Explainable CNN–BiLSTM Framework for Multi-Class Sleep Apnea Severity Detection Using Single-Lead ECG Signals: A Comprehensive Machine Learning Approach - Al-Quran, Fida’a; Jawarneh, Malik; AL-Mrayat, Omar Isam; Ibrahim, Dyala; Samara, Ghassan; Sheta, Alaa; Elmarhomy, Ghada; Baghdadi, Nadiah A.; Malki, Amer; Atlam, El-Sayed (Diagnostics, 2026)
Background/Objectives: Obstructivesleep apnea (OSA) is one of the most widespread forms of sleep disease, affecting over 936 million adults globally. The health consequences of obstructive sleep apnea (OSA) are well documented; however, it remains largely underdiagnosed because the current gold-standard diagnostic method, polysomnography (PSG), is often costly, time-consuming, and unavailable in many healthcare settings. To address these challenges, this study presents a novel explainable deep learning (DL) framework for automated multi-class OSA severity classification using single-lead electrocardiogram (ECG) signals. Methods: The proposed framework integrates a hybrid CNN–BiLSTM architecture with explainable artificial intelligence (XAI) techniques to generate clinically meaningful predictions and explanations across four OSA severity classes: Normal, Mild, Moderate, and Severe. The framework was evaluated using the publicly available PhysioNet Apnea-ECG dataset (70 recordings) together with an institutional ECG dataset (150 recordings), resulting in a combined cohort of 220 recordings. Results: The proposed framework achieved an overall classification accuracy of 94.7%, with sensitivity and specificity values of 92.3% and 96.1%, respectively. Furthermore, the proposed model consistently outperformed conventional machine learning algorithms, including Support Vector Machine (SVM), Random Forest, and XGBoost, by 5.5%, 4.2%, and 2.9%, respectively. To enhance transparency and clinical trust, SHAP (SHapley Additive exPlanations) was employed to identify the most influential physiological predictors driving model decisions. Heart rate variability features, particularly RMSSD and pNN50, emerged as the strongest indicators of OSA severity. Moreover, computational efficiency analysis revealed that the model required only 0.23 s to process a 60 s ECG epoch on a standard computing platform, supporting its suitability for real-time deployment. Conclusions: The findings demonstrate that explainable deep learning applied to ECG signals can provide accurate, interpretable, and computationally efficient assessment of OSA severity. The proposed framework may support OSA screening, clinical triage, and early intervention, particularly in resource-constrained healthcare environments.
Keywords: CNN; LSTM; SHAP; automated diagnosis; deep learning; dept.csc; electrocardiogram; explainable artificial intelligence; heart rate variability; obstructive sleep apnea; sleep disorders

Machine learning model for the detection of autism spectrum disorder using electroretinogram signals - Anwar, Muhammad Aqib; Islam, Mohammad Tariqul; Alam, Tanvir (Scientific Reports, 2026)
Keywords: dept.csc

Early Screening of Sleep-Disordered Breathing Using Metaheuristic-Optimized Extreme Learning Machines - Thaher, Thaer; Sheta, Alaa; Ashqar, Huthaifa I.; Chantar, Hamouda; Surani, Salim (Diagnostics, 2026)
Background/Objectives: Obstructive sleep apnea (OSA) is a common and serious sleep-related disorder that causes repeated interruptions in breathing during sleep. Traditional diagnostic methods, such as polysomnography, are accurate but costly, time-consuming, and unsuitable for large-scale screening. This study proposes and evaluates a lightweight diagnostic framework based on an Extreme Learning Machine (ELM) optimized by a set of basic and advanced metaheuristic optimizers. The model aims to evaluate whether metaheuristic optimization can improve ELM-based classification performance using structured demographic, clinical, and sleep-related predictors. Methods: Two real datasets were employed to train and evaluate the proposed framework: (i) a clinical OSA dataset with 274 subjects and 31 demographic/anthropometric and sleep-related predictors, and (ii) a public strongly imbalanced Sleep-Disordered Breathing (SDB) dataset with 500 subjects and 10 structured predictors. Metaheuristic algorithms are used to optimize ELM weights and biases, addressing the instability of random initialization and improving model generalization. The optimized models are evaluated against eight baseline classifiers, including logistic regression (LR), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), support vector machine (SVM), multilayer perceptron (MLP), XGBoost (XGB), and a standard ELM classifier. Results: Results show that metaheuristic optimization moderately improves ELM on the OSA dataset, increasing ROC-AUC from 0.6527 to about 0.73 and accuracy from 0.6573 to about 0.69–0.70, while on the highly imbalanced SDB dataset, it yields modest ROC-AUC gains (from 0.5132 to about 0.544–0.548) with small decreases in accuracy and F1-score. We additionally assess class-imbalance handling on the SDB dataset and analyze feature importance with permutation importance and SHAP, which shows the models rely heavily on diagnosis-derived predictors. Conclusions: The proposed framework provides a lightweight ELM-based decision-support approach with low inference cost after offline optimization. The results suggest potential value for screening-oriented OSA/SDB classification, but further validation with larger cohorts and a screening-only feature set is needed before clinical implementation.
Keywords: dept.csc; extreme learning machine; machine learning; metaheuristic optimization; obstructive sleep apnea; screening; sleep-disordered breathing

Hybrid Evolutionary Neural Network Models for Gold Price Prediction - Sheta, Alaa; Elashmawi, Walaa H.; Aljahdali, Sultan; Rauch, Peter; Othman, Emad S. (ACIT - Int. Arab Conf. Inf. Technol., Conf. Proc., 2025)
Gold serves as a safe-haven asset, an inflation hedge, and a store of value, among other economic purposes. History has a wealth of information about its connections to important macroeconomic and financial variables. We compare three supervised methods for one-day GLD (SPDR Gold Shares) prediction using cross-Asset drivers: SPX (S&P 500 Index), USO (United States Oil Fund), SLV (iShares Silver Trust), and EUR/USD (Euro-U.S. Dollar exchange rate). To fill this gap: (i) a linear regression baseline, (ii) a feedforward neural network (FFNN) trained by a Genetic Algorithm (GA), and (iii) an FFNN optimized through Particle Swarm Optimization (PSO). We use standard scaling, a held-out train/test split, and R2, Variance Accounted For (VAF), MSE, RMSE, MAE, and GA/PSO convergence curves to check how the optimizer works with daily data from 2008 to 2018. Empirically, PSO-FFNN does better than both the linear baseline and GA-FFNN. It has the best generalization (for example, Test MSE 38.99, R 0.929). This implies that (1) GLD possesses exploitable nonlinear structure concerning these predictors, and (2) PSO traverses the FFNN search space more effectively than GA in our context. The findings validate the application of evolutionary training for reliable and accurate gold price forecasts, with implications for risk management and strategic asset allocation. © 2025 IEEE.
Journal Abbreviation: ACIT - Int. Arab Conf. Inf. Technol., Conf. Proc.
Keywords: Gold prices; dept.csc; evolutionary algorithms; financial forecasting; neural networks; prediction models

Water Flow Prediction in the Black River (USA) Leveraging Evolutionary Feedforward Artificial Neural Networks and Crow Search Optimization - Elashmawi, Walaa H.; Sheta, Alaa (Springer Optimization and Its Applications, 2026)
Water flow prediction and planning significantly help decision-makers determine the most suitable irrigation strategy and crop type and help avoid risks from flooding, among other benefits. Conventional statistical and physical models are often challenged by the highly dynamic and nonlinear nature of hydrological processes. Recent advances in machine learning (ML), including artificial neural networks (ANNs), provide powerful tools for modeling these complex relationships. However, the performance of these models depends on the optimal parameter tuning. By combining ANN with Crow Search Optimization, we aim to improve prediction accuracy and robustness while providing a clever, adaptable, and reliable solution to real-world water flow forecasting problems. The Crow Search Algorithm (CSA) is one of the most recent metaheuristic algorithms used as a training algorithm for neural network models to achieve higher performance. This research provides an evolutionary-based model to predict the flow of the Black River, a well-known river in the USA. The adopted ANN model was used to train and predict daily flows at the initial Black Water River station (No. 02047500) near Dendron, Virginia. Among the well-known metaheuristic algorithms employed in this study for comparison are the Salp Swarm Algorithm (SSA), Particle Swarm Optimization (PSO), and the Dandelion Optimizer (DO). Based on comparative research, the CSA algorithm outperforms other training algorithms in predicting river flow, achieving an average fitness value of 0.0048926, which is 41% better than SSA, 81% better than PSO, and 49% better than DO. Furthermore, CSA has achieved a superior convergence curve, and high variance accounts for VAFs of up to 99.06% on the training data and 98.45% on the test data. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Keywords: Artificial neural network; Black water river flow; Crow search algorithm; Evolutionary ANN; Prediction; Water resource management; dept.csc

GeNeo2: An updated suite of bioinformatics tools for identifying tumor-specific neoepitopes for personalized cancer immunotherapies 2268 - Shcheglova, Tatiana; Al Seesi, Sahar; Sherafat, Elham; Force, Jordan; Srivastava, Pramod; Măndoiu, Ion (The Journal of Immunology, 2025)
Abstract Description The identification and prioritization of cancer-specific neoepitopes from next-generation sequencing data for personalized immunotherapies such as cancer vaccines remains challenging and requires the use of complex bioinformatics approaches. Here, we present GeNeo2, an updated version with enhanced features of the GeNeo toolbox for predicting neoepitopes from matched tumor/normal exome sequencing data coupled with tumor RNA-Seq data (Al Seesi et al., 2023). Unlike GeNeo, which identifies neoepitopes generated by single nucleotide variants, GeNeo2 also predicts neoepitopes generated by somatic indels. A distinguishing feature in GeNeo2 is that it integrates tools for analyzing mass spectrometry immunepeptidomic data, which can reveal neoantigens derived from both canonical and noncanonical sources. Finally, GeNeo2 integrates novel machine-learning approaches to improve the accuracy of somatic variant calling and peptide identification from mass spectrometry data. GeNeo2 tools can be accessed via web-based interfaces deployed on a Galaxy portal accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo2 locally is also available to academic users upon request. Topic Categories Computational and Systems Immunology (COMP)
Keywords: DNA sequencing; Exome; Exome sequencing; Identification (biology); Personalized medicine; Suite; Toolbox; Translational bioinformatics; dept.csc

From smog to search: studying the impact of air pollution on Google search trends for asthma - Aiyer, P.; Mominkhawaja, Aiza; Sheta, Alaa; Surani, Salim (American Journal of Respiratory and Critical Care Medicine, 2025)
Keywords: Air pollution; Asthma; Environmental health; Immunology; Medicine; dept.csc

A frameshift-generated cancer neoepitope that controls tumor burden in prophylaxis as well as therapy - George, Mariam; Brennick, Cory; Hagymasi, Adam; Shcheglova, Tatiana; Al Seesi, Sahar; Rosales, Tatiana; Baker, Brian; Măndoiu, Ion; Srivastava, Pramod (The Journal of Immunology, 2025)
Insertion or deletion of one or two base pairs within a coding region causes a frameshift, which has the potential to generate neoepitopes (InDel-generated neoepitopes) that lack a self-counterpart and are entirely novel. Despite the obvious appeal of InDel-generated neoepitopes, and the demonstration of such candidate neoepitopes that can elicit a CD8 T-cell response, no InDel-generated neoepitopes that actually control tumors in vivo have been reported thus far. Here, in a mouse colon carcinoma line, we identify 11 InDels, only one of which generates a neoepitope that elicits tumor control in vivo in models of prophylaxis as well as therapy. Although this neoepitope has no self-counterpart, it has a low affinity (IC50 33,937.60 nM) for its MHC I allele. Despite its low affinity for MHC I, this neoepitope elicits antitumor activity in vivo through CD8 T cells. Furthermore, CD8 T cells elicited by this InDel-generated neoepitope, like the neoepitopes created by point mutations, show notably less exhaustion than classical immunogenic epitopes. Ironically, this InDel-generated neoepitope follows the same rules as noted for most of the tumor control-mediating neoepitopes generated by point mutations that have a poor affinity for MHC I alleles.
Keywords: Biology; Cancer research; Frameshift mutation; Gene; Genetics; INDEL Mutation; Immunology; In vivo; Indel; Mutation; dept.csc

A gradient boosting machine learning model for neo-epitopes classification - Barde, Romi; Islam, Mohammad; Al Seesi, Sahar (2025 Women in Bioinformatics Workshop (WIBI), 2025)
Keywords: Artificial intelligence; Boosting (machine learning); Computer science; Gradient boosting; Machine learning; Random forest; dept.csc

Evolutionary design of a PID controller using metaheuristics search algorithms - Reddy, V.; Reddy, V.; Suresh, Karnati; Sheta, Alaa; Hassan, Walaa; Djellal, Adel; Baareh, Abdel (Journal of Computing and Communication, 2025)
Proportional-Integral-Derivative (PID) controllers are prominent due to their superior functionality and ease of use. However, optimizing their parameters presents a significant challenge. Adjusting parameters must be done carefully and cautiously because improper calibration can compromise the system’s stability. Although classic tuning techniques, such as the Ziegler-Nichols (ZN), are frequently employed, their efficiency is restricted due to the intricate and ever- changing nature of the systems, often leading to parameter settings that could be more optimal. Therefore, the need for a more accurate parameter-tuning technique is urgent. Various optimization strategies are used to fine-tune parameters with more precision. These methods include Gray Wolf Optimization (GWO), Genetic Algorithm (GA), Particle Swarm Optimization (PSO), and Ant Colony Optimization (ACO). These methods are applied to fine-tune the PID parameters for a Direct Current (DC) motor to achieve optimal performance, and a comparative analysis of the results is conducted. Various fitness functions encompass performance metrics such as rise time, overshoot, peak time, settling time, and mean square error (MSE). These metrics are incorporated into the corresponding optimization approaches to quantitatively assess the controller’s performance. Various test cases have been utilized and the GA outperforms other algorithms ranging from 17% to 28% where rise time, settling time, and MSE are significant in the fitness function.
Keywords: Algorithm; Artificial intelligence; Computer science; Control (management); Control engineering; Control theory (sociology); Engineering; Evolutionary algorithm; Mathematical optimization; Mathematics; Metaheuristic; PID controller; Temperature control; dept.csc

A Comparative Statistical Analysis of Machine Learning Models for Suspicious Email Detection - Sheta, Alaa; Pahnehkolaee, Nastaran Davudi; Braik, Malik; Baareh, Abdel Karim M.; Elashmawi, Walaa H.; Othman, Emad S. (Journal of Computing Science and Engineering, 2025)
The increasing volume of suspicious emails, commonly known as spam, has created a critical need for more reliable and robust anti-spam filters. These suspicious emails can be dangerous and can lead to the loss of personal information, underscoring the necessity for an effective spam filtering system. The application of machine learning methods has enhanced system security and improved the detection of suspicious messages. This research evaluates the effectiveness of seven machine learning algorithms for classifying suspicious email messages: random forest, support vector machine, artificial neural network, decision tree, gradient boosting classifier, and k-nearest neighbor. The primary focus of this evaluation is the accuracy achieved by each algorithm in identifying spam emails. Our analysis revealed that the random forest algorithm outperformed the other evaluated algorithms in terms of accuracy for spam email classification, achieving a remarkable 95%. The accuracy percentages of the various methods ranged from 88% to 93%. Copyright 2025. The Korean Institute of Information Scientists and Engineers.
Keywords: Classification; Machine learning; Random forest classifier; Security; Suspicious email; dept.csc

Analyzing Capacitive Swipe Gesture towards User Identification - Kc, Kiran; Hossain, Md Shafaeat; Tayeen, Abu Saleh Md (IEEE Int. Jt. Conf. Biom., IJCB, 2025)
Traditional physical biometrics - such as fingerprints, facial recognition, and iris scans - have long been utilized for user identification in areas like border control, military operations, law enforcement, and public safety. However, the rise of smartphone technology has introduced new avenues for security research. One emerging area is behavioral biometrics, particularly the use of touchscreen interaction data as a more accessible and user-friendly identification method. In this context, our study focuses on a novel form of touchscreen input: capacitive swipe gestures for user identification. We compiled a comprehensive dataset of capacitive swipe gestures collected over multiple sessions from 30 participants. To evaluate this modality, we conducted thorough experiments using established machine learning algorithms, including Support Vector Machine, Random Forest, and XGBoost. Additionally, we developed a new preprocessing algorithm tailored for capacitive swipe data. Our findings reveal that this algorithm significantly enhances identification performance compared to existing methods. Overall, our results highlight the strong potential of capacitive swipe gestures as a viable biometric modality for user identification. © 2025 IEEE.
Journal Abbreviation: IEEE Int. Jt. Conf. Biom., IJCB
Keywords: Accelerometer; Biometrics; Capacitive sensing; Gesture; Identification (biology); Keystroke logging; Modality (human–computer interaction); SwIPe; Touchscreen; dept.csc

Smart Agriculture: An Expert System for Tomato Plant Disease Diagnosis - Sheta, Alaa; Elashmawi, Walaa H.; Othman, Emad S. (Intelligent Systems, Blockchain, and Communication Technologies, 2026)
Tomato plant diseases pose a significant threat to agricultural productivity, resulting in substantial economic losses. Early and accurate diagnosis is crucial for effective disease management. This paper describes the design and implementation of expert systems for tomato disease detection using the CLIPS (C Language Integrated Production System) platform. The tool is designed to help farmers and agronomists accurately identify diseases affecting tomato crops by simulating knowledge from professional experts. We carefully developed a set of rules to distinguish leaf blight symptoms from those of other tomato diseases and provided recommendations to minimize crop losses and maximize yields. The expert system was developed using a forward-chaining inference engine, and its performance was evaluated through a set of real-world test cases, demonstrating a high level of accuracy and consistency in decision-making. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Journal Abbreviation: Lect. Notes Networks Syst.
Keywords: Blight; CLIPS; Consistency (knowledge bases); Expert System; Expert system; Inference engine; Knowledge Base; Plant Disease Diagnosis; Plant disease; Production (economics); Set (abstract data type); Tomato Diseases; dept.csc

Performance Analysis of Extractive Text Summarization Using Machine Learning - Zidan, Marwan; Sheta, Alaa; Yousef, Ahmed Hassan (Intelligent Systems, Blockchain, and Communication Technologies, 2026)
With the increasing interest in natural language processing, text summarization has become essential for condensing large volumes of data into concise and meaningful summaries. Extractive summarization, which involves selecting key sentences based on textual features, has gained attention due to its efficiency and effectiveness. This research explores extractive summarization using multiple machine learning classifiers, including Support Vector Machines (SVM), Logistic Regression (LR), Decision Trees (DT), K-Nearest Neighbors (KNN), and Random Forest (RF). Our findings indicate that the Random Forest model achieved the highest accuracy, reaching 80% in classifying sentences for summary generation. Additionally, we evaluated text classification on the same BBC dataset using ChatGPT, which attained an accuracy of 62%. Furthermore, comparisons with results from prior research confirm the competitive performance of our approach, reinforcing the potential of machine learning models in extractive summarization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2026.
Journal Abbreviation: Lect. Notes Networks Syst.
Keywords: Automatic summarization; Decision tree; Key (lock); Logistic regression; Random forest; Support vector machine; Training set; dept.csc

Intelligent Intrusion Detection System Using RF, SVM, and DT: A Comparison-Based KDD Data Set - Elashmawi, Walaa Hassan; Sheta, Alaa; Al-Qerem, Ahmad (Journal of Computer Science, 2025)
The rapid growth of technology has brought about many advantages, but has also made networks more susceptible to security threats. Intrusion Detection Systems (IDS) play a vital role in protecting computer networks against malicious activities. Given the dynamic and constantly evolving nature of cyber threats, these systems must continuously adapt to maintain their effectiveness. Machine Learning (ML) methods have gained prominence as effective tools for constructing IDS that offer both high accuracy and efficiency. This study conducts a performance assessment of several machine learning classifiers, including Random Forests (RF), Decision Trees (DT), and Support Vector Machines (SVM), in addressing multiclass intrusion detection as a means to counter cybersecurity threats. The NSL-KDD dataset, which includes various network attacks, served as the basis for our experimental evaluation. The research explores two classification scenarios: a five-class and a three-class model, analyzing their impact on detection performance. The results demonstrate that RF consistently achieves the highest accuracy (85.42%) on the three-class scenario testing set, highlighting its effectiveness in handling patterns and non-linear relationships within the intrusion data. Furthermore, reducing the classification complexity (three classes vs. five classes) significantly improves model generalization, as evidenced by the reduced performance gap between training and testing data. Friedman’s rank test and Holm’s post-hoc analysis were applied to ensure statistical rigor, confirming that RF outperforms DT and SVM in all evaluation metrics. These findings establish RF as the most robust classifier for intrusion detection and underscore the importance of simplifying classification tasks for improved IDS performance. © (2025), (Science Publications). All rights reserved.
Keywords: Intrusion Detection System; Multi-Class Classifications; Random Forest Classifier; dept.csc

ES-SVM Hyperparameter Optimization for Non-Invasive Coronary Heart Disease Classification - Sheta, Alaa; Elashmawi, Walaa H.; Surani, Salim; Baareh, Abdel Karim M.; Rauch, Peter; Othman, Emad S. (Int. Mobile, Intell., Ubiquitous Comput. Conf., MIUCC, 2025)
Coronary heart disease (CHD) is the leading global cause of death, making early detection essential. While coronary angiography is the diagnostic gold standard, its invasive nature poses risks, and non-invasive symptom-based methods often lack accuracy. Machine learning-powered computer-aided diagnostic systems can effectively address challenges in clinical decisionmaking. This work presents an Evolutionary Strategy-optimized Support Vector Machine (ES-SVM) model for classifying CHD based on non-invasive test results and patient characteristics. Using the Coronary Heart Disease dataset, the proposed ESSVM demonstrated significant precision and F1-scores, as well as the accuracy of the proposed model. The results indicate that SVM performance can be significantly enhanced through evolutionary hyperparameter tuning, resulting in a reliable, noninvasive diagnostic tool for initial CAD screening and supporting early intervention techniques. © 2025 IEEE.
Journal Abbreviation: Int. Mobile, Intell., Ubiquitous Comput. Conf., MIUCC
Keywords: CAD; Classification; Coronary Heart Disease; Coronary angiography; Coronary heart disease; Diagnostic accuracy; Evolutionary Strategies; Gold standard (test); Hyperparameter; Machine Learning; Non-Invasive Screening; Support Vector Machine; Support vector machine; dept.csc

ANN-MFO: Optimization of Neural Networks for Lipase Activity Modeling in Biotech Applications - Sheta, Alaa; Elashmawi, Walaa H. (Women Bioinform. Workshop, WIBI, 2025)
Modeling lipase activity aids researchers in optimizing features such as temperature, pH, and substrate concentration to boost enzyme performance. This is essential in biotechnology for progressing the productivity and yield of processes such as fermentation, biodiesel production, and bioremediation. Fermentation is a highly complex, multivariable, and non-linear biotechnological process that produces bioactive materials. This study leverages artificial neural networks (ANN) to predict lipase activity in batch fermentation processes, addressing the inherent challenges in weight learning optimization often encountered with traditional algorithms like Backpropagation (BP). Several metaheuristic algorithms were employed to optimize the Multilayered Perceptron (MLP) structure and weights, including moth-frequency optimization (MFO), Particle Swarm Optimization (PSO), Dandelion Optimizer Algorithm (DO), Crow Search Algorithm (CSA), and Salp Swarm Algorithm (SSA) to overcome these limitations. Among the tested algorithms, MFO emerged as the most effective approach, achieving superior performance in weight learning with the best fitness value (i.e., mean square error (MSE)) of 0.6006. MFO-optimized ANN models deliver the most accurate predictions for lipase activity, highlighting their potential as a powerful tool for advancing industrial fermentation process optimization. © 2025 IEEE.
Journal Abbreviation: Women Bioinform. Workshop, WIBI
Keywords: Artificial intelligence; Artificial neural network; Biochemical engineering; Biochemistry; Biology; Biotechnology; Box-Behnken; Chemistry; Computational biology; Computer science; Engineering; Enzyme; Lipase; Metaheuristics search algorithms; Moth-Flame Optimization Algorithm; Thermostable Lipase Enzyme; artificial neural networks; dept.csc

Medical image analysis for brain tumor classification using CNN architecture - Sheta, Alaa; Elashmawi, Walaa H.; Karim Baareh, Abdel; Rausch, Peter; Othman, Emad S. (Proceeding - 12th International Conference on Information Technology: Innovation Technologies, ICIT 2025, 2025)
Traditional brain tumor diagnosis and classification are time-consuming and heavily reliant on radiologist expertise. The ever-growing patient population generates vast data, rendering existing methods expensive and inefficient. Deep Learning (DL) is a promising approach for developing automated systems to diagnose or segment brain tumors with high accuracy in less time. Within Deep Learning, Convolutional Neural Networks (CNNs) are potent tools for image classification tasks. This is achieved through a series of specialized layers, including convolution layers that identify patterns within images, pooling layers that summarize these patterns, fully connected layers that ultimately classify the image, and a feedforward layer to produce the output class. This study employed a CNN to classify brain tumors in T1-weighted contrast-enhanced images with various image resolutions, including 30×30, 50×50, 70×70, 100×100, and 150×150 pixels. The model successfully distinguished between three tumor types: glioma, meningioma, and pituitary. The CNN's impressive accuracy on training data reached up to 86.38% for image resolution (30×30) and 94.64% for higher resolution (150×150). This indicates its potential as a valuable tool in real-world brain tumor classification tasks. © 2025 IEEE.
Journal Abbreviation: Proceeding - Int. Conf. Inf. Technol.: Innov. Technol., ICIT
Keywords: Brain Tumor; Brain tumor; Classification; Contextual image classification; Convolutional Neural Networks; Convolutional neural network; Deep learning; MRI; Pattern recognition (psychology); Pooling; Population; Rendering (computer graphics); dept.csc

A New Version of the Golden Eagle Optimizer Algorithm And Its Application For Solving A Trio-Objective Skillful Team Formation Problem In A Social Network - Elashmawi, Walaa; Sheta, Alaa; Alqadi, Basma; AbdElminaam, Diaa; Alsekait, Deema (Journal of Artificial Intelligence and Soft Computing Research, 2025)
Abstract Metaheuristic methods have demonstrated their utility in tackling global optimization problems with and without constraints. However, existing state-of-the-art (SOTA) algorithms often suffer from limitations such as premature convergence, inefficient exploration-exploitation balance, and poor adaptability to complex discrete optimization problems like Team Formation (TF). The Golden Eagle Optimizer (GEO) algorithm is a promising metaheuristic that addresses some of these challenges by effectively managing its hunting spiral motion using two control parameters: cruise (exploration) and attack (exploitation). Despite its strengths, the standard GEO algorithm requires modifications to handle the discrete and multi-objective nature of the TF problem effectively. This paper proposes an amended version of GEO, called AGEO, which integrates specialized operators to enhance its performance in TF scenarios. A skillful TF aims to form teams of experts with complementary skills in social networks (SN) while optimizing multiple objectives, including minimizing communication costs, maximizing the similarity score between team members, and achieving minimal team cardinality. AGEO preserves GEO’s powerful exploitation and exploration mechanisms while introducing tailored operator strategies to overcome the challenges inherent in TF. The AGEO undergoes testing on several well-established benchmark datasets, including Universiti Malaysia Pahang (UMP), Internet Movie Database (IMDB), Association for Computing Machinery (ACM), and Database Systems & Logic Programming (DBLP). Additionally, a comparative study against SOTA metaheuristic algorithms such as Particle Swarm Optimization (PSO), Butterfly Optimization Algorithm (BOA), Crow Search Algorithm (CSA), and Jaya Algorithm demonstrates AGEO’s superior performance in forming highly optimized teams with the least communication cost, lowest team cardinality, and highest similarity score.
Keywords: Adaptability; Algorithm; Artificial intelligence; Benchmark (surveying); Computer science; Machine learning; Mathematical optimization; Mathematics; Metaheuristic; Particle swarm optimization; dept.csc

Exploring capacitive swipe gesture for user authentication using a new large dataset - K.C., Kiran; Hossain, Shafaeat; Haberfeld, Carl (Computers & Security, 2025)
Keywords: Artificial intelligence; Authentication; Authentication (law); Biometrics; Capacitive sensing; Capacitive swipe gesture; Computer science; Computer security; Gesture; Operating system; Smartphone security; SwIPe; Touchscreen; dept.csc

Optimizing MPPT Control of DC-DC Boost Converters Using Metaheuristic Search Algorithms and Nonlinear PID (N-PID) Controllers - Khellaf, L.; Djellal, A.; Mayache, H.; Sheta, A. (Smart Computing and Control Renewable Energy Systems, 2025)
Photovoltaic systems have proven to be one of the most widely used renewable energies and the best replacement for conventional energy. Yet, their non-linear nature remains a challenge when it comes to extracting maximum power from photovoltaic modules. Therefore, in this work, a nonlinear PID controller has been used to meet the requirements of the photovoltaic system. In addition, to improve system performance and response, metaheuristic search algorithms were introduced into the tuning process of both the NPID controller and conventional PID controller parameters in order to compare them. The use of Artificial Intelligence to fine-tune the controller parameters will enable the optimum values of proportional, integral, derivative and nonlinear gains to be determined as system condition change. Finally, a comparison between the algorithms applied is conducted in terms of efficiency, rise time, settling time and overshoot as well as the overall system stability.
Keywords: Artificial intelligence; Computer science; Control; Control (management); Control engineering; Control theory (sociology); Converters; Electrical engineering; Engineering; Evolutionary Algorithm; Machine Learning; Nonlinear system; PID controller; Physics; Swarm Intelligence Algorithm; Temperature control; dept.csc

Stock Market Price Forecasting Using Metaheuristic Search Algorithms: A Comparative Analysis - Sheta, A.L.A.A.; Abdel-Raouf, A.M.A.L. (International Journal of Computing, 2024)
Stock market forecasting is an essential factor in the daily operations of many companies and individuals. However, the complex and nonlinear nature of the stock market and the unpredictable variations in factors affecting stock prices present significant challenges in accurate forecasting. To address this, we employ four model-based metaheuristic search algorithms (MHs), namely the Crow Search Algorithm (CSA), Particle Swarm Optimizer (PSO), Gray Wolf Optimizer (GWO), and Dandelion Optimizer (DO), to estimate the parameters of stock market prices models. The data utilized in our experiments are extracted from the widely recognized stock index of Standard & Poor's 500 (S&P 500), that serves as a representative benchmark for the United States stock market. Our findings demonstrate that the CSA outperforms other MHs by providing the best combination of parameters for modeling stock market prices. The optimized parameters for the CSA model yielded Variance-Account-For (VAF) values of 97.846% in the training set and 93.483% in the testing set. This suggests that CSA offers promising capabilities for enhancing the accuracy and effectiveness of stock market forecasting models. © (2024), (Research Institute of Intelligent Computer Systems). All rights reserved.
Keywords: Algorithm; Computer science; Econometrics; Economics; Engineering; Mathematical optimization; Mathematics; Metaheuristic; Particle swarm optimization; Stock (firearms); Stock market; Stock market forecasting; Stock market index; computational intelligence; crow search algorithm; dandelion optimizer; dept.csc; gray wolf optimizer; metaheuristic search algorithms; particle swarm optimizer; peer-reviewed

A pilot study on diabetes detection using handheld fundus camera and mobile app development - Al-Absi, Hamada R. H.; Muchori, Gilbert Njihia; Musleh, Saleh; Basit, Syed Abdullah; Islam, Mohammad Tariqul; Mou, Younss Ait; Alam, Tanvir (Discover Applied Sciences, 2025)
Diabetes, affecting more than 500 million individuals worldwide, is the most widespread non-communicable disease, globally. The early identification and effective management of diabetes are crucial for controlling its spread. Currently, the HbA1c test is the gold standard for the detection of diabetes with high confidence. But this is an invasive, expensive pathology test. Therefore, alternative non-invasive and inexpensive methods have been proposed in the literature for the early detection of diabetes.
Keywords: Android Mobile application; Artificial intelligence; Computer graphics (images); Computer science; Computer vision; DMPred; Diabetes; Diabetes mellitus; Diabetic retinopathy; Fundus (uterus); Fundus camera; Handheld camera; Human–computer interaction; Medicine; Mobile apps; Mobile device; Ophthalmology; Ophthalmoscopy; Retinal; Retinal fundus image; World Wide Web; dept.csc; peer-reviewed

Comprehensive Evaluation of Machine Learning Techniques for Obstructive Sleep Apnea Detection - Sheta, Alaa; Elashmawi, Walaa H.; Djellal, Adel; Braik, Malik; Surani, Salim; Aljahdali, Sultan; Subramanian, Shyam; Patel, Parth S. (International Journal of Advanced Computer Science and Applications (ijacsa), 2024)
Obstructive Sleep Apnea (OSA) is a prevalent health issue affecting 10-25% of adults in the United States (US) and is associated with significant economic consequences. Machine learning methods have shown promise in improving the efficiency and accessibility of OSA diagnoses, thus reducing the need for expensive and challenging tests. A comparative analysis of Logistic Regression (LR), Support Vector Machine (SVM), Gradient Boosting (GB), Gaussian Naive Bayes (GNB), Random Forest (RF), and K-Nearest Neighbors (KNN) algorithms was conducted to predict Obstructive Sleep Apnea (OSA). To improve the predictive accuracy of these models, Random Oversampling was applied to address the imbalance in the dataset, ensuring a more equitable representation of the minority class. Patient demographics, including age, sex, height, weight, BMI, neck circumference, and gender, were employed as predictive features in the models. The RFC provided outstanding training and testing accuracies of 87% and 65%, respectively, and a Receiver Operating Characteristic (ROC) score of 87%. The GBC and SVM classifiers also demonstrated good performance on the test dataset. The results of this study show that machine learning techniques may be effectively used to diagnose OSA, with the Random Forest Classifier demonstrating the best results.
Number: 12
Keywords: dept.csc; peer-reviewed

Machine Learning for Enhanced COPD Diagnosis: A Comparative Analysis of Classification Algorithms - Elashmawi, Walaa H.; Djellal, Adel; Sheta, Alaa; Surani, Salim; Aljahdali, Sultan (Diagnostics, 2024)
Background: In the United States, chronic obstructive pulmonary disease (COPD) is a significant cause of mortality. As far as we know, it is a chronic, inflammatory lung condition that cuts off airflow to the lungs. Many symptoms have been reported for such a disease: breathing problems, coughing, wheezing, and mucus production. Patients with COPD might be at risk, since they are more susceptible to heart disease and lung cancer. Methods: This study reviews COPD diagnosis utilizing various machine learning (ML) classifiers, such as Logistic Regression (LR), Gradient Boosting Classifier (GBC), Support Vector Machine (SVM), Gaussian Naïve Bayes (GNB), Random Forest Classifier (RFC), K-Nearest Neighbors Classifier (KNC), Decision Tree (DT), and Artificial Neural Network (ANN). These models were applied to a dataset comprising 1603 patients after being referred for a pulmonary function test. Results: The RFC has achieved superior accuracy, reaching up to 82.06% in training and 70.47% in testing. Furthermore, it achieved a maximum F score in training and testing with an ROC value of 0.0.82. Conclusions: The results obtained with the utilized ML models align with previous work in the field, with accuracies ranging from 67.81% to 82.06% in training and from 66.73% to 71.46% in testing.
Number: 24
Keywords: artificial neural network (ANN); chronic obstructive pulmonary disease (COPD); dept.csc; machine learning (ML); peer-reviewed; random forest classifier (RFC)

Evolutionary optimization of Yagi–Uda antenna design using grey wolf optimizer - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan; El-Hefnawi, Fatma; Al-Hiary, Heba; Elashmawi, Walaa H. (Neural Computing and Applications, 2024)
Meta-heuristic optimization algorithms have become widely used due to their outstanding features, such as gradient-free mechanisms, high flexibility, and great potential for avoiding local optimal solutions. This research explored the grey wolf optimizer (GWO) to find the ideal configuration for a six-element Yagi–Uda antenna. The GWO algorithm adjusted the lengths of the antenna wires and the spacings between them. The goal was to maximize the antenna’s ability to transmit signals (throughput gain). Optimal antenna selection relies on various parameters, including gain, bandwidth, impedance matching, frequency, side-lobe levels, etc. The optimization of a six-element Yagi–Uda antenna presents a challenging engineering design problem due to its multimodal and nonlinear nature. Achieving optimal performance hinges on the intricate interplay between the lengths of the constituent elements and the spacing configurations. To this end, a multiobjective function was adopted to design this antenna. The performance of several meta-heuristic algorithms, including genetic algorithms, biogeography-based optimization, simulated annealing, and grey wolf optimizer, was compared. The GWO-based approach has performed better than its competitors. This optimized antenna design based on GWO reported a gain of 14.21 decibel. Therefore, the GWO-based method optimizes antennas that can be further investigated for other antenna design problems.
Keywords: Antenna design; Artificial Intelligence; Gain optimization; Grey wolf optimizer; Meta-heuristic algorithms; Multi-objective optimization; dept.csc; peer-reviewed

Fear of Missing Out’s (FoMO) relationship with moral judgment and behavior - McKee, Paul C.; Senthilnathan, Ithika; Budnick, Christopher J.; Bind, Marie-Abèle; Antonios, Imad; Sinnott-Armstrong, Walter (PLOS ONE, 2024)
Across three online studies, we examined the relationship between the Fear of Missing Out (FoMO) and moral cognition and behavior. Study 1 (N = 283) examined whether FoMO influenced moral awareness, judgments, and recalled and predicted behavior of first-person moral violations in either higher or lower social settings. Study 2 (N = 821) examined these relationships in third-person judgments with varying agent identities in relation to the participant (agent = stranger, friend, or someone disliked). Study 3 (N = 604) examined the influence of recalling activities either engaged in or missed out on these relationships. Using the Rubin Causal Model, we created hypothetical randomized experiments from our real-world randomized experimental data with treatment conditions for lower or higher FoMO (median split), matched for relevant covariates, and compared differences in FoMO groups on moral awareness, judgments, and several other behavioral outcomes. Using a randomization-based approach, we examined these relationships with Fisher Tests and computed 95% Fisherian intervals for constant treatment effects consistent with the matched data and the hypothetical FoMO intervention. All three studies provide evidence that FoMO is robustly related to giving less severe judgments of moral violations. Moreover, those with higher FoMO were found to report a greater likelihood of committing moral violations in the past, knowing people who have committed moral violations in the past, being more likely to commit them in the future, and knowing people who are likely to commit moral violations in the future.
Keywords: Behavior; Interpersonal relationships; Machine learning; Memory recall; Moral philosophy; Personality; Religion; Social media; dept.csc; dept.psy; peer-reviewed

Estimating the Parameters of Software Reliability Growth Models Using the Grey Wolf Optimization Algorithm - Sheta, Alaa F.; Abdel-Raouf, Amal (International Journal of Advanced Computer Science and Applications (IJACSA), 2016)
In this age of technology, building quality software is essential to competing in the business market. One of the major principles required for any quality and business software product for value fulfillment is reliability. Estimating software reliability early during the software development life cycle saves time and money as it prevents spending larger sums fixing a defective software product after deployment. The Software Reliability Growth Model (SRGM) can be used to predict the number of failures that may be encountered during the software testing process. In this paper we explore the advantages of the Grey Wolf Optimization (GWO) algorithm in estimating the SRGM’s parameters with the objective of minimizing the difference between the estimated and the actual number of failures of the software system. We evaluated three different software reliability growth models: the Exponential Model (EXPM), the Power Model (POWM) and the Delayed S-Shaped Model (DSSM). In addition, we used three different datasets to conduct an experimental study in order to show the effectiveness of our approach.
Number: 4
Keywords: dept.csc; peer-reviewed

Barcode-less Fruits Classification Using Deep Learning - Abdel-raouf, Amal; Sheta, Alaa; Baareh, AbdelKarim; Rausch, Peter (IAES International Journal of Artificial Intelligence (IJ-AI), 2024)
Barcode-less fruit recognition technology has revolutionized the checkout process by eliminating manual barcode scanning. This technology automatically identifies and adds fruit items to the purchase list, significantly reducing waiting times at the cash register. Faster checkouts enhance customer convenience and optimize operational efficiency for retailers. Adding barcode to fruits require using adhesives on the fruit surface that may cause health hazards. Leveraging deep learning techniques for barcode-less fruit recognition brings valuable advantages to industries, including advanced automation, enhanced accuracy, and increased efficiency. These benefits translate into improved productivity, cost reduction, and superior quality control. This study introduces a Convolutional Neural Network (CNN) designed explicitly for automatic fruit recognition, even in challenging real-world scenarios. The proposed method assists fruit sellers in accurately identifying and distinguishing between different types of fruit that may exhibit similarities. A dataset that includes 44,406 images of different fruit types is used to train and test our technique. Employing a CNN, the developed model achieves an impressive classification accuracy of 97.4% during the training phase and 88.6% during the testing phase respectively, showcasing its effectiveness in precise fruit recognition.
Number: 3
Keywords: Classification; Convolution Neural Network; Fruit Detection; dept.csc; peer-reviewed

Heart disease diagnosis using decision trees with feature selection method - Sheta, A.; El-Ashmawi, W.; Baareh, A. (International Arab Journal of Information Technology, 2024)
The advancement in treating medical data grows significantly daily. An accurate data classification model can help determine patient disease and diagnose disease severity in the medical domain, thus easing doctors' treatment burdens. Nonetheless, medical data analysis presents challenges due to uncertainty, the correlations between various measurements, and the high dimensionality of the data. These challenges burden statistical classification models. Machine Learning (ML) and data mining approaches have proven effective in recent years in gaining a deeper understanding of the importance of these aspects. This research adopts a well-known supervised learning classification model named a Decision Tree (DT). DT is a typical tree structure consisting of a central node, connected branches, and internal and terminal nodes. In each node, we have a decision to be made, such as in a rule-based system. This type of model helps researchers and physicians better diagnose a disease. To reduce the complexity of the proposed DT, we explored using the Feature Selection (FS) method to design a simpler diagnosis model with fewer factors. This concept will help reduce the data collection stage. A comparative analysis has been conducted between the developed DT and other various ML models, such as Logistic Regression (LR), Support Vector Machine (SVM), and Gaussian Naive Bayes (GNB), to demonstrate the effectiveness of the developed model. The results of the DT model establish a notable accuracy of 93.78% and an ROC value of 0.94, which beats other compared algorithms. The developed DT model provided promising results and can help diagnose heart disease. © 2024, Zarka Private University. All rights reserved.
Keywords: Machine learning; classification; decision tree; dept.csc; diagnosis; heart disease; peer-reviewed

Utilizing various machine learning techniques for diabetes mellitus feature selection and classification - Sheta, Alaa; Elashmawi, Walaa H.; Al-Qerem, Ahmad; Othman, Emad S. (International Journal of Advanced Computer Science and Applications (IJACSA), 2024)
Diabetes mellitus is a chronic disease affecting over 38.4 million adults worldwide. Unfortunately, 8.7 million were undiagnosed. Early detection and diagnosis of diabetes can save millions of people’s lives. Significant benefits can be achieved if we have the means and tools for the early diagnosis and treatment of diabetes since it can reduce the ratio of cardiovascular disease and mortality rate. It is urgently necessary to explore computational methods and machine learning for possible assistance in the diagnosis of diabetes to support physician decisions. This research utilizes machine learning to diagnose diabetes based on several selected features collected from patients. This research provides a complete process for data handling and pre-processing, feature selection, model development, and evaluation. Among the models tested, our results reveal that Random Forest performs best in accuracy (i.e., 0.945%). This emphasizes Random Forest’s efficiency in precisely helping diagnose and reduce the risk of diabetes.
Number: 3
Keywords: dept.csc; peer-reviewed

Multi-disease Retinal Vessel Segmentation: A Deep Learning Approach - Islam, M.T.; Zaky, H.; Alam, T. (2023)
The structure of blood vessels in the retina is a crucial factor in identifying and forecasting various eye diseases like cardiovascular diseases, diabetes, and other diseases. Therefore, detecting the structure of blood vessels from retinal fundus images is a critical field of research in healthcare. This study employed a novel deep learning model to segment vessels for different diseases, including Glaucoma, Diabetic Retinopathy (DR), and Age-related Macular Degeneration (AMD). We considered multiple transfer learning-based models and discovered that the ResNet-based U-Net architecture was the most effective for vessel segmentation, achieving the highest Dice Score above 84% for disease-agnostic, and 82%-84% for disease-specific conditions. We believe the proposed methodology will help to advance retinal vessel segmentation process and enhance the screening process of diseases based on retinal fundus images in clinical settings of Qatar Biobank as well as other biobanks across the globe. © 2023 IEEE.
Keywords: Diabetic Retinopathy; Glaucoma; Qatar Biobank; Retina; Segmentation; dept.csc

Diabetic Macular Edema Detection based on Non-mydriatic Retinal Image - Gilbert, Njihia Muchori; Hamada, R. H. Al-Absi; Saleh, Musleh; Mohammad, Tariqul Islam; Tanvir, Alam (2023 3rd International Conference on Computing and Information Technology, 2023)
Keywords: dept.csc

River Flow Forecasting: A Comparison Between Feedforward and Layered Recurrent Neural Network - Aljahdali, Sultan; Sheta, Alaa; Turabieh, Hamza (Innovation in Information Systems and Technologies to Support Learning Research, 2020)
Forecasting the daily flows of rivers is a challenging task that have a significant impact on the environment, agriculture, and people life. This paper investigates the river flow forecasting problem using two types of Deep Neural Networks (DNN) structures, Long Short-Term Memory (LSTM) and Layered Recurrent Neural Networks (L-RNN) for two rivers in the USA, Black and Gila rivers. The data sets collected for a period of seven years for Black river (six years for training and one year for testing) and four years for Gila river (three years for training and one year for testing) were used for our experiments. An order selection method based partial auto-correlation sequence was employed to determine the appropriate order for the proposed models in both cases. Mean square errors (MSE), Root mean square errors (RMSE) and Variance (VAF) were used to evaluate to developed models. The obtained results show that the proposed LSTM is able to produce an excellent model in each case study.
Keywords: Forecasting; Long short-term memory; River Flow; dept.csc

Diagnosis of Brain Tumors in MR Images Using Metaheuristic Optimization Algorithms - Braik, Malik; Sheta, Alaa; Aljahdali, Sultan (Innovation in Information Systems and Technologies to Support Learning Research, 2020)
Image clustering presents a hot topic that researchers have chased extensively. There is always a need to a promising clustering technique due to its vital role in further image processing steps. This paper presents a compelling clustering approach for brain tumors and breast cancer in Magnetic Resonance Imaging (MRI). Driven by the superiority of nature-inspired algorithms in providing computational tools to deal with optimization problems, we propose Flower Pollination Algorithm (FPA) and Crow Search Algorithm (CSA) to present a clustering method for brain tumors and breast cancer. Evaluation clustering results of CSA and FPA were judged using two apposite criteria and compared with results of K-means, fuzzy c-means and other metaheuristics when applied to cluster the same benchmark datasets. The clustering method-based CSA and FPA yielded encouraging results, significantly outperforming those obtained by K-means and fuzzy c-means and slightly surpassed those of other metaheuristic algorithms.
Keywords: Clustering; Crow Search Algorithm; Flower Pollination Algorithm; Fuzzy C-mean; K-means; dept.csc

Development of a Sleep Monitoring System by Using a Depth Sensor: A Pilot Study - Park, Jangwoon; Kim, Jungyoon; Park, Jaehyun; Sheta, Alaa; Murphey, Christina; Um, Dugan (Advances in Human Factors and Ergonomics in Healthcare and Medical Devices, 2020)
Sleep is an essential part of health and longevity persons. As people grow older, the quality of their sleep becomes vital. Poor sleep quality can make negative physiological, psychological, and social impacts on the elderly population, causing a range of health problems including coronary heart disease, depression, anxiety, and loneliness. Early detection, proper diagnosis, and treatments for sleep disorders can be achieved by identifying sleep patterns through long-term sleep monitoring. Although many studies developed sleep monitoring systems by using non-invasive measures such as body temperature, pressure, or body movement signal, research is still limited to detect sleep position changes by using a depth camera. The present study is intended (1) to identify concerns on the existing sleep monitoring system based on the literature review and (2) propose to developing a non-invasive sleep monitoring system using an infrared depth camera. For the literature review, various journal/conference papers have been reviewed to understand the characteristics, tools, and algorithms of the existing sleep monitoring systems. For the system development and validation, we collected data for the sleep positions from two subjects (35 years old man and 84 years old women) during the four-hour sleep. Kinect II depth sensor was used for data collection. We found that the averaged depth data is useful measure to notify the participants’ positional changes during the sleep.
Keywords: Kinect II depth sensor; Sleep movement; Sleep pattern; Sleep quality; dept.csc

Statistical Models for Predicting a Patient’s Respiratory Disturbance Index - Park, Jangwoon; Sheta, Alaa; Surani, Salim (CHEST, 2019)
SESSION TITLE: Clinical Prediction and Diagnosis of OSA
Keywords: dept.csc

Evolving neural networks using bird swarm algorithm for data classification and regression applications - Aljarah, Ibrahim; Faris, Hossam; Mirjalili, Seyedali; Al-Madi, Nailah; Sheta, Alaa; Mafarja, Majdi (Cluster Computing, 2019)
This work proposes a new evolutionary multilayer perceptron neural networks using the recently proposed Bird Swarm Algorithm. The problem of finding the optimal connection weights and neuron biases is first formulated as a minimization problem with mean square error as the objective function. The BSA is then used to estimate the global optimum for this problem. A comprehensive comparative study is conducted using 13 classification datasets, three function approximation datasets, and one real-world case study (Tennessee Eastman chemical reactor problem) to benchmark the performance of the proposed evolutionary neural network. The results are compared with well-regarded conventional and evolutionary trainers and show that the proposed method provides very competitive results. The paper also considers a deep analysis of the results, revealing the flexibility, robustness, and reliability of the proposed trainer when applied to different datasets.
Keywords: Bird Swarm Algorithm; Classification; Multilayer perceptron; Neural networks; Optimization; Regression; dept.csc; peer-reviewed

Diagnosis of Sleep Apnea Using Artificial Neural Network and Binary Particle Swarm Optimization for Feature Selection - Surani, Salim; Sheta, Alaa; Turabieh, Hamza; Park, Jangwoon; Mathur, Sunil; Katangur, Ajay (CHEST, 2019)
SESSION TITLE: Clinical Prediction and Diagnosis of OSA
Keywords: dept.csc

Collision-Free Autonomous Robot Navigation in Unknown Environments Utilizing PSO for Path Planning - Krell, Evan; Sheta, Alaa; Balasubramanian, Arun Prassanth Ramaswamy; King, Scott A. (Journal of Artificial Intelligence and Soft Computing Research, 2019)
AbstractThe autonomous navigation of robots in unknown environments is a challenge since it needs the integration of a several subsystems to implement different functionality. It needs drawing a map of the environment, robot map localization, motion planning or path following, implementing the path in real-world, and many others; all have to be implemented simultaneously. Thus, the development of autonomous robot navigation (ARN) problem is essential for the growth of the robotics field of research. In this paper, we present a simulation of a swarm intelligence method is known as Particle Swarm Optimization (PSO) to develop an ARN system that can navigate in an unknown environment, reaching a pre-defined goal and become collision-free. The proposed system is built such that each subsystem manipulates a specific task which integrated to achieve the robot mission. PSO is used to optimize the robot path by providing several waypoints that minimize the robot traveling distance. The Gazebo simulator was used to test the response of the system under various envirvector representing a solution to the optimization problem.onmental conditions. The proposed ARN system maintained robust navigation and avoided the obstacles in different unknown environments. vector representing a solution to the optimization problem.
Keywords: dept.csc; peer-reviewed

Adaboosting Model for Detecting Osa - Surani, Salim; Sheta, Alaa; Turabieh, Hamza; Subramanian, Shyamsunder (CHEST, 2019)
SESSION TITLE: Clinical Prediction and Diagnosis of OSA
Keywords: dept.csc

Computer networks: data communications, internet and security - Elahi, Ata; Cushman, Alex (Springer International Publishing, 2024)
​The hands-on textbook covers both the theory and applications of data communications, the Internet, and network security technology, following the ACM guideline for courses in networking. The content is geared towards upper undergraduate and graduate students in information technology, communications engineering, and computer science. The book is divided into three sections: Data Communications, Internet Architecture, and Network Security. Topics covered include flow control and reliable transmission; modulation, DSL, cable modem, and FTTH; Ethernet and Fast Ethernet; gigabit and 10 gigabit Ethernet; and LAN interconnection devices, among others. The book also covers emerging topics such as IPv6 and software defined networks. The book is accompanied with a lab manual which uses Wireshark, Cisco Packet Tracer, and virtual machines to lead students through simulated labs.
Keywords: Communications Channels and Media; Communications Networks; Computers / Information Theory; Computers / Networking / Hardware; Data Communications; Internet Protocols; Language Arts & Disciplines / Library & Information Science / General; Network Security; SCSU-student; Technology & Engineering / Electrical; Technology & Engineering / Telecommunications; Wireless Local Area Network (WLAN); dept.csc

Diagnosis of obstructive sleep apnea using feature selection, classification methods, and data grouping based age, sex, and race - Sheta, Alaa; Thaher, Thaer; Surani, Salim R.; Turabieh, Hamza; Braik, Malik; Too, Jingwei; Abu-El-Rub, Noor; Mafarjah, Majdi; Chantar, Hamouda; Subramanian, Shyam (Diagnostics, 2023)
Obstructive sleep apnea (OSA) is a prevalent sleep disorder that affects approximately 3–7% of males and 2–5% of females. In the United States alone, 50–70 million adults suffer from various sleep disorders. OSA is characterized by recurrent episodes of breathing cessation during sleep, thereby leading to adverse effects such as daytime sleepiness, cognitive impairment, and reduced concentration. It also contributes to an increased risk of cardiovascular conditions and adversely impacts patient overall quality of life. As a result, numerous researchers have focused on developing automated detection models to identify OSA and address these limitations effectively and accurately. This study explored the potential benefits of utilizing machine learning methods based on demographic information for diagnosing the OSA syndrome. We gathered a comprehensive dataset from the Torr Sleep Center in Corpus Christi, Texas, USA. The dataset comprises 31 features, including demographic characteristics such as race, age, sex, BMI, Epworth score, M. Friedman tongue position, snoring, and more. We devised a novel process encompassing pre-processing, data grouping, feature selection, and machine learning classification methods to achieve the research objectives. The classification methods employed in this study encompass decision tree (DT), naive Bayes (NB), k-nearest neighbor (kNN), support vector machine (SVM), linear discriminant analysis (LDA), logistic regression (LR), and subspace discriminant (Ensemble) classifiers. Through rigorous experimentation, the results indicated the superior performance of the optimized kNN and SVM classifiers for accurately classifying sleep apnea. Moreover, significant enhancements in model accuracy were observed when utilizing the selected demographic variables and employing data grouping techniques. For instance, the accuracy percentage demonstrated an approximate improvement of 4.5%, 5%, and 10% with the feature selection approach when applied to the grouped data of Caucasians, females, and individuals aged 50 or below, respectively. Furthermore, a comparison with prior studies confirmed that effective data grouping and proper feature selection yielded superior performance in OSA detection when combined with an appropriate classification method. Overall, the findings of this research highlight the importance of leveraging demographic information, employing proper feature selection techniques, and utilizing optimized classification models for accurate and efficient OSA diagnosis.
0 citations (Crossref) [2023-10-31] Number: 14
Keywords: dept.csc; feature selection; grouping; machine learning; obstructive sleep apnea; peer-reviewed

DMEgrader: Android Mobile Application for Diabetic Macular Edema Grading Prediction - Al-Absi, Hamada R.H.; Muchori, Gilbert Njihia; Musleh, Saleh a; Islam, Mohammad Tariqul; Pai, Anant; Alam, Tanvir (2023)
More than half a billion people worldwide are affected by diabetes, which is a prevalent non-communicable disease that can lead to critical health conditions, including vision loss. Diabetic Macular Edema (DME) is a primary cause of vision impairment and can eventually lead to blindness in diabetic patients. Early detection of DME and proper health management are crucial to controlling the disease. Retinal image-based AI-enabled diabetes diagnosis has gained significant attention as a non-invasive, fast, and reasonably accurate method for diagnosing DME. To make this technology accessible to underserved communities or areas lacking proper clinical facilities, a mobile application-based solution could have a significant impact. In this article, we describe how we transformed our previously published AI-enabled model into an Android-based mobile application, which is part of a two-phase research study. In the first phase, we developed a deep learning-based model that predicts DME grading using retinal images. In the second phase, we built a mobile application DMEgrader to make our model accessible via a mobile device. To the best of our knowledge, this is the first article to demonstrate necessary steps and code snippets to support developers in transforming deep learning models into Android based mobile applications for DME grading prediction. © 2023 IEEE.
0 citations (Crossref) [2023-10-31]
Keywords: Deep Learning; Diabetes; Diabetic Macular Edema; Qatar Biobank; dept.csc

Improved versions of crow search algorithm for solving global numerical optimization problems - Sheta, Alaa; Braik, Malik; Al-Hiary, Heba; Mirjalili, Seyedali (Applied Intelligence, 2023)
Over recent decades, research in Artificial Intelligence (AI) has developed a broad range of approaches and methods that can be utilized or adapted to address complex optimization problems. As real-world problems get increasingly complicated, this requires an effective optimization method. Various meta-heuristic algorithms have been developed and applied in the optimization domain. This paper used and ameliorated a promising meta-heuristic approach named Crow Search Algorithm (CSA) to address numerical optimization problems. Although CSA can efficiently optimize many problems, it needs more searchability and early convergence. Its positioning updating process was improved by supporting two adaptive parameters: flight length (fl) and awareness probability (AP) to tackle these curbs. This is to manage the exploration and exploitation conducts of CSA in the search space. This process takes advantage of the randomization of crows in CSA and the adoption of well-known growth functions. These functions were recognized as exponential, power, and S-shaped functions to develop three different improved versions of CSA, referred to as Exponential CSA (ECSA), Power CSA (PCSA), and S-shaped CSA (SCSA). In each of these variants, two different functions were used to amend the values of fl and AP. A new dominant parameter was added to the positioning updating process of these algorithms to enhance exploration and exploitation behaviors further. The reliability of the proposed algorithms was evaluated on 67 benchmark functions, and their performance was quantified using relevant assessment criteria. The functionality of these algorithms was illustrated by tackling four engineering design problems. A comparative study was made to explore the efficacy of the proposed algorithms over the standard one and other methods. Overall results showed that ECSA, PCSA, and SCSA have convincing merits with superior performance compared to the others.
0 citations (Crossref) [2023-10-31]
Keywords: Crow search algorithm; Engineering problems; Meta-heuristics; Optimization; dept.csc; peer-reviewed

SoccerNet: A Gated Recurrent Unit-based model to predict soccer match winners - AlMulla, Jassim; Islam, Mohammad Tariqul; Al-Absi, Hamada R. H.; Alam, Tanvir (PLOS ONE, 2023)
Winning football matches is the major goal of all football clubs in the world. Football being the most popular game in the world, many studies have been conducted to analyze and predict match winners based on players’ physical and technical performance. In this study, we analyzed the matches from the professional football league of Qatar Stars League (QSL) covering the matches held in the last ten seasons. We incorporated the highest number of professional matches from the last ten seasons covering from 2011 up to 2022 and proposed SoccerNet, a Gated Recurrent Unit (GRU)-based deep learning-based model to predict match winners with over 80% accuracy. We considered match- and player-related information captured by STATS platform in a time slot of 15 minutes. Then we analyzed players’ performance at different positions on the field at different stages of the match. Our results indicated that in QSL, the defenders’ role in matches is more dominant than midfielders and forwarders. Moreover, our analysis suggests that the last 15–30 minutes of match segments of the matches from QSL have a more significant impact on the match result than other match segments. To the best of our knowledge, the proposed model is the first DL-based model in predicting match winners from any professional football leagues in the Middle East and North Africa (MENA) region. We believe the results will support the coaching staff and team management for QSL in designing game strategies and improve the overall quality of performance of the players.
0 citations (Crossref) [2023-10-31]
Keywords: Decision tree learning; Forecasting; Machine learning; Machine learning algorithms; Neural networks; Neurons; Qatar; Sports; dept.csc; peer-reviewed

Genetic Algorithm Based Service Broker Policy to find Optimal Datacenters in Cloud Services - Chowdhury, S.; Katangur, A.; Sheta, A.; Psayadala, N.R.; Liu, S. (2023 8th International Conference on Cloud Computing and Big Data Analytics, ICCCBDA 2023, 2023)
In modern-day computing, cloud services are widely used in every aspect of life. So, user satisfaction depends on the effectiveness and efficiency of cloud services. Service broker policy of the cloud maintains the effectiveness and efficiency of cloud services. Service broker policy provides the rules and norms based on which a data center is selected for a userbase request. This paper proposes a genetic algorithm-based service broker policy that provides the optimal sequence of data centers for different userbases based on their requirements. This research aims to find an optimal data center for userbases that can achieve user satisfaction by minimizing the cloud service's response time and data processing time. We have experimented with our proposed genetic algorithm-based service broker policy in the CloudAnalyst platform based on different real-world scenarios. Simulation results indicate that our proposed genetic algorithm outperforms existing traditional algorithms. © 2023 IEEE.
0 citations (Crossref) [2023-10-31]
Keywords: cloud computing; dept.csc; genetic algorithm; service broker policy

The Use of Census-Based Geographic Automata Systems in Understanding the Spread of Infectious Disease. - Vitale, Joseph (2010)
Keywords: dept.csc; faculty_collection; sabbatical_report

Google Hacking. - Lancour, Lisa (2005)
Work in the area of Google Hacking resulted in two presentations, 2 publications, a grant of $2500, and incorporation of results in two courses (Web Security CSC553)and (Information Security CSC453).
Keywords: dept.csc; faculty_collection; sabbatical_report

Bioinformatics Education. - BarandaranSeyed, Taraneh (2003)
Keywords: dept.csc; faculty_collection; sa

Assessing the impact of measurement artifacts on energy loss calculation in electrical distribution grids - Antonios, Imad; Schwefel, Hans-Peter (e-Energy 2023 - Proceedings of the 2023 14th ACM International Conference on Future Energy Systems, 2023)
Monitoring of electrical distribution grids requires the joint processing of electrical measurements from different grid locations. Such type of processing is influenced by inaccuracies in measurement data originating from measurement errors, non-ideal clocks in measurement devices, and from time averaging of measurands as part of the data collection process. This paper introduces an approach to assess the impact of these three different measurement artifacts in realistic measurement scenarios of electrical distribution grids. A case study of power loss calculation in a real-life medium-voltage grid is presented, covering both technical loss obtained from current measurement and total loss obtained from power measurements. The results show that total loss in general is more robust to aggregation of power measurements over longer measurement intervals, while it is more sensitive to measurement errors and clock offsets. The results of the study are important for quantifying the trustworthiness of the obtained loss values and for the future enhancement of the measurement data collection process. © 2023 ACM.
0 citations (Crossref) [2023-10-31]
Keywords: Energy loss; aggregation errors; dept.csc; electricity distribution grids; measurement errors; time alignment errors

Optical Disc Segmentation from Retinal Fundus Images Using Deep Learning - Islam, Mohammad Tariqul; Ahmed, Ferdaus; Househ, Mowafa; Alam, Tanvir (Studies in health technology and informatics, 2023)
The optical disc in the human retina can reveal important information about a person's health and well-being. We propose a deep learning-based approach to automatically identify the region in human retinal images that corresponds to the optical disc. We formulated the task as an image segmentation problem that leverages multiple public-domain datasets of human retinal fundus images. Using an attention-based residual U-Net, we showed that the optical disc in a human retina image can be detected with more than 99% pixel-level accuracy and around 95% in Matthew's Correlation Coefficient. A comparison with variants of UNet with different encoder CNN architectures ascertains the superiority of the proposed approach across multiple metrics.
0 citations (Crossref) [2023-10-31]
Keywords: CNN; Qatar Biobank; Retina; Segmentation; dept.csc; peer-reviewed

A Deep Learning-Based Approach Towards Simultaneous Localization of Optic Disc and Fovea from Retinal Fundus Images - Islam, Mohammad Tariqul; Ahmed, Ferdaus; Househ, Mowafa; Alam, Tanvir (Studies in health technology and informatics, 2023)
In this work, we propose a multi-task learning-based approach towards the localization of optic disc and fovea from human retinal fundus images using a deep learning-based approach. Formulating the task as an image-based regression problem, we propose a Densenet121-based architecture through an extensive set of experiments with a variety of CNN architectures. Our proposed approach achieved an average mean absolute error of only 13pixels (0.04%), mean squared error of 11 pixels (0.005%), and a root mean square error of only 0.02 (13%) on the IDRiD dataset.
0 citations (Crossref) [2023-10-31]
Keywords: Fovea; Optic Disc; Qatar Biobank; Retina; dept.csc; peer-reviewed

Computer Systems: Digital Design, Fundamentals of Computer Architecture and Assembly Language - Elahi, Ata (Springer Nature Switzerland AG, 2022)
Keywords: Assembly languages (Electronic computers); Computer engineering; Computer architecture; dept.csc; faculty_collection

Big Data Framework - Abdel-Raouf, Amal (2016)
The objective of my sabbatical leave project was to propose a new scheduling algorithm that extends the current MapReduce model to improve system performance. MapReduce, which has been popularized by Google, is a scalable tool that enables the processing of massive volumes of data.
Keywords: dept.csc; faculty_collection; sabbatical_report

Sabbatical leave report: Spring 2014 - Antonios, Imad (2014)
the page describes the 3 projects Antonios completed: Data Network Traffic Analysis, Gossiping Algorithm Development and Analysis, and ZigBee Networks
Keywords: dept.csc; faculty_collection; sabbatical_report

A Platform for Development and Test of Spatio-Temporal Algorithms. - Seyed, Taraneh Baradaran (2011)
In-depth research of theoretical foundations of spatio-temporal databases and their indexing, leading to the development of software.
Keywords: dept.csc; faculty_collection; sabbatical_report

Zigbee Wireless Sensor and Control Network - Elahi, Ata; Gschwender, Adam (Prentice Hall, 2010)
Topics include: Fundamental wireless concepts: OSI Model, error detection, the ISM Band, modulation, WLAN, FHSS, DSSS, Wireless MANs, Bluetooth ; ZigBee essentials: applications, characteristics, device types, topologies, protocol architecture, and expanded ZigBee PRO features ; Physical layer: includes frequency bands, data rate, channels, data/management services, transmitter power, and receiver sensitivity ; MAC layer: data/management services, MAC layer information base, access methods, and frames ; Network layer: data entities, NIB, device configuration, starting network, addressing, discovery, channel scanning ; Application support sublayer and application layer: includes profiles, cluster format, attributes, device discovery, and binding ; ZigBee network security: includes encryption, trust center, security modes, and security management primitives ; Address assignment and routing techniques ; Alternative technologies: 6lowpan, WirelessHART, and Z-wave.:–pub. desc.
Keywords: Industrial applications; Industrial applications; Radio frequency; Personal communication service systems; Standards; Sensor networks; Low voltage systems; dept.csc; faculty_collection

Data, Network, and Internet Communications Technology - Elahi, Ata; Elahi, Mehran (Thomson Delmar Learning, 2006)
"This thorough primer examines the technological aspects of networking through a practical approach. Readers will gain knowledge of local area networks (LANs), wide area networks (WANs), the Internet, wireless LANs, wireless MANs, voice over IP (VoIP), as well as asynchronous transfer mode (ATM) and network security. Introductory chapters on foundational topics, such as data communications and computer architecture give readers the knowledge base they need to understand more complex networking concepts. This book effectively utilizes a practical approach to networking rather than a strict focus on theory or math, and requires no prior background in communications technology."–BOOK JACKET.
Keywords: Computer networks; Textbooks; Textbooks; Data transmission systems; dept.csc; faculty_collection

Network Communications Technology - Elahi, Ata (Delmar, 2001)
Keywords: Computer networks; Data transmission systems; dept.csc; faculty_collection

A cost-effective Wi-Fi-based indoor positioning system for mobile phones - Wandell, R.; Hossain, M.S.; Hussain, I. (Wireless Networks, 2023)
Fingerprinting-based indoor positioning systems require a significant amount of time to set up due to the need for prior, offline signal map creation. We propose a mobile phone-based indoor positioning system that can be set up in a short amount of time in any environment with existing Wi-Fi infrastructure. We introduce interpolation into a fingerprinting-based system to reduce the number of reference points needed, leading to a reduction in signal map creation time. The proposed interpolation method is used in conjunction with a particle filter algorithm to provide an accuracy level comparable to the state-of-the-art. We created signal maps at three separate locations using a 100%, 50%, 20%, and 10% scan coverage in order to evaluate the effectiveness of our interpolation on the localization error on a lower scan percentage. We evaluated our signal maps before and after interpolation using 16 tests which included both motion and stationary tests, as well as tests taken 2 and 3 weeks after the initial data gathering. We show that our interpolation method is able to reduce the effects of a dimensional mismatch between signal map reference point vectors and a test sample vector, as well as reduce the effects of signal map aging. © 2023, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
0 citations (Crossref) [2023-10-31]
Keywords: Fingerprinting; Indoor positioning system; Interpolation; Mobile phones; Wi-Fi; dept.csc; peer-reviewed

Evolutionary design of a PSO-tuned multigene symbolic regression genetic programming model for river flow forecasting - Sheta, A.; Abdel-Raouf, A.; Fraihat, K.M.; Baareh, A. (International Journal of Advanced Computer Science and Applications, 2023)
The earth’s population is growing at a rapid rate, while the availability of water resources remains limited. Water is required for various purposes, including drinking, agriculture, industry, recreation, and development. Accurate forecasting of river flows can have a significant economic impact, particularly in agricultural water management and planning during water resource scarcity. Developing precise river flow forecasting models can greatly improve the management of water resources in many countries. In this study, we propose a two-phase model for predicting the flow of the Blackwater river located in the South Central United States. In the first phase, we use Multigene Symbolic Regression Genetic Programming (MG-GP) to develop a mathematical model. In the second phase, Particle Swarm Optimization (PSO) is employed to fine-tune the model parameters. Fine-tuning the MG-GP parameters improves the prediction accuracy of the model. The newly fine-tuned model exhibits 96% and 94% accuracy in training and testing cases, respectively © 2023, International Journal of Advanced Computer Science and Applications.All Rights Reserved.
0 citations (Crossref) [2023-10-31]
Keywords: River flow; dept.csc; evolutionary computation; forecasting; genetic programming; particle swarm optimization; peer-reviewed

GeNeo: A Bioinformatics Toolbox for Genomics-Guided Neoepitope Prediction - Al Seesi, S.; Al-Okaily, A.; Shcheglova, T.V.; Sherafat, E.; Alqahtani, F.H.; Hagymasi, A.T.; Kaur, A.; Srivastava, P.K.; Mǎndoiu, I.I. (Journal of Computational Biology, 2023)
High-Throughput DNA and RNA sequencing are revolutionizing precision oncology, enabling personalized therapies such as cancer vaccines designed to target tumor-specific neoepitopes generated by somatic mutations expressed in cancer cells. Identification of these neoepitopes from next-generation sequencing data of clinical samples remains challenging and requires the use of complex bioinformatics pipelines. In this paper, we present GeNeo, a bioinformatics toolbox for genomics-guided neoepitope prediction. GeNeo includes a comprehensive set of tools for somatic variant calling and filtering, variant validation, and neoepitope prediction and filtering. For ease of use, GeNeo tools can be accessed via web-based interfaces deployed on a Galaxy portal publicly accessible at https://neo.engr.uconn.edu/. A virtual machine image for running GeNeo locally is also available to academic users upon request. © Copyright 2023, Mary Ann Liebert, Inc., publishers 2023.
0 citations (Crossref) [2023-10-31]
Keywords: bioinformatics tools; cancer immunotherapy; dept.csc; neoepitope prediction; peer-reviewed; somatic variant calling

Swipe gestures for user authentication in smartphones - Chao, Jedrik; Hossain, Md Shafaeat; Lancor, Lisa (Journal of Information Security and Applications, 2023)
As more people rely on smartphones to store sensitive information, the need for robust security measures is all the more pressing. Because traditional one shot authentication methods like PINs and passwords are vulnerable to various attacks, we present a behavioral biometrics based smartphone authentication system using swipes. While previous research focused on a single kind of swipe, our data set features swipes using different fingers and directions collected from 36 users across three sessions. In our system, we experimented with support vector machine (SVM) and random forest (RF) classifiers. We investigated which finger, direction, and classifier provided the best individual swipe authentication results. Then, we analyzed whether fusion of different fingers and directions improved results. The best unimodal result came from a rightward swipe with right thumb using SVM, which resulted in an area under ROC curve (AUC) of 0.936 and an equal error rate (EER) of 0.135. We found that swipes using thumbs offered better performance. Fusion improves results for the most part, and our best result was the combination of a leftward swipe with right thumb and a leftward swipe with left thumb. This combination gave an AUC of 0.969 and EER of 0.081 with the SVM classifier.
0 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Smartphone security; Swipe gesture; Touchscreen; dept.csc; peer-reviewed

Comparing Machine Learning Models for Intron Classification - Kohnert, C.; Yu, N.; Rosenberg, P.; Torgerson, M.; Sobky, A.E.; Elkharboutly, R.; Seesi, S.A. (2022)
This work explores using Probabilistic Context Free Grammars and Artificial Neural Networks as possible machine learning models for classifying introns into major and minor introns. It presents an intron classification framework that combines probabilistic context free grammars and support vector machines. It also assesses the computational prediction power of these two models in comparison to the Position Weight Matrices technique, which is currently the exclusively used model for intron classification. The comparison is done through experimental analysis, and it shows promising results for Probabilistic Context Free Grammars and Artificial Neural Networks. © 2022 IEEE.
0 citations (Crossref) [2023-10-31]
Keywords: Artificial Neural Network; Position Weight Matrices; Probabilistic Context Free Grammars; U12 introns; U2 introns; dept.csc; intron classification; major introns; minor introns

Bad dream frequency predicts mental health needs during the SARS-CoV-2 pandemic - Simmons, Claire; McKee, Paul; Antonios, Imad; Smith, Anna; Sinnott-Armstrong, Walter (Journal of Affective Disorders Reports, 2022)
0 citations (Crossref) [2023-10-31]
Keywords: dept.csc; peer-reviewed

On error reduction by the symmetric rejection method in multi-stage biometric verification systems - Hossain, M.S.; Chen, J.; Rahman, K. (Pattern Analysis and Applications, 2022)
A multi-stage biometric verification system serially activates its verifiers and improves performance-cost trade-off by allowing users to submit a subset of the available biometrics. In the heart of a verifier in multi-stage systems lies the concept of ‘reject option’ where a reject region is used to identify a bad quality test sample. If the match-score falls inside the reject region, no binary (genuine/impostor) decision is made in the current stage and the verifier in the next stage is activated. Recent studies have demonstrated a significant promise of the ‘symmetric rejection method’ in choosing a suitable reject region for multi-stage verification systems. In this paper, we delve into the symmetric rejection method to gain more insights into its error reduction capabilities. Specifically, we develop a theory which mathematically proves that the symmetric rejection method reduces the false accept rate and false reject rate. Then, we empirically validate our theory. Results show that the symmetric rejection method significantly reduces the error rates, both the false accept rate and false reject rate. © 2022, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
0 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Multi-stage system; Serial fusion; Symmetric rejection method; dept.csc; peer-reviewed

Review of Application of Machine Learning as a Screening Tool for Diagnosis of Obstructive Sleep Apnea - Aiyer, Ishan; Shaik, Likhita; Sheta, Alaa; Surani, Salim (Medicina, 2022)
Obstructive sleep apnea syndrome (OSAS) is a pervasive disorder with an incidence estimated at 5–14 percent among adults aged 30–70 years. It carries significant morbidity and mortality risk from cardiovascular disease, including ischemic heart disease, atrial fibrillation, and cerebrovascular disease, and risks related to excessive daytime sleepiness. The gold standard for diagnosis of OSAS is the polysomnography (PSG) test which requires overnight evaluation in a sleep laboratory and expensive infrastructure, which renders it unsuitable for mass screening and diagnosis. Alternatives such as home sleep testing need patients to wear diagnostic instruments overnight, but accuracy continues to be suboptimal while access continues to be a barrier for many. Hence, there is a continued significant underdiagnosis and under-recognition of sleep apnea in the community, with at least one study suggesting that 80–90% of middle-aged adults with moderate to severe sleep apnea remain undiagnosed. Recently, we have seen a surge in applications of artificial intelligence and neural networks in healthcare diagnostics. Several studies have attempted to examine its application in the diagnosis of OSAS. Signals included in data analytics include Electrocardiogram (ECG), photo-pletysmography (PPG), peripheral oxygen saturation (SpO2), and audio signals. A different approach is to study the application of machine learning to use demographic and standard clinical variables and physical findings to try and synthesize predictive models with high accuracy in assisting in the triage of high-risk patients for sleep testing. The current paper will review this latter approach and identify knowledge gaps that may serve as potential avenues for future research.
4 citations (Crossref) [2023-10-31] Number: 11
Keywords: ML; artificial intelligence; dept.csc; peer-reviewed; screening; sleep apnea

College student Fear of Missing Out (FoMO) and maladaptive behavior: Traditional statistical modeling and predictive analysis using machine learning - McKee, Paul C.; Budnick, Christopher J.; Walters, Kenneth S.; Antonios, Imad (PLOS ONE, 2022)
This paper reports a two-part study examining the relationship between fear of missing out (FoMO) and maladaptive behaviors in college students. This project used a cross-sectional study to examine whether college student FoMO predicts maladaptive behaviors across a range of domains (e.g., alcohol and drug use, academic misconduct, illegal behavior). Participants (N = 472) completed hard copy questionnaire packets assessing trait FoMO levels and questions pertaining to unethical and illegal behavior while in college. Part 1 utilized traditional statistical analyses (i.e., hierarchical regression modeling) to identify any relationships between FoMO, demographic variables (socioeconomic status, living situation, and gender) and the behavioral outcomes of interest. Part 2 looked to quantify the predictive power of FoMO, and demographic variables used in Part 1 through the convergent approach of supervised machine learning. Results from Part 1 indicate that college student FoMO is indeed related to many diverse maladaptive behaviors spanning the legal and illegal spectrum. Part 2, using various techniques such as recursive feature elimination (RFE) and principal component analysis (PCA) and models such as logistic regression, random forest, and Support Vector Machine (SVM), showcased the predictive power of implementing machine learning. Class membership for these behaviors (offender vs. non-offender) was predicted at rates well above baseline (e.g., 50% at baseline vs 87% accuracy for academic misconduct with just three input variables). This study demonstrated FoMO’s relationships with these behaviors as well as how machine learning can provide additional predictive insights that would not be possible through inferential statistical modeling approaches typically employed in psychology, and more broadly, the social sciences. Research in the social sciences stands to gain from regularly utilizing the more traditional statistical approaches in tandem with machine learning.
4 citations (Crossref) [2023-10-31]
Keywords: Alcohol consumption; Behavior; Cannabis; Drug interactions; Forecasting; Machine learning; Machine learning algorithms; Principal component analysis; dept.csc; dept.psy; peer-reviewed

Do Online Firms Individualize Search Results? An Empirical Analysis of Individualization on Amazon - Ahmed, Rasha; Al Seesi, Sahar; Ruiz Sánchez, Gerardo (Journal of Theoretical and Applied Electronic Commerce Research, 2022)
Online markets offer sellers access to buyers’ information and, thus, the potential to alter prices and products accordingly. In light of this, we undertook an empirical analysis to test for individualization on Amazon.com. We collect data from individuals recruited to shop for household items. Our results indicate evidence of individualization of search results and net prices (via coupons). We found, contrary to what was expected, that demographic, geolocation, and account information play an insignificant role in individualization of search results. Thus, we conclude that individualization is based on more dynamic information, e.g., online browsing behavior. This highlights the fact that sellers’ need for (and use of) buyer information goes beyond the simple information accessible from the buyers’ accounts to a more rigorous monitoring of buyers’ online behavior.
1 citations (Crossref) [2023-10-31] Number: 3
Keywords: Amazon; dept.csc; individualization; peer-reviewed; price discrimination

A machine learning based study to assess bone health in a diabetic cohort - Musleh, Saleh; Nazeemudeen, Anjanarani; Islam, Mohammad Tariqul; El Hajj, Nady; Alam, Tanvir (Informatics in Medicine Unlocked, 2022)
Diabetes mellitus (DM) and osteoporosis/osteopenia affect millions of people globally and are major health conditions in several countries including Qatar. Bone mineral density (BMD) is a widely accepted indicator for diagnosing osteoporosis (OP) and osteopenia (OPN). The best method for determining bone mineral density and OP/OPN risk is via dual energy X-ray absorptiometry (DXA) technology. The risk of osteoporosis-related fracture may increase for people with diabetes. Therefore, it is necessary to develop a system that can support the early detection of OP/OPN in diabetic patients. In this study, we analyzed Qatar diabetic cohorts including 500 subjects, among which 68 were OP/OPN (target) subjects and 432 were without osteoporosis/osteopenia (control) subjects. The objective of this study is to develop an ML model to distinguish diabetic OP/OPN patients from diabetic non-OP/non-OPN subjects based on their bone health indicators from full body DXA scan measurements. Based on our experiments, AdaBoost model performed the best for classifying the target group from the control group. 10-fold cross validation-based results indicate that the proposed ML model was able to distinguish the target group from the control group at 80% sensitivity, 96% specificity. To the best of our knowledge, our study is the first ML-based approach to detect the early onset of OP/OPN in diabetic cohort from Qatar. Our analyses revealed the higher level of lean mass, fat mass and bone mass for the control group compared to the target group. Higher levels of BMC, BMD from different body parts in the control group compared to the osteoporosis/osteopenia group indicate the protective effects of obesity on bone health in the Qatari diabetic cohort. Moreover, higher value of anthropometric measurements in troch, lumbar spine (L1, L2, L3, L4), pelvis and other body parts in the control group indicates that the WHO guideline can be applied to the Qatari diabetic cohort for the early detection of OP/OPN based on the proposed ML model. Further research on OP/OPN in diabetic patients is warranted in future to confirm the role of DM on bone health.
1 citations (Crossref) [2023-10-31]
Keywords: Bone mineral density; DXA; Diabetes; Dual energy X-ray absorptiometry; Machine learning; Osteopenia; Osteoporosis; Qatar Biobank (QBB); dept.csc; peer-reviewed

Barriers and facilitators to exclusive breastfeeding among Black mothers: A qualitative study utilizing a modified Barrier Analysis approach - Tran, Victoria; Reese Masterson, Amelia; Frieson, Tomeka; Douglass, Frankie; Pérez-Escamilla, Rafael; O'Connor Duffany, Kathleen (Maternal & Child Nutrition, 2022)
Breastfeeding has health benefits for both infants and mothers, yet Black mothers and infants are less likely to receive these benefits. Despite research showing no difference in breastfeeding intentions by race or ethnicity, inequities in breastfeeding rates persist, suggesting that Black mothers face unique barriers to meeting their breastfeeding intentions. The aim of this study is to identify barriers and facilitators that Black women perceive as important determinants of exclusively breastfeeding their children for at least 3 months after birth. Utilizing a Barrier Analysis approach, we conducted six focus group discussions, hearing from Black mothers who exclusively breastfed for 3 months and those who did not. Transcripts were coded starting with a priori parent codes based on theory-derived determinants mapped onto the Socioecological Model; themes were analysed for differences between groups. Facilitators found to be important specifically for women who exclusively breastfed for 3 months include self-efficacy, lactation support, appropriate lactation supplies, support of mothers and partners, prior knowledge of breastfeeding, strong intention before birth and perceptions of breastfeeding as money-saving. Barriers that arose more often among those who did not exclusively breastfeed for 3 months include inaccessible lactation support and supplies, difficulties with pumping, latching issues and perceptions of breastfeeding as time-consuming. Lack of access to and knowledge of breastfeeding laws and policies, as well as negative cultural norms or stigma, were important barriers across groups. This study supports the use of the Socioecological Model to design multicomponent interventions to increase exclusive breastfeeding outcomes for Black women.
3 citations (Crossref) [2023-10-31] _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1111/mcn.13428
Keywords: barrier analysis; breastfeeding; dept.csc; dept.pch; focus group; peer-reviewed; public health; qualitative methods; social factors; sociocultural analyses

Autonomous Robot System for Pavement Crack Inspection Based Cnn Model - Sheta, A.; Mokhtar, S.A. (Journal of Theoretical and Applied Information Technology, 2022)
Maintaining the excellent state of the road is critical to secure driving and is an obligation of both transportation and regulatory maintenance authorities. For a safe driving environment, it is essential to inspect road surfaces for defects or degradation frequently. This process is found to be labor-intensive and necessitates primary expertise. Therefore, it is challenging to examine road cracks visually; thus, we must effectively employ computer visualization and robotics tools to support this mission. This research provides our initial idea of simulating an Autonomous Robot System (ARS) to perform pavement assessments. The ARS for crack inspection is a camera-equipped mobile robot (i.e., an Android phone) to collect images on the road. The proposed system is simulated using an mBot robot armed with an Android phone that gathers video streams to be processed on a server that has a pre-training Convolutional Neural Networks (CNN) that can recognize crack existence. The proposed CNN model attained 99.0% accuracy in the training case and 97.5% in the testing case. The results of this research are suitable for application with a commercial mobile robot as an autonomous platform for pavement inspections. © 2022 Little Lion Scientific.
Keywords: Automatic Detection; Autonomous Robot System; Crack Detection; Deep Neural Network; Pavement Crack; Road Maintenance; dept.csc; peer-reviewed; ⛔ No DOI found

Incorporating deep learning into capacitive images for smartphone user authentication - Hossain, Md Shafaeat; Islam, Mohammad T.; Akhtar, Zahid (Journal of Information Security and Applications, 2022)
We incorporate deep learning techniques into capacitive images of body parts (ear, four fingers, and thumb) to improve the performance of user authentication in smartphones. Use of a capacitive touchscreen as an image sensor has several advantages, such as it is less sensitive to poor illumination conditions, occlusions, and pose variations. Also, it does not need an additional hardware like iris or fingerprint scanner. Use of capacitive images for user authentication is not new. However, the performance, specially, false reject rates (FRRs) of the state-of-the-art capacitive image-based systems are poor. In this paper, we focus on improving the performance and leverage deep learning. Deep learning techniques demonstrated spectacular performance in previous physical biometrics-based research. However, to our knowledge, effectiveness of deep learning is still unexplored in capacitive touchscreen-based user authentication. In order to bridge this research gap, we devise a multi-modal deep learning model, namely UASNet, and compare its performance with a large set of uni- and multi-modal baselines. Using the UASNet, we achieve an accuracy of 99.77%, an EER of 0.48%, and an FRR of 1.19% at FAR of 0.06%.
0 citations (Crossref) [2023-10-31]
Keywords: Biometrics; CNN; Capacitive images; Deep learning; Fusion; Smartphone; Touchscreen; User authentication; dept.csc; peer-reviewed

Cardiovascular Disease Diagnosis from DXA Scan and Retinal Images Using Deep Learning - Al-Absi, Hamada R. H.; Islam, Mohammad Tariqul; Refaee, Mahmoud Ahmed; Chowdhury, Muhammad E. H.; Alam, Tanvir (Sensors, 2022)
Cardiovascular diseases (CVD) are the leading cause of death worldwide. People affected by CVDs may go undiagnosed until the occurrence of a serious heart failure event such as stroke, heart attack, and myocardial infraction. In Qatar, there is a lack of studies focusing on CVD diagnosis based on non-invasive methods such as retinal image or dual-energy X-ray absorptiometry (DXA). In this study, we aimed at diagnosing CVD using a novel approach integrating information from retinal images and DXA data. We considered an adult Qatari cohort of 500 participants from Qatar Biobank (QBB) with an equal number of participants from the CVD and the control groups. We designed a case-control study with a novel multi-modal (combining data from multiple modalities—DXA and retinal images)—to propose a deep learning (DL)-based technique to distinguish the CVD group from the control group. Uni-modal models based on retinal images and DXA data achieved 75.6% and 77.4% accuracy, respectively. The multi-modal model showed an improved accuracy of 78.3% in classifying CVD group and the control group. We used gradient class activation map (GradCAM) to highlight the areas of interest in the retinal images that influenced the decisions of the proposed DL model most. It was observed that the model focused mostly on the centre of the retinal images where signs of CVD such as hemorrhages were present. This indicates that our model can identify and make use of certain prognosis markers for hypertension and ischemic heart disease. From DXA data, we found higher values for bone mineral density, fat content, muscle mass and bone area across majority of the body parts in CVD group compared to the control group indicating better bone health in the Qatari CVD cohort. This seminal method based on DXA scans and retinal images demonstrate major potentials for the early detection of CVD in a fast and relatively non-invasive manner.
17 citations (Crossref) [2023-10-31] Number: 12
Keywords: DXA; Qatar Biobank (QBB); cardiovascular diseases; deep learning; dept.csc; machine learning; peer-reviewed; retina

Bidirectional reservoir networks trained using SVM$$+$$privileged information for manufacturing process modeling - Rodan, Ali; Sheta, Alaa F.; Faris, Hossam (Soft Computing, 2017)
In the last decade, a wide range of machine learning approaches were proposed and experimented to model highly nonlinear manufacturing processes. However, improving the performance of such models is challenging due to the complexity and high dimensionality of the manufacturing processes in general. In this paper, we propose bidirectional echo state reservoir networks (Bi-ESNs) trained using support vector machine privileged information method (SVM$$+$$) to model a winding machine process. The proposed model will be applied, tested and compared to reported models in the literature such as classical ESN with linear regression, ESN with a linear SVM readout, genetic programming, feedfoward neural network with backpropagation, radial basis function network, adaptive neural fuzzy inference system and local linear wavelet neural network. The developed results show that Bi-ESNs trained with SVM$$+$$are promising. It was able to provide better generalization performance compared to other models.
19 citations (Crossref) [2023-10-31]
Keywords: Engineering process; Privileged information; Recurrent neural network; Reservoir computing; Support vector machines; Winding machines; dept.csc; peer-reviewed

Zoom gesture analysis for age-inappropriate internet content filtering - Pulfrey, J.; Hossain, M.S. (Expert Systems with Applications, 2022)
The Internet contains large amounts of adult content. With only a few taps, or mis-taps, an under-aged user can be exposed to age-inappropriate content. Currently, this can be avoided by creating age-restricted profiles or restricting users to child-friendly applications (apps). However, these existing measures are time-consuming, laborious, and require a higher level of technical literacy than many parents can afford. We believe a better solution is to use a browser or an app that automatically detects the user's age then applies any appropriate content filters. For such a browser/app to be developed, we must learn that age estimation can indeed be performed with an acceptable rate of error. To that end, we created an Android app that collects biometric touchscreen data from elementary school, middle school, high school, and university students. Touch samples were collected from participants aged 5 to 61 on both smartphones and tablets. We focused exclusively on zoom-in and zoom-out touchscreen data samples. We made this decision because we found the zoom gesture to be rich with data and highly used among the most popular applications. Furthermore, we identify a niche within the current research landscape: no other machine learning experiments have leveraged the benefits of the zoom gesture for age estimation. We collected a total of 41,911 zoom data samples. From each zoom sample, 90 features were extracted. Those features were then used to train and test on six regressors and six classifiers to build a method that can accurately estimate the user's age from their touchscreen behavior. The regressors performed with the best mean absolute errors (MAEs) of 2.27 and 2.54 years for smartphones and tablets, respectively. The classifiers performed with the best accuracies of 90% and 91% for smartphones and tablets, respectively. Given these results, it is our belief that not only is touch-based age estimation viable, but developing a child-safe browser or a parental control app with this underlying technology is a worthwhile endeavor. © 2022 Elsevier Ltd
5 citations (Crossref) [2023-10-31]
Keywords: Age estimation; Child safety; Smartphone; Touch gesture; Zoom gesture; dept.csc; peer-reviewed

Three non-Bayesian methods of spam filtration: CRM114 at TREC 2007 - Kato, M.; Langeway, J.; Wu, Y.; Yerazunis, W.S. (NIST Special Publication, 2007)
For the TREC 2007 conference, the CRM114 team considered three non-Bayesian methods of spam filtration in the CRM114 framework - an SVM based on the "hyperspace" feature==document paradigm, a bit-entropy matcher, and substring compression based on LZ77. As a calibration yardstick, we used the well-tested and widely used CRM114 OSB markov random field system (basically unchanged since 2003). The results show that the SVM has a spam-filtering accuracy of about a factor of two to three better accuracy than the OSB system, that substring compression is somewhat more accurate than OSB, and that bit entropy is somewhat less accurate for the TREC 2007 test sets.
Journal Abbreviation: NIST Spec. Publ.
Keywords: Bayesian networks; Entropy; Information retrieval; Internet; Markov Random Fields; Substring compression; Test sets; dept.csc

A Double k-clustering approach for restructuring Distributed Object-Oriented software - Hamad, S.H.; Fergany, T.; Ammar, R.A.; El-Raouf, A.A.A. (Proc. IEEE Symp. Comput. Commun., 2008)
There have been a large number of projects based on the Distributed Object Oriented (DOO) approach for solving complex problems in various scientific fields. The Mismatch problem is one of the most important problems facing the DOO system, where the initial design of the DOO application does not give the best class distribution. In such a case, the DOO software may need to be restructured. In this paper, we propose a methodology for efficiently restructuring the DOO software classes to be mapped on a distributed system consisting of a set of nodes. The proposed methodology consists of two phases. The first phase introduces a recursive graph clustering technique to partition the OO system into subsystems with low coupling. The second phase is concerned with mapping the generated partitions to the set of available machines in the target distributed architecture. A simulation evaluation was carried out for a set of randomly generated DOO software designs. Then the results were compared with those of the K-Partitioning algorithm in terms of the overall inter-class communication cost. © 2008 IEEE.
3 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. IEEE Symp. Comput. Commun.
Keywords: Class distributions; Clustering approaches; Communication costs; Complex problems; Computer software; Distributed architectures; Distributed objects; Distributed systems; Flow of solids; Graph clustering; Initial designs; Low couplings; Mismatch problems; Object oriented programming; Oo systems; Partitioning algorithms; Problem solving; Scientific fields; Simulation evaluations; Software design; Sub systems; Two phases; dept.csc

An information system for multi-instrument atmospheric laser radar studies - Sharma, N.; Parikh, J.A. (WMSCI - World Multi-Conf. Syst., Cybern. Informatics, Proc., 2010)
In this paper we describe an information system that we have designed for students and researchers to conduct atmospheric studies using data that they have collected from multiple atmospheric instruments including two laser radar (lidar) systems. The lidar systems available for research include a monostatic Micro Pulse Lidar System and a bistatic imaging CLidar system. Complementary instruments for data analysis and ground truth specification include a nephelometer, sunphotometers and a weather station. Information structures within the system allow users to 1) label, describe and archive raw and derived datasets from multiple atmospheric instruments with associated metadata using NetCDF format, 2) link together coincident and co-located datasets from different instruments and 3) identify owner and verify user access rights of raw and derived datasets. Data analysis software tools have been developed in MATLAB to characterize and remove instrument artifacts based on experimental lidar studies, to analyze clear sky data to determine variability in atmospheric aerosol content over time and altitude, and to investigate cloud and aerosol patterns.
Journal Abbreviation: WMSCI - World Multi-Conf. Syst., Cybern. Informatics, Proc.
Keywords: Aerosol patterns; Atmospheric aerosols; Atmospheric instruments; Atmospheric laser; Atmospheric studies; Bistatic; Clear sky; Co-located; Cybernetics; Data analysis softwares; Data sets; Ground truth; Information science; Information structures; Information systems; Instruments; Laser radar; Lasers; Lidar; Lidar systems; Metadata; Micro pulse lidar; Monostatic; Optical radar; Sun-photometers; User access; Weather stations; dept.csc

Meeting deadlines within object-oriented systems - El-Raouf, A.A. (International Conference on High Performance Computing and Simulation, 2011)
In real-time software systems, meeting deadlines is crucial. Software engineers face many challenges to model the object-oriented software system to handle complex real-time constraints. The accurate estimating of the performance time is a key criterion for a precise scheduling decision. This paper presents an object-oriented performance model that analyzes the behavior of the real-time objects' tasks whose executions are controlled by a scheduler. Each task is subject to a time/utility function (TUF) that determines the accrued utility of the task according to its completion time. The scheduling scheme uses both the estimated time generated by the object-oriented performance model and the time utility function (TUF) of each task in the object-oriented system in order to maximize the total accrued utility. In addition, we implemented a software tool to conduct experimental study in order to show the effectiveness of our approach. © 2011 IEEE.
0 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. Int. Conf. High Perform. Comput. Simul., HPCS
Keywords: Completion time; Computer software selection and evaluation; Experimental studies; Object oriented; Object oriented software; Object-oriented software systems; Object-oriented system; Performance Model; Performance analysis; Real time constraints; Real time systems; Real-time software; Scheduling; Scheduling decisions; Scheduling schemes; Software engineers; Software tool; Time-utility functions; deadline; dept.csc; object-oriented software; performance analysis; scheduling; time utility function

ARM Assembly Language with Hardware Experiments - Elahi, Ata; Arjeski, Trevor (Springer, 2015)
This book provides a hands-on approach to learning ARM assembly language with the use of a TI microcontroller. The book starts with an introduction to computer architecture and then discusses number systems and digital logic. The text covers ARM Assembly Language, ARM Cortex Architecture and its components, and Hardware Experiments using TILM3S1968. Written for those interested in learning embedded programming using an ARM Microcontroller. © Springer International Publishing Switzerland 2015.
Keywords: ARM microcontrollers; ARM processors; Arm cortices; Assembly language; Assembly languages (Electronic computers); Microprocessors; Computer architecture; Computer hardware; Digital logic; Education; Embedded programming; Hardware; Hardware experiment; Microcontrollers; Number system; Numbering systems; Programming; dept.csc; faculty_collection

Analyzing HTTP-Based Information Exfiltration of Malicious Android Applications - Kelkar, S.; Kraus, T.; Morgan, D.; Zhang, J.; Dai, R. (Proc. - IEEE Int. Conf. Trust, Secur. Priv. Comput. Commun. IEEE Int. Conf. Big Data Sci. Eng., Trustcom/BigDataSE, 2018)
Exfiltrating sensitive information from smartphones has become one of the most significant security threats. We have built a system to identify HTTP-based information exfiltration of malicious Android applications. In this paper, we discuss the method to track the propagation of sensitive information in Android applications using static taint analysis. We have studied the leaked information, destinations to which information is exfiltrated, and their correlations with types of sensitive information. The analysis results based on 578 malicious Android applications have revealed that a significant portion of these applications are interested in identity-related sensitive information. The vast majority of malicious applications leak multiple types of sensitive information. We have also identified servers associated with three country codes including CN, US, and SG are most active in collecting sensitive information. The analysis results have also demonstrated that a wide range of non-default ports are used by suspicious URLs. © 2018 IEEE.
4 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. - IEEE Int. Conf. Trust, Secur. Priv. Comput. Commun. IEEE Int. Conf. Big Data Sci. Eng., Trustcom/BigDataSE
Keywords: Android (operating system); Android Applications; Android applications; Big data; Data privacy; Exfiltration; HTTP; HTTP Based; Information Exfiltration; Malicious android applications; Security threats; Sensitive informations; dept.csc

Nonperturbative strange-quark sea from lattice QCD, light-front holography, and meson-baryon fluctuation models - Sufian, R.S.; Liu, T.; De Téramond, G.F.; Dosch, H.G.; Brodsky, S.J.; Deur, A.; Islam, M.T.; Ma, B.-Q.; HLFHS Collaboration (Physical Review D, 2018)
We demonstrate that a nonzero strangeness contribution to the spacelike electromagnetic form factor of the nucleon is evidence for a strange-antistrange asymmetry in the nucleon's light-front wave function, thus implying different nonperturbative contributions to the strange and antistrange quark distribution functions. A recent lattice QCD calculation of the nucleon strange quark form factor predicts that the strange quark distribution is more centralized in coordinate space than the antistrange quark distribution, and thus the strange quark distribution is more spread out in light-front momentum space. We show that the lattice prediction implies that the difference between the strange and antistrange parton distribution functions, s(x)-s(x), is negative at small-x and positive at large-x. We also evaluate the strange quark form factor and s(x)-s(x) using a baryon-meson fluctuation model and a novel nonperturbative model based on light-front holographic QCD. This procedure leads to a Veneziano-like expression of the form factor, which depends exclusively on the twist of the hadron and the properties of the Regge trajectory of the vector meson which couples to the quark current in the hadron. The holographic structure of the model allows us to introduce unambiguously quark masses in the form factors and quark distributions preserving the hard scattering counting rule at large-Q2 and the inclusive counting rule at large-x. Quark masses modify the Regge intercept which governs the small-x behavior of quark distributions, therefore modifying their small-x singular behavior. Both nonperturbative approaches provide descriptions of the strange-antistrange asymmetry and intrinsic strangeness in the nucleon consistent with the lattice QCD result. © 2018 authors. Published by the American Physical Society.
18 citations (Crossref) [2023-10-31]
Keywords: dept.csc; peer-reviewed

Authorship identification on limited samplings - Boran, T.; Martinaj, M.; Hossain, M.S. (Computers and Security, 2020)
The internet has changed the way that many people access written works. Books and articles, of various lengths, in several formats can be bought and accessed online, both legally and illegally. Texts in even shorter form are originating through forums, SMS, blogs, emails, and social media. Automating the process of determining the authorship of posted texts would help combat online piracy of copyrighted text and plagiarism. In addition, authorship identification could help detect fraudulent email messages from dangerous sources and combat cyberattacks by identifying authentic sources. We experiment with several machine learning algorithms on a limited set of public domain literature to identify the most efficient method of authorship identification using the least amount of samples. Different sized data sets are created by 5 predefined rounds of random sampling of 1500 word blocks on a total of 28 text books from a corpus of 7 authors. Traditional methods of authorship identification, such as Naive Bayes, Artificial Neural Network, and Support Vector Machine are implemented in addition to using a modern Deep Learning Neural Network for classification. Thirteen stylometric features are extracted ranging from character based, word based, and syntactic features. Our model consistently showed that Support Vector Machine out performs other classification methods. © 2020
6 citations (Crossref) [2023-10-31]
Keywords: Authorship identification; Classification methods; Dangerous sources; Deep learning; Electronic mail; Email messages; Learning algorithms; Learning neural networks; Learning systems; Neural networks; Phishing attacks; Plagiarism; Random sampling; Stylometric features; Support vector machines; Syntactic features; dept.csc; peer-reviewed

A user-centered design of a major and minor intron annotation database (MMIAD) - ElKharboutly, R.; Gorman, K.; Eccles, W.; Seesi, S.A. (Proc. IADIS Int. Conf. Interfaces Hum. Comput. Interact., IHCI Proc. IADIS Int. Conf. Game Entertain. Technol., GET - Part Multi Conf. Comput. Sci. Inf. Syst., MCCSIS, 2020)
Biologists and bioinformaticians heavily rely on data portals and repositories accessible through web application. While they mostly agree that the data is valuable, they find the interfaces hard to use and non-intuitive. In this paper we present a user-centered design of a database for classification and annotation for major and minor introns in various species. Our design is based on surveying and interviewing minor intron researchers and comparing the features of existing databases. In addition to its ease of use, the proposed database, Major and Minor Intron Annotation Database (MMIAD) offers high flexibility in querying and downloading subsets of information that interest the user in multiple commonly used file formats. © Proceedings of the 14th IADIS International Conference Interfaces and Human Computer Interaction 2020, IHCI 2020 and Proceedings of the 13th IADIS International Conference Game and Entertainment Technologies 2020, GET 2020 - Part of the 14th Multi Conference on Computer Science and Information Systems, MCCSIS 2020. All rights reserved.
Journal Abbreviation: Proc. IADIS Int. Conf. Interfaces Hum. Comput. Interact., IHCI Proc. IADIS Int. Conf. Game Entertain. Technol., GET - Part Multi Conf. Comput. Sci. Inf. Syst., MCCSIS
Keywords: Bioinformaticians; Bioinformatics; Classification (of information); Computer games; Data portal; Database systems; Ease-of-use; Entertainment; File formats; High flexibility; Human computer interaction; Information systems; Information use; Introns; Query processing; User centered design; User-Centered Development; WEB application; dept.csc

Touch behavior based age estimation toward enhancing child safety - Hossain, M.S.; Haberfeld, C. (IJCB - IEEE/IAPR Int. Jt. Conf. Biom., 2020)
Adult content on the Internet may be accessed by children with only a few keystrokes. While separate child-safe accounts may be established, a better approach could be incorporating automatic age estimation capability into the browser. We envision a safer browsing experience by implementing child-safe browsers combined with Internet content rating similar to the film industry. Before such a browser is created it was necessary to test the age estimation module to see whether acceptable error rates are possible. We created an Android application for collecting biometric touch data, specifically tapping data. We arranged with an elementary school, a middle school, a high school, and a university and collected samples from 262 user sessions (ages 5 to 61). From the tapping data, feature vectors were constructed, which were used to train and test 14 regressors and classifiers. Results for regression show the best mean absolute errors of 3.451 and 3.027 years, respectively, for phones and tablets. Results for classification show the best accuracies of 73.63% and 82.28%, respectively, for phones and tablets. These results demonstrate that age estimation, and hence, a child-safe browser, is feasible, and is a worthwhile objective. © 2020 IEEE.
4 citations (Crossref) [2023-10-31] Journal Abbreviation: IJCB - IEEE/IAPR Int. Jt. Conf. Biom.
Keywords: Age estimation; Android applications; Behavior-based; Biometrics; Elementary schools; Entertainment industry; Feature vectors; Internet content; Mean absolute error; Telephone sets; User sessions; dept.csc

Continuous authentication using creative writing - Hossain, M.S.; Haberfeld, C.; Yuan, K.; Chen, J.; Rahman, K.A.; Hussain, I. (Int. Symp. Networks, Comput. Commun., ISNCC, 2020)
Traditional keyboards remain the input device of choice for typing-heavy environments. When attached to sensitive data, security is a major concern. To continuously authenticate users in these environments, use of keystroke dynamics can be a preferred choice. An integral part of user enrollment in a keystroke based continuous authentication system is the writing instruction (prompt) given to the users, to use as a basis for their improvised writing. There are many prompts possible, and they directly impact the performance of authentication systems. Hence, prompts should be designed carefully, and with purpose. In this paper, we bridge the gap between cognitive psychology and computer science and attempt to influence the mental state of the users to acquire a better authentication performance. We compare two kinds of writing prompts, creative and factual, for generating reference samples. In addition, we perform two robustness tests: robustness to dissimilar writing style (e.g., creative reference and factual test) and robustness to surface (e.g., hard surface reference and soft surface test). We collect data from thirty participants in four weekly sessions. We experiment with three features: key interval, key press, and key hold latencies. We use Relative (R) measure to generate the match score between the reference and test samples. Results show that creative writing consistently performs better than the factual one. Both writing prompts perform well with dissimilar style in testing, i.e., continuous authentication is found robust to writing style. Also, we find that the surface (hard or soft) used in testing need not match that used for the reference, thus continuous authentication is also surface robust. © 2020 IEEE.
0 citations (Crossref) [2023-10-31] Journal Abbreviation: Int. Symp. Networks, Comput. Commun., ISNCC
Keywords: Authentication; Authentication systems; Cognitive psychology; Computer networks; Continuous authentication; Continuous authentications; Creative writing; Creative writings; Data privacy; Free text; Keystroke dynamics; Relative measure; Robustness tests; Sensitive datas; Surface testing; Well testing; Writing instruction; dept.csc

Smartwatch dynamics: A novel modality and solution to attacks on cyber-behavioral biometrics for continuous verification? - Rahman, K.A.; Alam, N.; Musarrat, J.; Madarapu, A.; Hossain, M.S. (Int. Symp. Networks, Comput. Commun., ISNCC, 2020)
The cyber-behavioral biometric modalities such as keystroke dynamics, mouse dynamics, and touch screen dynamics have come under attacks of different forms in recent days. To address these attacks and other security issues, we present a novel concept of using smartwatch sensor data to continuously verify users in cyberspace and show its potential to be a new standalone cyber-behavioral biometric modality. For our experiments, smartwatch gyroscope and accelerometer data collected from 49 subjects while typing in desktop computer have been considered. We implemented six pattern matching classifiers to compare each verification attempt against the user profile. Experimental results comprising of 282, 240 classification attempts show significantly high True Positive (TP) rates and extremely low False Positive (FP) rates with the highest achieved TP rate of 87.2% and lowest FP rate of 0.2%. With this level of accuracy and natural resiliency to attacks comes with physical biometric property as such in hand movement, we opine that smartwatch movement dynamics, besides being a new biometric trait, can be a solution to the security loopholes in existing cyber-behavioral biometric modalities for continuous verification. © 2020 IEEE.
5 citations (Crossref) [2023-10-31] Journal Abbreviation: Int. Symp. Networks, Comput. Commun., ISNCC
Keywords: Accelerometer; Accelerometer data; Behavioral biometrics; Biometric traits; Biometrics; Computer networks; Continuous verification; Dynamics; False positive; Gyroscope; Keystroke dynamics; Mammals; Mouse dynamics; Pattern matching; Personal computers; Security issues; Smartwatch dynamics; Touch screens; True positive rates; Wearable computers; dept.csc

DeePEL: Deep learning architecture to recognize p-lncRNA and e-lncRNA promoters - Alam, T.; Islam, M.T.; Schmeier, S.; Househ, M.; Al-Thani, D.A. (Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM, 2019)
Promoter regions of long non-coding RNA (lncRNA) genes are crucial to understand their transcriptional regulatory pattern. LncRNA genes, being more cryptic than protein-coding genes in terms of their functionality and biogenesis divergence, are lacking in number of existing studies to elucidate the roles of their promoters compared to their counterparts. Based on the overlap between epigenetic marks and transcription start sites, human lncRNAs were categorized into two broad categories: enhancer-originated lncRNAs (e-lncRNAs) and promoter-originated lncRNAs (p-lncRNAs) and hence these two groups are subject to distinct transcriptional regulatory programs. To understand the difference in the transcriptional regulatory mechanisms that governs p- and e-lncRNAs, we studied the promoter sequences of these two groups of lncRNAs including distinct transcription factor (TF) proteins that favor p-over e-lncRNA (and vice versa). In addition, we developed a convolution neural network (CNN) based deep learning (DL) framework DeePEL (deep p-, e-lncRNA promoter recognizer), to classify the promoter of p- and e-lncRNAs. To the best of our knowledge, this is the first attempt to classify these two groups of lncRNA promoters, using sequence and TF information, based on DL framework. We report several sequence specific signatures in the promoter regions as well as several distinct TFs specific to groups of lncRNAs that will help in understanding the promoter-proximal transcriptional regulation of p-lncRNAs and e-lncRNAs. © 2019 IEEE.
0 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. - IEEE Int. Conf. Bioinform. Biomed., BIBM
Keywords: Bioinformatics; Classification (of information); Convolution neural network; Deep learning; FANTOM; Learning architectures; Promoter sequences; Protein-coding genes; Proteins; Regulatory mechanism; Regulatory patterns; Transcription; Transcription start site; Transcriptional regulation; convolution neural network; deep learning; dept.csc; long non-coding RNA; promoter

Deep learning based plant disease detection for smart agriculture - Ale, L.; Sheta, A.; Li, L.; Wang, Y.; Zhang, N. (IEEE Globecom Workshops, GC Wkshps - Proc., 2019)
Deep learning is a promising approach for fine- grained disease severity classification for smart agriculture, as it avoids the labor-intensive feature engineering and segmentation-based threshold. In this work, we first propose a Densely Connected Convolutional Networks (DenseNet) based transfer learning method to detect the plant diseases, which expects to run on edge servers with augmented computing resources. Then, we propose a lightweight Deep Neural Networks (DNN) approach that can run on Internet of Things (IoT) devices with constrained resources. To reduce the size and computation cost of the model, we further simplify the DNN model and reduce the size of input sizes. The proposed models are trained with different image sizes to find the appropriate size of the input images. Experiment results are provided to evaluate the performance of the proposed models based on real- world dataset, which demonstrate the proposed models can accurately detect plant disease using low computational resources. © 2019 IEEE.
30 citations (Crossref) [2023-10-31] Journal Abbreviation: IEEE Globecom Workshops, GC Wkshps - Proc.
Keywords: Agriculture; Classification; Classification (of information); Computational resources; Computing resource; Constrained resources; Convolution; Convolutional Neural Network; Convolutional networks; Convolutional neural networks; Corp Disease; Deep learning; Deep neural networks; Detection; Digital Epidemiology; Error detection; Feature engineerings; Internet of Things (IOT); Internet of things; Learning systems; Plants Disease; Smart agricultures; Transfer learning; Transfer learning methods; dept.csc

Application of electrical capacitance tomography for imaging conductive materials in industrial processes - Deabes, W.; Sheta, A.; Bouazza, K.E.; Abdelrahman, M. (Journal of Sensors, 2019)
This paper presents highly robust, novel approaches to solving the forward and inverse problems of an Electrical Capacitance Tomography (ECT) system for imaging conductive materials. ECT is one of the standard tomography techniques for industrial imaging. An ECT technique is nonintrusive and rapid and requires a low burden cost. However, the ECT system still suffers from a soft-field problem which adversely affects the quality of the reconstructed images. Although many image reconstruction algorithms have been developed, still the generated images are inaccurate and poor. In this work, the Capacitance Artificial Neural Network (CANN) system is presented as a solver for the forward problem to calculate the estimated capacitance measurements. Moreover, the Metal Filled Fuzzy System (MFFS) is proposed as a solver for the inverse problem to construct the metal images. To assess the proposed approaches, we conducted extensive experiments on image metal distributions in the lost foam casting (LFC) process to light the reliability of the system and its efficiency. The experimental results showed that the system is sensible and superior. © 2019 Wael Deabes et al.
15 citations (Crossref) [2023-10-31]
Keywords: Capacitance; Capacitance measurement; Conductive materials; Electric impedance tomography; Electrical Capacitance Tomography; Electrical capacitance tomography systems; Image reconstruction; Image reconstruction algorithm; Industrial processs; Inverse problems; Its efficiencies; Lost foam castings; Metal distributions; Neural networks; Reconstructed image; dept.csc; peer-reviewed

Multiple Waypoint Mobile Robot Path Planning Using Neighborhood Search Genetic Algorithms - Maddi, D.R.; Sheta, A.; Mahdy, A.; Turabieh, H. (ACM Int. Conf. Proc. Ser., 2019)
In this paper, we present a Neighborhood Search Genetic Algorithms (NSGAs) for mobile robot path planning. GAs have been used successfully in a variety of path planning problem because they can search the space of all possible paths and provide the optimal one. The convergence process of GAs might be lengthy compared to traditional search techniques that depend on local search methods. We propose a hybrid approach that allows GAs to combine both the advantages of GAs and local search algorithms. GAs will create a multiple waypoint path allowing a mobile robot to navigate through static obstacles and finding the optimal path in order to approach the target location without collision. The proposed NSGAs has been examined over four different path planning case studies with varying complexity. The performance of the enhanced GA has been compared with A-star algorithm (A∗) standard GA, particle swarm optimization (PSO) algorithm. The obtained results show that the proposed approach is able to get good results compared to other algorithms. © 2019 ACM.
1 citations (Crossref) [2023-10-31] Journal Abbreviation: ACM Int. Conf. Proc. Ser.
Keywords: Air navigation; Convergence process; Gases; Genetic Algorithm; Genetic algorithms; Local search (optimization); Local search algorithm; Local search method; Mobile robots; Neighborhood Search; Neighborhood search; Particle swarm optimization (PSO); Particle swarm optimization algorithm; Path Planning; Path planning problems; Robot path-planning; Robot programming; Robotics; Search technique; dept.csc

Medical Data Classification Using Binary Brain Storm Optimization Algorithm - Ogwo, O.; Turabieh, H.; Sheta, A.; King, S. (ACM Int. Conf. Proc. Ser., 2019)
With the growing access to technology in the medical domain, an increased volume of medical data is recorded. The size and complexity of these data make the process of analysis of meaningful discoveries of beneficial patterns more challenging. This problem has attracted numerous researchers around the world. Statistical methods have been employed to handle medical data for diagnosis purposes. Unfortunately, these methods were less capable of dealing with these massive and complex datasets. To solve this problem, we suggest a process to classify medical data which includes feature selection and classification using a number of supervised learning techniques. Binary Brain Storm Optimization (BBSO) is used for feature selection, which is a population search approach that simulates the process of electing the best idea (solution), among others. We simulated six different classifiers: Naive-Bayes, K-Nearest Neighbor, Support Vector Machine, Linear Discriminant Analysis, Decision Tree and Random Forest. Five datasets adopted from the UCI Machine Learning Repository, (Breast Cancer, Diabetes, Heart Disease, Chronic Kidney, and SPECT), are employed as a benchmark test data. The performance of BBSO is evaluated using accuracy on the datasets using the various classifiers. Experimental results show that the proposed approach improves the classification performance for better medical diagnosis. © 2019 ACM.
1 citations (Crossref) [2023-10-31] Journal Abbreviation: ACM Int. Conf. Proc. Ser.
Keywords: Benchmarking; Classification performance; Complex datasets; Computer aided diagnosis; Decision trees; Discriminant analysis; Diseases; Feature extraction; Feature selection and classification; K-nearest neighbors; Learning systems; Linear discriminant analysis; Medical domains; Nearest neighbor search; Optimization algorithms; Robotics; Storms; Support vector machines; UCI machine learning repository; classification; dept.csc; feature selection; medical diagnosis

Web user authentication using chosen word keystroke dynamics - Rahman, K.A.; Neupane, D.; Zaiter, A.; Hossain, M.S. (Proc. - IEEE Int. Conf. Mach. Learn. Appl., ICMLA, 2019)
Keystroke dynamics has been used as a form of one-time user authentication and continuous verification especially when it comes to securing the cyberspace. In this paper, we present the idea of using keystroke dynamics as a form of second layer authentication in web applications. We showed that this method can authenticate a user with high accuracy and can be used as an alternate to CAPTCHA tests, security questions and image selections that are being used today. We have developed a working web-based platform in a browser environment that enforces the proposed second-layer security. We performed penetration test experiments by launching a total of 598,500 impostor and genuine authentication attempts and found the Equal Error Rate (EER) as 10.5%. © 2019 IEEE.
5 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. - IEEE Int. Conf. Mach. Learn. Appl., ICMLA
Keywords: Authentication; CAPTCHA; CAPTCHAs; Cyber security; Cybersecurity; Dynamics; Electronic mail filters; Equal error rate; Keystroke dynamics; Machine learning; Network security; Penetration test; User authentication; WEB application; Web based platform; Websites; dept.csc

Cascaded layered recurrent neural network for indoor localization in wireless sensor networks - Turabieh, H.; Sheta, A. (Int. Conf. New Trends Comput. Sci., ICTCS - Proc., 2019)
The growth in using various smart wireless devices in the last few decades has given rise to indoor localization service (ILS). Indoor localization is defined as the process of locating a user location in an indoor environment. Indoor device localization has been widely studied due to its popular applications in public settlement planning, health care zones, disaster management, the implementation of location-based services (LBS) and the Internet of Things (IoT). The ILS problem can be formulated as a learning problem utilizing Wi-Fi technology. The measured Wi-Fi signal strength can be used as an indication of the distribution of users in a various indoor location. Developing a classification model with high accuracy can be achieved using a machine learning approach. Artificial Neural Network is one of the most successful trends in machine learning. In this article, we provide our initial idea of using Cascaded Layered Recurrent Neural Network (L-RNN) for the classification of user localization in an indoor environment. Several neural network models were trained, with the best performance attainment is reported. The experimental results marked that the presented L-RNN model is highly accurate for indoor localization and can be utilized for many applications. © 2019 IEEE.
5 citations (Crossref) [2023-10-31] Journal Abbreviation: Int. Conf. New Trends Comput. Sci., ICTCS - Proc.
Keywords: Classification models; Disaster management; Disaster prevention; Disasters; Forecasting; Indoor Environment; Indoor environment; Indoor localization; Indoor positioning systems; Internet of thing (IOT); Internet of things; Layered Recurrent Neural Network; Location; Location based services; Machine learning; Machine learning approaches; Multilayer neural networks; Network layers; Neural network model; Prediction; Recurrent neural networks; Telecommunication services; User Localization; User localization; Wireless local area networks (WLAN); Wireless sensor networks; dept.csc

Evaluating and improving accessibility of web applications: Zulip chat case study - Barde, R.; Raouf, A.A. (2021)
Web applications are built to be accessible to everyone. Unfortunately, many web applications are inaccessible to people with special needs or disabilities. In this work, we show a methodology used to make web applications more accessible to a diverse group of people. The process includes two phases: evaluation and improvement. In the first phase, the Web Accessibility Barrier (WAB) score metric together with the Accessibility Failure Rate (AFR) metric are used to evaluate web applications. In the second phase changes suggested by accessibility checker tool are implemented in the software to enhance the metrics values and reach the target level of accessibility. The open-source chat application, Zulip, is used as a case study to show the effectiveness of this approach. © 2021 IEEE.
0 citations (Crossref) [2023-10-31] tex.author_keywords: Accessibility; Failure Rate; Usability; WAVE; Web Accessibility Barrier Score; Web Accessibility Standards tex.document_type: Conference Paper tex.source: Scopus
Keywords: Case-studies; Failure rate; Open source software; Special needs; Two phase; WEB application; Web accessibility; Web accessibility barrier score; Web accessibility barriers; Web accessibility standard; Web applications; Websites; dept.csc

Platform for comparative study of focus and retention in virtual reality and online learning - Macesker, M.; Morrow, R.; Too, G.W.; Artan, N.S.; Dong, Z.; Gu, H. (2021)
Our research objective is to compare the effectiveness of standard online learning methods versus the utilization of virtual reality in education in terms of student focus and information retention. Our proposed platform will have identical lesson plans in virtual reality as our online learning methods. Eye gaze tracking and a recall test will be used on both platforms to measure focus on the screen and retention, respectively. The ultimate goal of the project is to use this data to evaluate the effectiveness of VR as a digital learning environment. © 2021 IEEE.
0 citations (Crossref) [2023-10-31] tex.author_keywords: Focus; Retention; Standard online learning tools; Student engagement; Virtual reality tex.document_type: Conference Paper tex.source: Scopus
Keywords: Comparatives studies; Computer aided instruction; E-learning; Eye tracking; Information retention; Learning systems; On-line learning methods; Online learning; Online learning tools; Reality learning; Research objectives; Retention; Standard online learning tool; Student engagement; Students; Virtual reality; dept.csc

LNCRI: Long non-coding RNA identifier in multiple species - Musleh, S.; Islam, M.T.; Alam, T. (IEEE access : practical innovations, open solutions, 2021)
The pervasive nature of long non-coding RNA (lncRNA) transcription in the mammalian genomes has changed our protein-centric view of genomes. But the identification of lncRNAs is an important task to discover their functional role in species. The rapid development of next-generation sequencing technology leveraged the opportunity to discover many lncRNA transcripts. However, the cost and time-consuming nature of transcriptomics verification techniques barred the research community from focusing on lncRNA identification. To overcome these challenges we developed LNCRI (Long Non-Coding RNA Identifier), a novel machine learning (ML)-based tool for the identification of lncRNA transcripts. We leveraged weighted k-mer, pseudo nucleotide composition, hexamer usage bias, Fickett score, information of open reading frame, UTR regions, and HMMER score as a feature set to develop LNCRI. LNCRI outperformed other existing models in the task of distinguishing lncRNA transcripts from protein-coding mRNA transcripts with high accuracy in human and mouse. LNCRI also outperformed the existing tools for cross-species prediction on chimpanzee, monkey, gorilla, orangutan, cow, pig, frog and zebrafish. We applied the SHAP algorithm to demonstrate the importance of most dominating features that were leveraged in the model. We believe our tool will support the research community to identify the lncRNA transcripts in a highly accurate manner. The benchmark datasets and source code are available in GitHub: http://github.com/smusleh/LNCRI. © 2013 IEEE.
0 citations (Crossref) [2023-10-31] tex.author_keywords: lncRNA; Long non-coding RNA; machine learning; mRNA; sequence analysis tex.document_type: Article tex.source: Scopus
Keywords: Artificial intelligence; Bioinformatics; Codes (symbols); Genes; Learning systems; Long non-coding RNA; MRNA; Mammalian genomes; Mammals; Multiple species; Next-generation sequencing; Proteins; RNA; RNA transcription; Research communities; Sequence analysis; Transcriptomics; Verification techniques; dept.csc; peer-reviewed

Memory based hybrid crow search algorithm for solving numerical and constrained global optimization problems - Braik, M.; Al-Zoubi, H.; Ryalat, M.; Sheta, A.; Alzubi, O. (Artificial Intelligence Review, 2022)
Crow Search Algorithm (CSA) is a promising meta-heuristic method developed based on the intelligent conduct of crows in nature. This algorithm lacks a good representation of its individuals’ memory, and as with many other meta-heuristics it faces a problem in efficiently balancing exploration and exploitation. These defects may lead to early convergence to local optima. To cope with such issues, we proposed a Memory based Hybrid CSA (MHCSA) with the use of Particle Swarm Optimization (PSO) algorithm. This hybridization approach was proposed to reinforce the diversity ability of CSA and balance its search abilities for promising solutions to achieve robust search performance. The memory element of MHCSA was initialized with the best solution (pbest) of PSO to exploit the most promising search areas. The best positions of the CSA’s individuals are improved using the best solution found so far (gbest) and (pbest) of PSO. Another flaw of CSA is the use of fixed flight length and awareness probability for crows to control exploration and exploitation features, respectively. This issue was circumvented here by replacing these constants with adaptive functions in order to provide a better balance between exploration and exploitation over the course of iterations. The competence of MHCSA was revealed by testing it on seventy-three standard and computationally complex benchmark functions. Its applicability was substantiated by solving seven engineering design problems. The results showed that the problem of early convergence was eliminated by MHCSA and that the balance of exploration and exploitation was further improved. Further, MHCSA ranked first among CSA, PSO, robust variants of CSA and other strong competing methods in terms of accuracy and stability. © 2022, The Author(s), under exclusive licence to Springer Nature B.V.
14 citations (Crossref) [2023-10-31] tex.author_keywords: Crow search algorithm; Meta-heuristics; Optimization techniques; Particle swarm optimization tex.coden: AIRVE tex.document_type: Article tex.source: Scopus
Keywords: Algorithm for solving; Balancing; Constrained global optimization; Constrained optimization; Crow search algorithm; Early convergence; Exploration and exploitation; Global optimization; Global optimization problems; Heuristic methods; Learning algorithms; Meta-heuristic methods; Metaheuristic; Optimization techniques; Particle swarm optimization (PSO); Search Algorithms; dept.csc; peer-reviewed

Enhanced cuckoo search algorithm for industrial winding process modeling - Braik, M.; Sheta, A.; Al-Hiary, H.; Aljahdali, S. (Journal of Intelligent Manufacturing, 2022)
Modeling of nonlinear industrial systems embraces two key stages: selection of a model structure with a compact parameter list, and selection of an algorithm to estimate the parameter list values. Thus, there is a need to develop a sufficiently adequate model to characterize the behavior of industrial systems to represent experimental data sets. The data collected for many industrial systems may be subject to the existence of high non-linearity and multiple constraints. Meanwhile, creating a thoroughgoing model for an industrial process is essential for model-based control systems. In this work, we explore the use of a proposed Enhanced version of the Cuckoo Search (ECS) algorithm to address a parameter estimation problem for both linear and nonlinear model structures of a real winding process. The performance of the developed models was compared with other mainstream meta-heuristics when they were targeted to model the same process. Moreover, these models were compared with other models developed based on some conventional modeling methods. Several evaluation tests were performed to judge the efficiency of the developed models based on ECS, which showed superior performance in both training and testing cases over that achieved by other modeling methods. © 2022, This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply.
4 citations (Crossref) [2023-10-31] tex.author_keywords: Cuckoo search algorithm; Industrial winding process; Linear model; Nonlinear model tex.coden: JIMNE tex.document_type: Article tex.source: Scopus
Keywords: Cuckoo search algorithms; Developed model; Industrial systems; Industrial winding process; Learning algorithms; Linear modeling; Model method; Model structures; Non-linear modelling; Nonlinear systems; Optimization; Parameter estimation; Performance; Process-models; Winding; Winding process; dept.csc; peer-reviewed

ALLD: Acute lymphoblastic leukemia detector - Musleh, S.; Islam, M.T.; Alam, M.T.; Househ, M.; Shah, Z.; Alam, T. (Studies in Health Technology and Informatics, 2022)
Acute Lymphoblastic Leukemia (ALL) is a life-threatening type of cancer wherein mortality rate is unquestionably high. Early detection of ALL can reduce both the rate of fatality as well as improve the diagnosis plan for patients. In this study, we developed the ALL Detector (ALLD), which is a deep learning-based network to distinguish ALL patients from healthy individuals based on blast cell microscopic images. We evaluated multiple DL-based models and the ResNet-based model performed the best with 98% accuracy in the classification task. We also compared the performance of ALLD against state-of-the-art tools utilized for the same purpose, and ALLD outperformed them all. We believe that ALLD will support pathologists to explicitly diagnose ALL in the early stages and reduce the burden on clinical practice overall. © 2022 The authors and IOS Press.
0 citations (Crossref) [2023-10-31] ISBN: 9781643682501 tex.author_keywords: Acute lymphoblastic leukemia; Computer aided diagnosis (CAD); Deep learning; Leukemia tex.document_type: Conference Paper tex.pubmed_id: 35062096 tex.source: Scopus
Keywords: Acute lymphoblastic leukaemias; Classification tasks; Computer; Computer aided diagnose; Computer aided diagnosis; Computer aided instruction; Deep Learning; Deep learning; Diseases; Healthy individuals; Humans; Individual-based; Medical informatics; Microscopic image; Mortality rate; Neural Networks; Performance; Precursor Cell Lymphoblastic Leukemia-Lymphoma; State of the art; acute lymphoblastic leukemia; dept.csc; human; peer-reviewed

Summarizing consumer reviews - Peal, M.; Hossain, M.S.; Chen, J. (Journal of Intelligent Information Systems, 2022)
E-commerce giants like Amazon rely on consumer reviews to allow buyers to inform other potential buyers about a product’s pros and cons. While these reviews can be useful, they are less so when the number of reviews is large; no consumer can be expected to read hundreds or thousands of reviews in order to gain better understanding about a product. In an effort to provide an aggregate representation of reviews, Amazon offers an average user rating represented by a 1- to 5-star score. This score only represents how reviewers feel about a product without providing insight into why they feel that way. In this work, we propose an AI technique that generates an easy-to-read, concise summary of a product based on its reviews. It provides an overview of the different aspects reviewers emphasize in their reviews and, crucially, how they feel about those aspects. Our methodology generates a list of the topics most-mentioned by reviewers, conveys reviewer sentiment for each topic and calculates an overall summary score that reflects reviewers’ overall sentiment about the product. These sentiment scores adapt the same 1- to 5-star scoring scale in order to remain familiar to Amazon users. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
1 citations (Crossref) [2023-10-31] tex.author_keywords: Affective computing; Amazon consumer reviews; Aspect-based sentiment analysis; Language summarization; Topic extraction tex.coden: JIISE tex.document_type: Article tex.source: Scopus
Keywords: AI techniques; Affective Computing; Amazon consumer review; Aspect-based sentiment analyse; Consumer reviews; Language summarization; Potential buyers; Sentiment analysis; Topic extraction; User rating; dept.csc; peer-reviewed

The linkage between bone densitometry and cardiovascular disease - Refaee, M.A.; Al-Absi, H.R.H.; Islam, M.T.; Househ, M.; Shah, Z.; Rahman, M.S.; Alam, T. (Studies in Health Technology and Informatics, 2022)
Dual-energy X-ray absorptiometry (DXA) has been traditionally used to assess body composition covering bone, fat and muscle content. Cardiovascular disease (CVD) has deleterious effects on bone health and fat composition. Therefore, early detection of bone health, fat and muscle composition would help to anticipate a proper diagnosis and treatment plan for CVD patients. In this study, we leveraged machine learning (ML)-based models to predict CVD using DXA, demonstrating that it can be considered an innovative approach for early detection of CVD. We leveraged state-of-the-art ML models to classify the CVD group from non-CVD group. The proposed logistic regression-based model achieved nearly 80% accuracy. Overall, the bone mineral density, fat content, muscle mass and bone surface area measurements were elevated in the CVD group compared to non-CVD group. Ablation study revealed a more successful discriminatory power of fat content and bone mineral density than muscle mass and bone areas. To the best of our knowledge, this work is the first ML model to reveal the association between DXA measurements and CVD in the Qatari population. We believe this study will open new avenues of introducing DXA in creating the diagnosis and treatment plan of cardiovascular diseases. © 2022 The authors and IOS Press.
0 citations (Crossref) [2023-10-31] ISBN: 9781643682501 tex.author_keywords: Bone densitometry; Cardiovascular disease; Dual-energy X-ray absorptiometry (DXA); Qatar Biobank (QBB) tex.document_type: Conference Paper tex.pubmed_id: 35062138 tex.source: Scopus
Keywords: Absorptiometry; Adipose Tissue; Biobanks; Biochemistry; Body Composition; Bone Density; Bone densitometry; Bone mineral density; Cardiology; Cardiovascular Diseases; Cardiovascular disease; Diagnosis; Diseases; Dual-energy X-ray; Dual-energy X-ray absorptiometry; Humans; Light absorption; Machine learning models; Muscle; Patient treatment; Photon; Qatar biobank; Treatment plans; X-ray absorptiometry; adipose tissue; body composition; bone density; cardiovascular disease; dept.csc; diagnostic imaging; human; peer-reviewed; photon absorptiometry

An Enhanced Evolutionary Student Performance Prediction Model Using Whale Optimization Algorithm Boosted with Sine-Cosine Mechanism - Thaher, Thaer; Zaguia, Atef; Al Azwari, Sana; Mafarja, Majdi; Chantar, Hamouda; Abuhamdah, Anmar; Turabieh, Hamza; Mirjalili, Seyedali; Sheta, Alaa (Applied Sciences, 2021)
The students’ performance prediction (SPP) problem is a challenging problem that managers face at any institution. Collecting educational quantitative and qualitative data from many resources such as exam centers, virtual courses, e-learning educational systems, and other resources is not a simple task. Even after collecting data, we might face imbalanced data, missing data, biased data, and different data types such as strings, numbers, and letters. One of the most common challenges in this area is the large number of attributes (features). Determining the highly valuable features is needed to improve the overall students’ performance. This paper proposes an evolutionary-based SPP model utilizing an enhanced form of the Whale Optimization Algorithm (EWOA) as a wrapper feature selection to keep the most informative features and enhance the prediction quality. The proposed EWOA combines the Whale Optimization Algorithm (WOA) with Sine Cosine Algorithm (SCA) and Logistic Chaotic Map (LCM) to improve the overall performance of WOA. The SCA will empower the exploitation process inside WOA and minimize the probability of being stuck in local optima. The main idea is to enhance the worst half of the population in WOA using SCA. Besides, LCM strategy is employed to control the population diversity and improve the exploration process. As such, we handled the imbalanced data using the Adaptive Synthetic (ADASYN) sampling technique and converting WOA to binary variant employing transfer functions (TFs) that belong to different families (S-shaped and V-shaped). Two real educational datasets are used, and five different classifiers are employed: the Decision Trees (DT), k-Nearest Neighbors (k-NN), Naive Bayes (NB), Linear Discriminant Analysis (LDA), and LogitBoost (LB). The obtained results show that the LDA classifier is the most reliable classifier with both datasets. In addition, the proposed EWOA outperforms other methods in the literature as wrapper feature selection with selected transfer functions.
8 citations (Crossref) [2023-10-31] Number: 21
Keywords: ADASYN; Sine Cosine Algorithm (SCA); Whale Optimization Algorithm (WOA); dept.csc; educational data mining (EDM); feature selection; peer-reviewed; student performance

Optimization of PID Controller to Stabilize Quadcopter Movements Using Meta-Heuristic Search Algorithms - Sheta, Alaa; Braik, Malik; Maddi, Dheeraj Reddy; Mahdy, Ahmed; Aljahdali, Sultan; Turabieh, Hamza (Applied Sciences, 2021)
Quadrotor UAVs are one of the most preferred types of small unmanned aerial vehicles, due to their modest mechanical structure and propulsion precept. However, the complex non-linear dynamic behavior of the Proportional Integral Derivative (PID) controller in these vehicles requires advanced stabilizing control of their movement. Additionally, locating the appropriate gain for a model-based controller is relatively complex and demands a significant amount of time, as it relies on external perturbations and the dynamic modeling of plants. Therefore, developing a method for the tuning of quadcopter PID parameters may save effort and time, and better control performance can be realized. Traditional methods, such as Ziegler–Nichols (ZN), for tuning quadcopter PID do not provide optimal control and might leave the system with potential instability and cause significant damage. One possible approach that alleviates the tough task of nonlinear control design is the use of meta-heuristics that permit appropriate control actions. This study presents PID controller tuning using meta-heuristic algorithms, such as Genetic Algorithms (GAs), the Crow Search Algorithm (CSA) and Particle Swarm Optimization (PSO) to stabilize quadcopter movements. These meta-heuristics were used to control the position and orientation of a PID controller based on a fitness function proposed to reduce overshooting by predicting future paths. The obtained results confirmed the efficacy of the proposed controller in felicitously and reliably controlling the flight of a quadcopter based on GA, CSA and PSO. Finally, the simulation results related to quadcopter movement control using PSO presented impressive control results, compared to GA and CSA.
14 citations (Crossref) [2023-10-31] Number: 14
Keywords: Crow Search Algorithm; Genetic Algorithms; PID controller; Particle Swarm Optimization; dept.csc; peer-reviewed; quadcopter

Diagnosis of Obstructive Sleep Apnea from ECG Signals Using Machine Learning and Deep Learning Classifiers - Sheta, Alaa; Turabieh, Hamza; Thaher, Thaer; Too, Jingwei; Mafarja, Majdi; Hossain, Md Shafaeat; Surani, Salim R. (Applied Sciences, 2021)
Obstructive sleep apnea (OSA) is a well-known sleep ailment. OSA mostly occurs due to the shortage of oxygen for the human body, which causes several symptoms (i.e., low concentration, daytime sleepiness, and irritability). Discovering the existence of OSA at an early stage can save lives and reduce the cost of treatment. The computer-aided diagnosis (CAD) system can quickly detect OSA by examining the electrocardiogram (ECG) signals. Over-serving ECG using a visual procedure is challenging for physicians, time-consuming, expensive, and subjective. In general, automated detection of the ECG signal’s arrhythmia is a complex task due to the complexity of the data quantity and clinical content. Moreover, ECG signals are usually affected by noise (i.e., patient movement and disturbances generated by electric devices or infrastructure), which reduces the quality of the collected data. Machine learning (ML) and Deep Learning (DL) gain a higher interest in health care systems due to its ability of achieving an excellent performance compared to traditional classifiers. We propose a CAD system to diagnose apnea events based on ECG in an automated way in this work. The proposed system follows the following steps: (1) remove noise from the ECG signal using a Notch filter. (2) extract nine features from the ECG signal (3) use thirteen ML and four types of DL models for the diagnosis of sleep apnea. The experimental results show that our proposed approach offers a good performance of DL classifiers to detect OSA. The proposed model achieves an accuracy of 86.25% in the validation stage.
29 citations (Crossref) [2023-10-31] Number: 14
Keywords: classification; deep learning; dept.csc; machine learning; notch filter; obstructive sleep apnea; peer-reviewed

BHHO-TVS: A Binary Harris Hawks Optimizer with Time-Varying Scheme for Solving Data Classification Problems - Chantar, Hamouda; Thaher, Thaer; Turabieh, Hamza; Mafarja, Majdi; Sheta, Alaa (Applied Sciences, 2021)
Data classification is a challenging problem. Data classification is very sensitive to the noise and high dimensionality of the data. Being able to reduce the model complexity can help to improve the accuracy of the classification model performance. Therefore, in this research, we propose a novel feature selection technique based on Binary Harris Hawks Optimizer with Time-Varying Scheme (BHHO-TVS). The proposed BHHO-TVS adopts a time-varying transfer function that is applied to leverage the influence of the location vector to balance the exploration and exploitation power of the HHO. Eighteen well-known datasets provided by the UCI repository were utilized to show the significance of the proposed approach. The reported results show that BHHO-TVS outperforms BHHO with traditional binarization schemes as well as other binary feature selection methods such as binary gravitational search algorithm (BGSA), binary particle swarm optimization (BPSO), binary bat algorithm (BBA), binary whale optimization algorithm (BWOA), and binary salp swarm algorithm (BSSA). Compared with other similar feature selection approaches introduced in previous studies, the proposed method achieves the best accuracy rates on 67% of datasets.
16 citations (Crossref) [2023-10-31] Number: 14
Keywords: HHO; Harris Hawks Optimizer; dept.csc; feature selection; metaheuristics; peer-reviewed; time-varying; transfer functions

On the calculation of time alignment errors in data management platforms for distribution grid data. - Schwefel, Hans-Peter; Antonios, Imad; Lipsky, Lester (Sensors (Basel, Switzerland), 2021)
The operation and planning of distribution grids require the joint processing of measurements from different grid locations. Since measurement devices in low-and medium-voltage grids lack precise clock synchronization, it is important for data management platforms of distribution system operators to be able to account for the impact of nonideal clocks on measurement data. This paper formally introduces a metric termed Additive Alignment Error to capture the impact of misaligned averaging intervals of electrical measurements. A trace-driven approach for retrieval of this metric would be computationally costly for measurement devices, and therefore, it requires an online estimation procedure in the data collection platform. To overcome the need of transmission of high-resolution measurement data, this paper proposes and assesses an extension of a Markov-modulated process to model electrical traces, from which a closed-form matrix analytic formula for the Additive Alignment Error is derived. A trace-driven assessment confirms the accuracy of the model-based approach. In addition, the paper describes practical settings where the model can be utilized in data management platforms with significant reductions in computational demands on measurement devices. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.
0 citations (Crossref) [2023-10-31] https://www.mdpi.com/1424-8220/21/20/6903/xml ; https://dblp.uni-trier.de/db/journals/sensors/sensors21.html#SchwefelAL21 ; https://www.mdpi.com/1424-8220/21/20/6903 ; https://vbn.aau.dk/en/publications/on-the-calculation-of-time-alignment-errors-in-data-management-pl
Keywords: Additives; Alignment error; Clock Synchronization; Distribution grid; Electrical distribution; Electrical distribution grid; Grid data; Information management; Management platforms; Measurement data; Measurement device; Time alignment; article; averaging; calculation; dept.csc; human; information retrieval; measurement error; peer-reviewed

The effectiveness of zoom touchscreen gestures for authentication and identification and its changes over time - Wang, Leran; Hossain, Shafaeat; Pulfrey, Joshua; Lancor, Lisa (Computers & Security, 2021)
This paper focuses on how zoom touchscreen gestures can be used to continuously authenticate and identify smartphone users. The zoom gesture is critically under-researched as a behavioral biometric despite richness of data found in this gesture. Furthermore, analysing how the zoom gesture performs over time is a novel line of inquiry. Zoom samples from three different data collection sessions were sourced. In these sessions, each participant zoomed in and out on three images. Eighty-five features were extracted from each gesture. The classification models used were Support Vector Machine (SVM), Random Forest (RF), and K-nearest Neighbor (KNN). The best authentication performance of AUC 0.937 and EER 10.6% were achieved using the SVM classifier. The best identification performance of 65.5% accuracy, 69.6% precision, and 67.9% recall were achieved using the RF classifier. In terms of stability over time, SVM proved to be the most stable classifier, with an AUC degradation of only 0.007 after two weeks had elapsed. This analysis proves that zoom gestures demonstrate promise for use in continuous smartphone authentication and identification applications. © 2021 Elsevier Ltd
6 citations (Crossref) [2023-10-31]
Keywords: Authentication; Behavioural Biometric; Biometrics; Change-over time; Classification models; Data collection; Decision trees; Identification; Nearest neighbor search; Performance; Smart phones; Smartphones; Support vector machines; Support vectors machine; Touch gesture; Zoom; dept.csc; peer-reviewed

DAAB: Deep authorship attribution in bengali - Dipongkor, Atish Kumar; Islam, Saiful; Kayesh, Humayun; Hossain, Shafaeat; Anwar, Adnan; Rahman, Khandaker Abir; Razzak, Imran (2021 international joint conference on neural networks (IJCNN), 2021)
Authorship attribution identifies the true author of an unknown document. Authorship attribution plays a crucial role in plagiarism detection and blackmailer identification, however, the existing studies on authorship attribution in Bengali are limited. In this paper, we propose an instance-based deep authorship attribution model, called DAAB, to identify authors in Bengali. Our DAAB model fuses features from convolutional neural networks and another set of features from an artificial neural network to learn the stylometry of an author for authorship attribution. Extensive experiments with three real benchmark datasets such as Bengali-Quora and two online Bengali Corpus demonstrate the superiority of our authorship attribution model. © 2021 IEEE.
2 citations (Crossref) [2023-10-31]
Keywords: Authorship attribution; Benchmark datasets; Bengalis; Convolution; Convolutional neural network; Convolutional neural networks; Deep learning; Deep neural networks; Learn+; Plagiarism detection; Sets of features; Stylometry; dept.csc

Enhancing performance and user convenience of multi-biometric verification systems - Hossain, Shafaeat; Phoha, Vir V. (Pattern Analysis and Applications, 2021)
A multi-biometric verification system lowers the verification errors by fusing information from multiple biometric sources. Information can be fused in parallel or serial modes. While parallel fusion gives a higher accuracy, it may suffer from a serious problem of taking a longer verification time. Serial fusion can alleviate this problem by allowing the users to submit a subset of the available biometric characteristics. Unfortunately, several studies show that serial fusion may not reach the level of accuracy of parallel fusion. In this paper, we propose a fusion framework which combines the advantages of both parallel and serial fusion. The core of the framework is a new concept of “confident reject region” which incurs nearly zero verification error. We evaluate our framework by performing experiments on two multi-biometric verification systems built with NIST biometric scores set release 1. The experimental results show that our framework achieves a lower equal error rate and takes a shorter verification time than standard parallel fusion. © 2021, The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature.
2 citations (Crossref) [2023-10-31] https://experts.syr.edu/en/publications/enhancing-performance-and-user-convenience-of-multi-biometric-ver ; https://dblp.uni-trier.de/db/journals/paa/paa24.html#HossainP21
Keywords: dept.csc; peer-reviewed

Quad Swipe Pattern: A New Point-of-Entry Security Measure for Smartphone Users - Brown, James F.; Hossain, Md S.; Lancor, Lisa (IEEE access : practical innovations, open solutions, 2021)
In this paper, we develop a new point-of-entry security measure for smartphone users. We devise a concept, the “Quad Swipe Pattern”, which includes four swipes from a user in four directions and utilizes the user’s swipe behavior for authentication. The Quad Swipe Pattern overcomes several shortcomings present in current point-of-entry security measures. We performed several experiments to demonstrate the effectiveness of the Quad Swipe Pattern in smartphone user authentication. We evaluated the Quad Swipe Pattern using five machine learning classifiers, three datasets of different sizes, and five different fingers. In addition, we studied how fusion of information from multiple fingers and multiple classifiers can improve the performance of Quad Swipe Pattern. All of our experimental results show significant promise of the Quad Swipe Pattern as a new point-of-entry security measure for smartphones. With a Neural Network model, the Quad Swipe Pattern achieves the Accuracy of 99.7%, False Acceptance Rate of 0.4%, and False Rejection Rate of 0%. With Support Vector Machine, the Quad Swipe Pattern achieves the Accuracy of 99.5%, False Acceptance Rate of 0.4%, and False Rejection Rate of 1.7%. With fusion of two best fingers, the Quad Swipe Pattern demonstrates an excellent performance of a zero Equal Error Rate.
2 citations (Crossref) [2023-10-31] Conference Name: IEEE Access
Keywords: 'current; Authentication; Biometrics; Biometrics (access control); Classification (of information); Face recognition; False acceptance rate; False rejection rate; Mobile security; Passwords; Performance; Pins; Point of entries; Security; Security measure; Smart phones; Smartphone securities; Smartphones; Support vector machines; Swipe gesture; User authentication; authentication; dept.csc; peer-reviewed; smartphone security; swipe gesture; touchscreen

ECT-LSTM-RNN: An electrical capacitance tomography model-based long short-term memory recurrent neural networks for conductive materials - Deabes, W.A.; Sheta, Alaa; Braik, Malik (IEEE access : practical innovations, open solutions, 2021)
Image reconstruction for industrial applications based on Electrical Capacitance Tomography (ECT) has been broadly applied. The goal of image reconstruction based ECT is to locate the distribution of permittivity for the dielectric substances along the cross-section based on the collected capacitance data. In the ECT-based image reconstruction process: (1) the relationship between capacitance measurements and permittivity distribution is nonlinear, (2) the capacitance measurements collected during image reconstruction are inadequate due to the limited number of electrodes, and (3) the reconstruction process is subject to noise leading to an ill-posed problem. Thence, constructing an accurate algorithm for real images is critical to overcoming such restrictions. This paper presents novel image reconstruction methods using Deep Learning for solving the forward and inverse problems of the ECT system for generating high-quality images of conductive materials in the Lost Foam Casting (LFC) process. Here, Long Short-Term Memory Recurrent Neural Network (LSTM-RNN) models were implemented to predict the distribution of metal filling for the LFC process-based ECT. The recurrent connection and the gating mechanism of the LSTM is capable of extracting the contextual information that is repeatedly passing through the neural network while filtering out the noise caused by adverse factors. Experimental results showed that the presented ECT-LSTM-RNN model is highly reliable for industrial applications and can be utilized for other manufacturing processes. © 2013 IEEE.
5 citations (Crossref) [2023-10-31] https://dblp.uni-trier.de/db/journals/access/access9.html#DeabesSB21
Keywords: Brain; Capacitance; Capacitance measurement; Casting; Conductive materials; Contextual information; Deep learning; Dielectric substances; Distribution of metal; Electric impedance tomography; Electrical Capacitance Tomography; Image reconstruction; Information filtering; Inverse problems; Long short-term memory; Manufacturing process; Novel image reconstruction; Permittivity; Permittivity distributions; Reconstruction process; dept.csc; peer-reviewed

SAS Mobile Application for Diagnosis of Obstructive Sleep Apnea Utilizing Machine Learning Models - Haberfeld, Carl; Sheta, Alaa; Hossain, Md Shafaeat; Turabieh, Hamza; Surani, Salim (2020 11th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON), 2020)
In this paper, we provide a consistent, inexpensive, and easy to use graphical user interface (GUI) smart phone application named Sleep Apnea Screener (SAS) that can diagnosis Obstructive Sleep Apnea (OSA) based on demographic data such as: gender, age, height, BMI, neck circumference, waist, etc., allowing a tentative diagnosis of OSA without the need for overnight tests. The developed smart phone application can diagnosis sleep apnea using a model trained with 620 samples collected from a sleep center in Corpus Christi, TX. Two machine learning classifiers (i.e., Logistic Regression (LR) and Support Vector Machine (SVM)) were used to diagnosis OSA. Our preliminary results show that at-home OSA screening is indeed possible, and that our application is effective method for covering large numbers of undiagnosed cases.
7 citations (Crossref) [2023-10-31]
Keywords: COVID-19; Corpus christi; Demographic data; Graphical user interfaces; Graphical user interfaces (GUI); Learning systems; Logistic regression; Logistics; Machine Learning; Machine learning; Machine learning models; Mathematical model; Medical services; Mobile applications; Mobile telecommunication systems; OSA; Obstructive sleep apnea; SVM; Sleep Apnea; Sleep apnea; Sleep research; Smart phones; Smart-phone applications; Smartphones; Support vector machines; Support vector regression; Two machines; Ubiquitous computing; dept.csc; pretesting

Multimodal person detection system - Barello, Philip; Hossain, Shafaeat (Multimedia Tools and Applications, 2021)
Person detection is often critical for personal safety, property protection, and national security. Most person detection technologies implement unimodal classification, making predictions based on a single sensor data modality, which is most often vision. There are many ways to defeat unimodal person detectors, and many more reasons to ensure technologies responsible for detecting the presence of a person are accurate and precise. In this paper, we design and implement a multimodal person detection system which can acquire data from multiple sensors and detect persons based on a variety of unimodal classifications and multimodal fusions. We present two methods of generating system-level predictions: (1) device perspectives which makes a final decision based on multiple device-level predictions and (2) system perspectives which combines data samples from multiple devices into a single data sample and then makes a decision. Our experimental results show that system-level predictions from system perspectives are generally more accurate than system-level predictions from device perspectives. We achieve an accuracy of 100%, zero false positive rate and zero false negative rate with fusion of system perspectives motion and distance data. © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC part of Springer Nature.
1 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85099450439
Keywords: Design and implements; False negative rate; False positive rates; Forecasting; Generating system; Multi-modal fusion; Multiple devices; National security; Person detection; Property protection; dept.csc; peer-reviewed

Risk factors and comorbidities associated to cardiovascular disease in qatar: A machine learning based case-control study - Al-Absi, Hamada R. H.; Refaee, Mahmoud Ahmed; Rehman, Atiq ur; Islam, Mohammad Tariqul; Belhaouari, Samir Brahim; Alam, Tanvir (IEEE access : practical innovations, open solutions, 2021)
Cardiovascular disease (CVD) is reported to be the leading cause of mortality in the middle eastern countries, including Qatar. But no comprehensive study has been conducted on the Qatar specific CVD risk factors identification. The objective of this case-control study was to develop machine learning (ML) model distinguishing healthy individuals from people having CVD, which could ultimately reveal the list of potential risk factors associated to CVD in Qatar. To the best of our knowledge, this study considered the largest collection of biomedical measurements representing the anthropometric measurements, clinical biomarkers, bioimpedance, spirometry, VICORDER readings, and behavioral factors of the CVD group from Qatar Biobank (QBB). CatBoost model achieved 93% accuracy, thereby outperforming the existing model for the same purpose. Interestingly, combining multimodal datasets into the proposed ML model outperformed the ML model built upon currently known risk factors for CVD, emphasizing the importance of incorporating other clinical biomarkers into consideration for CVD diagnosis plan. The ablation study on the multimodal dataset from QBB revealed that physio-clinical and bioimpedance measurements have the most distinguishing power to classify these two groups irrespective of gender and age of the participants. Multiple feature subset selection techniques confirmed known CVD risk factors (blood pressure, lipid profile, smoking, sedentary life, and diabetes), and identified potential novel risk factors linked to CVD-related comorbidities such as renal disorder (e.g., creatinine, uric acid, homocysteine, albumin), atherosclerosis (intima media thickness), hypercoagulable state (fibrinogen), and liver function (e.g., alkaline phosphate, gamma-glutamyl transferase). Moreover, the inclusion of the proposed novel factors into the ML model provides better performance than the model with traditional known risk factors for CVD. The association of the proposed risk factors and comorbidities are required to be investigated in clinical setup to understand their role in CVD better. © 2013 IEEE.
17 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85100930619
Keywords: Anthropometric measurement; Anthropometry; Bioimpedance measurement; Biomarkers; Biomedical measurements; Blood pressure; Cardiology; Cardiovascular disease; Classification (of information); Diagnosis; Disease control; Diseases; Healthy individuals; Intima-media thickness; Machine learning; Multi-modal dataset; Multimodal datasets; Turing machines; dept.csc; peer-reviewed

Open code biometric tap pad for smartphones - Haberfeld, Carl; Hossain, Shafaeat; Lancor, Lisa (Journal of Information Security and Applications, 2021)
Poor security practices among smartphone users, such as the use of simple, easily guessed passcodes for logins, are a result of the effort required to memorize stronger ones. In this paper, we devise a concept of “open code” biometric tap pad to authenticate smartphone users, which eliminates the need of memorizing secret codes. A biometric tap pad consists of a grid of buttons each labeled with a unique digit. The user attempting to log into the phone will tap these buttons in a given sequence. He/she will not memorize this tap sequence. Instead, the sequence will be displayed on the screen. The focus here is how the user types the sequence. This typing behavior is used for authentication. An open code biometric tap pad has several advantages, such as (1) users do not need to memorize passcodes, (2) manufacturers do not need to include extra sensors, and (3) onlookers have no chance to practice shoulder-surfing. We designed three tap pads and incorporated them into an Android app. We evaluated the performance of these tap pads by experimenting with three sequence styles and five different fingers: two thumbs, two index fingers, and the “usual” finger. We collected data from 33 participants over two weeks. We tested three machine learning algorithms: Support Vector Machine, Artificial Neural Network, and Random Forest. Experimental results show significant promise of open code biometric tap pads as a solution to the problem of weak smartphone security practices used by a large segment of the population.
1 citations (Crossref) [2023-10-31]
Keywords: Authentication; Biometrics; Decision trees; Extra sensors; Fasteners; Index finger; Learning algorithms; Mobile security; Neural networks; Open codes; Secret code; Security Practice; Security systems; Shoulder surfing; Smartphone securities; Smartphones; Support vector machines; Typing behaviors; dept.csc; peer-reviewed

DiaNet: A deep learning based architecture to diagnose diabetes using retinal images only - Islam, Mohammad Tariqul; Al-Absi, Hamada R. H.; Ruagh, Essam A.; Alam, Tanvir (IEEE Access, 2021)
Diabetes is one of the leading fatal diseases globally, putting a huge burden on the global healthcare system. Early diagnosis of diabetes is hence, of utmost importance and could save many lives. However, current techniques to determine whether a person has diabetes or has the risk of developing diabetes are primarily reliant upon clinical biomarkers. In this article, we propose a novel deep learning architecture to predict if a person has diabetes or not from a photograph of his/her retina. Using a relatively small-sized dataset, we develop a multi-stage convolutional neural network (CNN)-based model DiaNet that can reach an accuracy level of over 84% on this task, and in doing so, successfully identifies the regions on the retina images that contribute to its decision-making process, as corroborated by the medical experts in the field. This is the first study that highlights the distinguishing capability of the retinal images for diabetes patients in the Qatari population to the best of our knowledge. Comparing the performance of DiaNet against the existing clinical data-based machine learning models, we conclude that the retinal images contain sufficient information to distinguish the Qatari diabetes cohort from the control group. In addition, our study reveals that retinal images may contain prognosis markers for diabetes and other comorbidities like hypertension and ischemic heart disease. The results led us to believe that the inclusion of retinal images into the clinical setup for the diagnosis of diabetes is warranted in the near future.
18 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85099733279
Keywords: Clinical biomarkers; Convolutional neural network; Convolutional neural networks; Decision making; Decision making process; Deep learning; Diabetes; Diabetes patients; Diagnosis; Feature extraction; Health-care system; Ischemic heart disease; Learning architectures; Licenses; Machine learning models; Medical diagnostic imaging; Medical experts; Medical imaging; Network architecture; Ophthalmology; Qatar; Qatar Biobank (QBB); Retina; Retinopathy; Task analysis; deep learning; dept.csc; diabetes; machine learning; retina

Road Damage Detection Utilizing Convolution Neural Network and Principal Component Analysis - Endri, Elizabeth; Sheta, Alaa; Turabieh, Hamza (International Journal of Advanced Computer Science and Applications (IJACSA), 2020)
Roads should always be in a reliable con-dition and maintained regularly. One of the problems that should be maintained well is the pavement cracks problem. This a challenging problem that faces road engineers, since maintaining roads in a stable condition is needed for both drivers and pedestrians. Many meth-ods have been proposed to handle this problem to save time and cost. In this paper, we proposed a two-stage method to detect pavement cracks based on Principal Component Analysis (PCA) and Convolutional Neural Network (CNN) to solve this classification problem. We employed a Principal Component Analysis (PCA) method to extract the most significant features with a di˙erent number of PCA components. The proposed approach was trained using a Mendeley Asphalt Crack dataset, which contains 400 images of road cracks with a 480×480 resolution. The obtained results show how PCA helped in speeding up the learning process of CNN.
2 citations (Crossref) [2023-10-31] Number: 6
Keywords: Condition; Convolution; Convolution neural network; Convolutional neural network; Convolutional neural network (CNN); Crack detection; Crack problems; Damage detection; Network component analysis; Neural networks; Pavement crack; Pavement cracks; Pavements; Principal component analyse; Principal component analysis; Principal component analysis (PCA); Principal-component analysis; Problem solving; Road damage; dept.csc; peer-reviewed

A Deep Learning Approach to Urban Street Functionality Prediction Based on Centrality Measures and Stacked Denoising Autoencoder - Noori, Fatemeh; Kamangir, Hamid; A. King, Scott; Sheta, Alaa; Pashaei, Mohammad; SheikhMohammadZadeh, Abbas (Isprs International Journal of Geo-Information, 2020)
In urban planning and transportation management, the centrality characteristics of urban streets are vital measures to consider. Centrality can help in understanding the structural properties of dense traffic networks that affect both human life and activity in cities. Many cities classify urban streets to provide stakeholders with a group of street guidelines for possible new rehabilitation such as sidewalks, curbs, and setbacks. Transportation research always considers street networks as a connection between different urban areas. The street functionality classification defines the role of each element of the urban street network (USN). Some potential factors such as land use mix, accessible service, design goal, and administrators’ policies can affect the movement pattern of urban travelers. In this study, nine centrality measures are used to classify the urban roads in four cities evaluating the structural importance of street segments. In our work, a Stacked Denoising Autoencoder (SDAE) predicts a street’s functionality, then logistic regression is used as a classifier. Our proposed classifier can differentiate between four different classes adopted from the U.S. Department of Transportation (USDT): principal arterial road, minor arterial road, collector road, and local road. The SDAE-based model showed that regular grid configurations with repeated patterns are more influential in forming the functionality of road networks compared to those with less regularity in their spatial structure.
2 citations (Crossref) [2023-10-31] WOS:000556509900001
Keywords: Centrality measures; Deep learning; Machine learning; Stacked denoising autoencoder; Street functionality classification; Urban transportation network; centrality measures; deep learning; dept.csc; machine learning; peer-reviewed; stacked denoising autoencoder; street functionality classification; urban transportation network

A novel lifetime scheme for enhancing the convergence performance of salp swarm algorithm - Braik, Malik; Sheta, Alaa; Turabieh, Hamza; Al-Hiary, Heba (Soft Computing, 2021)
The performance of any meta-heuristic algorithm depends highly on the setting of dependent parameters of the algorithm. Different parameter settings for an algorithm may lead to different outcomes. An optimal parameter setting should support the algorithm to achieve a convincing level of performance or optimality in solving a range of optimization problems. This paper presents a novel enhancement method for the salp swarm algorithm (SSA), referred to as enhanced SSA (ESSA). In this ESSA, the following enhancements are proposed: First, a new position updating process was proposed. Second, a new dominant parameter different from that used in SSA was presented in ESSA. Third, a novel lifetime convergence method for tuning the dominant parameter of ESSA using ESSA itself was presented to enhance the convergence performance of ESSA. These enhancements to SSA were proposed in ESSA to augment its exploration and exploitation capabilities to achieve optimal global solutions, in which the dominant parameter of ESSA is updated iteratively through the evolutionary process of ESSA so that the positions of the search agents of ESSA are updated accordingly. These improvements on SSA through ESSA support it to avoid premature convergence and efficiently find the global optimum solution for many real-world optimization problems. The efficiency of ESSA was verified by testing it on several basic benchmark test functions. A comparative performance analysis between ESSA and other meta-heuristic algorithms was performed. Statistical test methods have evidenced the significance of the results obtained by ESSA. The efficacy of ESSA in solving real-world problems and applications is also demonstrated with five well-known engineering design problems and two real industrial problems. The comparative results show that ESSA imparts better performance and convergence than SSA and other meta-heuristic algorithms.
19 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85087610380
Keywords: Benchmarking; Comparative performance analysis; Convergence performance; Engineering design problems; Exploration and exploitation; Global optimum solutions; Heuristic algorithms; Iterative methods; Meta heuristic algorithm; Optimal parameter settings; Optimization; Parameter estimation; Real-world optimization; Testing; dept.csc; peer-reviewed

Capacitive swipe gesture based smartphone user authentication and identification - Rilvan, Mohamed Azard; Chao, Jedrik; Hossain, Md Shafaeat (Proceedings - 2020 IEEE International Conference on Cognitive and Computational Aspects of Situation Management, CogSIMA 2020, 2020)
Weaknesses in smartphone security pose a severe privacy threat to users. Currently, smartphones are secured through methods such as passwords, fingerprint scanners, and facial recognition cameras. To explore new methods and strengthen smartphone security, we developed a capacitive swipe based user authentication and identification technique. Swipe is a gesture that a user performs throughout the usage of a smartphone. Our methodology focuses on using the capacitive touchscreen to capture the user's swipe. While the user swipes, a series of capacitive frames are captured for each swipe. We developed an algorithm to process this series of capacitive frames pertaining to the swipe. While different swipes may contain different numbers of capacitive frames, our algorithm normalizes the frames by constructing the same number of frames for every swipe. After applying the algorithm, we transform the normalized frames into gray scale images. We apply principal component analysis (PCA) to these images to extract principal components, which are then used as features to authenticate/identify the user. We tested random forest (RF) and support vector machine (SVM) algorithms as classifiers. For authentication, the performance of SVM (tested with left swipes) was more promising than RF, yielding a maximum accuracy of 79.88% with an FAR and FRR of 15.84% and 50%, respectively. SVM (tested with right swipes) produced our maximum identification accuracy at 57.81% along with an FAR and FRR of 0.60% and 42.18%, respectively. © 2020 IEEE.
7 citations (Crossref) [2023-10-31] WOS:000628978500009
Keywords: Authentication; Decision trees; Face recognition; Facial recognition; Fingerprint scanners; Identification accuracy; Identification techniques; Mobile security; Principal Components; Privacy by design; Smartphone securities; Smartphone security; Smartphones; Support vector machine algorithm; Support vector machines; User authentication; authentication; capacitive swipe; capacitive touchscreen; dept.csc; identification

A novel meta-heuristic search algorithm for solving optimization problems: capuchin search algorithm - Braik, Malik; Sheta, Alaa; Heba, Al-Hiary (Neural Computing & Applications, 2021)
Meta-heuristic search algorithms were successfully used to solve a variety of problems in engineering, science, business, and finance. Meta-heuristic algorithms share common features since they are population-based approaches that use a set of tuning parameters to evolve new solutions based on the natural behavior of creatures. In this paper, we present a novel nature-inspired search optimization algorithm called the capuchin search algorithm (CapSA) for solving constrained and global optimization problems. The key inspiration of CapSA is the dynamic behavior of capuchin monkeys.The basic optimization characteristics of this new algorithm are designed by modeling the social actions of capuchins during wandering and foraging over trees and riverbanks in forests while searching for food sources. Some of the common behaviors of capuchins during foraging that are implemented in this algorithm are leaping, swinging, and climbing. Jumping is an effective mechanism used by capuchins to jump from tree to tree. The other foraging mechanisms exercised by capuchins, known as swinging and climbing, allow the capuchins to move small distances over trees, tree branches, and the extremities of the tree branches. These locomotion mechanisms eventually lead to feasible solutions of global optimization problems. The proposed algorithm is benchmarked on 23 well-known benchmark functions, as well as solving several challenging and computationally costly engineering problems. A broad comparative study is conducted to demonstrate the efficacy of CapSA over several prominent meta-heuristic algorithms in terms of optimization precision and statistical test analysis. Overall results show that CapSA renders more precise solutions with a high convergence rate compared to competitive meta-heuristic methods. © 2020, Springer-Verlag London Ltd., part of Springer Nature.
94 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85087487517
Keywords: Biomimetics; Constrained optimization; Cost engineering; Effective mechanisms; Engineering problems; Forestry; Global optimization; Global optimization problems; Heuristic algorithms; Heuristic methods; Learning algorithms; Locomotion mechanism; Meta heuristic algorithm; Meta-heuristic methods; Meta-heuristic search algorithms; Optimization problems; dept.csc; peer-reviewed

Effectiveness of symmetric rejection for a secure and user convenient multistage biometric system - Hossain, Md. S.; Balagani, Kiran S.; Phoha, Vir V. (Pattern Analysis and Applications, 2021)
A multistage biometric verification system uses multiple biometrics and/or multiple biometric verifiers to generate a verification decision. The core of a multistage biometric verification system is reject option which allows a stage not to give a genuine/impostor decision when it is not confident enough. This paper studies the effectiveness of symmetric rejection for multistage biometric verification systems. The symmetric rejection method determines the reject region by symmetrically rejecting equal proportion of genuine and impostor scores. The applicability of a multistage biometric verification system depends on how secure and user convenient it is, which is measured by the performance–cost trade-off. This paper analyzes the performance–cost trade-off of symmetric rejection method by conducting extensive experiments. Experiments are performed on two biometric databases: (1) publicly available NIST database and (2) a keystroke database. In addition, the symmetric rejection method is empirically compared with two existing rejection methods: (1) sequential probability ratio test-based method, which uses score-fusion and (2) Marcialis et al.’s method, which does not use score fusion. Results demonstrate strong effect of symmetric rejection method on creating a secure and user convenient multistage biometric verification system.
5 citations (Crossref) [2023-10-31] Type: Article tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85087048573
Keywords: dept.csc; peer-reviewed

Who are My Family Members? A Solution Based on Image Processing and Machine Learning - Kiley, Matthew R.; Hossain, Md Shafaeat (International Journal of Image and Graphics, 2020)
Image creation and retention are growing at an exponential rate. Individuals produce more images today than ever in history and often these images contain family. In this paper, we develop a framework to detect or identify family in a face image dataset. The ability to identify family in a dataset of images could have a critical impact on finding lost and vulnerable children, identifying terror suspects, social media interactions, and other practical applications. We evaluated our framework by performing experiments on two facial image datasets, the Y-Face and KinFaceW, comprising 37 and 920 images, respectively. We tested two feature extraction techniques, namely PCA and HOG, and three machine learning algorithms, namely K-Means, agglomerative hierarchical clustering, and K nearest neighbors. We achieved promising results with a maximum detection rate of 94.59% using K-Means, 89.18% with agglomerative clustering, and 77.42% using K-nearest neighbors. © 2020 World Scientific Publishing Company.
1 citations (Crossref) [2023-10-31] WOS:000589375900007
Keywords: Agglomerative clustering; Agglomerative hierarchical clustering; Exponential rates; Facial images; Family detection; Feature extraction techniques; Hierarchical clustering; Image creation; K -means; K-means clustering; K-nearest neighbors; Learning algorithms; Machine learning; Maximum detection rates; Motion compensation; Nearest neighbor search; agglomerative hierarchical clustering; dept.csc; face image clustering; family identification; peer-reviewed

Hybrid neural network models for forecasting ozone and particulate matter concentrations in the Republic of China - Braik, Malik; Sheta, Alaa; Al-Hiary, Heba (Air Quality, Atmosphere & Health, 2020)
Ozone is a toxic gas with massive distinct chemical components from oxygen. Breathing ozone in the air can cause severe effects on human health, especially people who have asthma. It can cause long-lasting damage to the lungs and heart attacks and might lead to death. Forecasting the ozone concentration levels and related pollutant attribute is critical for developing sophisticated environment safety policies. In this paper, we present three artificial neural network (ANN) models to forecast the daily ozone (O3), coarse particulate matter (PM10), and particulate matter (PM2.5) concentrations in a highly polluted city in the Republic of China. The proposed models are (1) recurrent multilayer perceptron (RMLP), (2) recurrent fuzzy neural network (RFNN), and (3) hybridization of RFNN and grey wolf optimizer (GWO), which are referred to as RMLP-ANN, RFNN, and RFNN-GWO models, respectively. The performance of the proposed models is compared with other conventional models previously reported in the literature. The comparative results showed that the proposed models presented outstanding performance. The RFNN-GWO model revealed superior results in the modeling of O3, PM10, and PM2.5 compared with the RMLP-ANN and RFNN models. © 2020, Springer Nature B.V.
8 citations (Crossref) [2023-10-31] tex.ids: braikHybridNeuralNetwork2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85086159416 type: Article
Keywords: China; Forecasting; Grey wolf optimizer; Neural network; Recurrent fuzzy neural networks; Recurrent multilayer perceptron; artificial neural network; cause of death; concentration (composition); dept.csc; ozone; particulate matter; peer-reviewed; urban pollution

Analyzing the sentiment correlation between regular tweets and retweets - Chen, Jundong; Hossain, Shafaeat; Zhang, Huan (Social Network Analysis and Mining, 2020)
Retweeting is an important way of information propagation on Twitter. In this paper, we investigate the sentiment correlation between regular tweets and retweets. We anticipate our investigation sheds a light on how the sentiment of regular tweets impacts the retweets of different sentiments. We propose a method for measuring the sentiment of tweets. We categorize the Twitter users into different groups by different norms, which are the follower count, the betweenness connectivity, a combination of follower count and betweenness centrality,and the amount of tweets. Then, we calculate the sentiment correlation for different groups to examine the influential factors for retweeting a message with a certain sentiment.We find that the users with higher betweenness centrality and higher tweets amount tend to exhibit a higher sentiment correlation. The users with medium-level followers_count show the highest sentiment correlation compared to the low-level and high-level followers_count. After combining the two factors of followers_count and betweenness centrality, we discover that specifically at low-level betweenness centrality the users with medium-level followers_count have the highest sentiment correlation. Our last observation is that the difference for correlation coefficients exists between different types of users. Our study on the sentiment correlation provides instructional information for modeling information propagation in human society. © 2020, Springer-Verlag GmbH Austria, part of Springer Nature.
21 citations (Crossref) [2023-10-31] tex.ids: chenAnalyzingSentimentCorrelation2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85078901417 type: Article
Keywords: Betweenness; Betweenness centrality; Correlation coefficient; Human society; Influential factors; Information dissemination; Information propagation; Instructional information; Learning systems; Machine learning; Model informations; Sentiment analysis; Social network; Social networking (online); Twitter classification; dept.csc; peer-reviewed

Machine Learning Models Reveal the Importance of Clinical Biomarkers for the Diagnosis of Alzheimer's Disease - Refaee, Mahmoud Ahmed; Ali, Amal Awadalla Mohamed; Elfadl, Asma Hamid; Abujazar, Maha F. A.; Islam, Mohammad Tariqul; Kawsar, Ferdaus Ahmed; Househ, Mowafa; Shah, Zubair; Alam, Tanvir (Importance of Health Informatics in Public Health During a Pandemic, 2020)
Alzheimer's Disease (AD) is a neurodegenerative disease that causes complications with thinking capability, memory and behavior. AD is a major public health problem among the elderly in developed and developing countries. With the growth of AD around the world, there is a need to further expand our understanding of the roles different clinical measurements can have in the diagnosis of AD. In this work, we propose a machine learning-based technique to distinguish control subjects with no cognitive impairments, AD subjects, and subjects with mild cognitive impairment (MCI), often seen as precursors of AD. We utilized several machine learning (ML) techniques and found that Gradient Boosting Decision Trees achieved the highest performance above 84% classification accuracy. Also, we determined the importance of the features (clinical biomarkers) contributing to the proposed multi-class classification system. Further investigation on the biomarkers will pave the way to introduce better treatment plan for AD patients. © 2020 The authors and IOS Press.
WOS:000630065600122
Keywords: ADNI; Adaptive boosting; Alzheimer Disease; Alzheimer disease; Alzheimer's Disease; Alzheimer's disease; Biomarkers; Brain; Classification accuracy; Clinical biomarkers; Clinical measurements; Cognitive Dysfunction; Cognitive impairment; Decision trees; Dementia; Developing countries; Diagnosis; Humans; Machine Learning; Machine learning; Machine learning models; Magnetic Resonance Imaging; Medical informatics; Mild Cognitive Impairment; Mild cognitive impairments (MCI); Multi-class classification; Neurodegenerative diseases; Patient treatment; Public health; biological marker; brain; cognitive defect; dept.csc; human; machine learning; nuclear magnetic resonance imaging

An approach to microCT image processing - Clark, Michael R; Daponte, John S; Sadowski, Thomas (Northeast Bioengineering Conference, 2005)
In scientific imaging, it is crucial to obtain precise images to facilitate accurate observations for the given application. However, often times the imaging equipment used to acquire such images introduces error into the observed image. Therefore, there is a fundamental need to remove the error associated with these images in order to facilitate accurate observations. This study investigates the effectiveness of an image processing technique utilizing an iterative deconvolution algorithm to remove error from micro-CT images. This technique is applied to several sets of in-vivo micro CT scans of mice, and its effectiveness is evaluated by qualitative comparison of the resultant thresholded binary images to thresholded binary images produced by more conventional image processing techniques; namely Gaussian filtering and straight thresholding. Results for this study suggest that iterative deconvolution as a pre-processing step produces superior qualitative results as compared to the more conventional methods tested. The groundwork for future quantitative verification is motivated. ©2005 IEEE.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Algorithms; Binary images; Biomedical engineering; Computerized tomography; Deconvolution algorithms; Errors; Image processing; Imaging systems; Iterative methods; Micro-CT image processing; Natural sciences computing; Scientific imaging; dept.csc

Visual enhancement of micro CT bone density images - Daponte, John S; Clark, Michael R; Damon, Megan; Kamins, Rebecca; Sadowski, Thomas; Tirrell, Charles (Visual Information Processing Conference, 2005)
The primary goal of this research was to provide image processing support to aid in the identification of those subjects most affected by bone loss when exposed to weightlessness and provide insight into the causes for large variability. Past research has demonstrated that genetically distinct strains of mice exhibit different degrees of bone loss when subjected to simulated weightlessness. Bone loss is quantified by in vivo computed tomography (CT) imaging. The first step in evaluating bone density is to segment gray scale images into separate regions of bone and background. Two of the most common methods for implementing image segmentation are thresholding and edge detection. Thresholding is generally considered the simplest segmentation process which can be obtained by having a user visually select a threshold using a sliding scale. This is a highly subjective process with great potential for variation from one observer to another. One way to reduce inter-observer variability is to have several users independently set the threshold and average their results but this is a very time consuming process. A better approach is to apply an objective adaptive technique such as the Riddler / Calvard method. In our study we have concluded that thresholding was better than edge detection and pre-processing these images with an iterative deconvolution algorithm prior to adaptive thresholding yields superior visualization when compared with images that have not been pre-processed or images that have been pre-processed with a filter.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Adaptive technique; Algorithms; Bone; Bone density; Bone loss; Computerized tomography; Edge detection; Gray scale images; Image analysis; Image enhancement; Image segmentation; Medical imaging; Visualization; dept.csc

On the relationship between packet size and router performance for heavy-tailed traffic - Antonios, Imad; Lipsky, Lester (Network Computing and Applications, 2004)
The problem of characterizing the relationship between packet size and network delay has received little attention in the field. Research in that area has been limited to either simulation studies or empirical observations that are detached from analytic traffic modeling. From a queueing viewpoint, it is simple to show that these three variables are inter-related, which necessitates a more careful study. We present a traffic model of a router fed by ON/OFF-type sources with heavy-tailed burst sizes. The traffic model considered is consistent with the evidence that Web traffic is heavy-tailed. The analysis cases that are considered establish a quantitative characterization of the complex relationship among packet payload and header sizes, traffic burstiness, and router queueing delay. © 2004 IEEE.
3 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Automatic teller machines; Computer simulation; Local area networks; Mathematical models; Packet networks; Packet payloads; Poisson distribution; Queueing delay; Queueing networks; Queueing theory; Routers; Telecommunication traffic; Traffic models; Web traffic; World Wide Web; dept.csc

Genetic algorithm for network cost minimization using threshold based discounting - Podnar, Hrvoje; Skorinkapov, Jadranka (Journal of Applied Mathematics and Decision Sciences, 2003)
We present a genetic algorithm for heuristically solving a cost minimization problem applied to communication networks with threshold based discounting. The network model assumes that every two nodes can communicate and offers incentives to combine flow from different sources. Namely, there is a prescribed threshold on every link, and if the total flow on a link is greater than the threshold, the cost of this flow is discounted by a factor α. A heuristic algorithm based on genetic strategy is developed and applied to a benchmark set of problems. The results are compared with former branch and bound results using the CPLEX® solver. For larger data instances we were able to obtain improved solutions using less CPU time, confirming the effectiveness of our heuristic approach. Copyright© 2003, Lawrence Erlbaum Associates, Inc.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/121-335-792-346-263 tex.type: [object Object]
Keywords: Genetic algorithm; Mixed integer programming; Network design; Threshold based discounting; dept.csc

Tracking remotely sensed cloud images in time and space - Daponte, John S; Vitale, Joseph N; Parikh, Jo Ann; Tselioudis, George (Proceedings of SPIE, 2001)
Temporal and spatial analysis was applied to a sequence of cloud top pressure (CTP) images and cloud optical thickness (TAU) images, and a storm tracking algorithm was proposed. A sequence of storm tracks from the satellite images was developed from the satellite images. Composite images were created by projecting ahead in time and substituting the first valid pixel for missing data, and a variety of CTP and TAU cut-off values were used to identify regions of interest. The region correspondences were determined from one time frame to another which yielded the storm center coordinates. The obtained tracks were compared to the storm tracks computed from sea level pressure data by matching the results first in time and then in spatial distance.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/054-660-919-588-122 tex.type: [object Object]
Keywords: Algorithms; Climatology; Cloud top pressures (CTP); Clouds; Correspondence problem; Data reduction; Image analysis; Mathematical models; Radar tracking; Remote sensing; Satellite images; Sea level; Storm tracks; Storms; Temporal and spatial analysis; Weather forecasting; Weather satellites; dept.csc

Comparison of a geometric-based and an evolutionary technique for tracking storm systems - Parikh, Jo Ann; DaPonte, John S.; Vitale, Joseph N.; Tselioudis, George (Applications and Science of Computational Intelligence III, 2000)
The objective of this study is to compare geometric-based and evolutionary techniques for tracking storm systems from sequences of satellite images. Analysis was applied to the International Satellite Cloud Climatology Project low resolution D1 database for selected storm systems during the month of September, 1988. During this time period there were two exceptionally long tracks of major hurricane systems, Hurricanes Gilbert and Helene. Cloud top pressure and cloud optical thickness were used to identify storm systems. The ability of the geometric-based and evolutionary techniques to generate tracks through storm regions was assessed. Differences in final tracking results between the two techniques resulted not only from the differences in methodology but also form differences in the type of preprocessed input used by each of the techniques. Tracking results were compared to results disseminated by the Colorado State/Tropical Prediction Center and maintained by the National Hurricane Center in Miami, Florida. For the hurricanes investigated in this study, both techniques were able to generate tracks which followed either most or some of the portions of the hurricanes. The evolutionary algorithm was in general able to maintain good continuity along the tracks but, with no knowledge of overall region movement, was unable to discern which of two possible directions would be best to pursue in cases where there were tow or more equally close storm systems components. The geometric method was able to maintain a smooth track close to the course of the hurricane except for confusion primarily at the beginning and/or end of tracks.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/150-990-700-698-062, pop00221
Keywords: Algorithms; Computational geometry; Database systems; Evolutionary computation; Image analysis; Satellite image processing; Satellites; Storm tracking; Storms; dept.csc

Switching theory approach to alarm correlation in network management - Aboelela, E; Douligeris, Christos (Local Computer Networks, 2000)
Several working or experimental management systems use expert systems techniques for fault management purposes. Although the effort in the area is still growing, most of the expert fault management systems developed were built in an ad-hoc and unstructured basis simply transferring the knowledge of the human expert into an automated system. However, to meet future challenges, a theoretical foundation for fault management must be established aiming to bridge the gap between the working systems and research, and to provide a general structured model easily expandable to future networks. In this paper an algorithm is proposed to simplify the set of clustered alarms. This algorithm is based on the techniques widely used in the Logic Design field to simplify switching functions. The performance of the proposed algorithm is analyzed and the results are compared to those of a traditional algorithm.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Alarm correlations; Algorithms; Correlation theory; Expert systems; Network management; Switching theory; Telecommunication networks; dept.csc

Field-by-field evaluation of intraoperative transoesophageal echocardiography interpretative skills - Griffin, Michael J; Edwards, Bruce; Judd, Jon; Workman, Robert; Rafferty, T (Physiological Measurement, 2000)
A quality assurance system is essential for the credibility and structured growth of anaesthesiology-based transoesophageal echocardiography (TEE) programmes. We have developed software (Q/A Kappa), involving a 400- line source code, capable of directly reporting kappa correlation coefficient values, using external reviewer interpretations as the 'gold standard', and thereby allowing systematic assessment of the validity of intraoperative echocardiographic interpretation. This paper presents assessment of the validity of 240 intraoperative anaesthesiologists' echocardiographic interpretations, and, in addition, the results of field testing of this prototypical software. Data, derived from consecutive cardiac surgery patients, consisted of standardized two-dimensional transoesophageal echocardiographic, colour flow and Doppler imaging sequences. Intraoperative and off-line 'gold standard' TEE interpretations were compared for 19 fields or variables using the Q/A Kappa program. The kappa correlation coefficients were highly variable and dependent on the examination field, ranging from 0.08 for apical regional wall motion scores to 1.00 for tricuspid regurgitation grade, left atrial measurement, aortic valve anatomy and left ventricular long axis and short axis global function. The correlation coefficients were also operator dependent. These data (480 interpretations) were also manually integrated into the equation required for calculation of values of the variable kappa correlation coefficient. The relationship between Q/A Kappa-derived values and manually calculated values was highly significant (p < 0.001; r = 1.0). The implications and possible explanations of the results for particular examination fields are discussed. This study also demonstrates successful seamless functioning of this software program from data entry, segmentation into tables and valid statistical analysis. These findings suggest that it is practical to provide sophisticated continuous quality improvement TEE data on a routine basis.
2 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/049-767-860-096-746 tex.type: [object Object]
Keywords: Cardiovascular surgery; Coefficient values; Correlation coefficient; Echocardiography; Field-by-field evaluation; Gold standards; Intra-operative; Intraoperative; Kappa correlation; Line sources; Quality assurance; Quality assurance systems; Quality control; Software testing; Source codes; Transesophageal echocardiographies; Transoesophageal echocardiography; article; competence; controlled study; correlation function; dept.csc; heart surgery; human; intraoperative period; major clinical study; priority journal; quality control; transesophageal echocardiography

Hierarchical clustering of distributed object-oriented software systems: a generic solution for software-hardware mismatch problem - El-Raouf, Amal Abd (WSEAS Transactions on Computers archive, 2009)
During the software lifecycle, the software structure is subject to many changes in order to fulfill the customer's requirements. In Distributed Object Oriented systems, software engineers face many challenges to solve the software-hardware mismatch problem in which the software structure does not match the customer's underlying hardware. A major design problem of Object Oriented software systems is the efficient distribution of software classes among the different nodes in the system while maintaining two features: low-coupling and high software quality. In this paper, we present a new methodology for efficiently restructuring Distributed Object Oriented software systems to improve the overall system performance and to solve the softwarehardware mismatch problem. Our method has two main phases. In the first phase, we use the hierarchical clustering method to restructure the target software application. As a result, all the possible clustering solutions that could be applied to the target software application are generated. In the second phase, we decide on the best-fit clustering solution according to the customer hardware organization.
Keywords: Clustering solutions; Computer software selection and evaluation; Customer satisfaction; Design problems; Distributed object-oriented systems; Distributed objects; Distributed systems; Generic solutions; Hier-archical clustering; Hierarchical clustering; Hierarchical clustering methods; Hierarchical systems; Low coupling; Mismatch problems; Object oriented programming; Object oriented software; Object-oriented software systems; Performance analysis; Sales; Second phase; Software Quality; Software applications; Software engineers; Software life cycles; Software restructuring; Software structures; dept.csc; ⛔ No DOI found

Visualizing multidimensional climate model data - DaPonte, John S.; Munhutu, Paidemwoyo (Visual Information Processing XVIII, 2009)
The primary purpose of this study was to investigate the feasibility of using simulated data from the United Kingdom Meteorological Office (UKMO) global climate mathematical model to serve as boundary values for a regional model RM3 which has been used by NASA to make predictions about climate dynamics in West Africa. In the past, historical data has been used successfully as boundary data but this approach limits outcomes to time periods in the past. The advantage of using the UKMO data is its potential to provide input boundary data for future time periods resulting in future regional predictions. This study has provided NASA scientists with graphical and statistical summaries including visual animations that provide qualitative and quantitative information necessary for evaluating whether the UKMO data can be used as a driving force for the RM3 model. One definite conclusion of this investigation is that both spatial and temporal interpolation of UKMO results will be necessary in order to make its results compatible with the RM3 model.
0 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/085-256-486-320-082, pop00289 tex.type: [object Object]
Keywords: Boundary data; Boundary values; Climate dynamics; Climate model; Data processing; Driving forces; Forecasting; Global climate model; Global climates; Historical data; Mathematical models; NASA; Quantitative information; Regional model; Regional model and visual animations; Simulated data; Spatial and temporal interpolation; Statistical summary; Time periods; United kingdom; Visual communication; West Africa; dept.csc

Restructuring distributed object-oriented software using hierarchical clustering - El-Raouf, Amal Abd (Annual Conference on Computers, 2009)
The software restructuring techniques present solutions for the software-hardware mismatch problem in which the software structure does not match the available hardware platform. In Distributed Object Oriented (DOO) systems, software engineers face many challenges to solve the software-hardware mismatch problem. One important aspect of DOO software systems is the efficient distribution of software classes among the different nodes while maintaining low-coupling and high software quality. In this paper, we present a new methodology for efficiently restructuring the DOO software systems to improve the performance and to solve the software-hardware mismatch problem. In our method, we use the hierarchical clustering technique to opt the classes to be grouped together and according to the customer hardware organization, we pick the level of the hierarchy that have the appropriate number of clusters to be allocated to the set of available nodes in the customer distributed system.
tex.type: Proceedings paper
Keywords: Computer hardware; Computer software selection and evaluation; Distributed objects; Distributed systems; Hardware; Hardware platform; Hier-archical clustering; Hierarchical clustering; Hierarchical systems; Low coupling; Mismatch problems; Number of clusters; Object oriented programming; Object oriented software; Performance analysis; Re-structuring techniques; Software Quality; Software engineers; Software restructuring; Software structures; Software systems; dept.csc; ⛔ No DOI found

Computer assisted analysis of microscopy images - Sawicki, M; Munhutu, P; Daponte, John S; Caragianisbroadbridge, Christine; Lehman, Ann; Sadowski, Thomas; Garcia, E; Heyden, C; Mirabelle, L; Benjamin, P (Visualization and Data Analysis, 2009)
The use of Transmission Electron Microscopy (TEM) to characterize the microstructure of a material continues to grow in importance as technological advancements become increasingly more dependent on nanotechnology 1. Since nanoparticle properties such as size (diameter) and size distribution are often important in determining potential applications, a particle analysis is often performed on TEM images. Traditionally done manually, this has the potential to be labor intensive, time consuming, and subjective 2. To resolve these issues, automated particle analysis routines are becoming more widely accepted within the community 3. When using such programs, it is important to compare their performance, in terms of functionality and cost. The primary goal of this study was to apply one such software package, ImageJ to grayscale TEM images of nanoparticles with known size. A secondary goal was to compare this popular open-source general purpose image processing program to two commercial software packages. After a brief investigation of performance and price, ImageJ was identified as the software best suited for the particle analysis conducted in the study. While many ImageJ functions were used, the ability to break agglomerations that occur in specimen preparation into separate particles using a watershed algorithm was particularly helpful 4. © 2009 SPIE-IS&T.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Agglomeration; Commercial softwares; Computer aided analysis; Computer-assisted analysis; Data visualization; Electron microscopes; General purpose; Gray-scale; Image processing; Imaging systems; Labor intensives; Microscopy; Microscopy images; Nanoparticles; Open sources; Particle analysis; Particle size analysis; Potential applications; Semiconductor quantum wires; Software packages; Specimen preparation; TEM images; Technological advancements; Tem; Thresholding; Transmission Electron Microscope (TEM); Transmission electron microscopy; Visualization; Water-shed algorithms; dept.csc; dept.phy

Data analyis system design for lidar experimentation - Sharma, Nimmi C P; Parikh, Jo Ann (International Geoscience and Remote Sensing Symposium, 2008)
A web-based lidar experimentation and data analysis system (LEDAS) was developed, with support from a National Science Foundation award, to support resource sharing of lidar equipment, datasets and data analysis routines and collaboration between members of the Connecticut State University System (CSUS) Lidar Collaboratory. The system allows users at different geographical locations to conduct remote sensing research and education over the Web through remote access and control of a single shared lidar system and web-based data analysis. Users need not have any specialized instrumentation or software at their institutions, thereby making real remote sensing research available to students and faculty from institutions which may not have the internal budgets for such facilities. An original structure providing basic functionality was developed and implemented. This paper describes the second generation data analysis system which provides significant new enhancements and capabilities. © 2008 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Access control; Budget control; Collaboratories; Data analysis; Data analysis system; Data reduction; Education; Geographical locations; Geology; Information analysis; National Science Foundations; Optical radar; Original structures; Remote control; Remote sensing; Resource sharing; Second generation; University system; Websites; dept.csc; lidar

Characterization of nanoparticles by computer imaging particle analysis - DaPonte, J.; Sadowski, T.; Broadbridge, C. C.; Munhutu, P.; Lehman, A.; Krishnamoorthy, D.; Garcia, E. C.; Sawicki, M. S.; Heyden, C.; Mirabelle, L.; Benjamin, P. (Nanomaterials Synthesis, Interfacing, and Integrating in Devices, Circuits, and Systems II, 2007)
Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their decreased size may give rise to certain properties that are very different from those exhibited by bulk materials. Further advancement of nanotechnology cannot be realized without an increased understanding of nanoparticle properties such as size (diameter) and size distribution. Frequently, these parameters are evaluated using numerous imaging modalities including transmission electron microscopy (TEM) and atomic force microscopy (AFM). In the past, these parameters have been obtained from digitized images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. Recently, computer imaging particle analysis routines that count and measure objects in a binary image^1 have emerged as an objective and rapid alternative to manual techniques. In this paper a procedure is described that can be used to preprocess a set of gray scale images so that they are correctly thresholded into binary images prior to a particle analysis ultimately resulting in a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Particle analysis was performed on two types of calibration samples imaged using AFM and TEM. Additionally, results of particle analysis can be used for identifying and removing small noise particles from the image. This filtering technique is based on identifying the location of small particles in the binary image, assessing their size, and removing them without affecting the size of other larger particles.
1 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/032-875-618-829-762, pop00276
Keywords: Atomic force microscopy; Bulk materials; Computer imaging; Device fabrication; Imaging techniques; Nanoparticles; Particle analysis; Quantum computers; Size distribution; Thresholding; Transmission electron microscopy; dept.csc; dept.phy

Application of particle analysis to transmission electron microscopy (TEM) - Daponte, John S; Sadowski, Thomas; Broadbridge, Christine; Day, D; Lehman, A; Krishna, D; Marinella, L; Munhutu, P; Sawicki, M (Visual Information Processing Conference, 2007)
Nanoparticles, particles with a diameter of 1-100 nanometers (nm), are of interest in many applications including device fabrication, quantum computing, and sensing because their size may give them properties that are very different from bulk materials. Further advancement of nanotechnology cannot be obtained without an increased understanding of nanoparticle properties such as size (diameter) and size distribution frequently evaluated using transmission electron microscopy (TEM). In the past, these parameters have been obtained from digitized TEM images by manually measuring and counting many of these nanoparticles, a task that is highly subjective and labor intensive. More recently, computer imaging particle analysis has emerged as an objective alternative by counting and measuring objects in a binary image. This paper will describe the procedures used to preprocess a set of gray scale TEM images so that they could be correctly thresholded into binary images. This allows for a more accurate assessment of the size and frequency (size distribution) of nanoparticles. Several preprocessing methods including pseudo flat field correction and rolling ball background correction were investigated with the rolling ball algorithm yielding the best results. Examples of particle analysis will be presented for different types of materials and different magnifications. In addition, a method based on the results of particle analysis for identifying and removing small noise particles will be discussed. This filtering technique is based on identifying the location of small particles in the binary image and removing them without affecting the size of other larger particles.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Computer imaging; Flat field correction; Flat field correction and rolling ball background correction; Imaging particle analysis; Imaging systems; Magnification; Microfabrication; Nanoparticles; Nanotechnology; Quantum computers; Rolling ball background correction; Size distribution; Transmission electron microscopy; dept.csc; dept.phy

Comparison of thresholding techniques on nanoparticle images - Daponte, John S; Sadowski, Thomas; Broadbridge, Christine; Day, D; Lehman, A; Krishna, D; Marinella, L; Munhutu, P; Sawicki, M (Visual Information Processing Conference, 2007)
Thresholding is an image processing procedure used to convert an image consisting of gray level pixels into a black and white binary image. One application of thresholding is particle analysis. Once foreground objects are separated from the background, a quantitative analysis that characterizes the number, size and shape of particles is obtained which can then be used to evaluate a series of nanoparticle samples. Numerous thresholding techniques exist differing primarily in how they deal with variations in noise, illumination and contrast. In this paper, several popular thresholding algorithms are qualitatively and quantitatively evaluated on transmission electron microscopy (TEM) and atomic force microscopy (AFM) images. Initially, six thresholding algorithms were investigated: Otsu, Riddler-Calvard, Kittler, Entropy, Tsai and Maximum Likelihood. The Riddler-Calvard algorithm was not included in the quantitative analysis because it did not produce acceptable qualitative results for the images in the series. Two quantitative measures were used to evaluate these algorithms. One is based on comparing object area the other on diameter before and after thresholding. For AFM images the Kittler algorithm yielded the best results followed by the Entropy and Maximum Likelihood techniques. The Tsai algorithm yielded the top results for TEM images followed by the Entropy and Kittler methods.
2 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Algorithms; Atomic force microscopy; Binary images; Maximum likelihood; Nanoparticle; Nanoparticle samples; Nanoparticles; Pixels; Riddler-Calvard algorithm; Thresholding; Transmission electron microscopy; Tsai algorithm; dept.csc; dept.phy

Using Google hacking to enhance defense strategies - Lancor, Lisa; Workman, Robert (Technical Symposium on Computer Science Education, 2007)
This paper provides a description of how the topic of Google hacking was incorporated into a graduate course on web security which was offered in the Fall of 2005. It begins by providing an overview of Google hacking and describes what it is, how it is used, and most importantly how to defend against it. The paper then describes a series of exercises that students must complete providing them with hands-on Google hacking strategies, techniques and countermeasures. Copyright 2007 ACM.
6 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Computer crime; Education; Google hacking; Hacking; Information assurance; Information security; Security of data; Strategic planning; Students; Teaching; Web security; Websites; dept.csc

Performability models for multi-server systems with high-variance repair durations - Schwefel, H P; Antonios, I (Dependable Systems and Networks, 2007)
We consider cluster systems with multiple nodes where each server is prone to run tasks at a degraded level of service due to some software or hardware fault. The cluster serves tasks generated by remote clients, which are potentially queued at a dispatcher. We present an analytic queueing model of such systems, represented as an M/MMPP/1 queue, and derive and analyze exact numerical solutions for the mean and tail-probabilities of the queue-length distribution. The analysis shows that the distribution of the repair time is critical for these performability metrics. Additionally, in the case of high-variance repair times, the model reveals so-called blow-up points, at which the performance characteristics change dramatically. Since this blowup behavior is sensitive to a change in model parameters, it is critical for system designers to be aware of the conditions under which it occurs. Finally, we present simulation results that demonstrate the robustness of this qualitative blow-up behavior towards several model variations. © 2007 IEEE.
4 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Cluster analysis; Computer software; Mathematical models; Multi-server systems; Performability models; Queueing networks; Remote clients; Repair durations; Robustness (control systems); Servers; Systems analysis; dept.csc

Performance-based modeling and restructuring of object oriented software for pipeline architecture - Abdel-Raouf, Amal; Ammar, Reda A; Fergany, Tahany A (Computer Science and Software Engineering, 2006)
Pipelining is the suitable architecture to adopt applications that are naturally divided into stages. Recently, applications tend to be Object Oriented (OO). Within the context of OO, there are a lot of interactions among different objects that result in many communication activities. Besides the feed-forward communication activities, many bypassing activities are generated in the pipeline structure. In this paper, we present a performance model that analyzes and evaluates the execution and communication times of OO software that runs on pipeline architecture. The model realizes both the feed-forward and the bypassing communication. We utilize the model to restructure the target software to achieve better performance. The restructuring algorithm has two phases; the first phase is concerned with maximizing the throughput. The second phase aims to minimize the latency and fully exploit the system resources. © 2006 - IOS Press and the authors. All rights reserved.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Communication activities; Distributed systems; Feed-forward communication; Object oriented programming; Object oriented software; Performance modeling and analysis; Performance-based models; Pipeline architecture; Pipelines; Restructuring algorithms; dept.csc; distributed systems; object-oriented software; pipeline architecture; software restructuring

COinS for the link trail - Chudnov, Daniel (Library Journal, 2006)
tex.unique-id: ISI:000239013700003
Keywords: dept.csc; ⛔ No DOI found

Quantitative confirmation of visual improvements to micro-CT bone density images - Daponte, John S; Clark, Michael R; Nelson, Paul; Sadowski, Thomas; Wood, Elizabeth (Visual Information Processing Conference, 2006)
The primary goal of this research was to investigate the ability of quantitative variables to confirm qualitative improvements of the deconvolution algorithm as a preprocessing step in evaluating micro CT bone density images. The analysis of these types of images is important because they are necessary to evaluate various countermeasures used to reduce or potentially reverse bone loss experienced by some astronauts when exposed to extended weightlessness during space travel. Nine low resolution (17.5 microns) CT bone density image sequences, ranging from between 85 to 88 images per sequence, were processed with three preprocessing treatment groups consisting of no preprocessing, preprocessing with a deconvolution algorithm and preprocessing with a Gaussian filter. The quantitative parameters investigated consisted of Bone Volume to Total Volume Ratio, the Structured Model Index, Fractal Dimension, Bone Area Ratio, Bone Thickness Ratio, Euler's Number and the Measure of Enhancement. Trends found in these quantitative variables appear to corroborate the visual improvements observed in the past and suggest which quantitative parameters may be capable of distinguishing between groups that experience bone loss and others that do not.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Algorithms; Bone Thickness Ratio; Computerized tomography; Digital filters; Euler's Number; Gaussian filters; Image enhancement; Image sequences; Medical imaging; Optical resolving power; dept.csc; dept.phy

Animating climate model data - Daponte, John S; Sadowski, Thomas; Thomas, Paul (Visual Information Processing Conference, 2006)
This paper describes a collaborative project conducted by the Computer Science Department at Southern Connecticut State University and NASA's Goddard Institute for Space Science (GISS). Animations of output from a climate simulation math model used at GISS to predict rainfall and circulation have been produced for West Africa from June to September 2002. These early results have assisted scientists at GISS in evaluating the accuracy of the RM3 climate model when compared to similar results obtained from satellite imagery. The results presented below will be refined to better meet the needs of GISS scientists and will be expanded to cover other geographic regions for a variety of time frames.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Animation; Climate model data; Computer simulation; Data reduction; Geographical regions; Imaging systems; Mathematical models; Satellite imagery; Satellites; Simulation math model; dept.csc; dept.phy

Authorship categorization of public domain literature - Boran, Tudor; Voss, John; Hossain, Shafaeat (Ubiquitous Computing, 2016)
We defined a set of quantifiable features for authorship categorization. We performed our experiments on public domain literature - all books analyzed were obtained in plain text format through Project Gutenberg's online repository of classic books. We tested three machine learning algorithms: Artificial Neural Network, Naïve Bayes Classifier, and Support Vector Machine with our features. We found that certain features, such as punctuation and various suffixes result in a higher accuracy. In addition, the Support Vector Machine classifier produces repeatedly higher accuracies than other classifiers and seems to be a far superior method of classification in terms of authorship categorization. © 2016 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Authorship categorization; Bayes Classifier; Classification (of information); Learning algorithms; Learning systems; Mobile telecommunication systems; Neural networks; Online repositories; Plain text; Public domains; Support vector machine classifiers; Support vector machines; Ubiquitous computing; classification; dept.csc; public domain literature; support vector machine

On finding appropriate reject region in serial fusion based biometric verification - Hossain, Shafaeat (IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision Support, 2016)
We give the theoretical foundation for finding a reject region which gives the minimum equal error rate in serial fusion based biometric verification. Given a user-specified tolerance of x percent genuine score reject rate, we prove that there exists a unique reject region inside which the false alarm rate and impostor pass rate curves overlap, and this reject region gives the minimum equal error rate. Our theory leads to new algorithms for finding reject regions, which have two key advantages over the state-of-the-art: (1) the algorithms allow the system administrator to control the proportion of genuine scores that a reject region can erroneously reject and (2) the algorithms determine reject regions directly from the scores, without the need to estimate score distributions. Our proofs do not rely on data belonging to any particular distribution, which makes them applicable to a wide range of biometric modalities including face, finger, iris, speech, gait, and keystrokes. © 2016 IEEE.
3 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Biometric verification; Biometrics; Decision support systems; Equal error rate; False alarm rate; Genuine scores; Particular distribution; State of the art; System administrators; Theoretical foundations; dept.csc

User authentication and identification on smartphones by incorporating capacitive touchscreen - Rilvan, Mohamed Azard; Lacy, Kolby Isiah; Hossain, Shafaeat; Wang, Bing (International Performance, Computing, and Communications Conference, 2016)
Smartphones, while providing users ease of access to sensitive information on the go, also present severe security risks if an attacker is able to gain access to them. To strengthen the user authentication and identification in a smartphone, we develop a biometric authentication and identification system which uses the capacitive touchscreen that is featured in all current smartphones. Our methodology focuses on using the touchscreen as a sensor to capture the image of a user's ear, thumb or four fingers. We extract the capacitive raw data from the touched body part to obtain a capacitive image, and then use it to capture geometric features (e.g., length and width of a finger) and principal components. After that, we experiment with Support Vector Machine (SVM) and Random Forest (RF) classifiers to verify and also identify each user. We achieved the maximum authentication accuracy of 98.84% by four fingers with SVM, and maxinum identification accuracy of 97.61% by four fingers with RF. © 2016 IEEE.
4 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Authentication; Biometric; Biometric authentication; Biometrics; Decision trees; Geometric feature; Identification (control systems); Identification accuracy; Principal Components; Random forests; Security risks; Sensitive informations; Smartphones; Support vector machines; User authentication; authentication; capacitive touchscreen; dept.csc; identification; smart-phones

Exploring search task difficulty reasons in different task types and user knowledge groups - Liu, Jingjing; Kim, Chang Suk; Creel, Caitlin (Information Processing and Management, 2015)
Search task difficulty has been attracting much research attention in recent years, mostly regarding its relationship with searchers' behaviors and the prediction of task difficulty from search behaviors. However, it remains unknown what makes searchers feel the difficulty. A study consisting of 48 undergraduate students was conducted to explore this question. Each participant was given 4 search tasks that were carefully designed following a task classification scheme. Questionnaires were used to elicit participants' ratings on task difficulty and why they gave those ratings. Based on the collected difficulty reasons, a coding scheme was developed, which covered various aspects of task, user, and user-task interaction. Difficulty reasons were then categorized following this scheme. Results showed that searchers reported some common reasons leading to task difficulty in different tasks, but most of the difficulty reasons varied across tasks. In addition, task difficulty had some common reasons between searchers with low and high levels of topic knowledge, although there were also differences in top task difficulty reasons between high and low knowledge users. These findings further our understanding of search task difficulty, the relationship between task difficulty and task type, and that between task difficulty and knowledge level. The findings can also be helpful with designing tasks for information search experiments, and have implications on search system design both in general and for personalization based on task type and searchers' knowledge. (C) 2014 Elsevier Ltd. All rights reserved.
21 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/112-166-586-835-305 tex.type: [object Object]
Keywords: Information search; Knowledge level; Personalizations; Search engines; Students; Surveys; Task classification; Task difficulty; Task difficulty reason; Task type; Topic knowledge; Undergraduate students; dept.csc

Asynchronous gossip-based data propagation protocol - Andrew, Priyanka; Antonios, Imad (Digital Information and Communication Technology and Its Applications, 2015)
Data dissemination protocols govern interaction and exchange of data among nodes in a distributed system. An understanding of data transfer protocols provides insight into efficient middleware management. Due to their simplicity, scalability and fault-tolerance, gossip-based protocols are researched widely as an effective communication strategy. The Shuffle protocol presented in [1], is an example of a decentralized, gossip-based data transfer protocol used to spread information in a wireless network via probabilistic exchange of data. This paper presents, an asynchronous variant of the Shuffle protocol and a system model that captures variability in data transmission times. This transmission time variability is inherent in dynamic networks, where such algorithms are typically deployed. A simulation-based analysis of the protocol's performance behavior is presented. Results show the effects of transmission variability, on data replication and its coverage. Also examined is the relationship between available storage and the performance of the protocol, expressed using measures such as propagation time and work. © 2015 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Data communication systems; Data dissemination; Data dissemination protocols; Data propagation protocols; Data transfer; Data transfer protocols; Digital storage; Distributed database systems; Effective communication; Fault tolerance; Fault tolerant computer systems; Gossip-based algorithms; Gossip-based protocol; Internet protocols; Middleware; Shuffle protocol; Simulation-based analysis; Wireless networks; asynchronous gossip-based algorithm; data dissemination; data transfer protocol; dept.csc; wireless network

ARM Assembly language programming Using Keil Development Tools Introduction - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
Manufacturers of CPUs publish a document that contains information about the processor that includes: list of registers, function of each register, size of data bus, size of address bus and list of instructions that can be executed by the CPU. Each CPU has a known instruction set that a programmer can use to write assembly language programs. Instruction sets are specific to each type of processor. That being said, Pentium processors use a different instruction set than ARM processors. Using the Instructions a of processor to write a program is called assembly language and function of an assembler is to convert assembly language to machine code (binary) that the CPU can understand.
Citation Key Alias: lens.org/088-348-966-769-812
Keywords: dept.csc

ARM Cortex-M3 Processor and MBED NXP LPC1768 - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
ARM offers variety of the core processor base on their applications and they are: Cortex A series: Cortex A series is a High performance processor for open operating system, the Cortex-A50 is a 64 bit process, application of Cortex-A series are Smart phones, Netbook, Digital TV, and eBook readers
Citation Key Alias: lens.org/031-467-574-875-492
Keywords: dept.csc

ARM Instructions and Part II - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
The data transfer instructions are used to transfer data from memory to registers and from registers to memory and they are Load (LDR) and Store (STR) instructions.
Citation Key Alias: lens.org/013-090-628-194-319
Keywords: dept.csc

ARM Instructions Part I - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
Advanced RISC Machine (ARM) was developed by the Acorn Company. ARM is a leader supplier of microprocessors in the world, ARM develop the core CPU and thousand of suppliers add more functional units to the core. ARM uses two types instruction called Thumb and Thumb-2. Thumb instructions are 16 bits and thumb-2 instructions are 32 bits, currently most ARM processors uses 32 bit instructions.
Citation Key Alias: lens.org/004-692-447-907-924
Keywords: dept.csc

Logic Gates and Introduction to Computer Architecture - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
The basic components of an Integrated Circuit (IC) is logic gates which made of transistors, in digital system there are three basic logic operations and they are called AND, OR and NOT.
Citation Key Alias: lens.org/102-318-987-004-411
Keywords: dept.csc

Number Systems and Data Communication - Elahi, Ata; Arjeski, Trevor (ARM Assembly Language with Hardware Experiments, 2015)
In order to understand network technology it is important to know how information is represented for transmission from one computer to another. Information can be transferred between computers in one of two ways: an analog signal or a digital signal.
Citation Key Alias: lens.org/038-175-763-396-415
Keywords: Analog Signal; Binary Number; Digital Signal; Number System; Transmission Method; dept.csc

Enhancing EDP of multicore processors through DVFS - Hajiamini, Shervin; Shirazi, Behrooz; Rilvan, Mohamed Azard (International Green and Sustainable Computing Conference, 2015)
Dynamic voltage and frequency scaling (DVFS) is a well-known technique to optimize the power dissipation of electronic systems without significantly compromising overall system performance. DVFS exploits the periods of inter-core data exchange (memory-bound operations) to reduce the voltage and frequency (V/F) of the cores in order to reduce the power dissipation during the execution flow of an application running on the CMP. As the lengths of the idle and busy periods of the cores vary depending on the benchmarks, it is crucial for any DVFS technique to maximize the power saving without losing a significant performance. In this work we present two power optimization methodologies that are integrated into a full-system simulator to make online predictions about the voltage and frequency of the cores during the execution time of the benchmarks. We evaluate these methodologies in terms of the V/F predictions vs. the actual utilization of each core periodically. We also compare the overall execution time, energy dissipation, and energy-delay product (EDP) of the power optimization methodologies for various benchmarks. © 2015 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Benchmarking; DVFS; Dynamic frequency scaling; Dynamic voltage and frequency scaling; Electric losses; Electronic data interchange; Electronic systems; Energy delay product; Energy dissipation; Energy efficiency; Forecasting; Full system simulators; Multi core; Multi-core processor; Multicore; Power Optimization; V/F tracking; Voltage scaling; dept.csc; energy efficiency

Free indulgences: Enhanced zero-price effect for hedonic options - Hossain, Mehdi T; Saini, Ritesh (International Journal of Research in Marketing, 2015)
We find that the relative preference of hedonic products is disproportionately enhanced when they are offered at a free price. This “free price bounce” is more subdued for utilitarian products. This is surprising because rational choice theory posits that relative preference amidst two options - say a hedonic and a utilitarian product - remains intact as long as the price difference between them is constant. We propose and demonstrate that this axiom is violated when a hedonic product is offered for free. (C) 2015 Elsevier B.V. All rights reserved.
14 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/107-412-613-199-640 tex.type: [object Object]
Keywords: Affective appraisal; Behavioral pricing; Hedonic-utilitarian differences; Sales promotion; Zero price; dept.csc

Analyzing PHP frameworks for use in a project-based software engineering course - Lancor, Lisa; Katha, Samyukta (Technical Symposium on Computer Science Education, 2013)
Given the popularity of PHP frameworks used in developing webbased applications, a comparative study is conducted to determine which framework is best suited for incorporation into the curriculum of an undergraduate software engineering course that uses project-based learning. The top six PHP frameworks (Zend, Yii, CakePHP, CodeIgniter, PRADO, and Symphony) were initially considered and then narrowed down to two (CakePHP and CodeIgniter) based on their alignment with common functionality in previous class projects, framework complexity for those new to frameworks (learning curve), and developer friendliness (availability of documentation and online resources). An in-depth comparative study is conducted by developing a functionally-equivalent web application using each of the two frameworks as well as plain PHP (no framework). This work was motivated by the difficulties that were encountered in an evolving, content-rich software engineering course and discusses the educational changes that were made to align student learning with sound software engineering principles and current software development practices used in the computing industry. Copyright © 2013 ACM.
4 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Application programs; Curricula; Education computing; Engineering education; PHP frameworks; Project based learning; Project-based learning; Software design; Software design and development; Software development practices; Software engineering; Software engineering course; Software engineering principles; Technical presentations; Undergraduate software engineering; Web-based applications; dept.csc

Why do users perceive search tasks as difficult? Exploring difficulty in different task types - Liu, Jingjing; Kim, Chang Suk (Symposium on Human-Computer Interaction and Information Retrieval, 2013)
In this paper, we examined why information searchers perceive search tasks as difficult, and what factors/reasons make them perceive tasks as difficult. We also examined if task difficulty reasons vary across different tasks (task types). Data was collected through a controlled laboratory experiment in which tasks were designed following a classification scheme. A total of 32 undergraduate students participated, each was given 4 search tasks, and they were asked in questionnaires both before and after the tasks for task difficulty ratings and why they gave those ratings. We developed a coding scheme based on the difficulty reasons users gave, which covered various aspects of task, user, and user-task interaction. Difficulty reasons were categorized following this scheme. Results showed that searchers had some common reasons for task difficulty in different tasks, but most of the difficulty reasons varied across tasks. For each task, there were also common reasons for task difficulty, although there was some variation here as well. Task difficulty was also found to be negatively correlated with users topic knowledge, previous experience, and topic interest. Our findings help understand search task difficulty, as well as the relationships between task difficulty and task type, knowledge background, etc. These can also be helpful with experiment task design. © 2013 ACM.
5 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Classification scheme; Coding scheme; Controlled laboratories; Experiments; Human computer interaction; Information retrieval; Search tasks; Students; Surveys; Task difficulty; Task difficulty reason categories; Undergraduate students; dept.csc; task features; topic knowledge

Why do users feel search task difficult - Liu, Jingjing; Kim, Chang Suk; Creel, Caitlin (ASIST '13 Proceedings of the 76th ASIS&T Annual Meeting: Beyond the Cloud: Rethinking Information Boundaries, 2013)
This paper reports on an examination of why information searchers find search tasks difficult and the specific reasons they feel difficult. Data was collected through a controlled laboratory experiment with 32 participants, each working with 4 search tasks. They were asked in questionnaires, both before and after the tasks, for task difficulty ratings and their reasoning for giving those ratings. We developed a coding scheme based on the difficulty reasons users gave, which covered various aspects of task, user, and user-task interaction. This helps us understand search task difficulty, as well as provides a scheme for task difficulty control in experiment task design.
6 citations (Crossref) [2023-10-31]
Keywords: Task difficulty reason categories; Task features; Topic knowledge; dept.csc

Software environments for atmospheric lidar remote sensing - Sharma, Nimmi C P; Parikh, Jo Ann (International Geoscience and Remote Sensing Symposium, 2013)
Research and training in atmospheric lidar remote sensing requires highly versatile software environments for both experimentation and analysis. Experimentation and analysis may be conducted either directly on site at the location of the lidar equipment and/or institutional computer center or remotely from a distant location. The requirements for lidar software environments depend not only on type of user access (remote or onsite) but also on the nature of the teaching or research missions they support and the characteristics of the lidar systems for which they are used. The software environments discussed in this paper have been used to support lidar aerosol studies in settings ranging from urban locations to a remote atmospheric baseline station. Experiments and data analysis studies have been conducted for two different ground-based lidar systems, a monostatic Micro Pulse Lidar system and a bistatic imaging lidar system. © 2013 IEEE.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: aerosols; dept.csc; laser radar; remote sensing software

A performance model of gossip-based update propagation - Antonios, Imad; Dhar, Reetu; Zhang, Feng; Lipsky, Lester (Network Computing and Applications, 2010)
We consider the problem of propagating an update to nodes in a distributed system using two gossiping protocols. The first is an idealized algorithm with static and dynamic knowledge of the system, and the second is a simple randomized algorithm. We construct a theoretical model that allows us to derive work and completion time statistics under varying transmission delay distributions. Numerical results are obtained for both exponential and nonexponential transmission times using linear-algebraic queueing theory techniques. Additionally, we present the results of simulation experiments showing that under node churn assumptions, the randomized algorithm's performance is qualitatively different than in a fault-free system. © 2010 IEEE.
0 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Algorithms; Completion time; Distributed systems; Gossiping protocols; Linear-algebraic; Numerical results; Performance Model; Queueing theory; Randomized Algorithms; Simulation experiments; Static and dynamic; Theoretical models; Transmission delays; dept.csc

Timing Prediction for Dynamic Application Migration on Multi-core Embedded Systems - Li, Zheng; Wu, Hao; He, Shuibing (2018 IEEE 4th International Conference on Big Data Security on Cloud (BigDataSecurity), IEEE International Conference on High Performance and Smart Computing, (HPSC) and IEEE International Conference on Intelligent Data and Security (IDS), 2018)
To accommodate execution mode change and hardware malfunction, dynamic system reconfiguration, which invokes application migration across different processing cores, needs to be supported on multi-core embedded systems. Different application migration strategies will impact system's timing behaviors in different manners, it is important to select an appropriate one such that the system's timing performance after the migration process is still acceptable. The focus of our research is to predict the system's timing change of possible migration strategies and upon which to choose the optimal one. Extensive experiments have been set up by running multiple benchmarks and experimental results validate the effectiveness of our proposed approach.
0 citations (Crossref) [2023-10-31]
Keywords: Application Migration; Application migrations; Big data; Dynamic applications; Dynamic system reconfigurations; Embedded systems; Forecasting; Kernel; Migration process; Migration strategy; Multi Core; Multi core; Multi-core embedded systems; Multicore processing; Network security; Predictive models; Timing; Timing Prediction; Timing circuits; Timing performance; Training; Training data; dept.csc; dynamic application migration; dynamic system reconfiguration; embedded systems; execution mode; hardware malfunction; multicore embedded systems; multiprocessing systems; systems timing behaviors; timing prediction

Approximation of the time alignment error for measurements in electricity grids - Antonios, I.; Schwefel, H.-P.; Lipsky, L. (Proc. - Eur. Dependable Comput. Conf., EDCC, 2019)
Measurements of parameters in electricity grids are frequently captured as average values over some time interval. In scenarios of distributed measurements such as in distribution grids, offsets of local clocks can result in misaligned averaging intervals. This paper investigates the properties of the so-called time alignment error of such measurands that is caused by shifts of the averaging interval. We extend a previously derived Markov-modulated model and provide an approximation of the variance of the time alignment error. The model accounts for slow-decaying correlation structure found in actual traces of electrical measures. We compare results of three electrical measures for 20 traces with numerical results and simulations from the the fitted Markov model. © 2019 IEEE.
2 citations (Crossref) [2023-10-31] Journal Abbreviation: Proc. - Eur. Dependable Comput. Conf., EDCC
Keywords: Alignment; Clock Synchronization; Clock synchronization; Clocks; Decaying correlations; Distributed measurements; Distribution grid; Electric power transmission networks; Electric variables measurement; Electrical distribution; Electrical distribution grids; Electricity grids; Errors; Markov processes; Measurement errors; Modulated model; Numerical results; dept.csc

Nature-inspired metaheuristics search algorithms for solving the economic load dispatch problem of power system: A comparison study - Sheta, Alaa; Faris, Hossam; Braik, Malik; Mirjalili, Seyedali (Applied Nature-Inspired Computing: Algorithms and Case Studies, 2020)
This work proposes a new approach in addressing Economic Load Dispatch (ELD) optimization problem in power unit systems using nature-inspired metaheuristics search algorithms. Solving such a problem requires a degree of maximization of the economic pact of a power network system, where this is possible with some existing population-based metaheuristic search algorithms. The key issue to be handled here is how to maximize the economic benevolence of a power network under a variety of operational constraints, taking into account the reduction in the generated fuel cost as well as the aggregate power loss in the transmission power network system. Some nature-inspired metaheuristics will be explored. Meanwhile, we shall focus our attention on a newly developed nature-inspired search algorithm, referred to as the Crow Search Algorithm or CSA for short, as well as the Differential Evolution (DE) that is commonly known as a metaheuristic. The CSA emerged to light by simulating the intelligent flocking behavior of crows. The practicability of the proposed approach-based CSA was conducted to common types of power generators, including three and six buses (nodes) in addition to the IEEE 30-bus standard system. The results of the presented approaches were compared to other results developed using existing nature-inspired metaheuristic algorithms like particle swarm optimization and genetic algorithms and also compared to traditional approaches such as quadratic programming method. The results reported here support that CSA has achieved an outstanding performance in solving the problem of ELD in power systems, demonstrating their good optimization capabilities through arriving at a combination of power loads that consummate the constraints of the ELD problem while simultaneously lessening the entire fuel cost. The experimental results also showed that the CSA solutions were capable of maximizing the reliability of the power supplied to the customers, and also reducing both the generated power cost and the loss of power in the transmission power systems.
Citation Key Alias: lens.org/053-057-671-070-617
Keywords: 3-units power system; 6-units power system; Crow search algorithm; Differential evolution; Economic load dispatch; IEEE 30-bus standard system; Nature-inspired metaheuristic algorithms; dept.csc

Diagnosis of Obstructive Sleep Apnea Using Logistic Regression and Artificial Neural Networks Models - Sheta, Alaa; Turabieh, Hamza; Braik, Malik; Surani, Salim R. (Proceedings of the Future Technologies Conference (FTC) 2019, 2020)
Regrettably, a large proportion of likely patients with sleep apnea are underdiagnosed. Obstructive sleep apnea (OSA) is one of the main causes of hypertension, type II diabetes, stroke, coronary artery disease, and heart failure. OSA affects not only adults but also children where it forms one of the sources of learning disabilities for children. This study aims to provide a classification model for one of the well-known sleep disorders known as OSA, which causes a serious malady that affects both men and women. OSA affects both genders with different scope. Men versus women diagnosed with OSA are about 8:1. In this research, logistic regression (LR) and artificial neural networks were applied successfully in several classification applications with promising results, particularly in the bio-statistics area. LR was used to derive a membership probability for a potential OSA system from a range of anthropometric features including weight, height, body mass index (BMI), hip, waist, age, neck circumference, modified Friedman, snoring, Epworth sleepiness scale (ESS), sex, and daytime sleepiness. We developed two models to predict OSA, one for men and one for women. The proposed sleep apnea diagnosis model has yielded accurate classification results and possibly a prototype software module that can be used at home. These findings shall reduce the patient’s need to spend a night at a laboratory and make the study of sleep apnea to implement at home.
2 citations (Crossref) [2023-10-31] tex.ids: pop00118, shetaDiagnosisObstructiveSleep2020a tex.citation: https://api.elsevier.com/content/abstract/scopus_id/85075663726 tex.type: [object Object] type: Conference paper
Keywords: Artificial neural networks; Classification; Classification (of information); Classification results; Computer aided diagnosis; Coronary artery disease; Diseases; Epworth sleepiness scale; Features selection; Logistic regression; Logistic regressions; Membership probability; Neural networks; Obstructive sleep apnea; Regression analysis; Scales (weighing instruments); Sleep apnea; Sleep research; Software prototyping; dept.csc

On enhancing serial fusion based multi-biometric verification system - Hossain, Md; Chen, Jundong; Rahman, Khandaker (Applied Intelligence, 2018)
Design of a serial fusion based multi-biometric verification system requires fixing several parameters, such as reject thresholds at each stage of the architecture and the order in which each individual verifier is placed within the multi-stage system. Selecting the order of verifier is a crucial parameter to fix because of its high impact on verification errors. A wrong choice of verifier order might lead to tremendous user inconvenience by denying a large number of genuine users and might cause severe security breach by accepting impostors frequently. Unfortunately, this design issue has been poorly investigated in multi-biometric literature. In this paper, we address this design issue by performing experiments using three different serial fusion based multi-biometric verification schemes. We did our experiments on publicly available NIST multi-modal dataset. We tested 24 orders—all possible orders originated from four individual verifiers—on a four-stage biometric verification system. Our experimental results show that the verifier order “best-to-worst”, where the best performing individual verifier is placed in the first stage, the next best performing individual verifier is placed in the second stage, and so on, is the top performing order. In addition, we have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems.
9 citations (Crossref) [2023-10-31] Citation Key Alias: hossainEnhancingSerialFusion2018a, lens.org/017-508-366-099-573, pop00310 tex.type: [object Object]
Keywords: Architecture; Biometric verification; Biometrics; Multi biometrics; Multi-biometric verification; Multi-modal dataset; Proposed architectures; Reject option; Serial fusion; Traditional architecture; Verifier order; dept.csc; peer-reviewed

Impact of time interval alignment on data quality in electricity grids - Schwefel, Hans-Peter; Antonios, Imad; Lipsky, Lester (2018 IEEE International Conference on Communications, Control, and Computing Technologies for Smart Grids (SmartGridComm), 2018)
Measurements of parameters in electricity grids are frequently average values over some time interval. In scenarios of distributed measurements such as in distribution grids, offsets of local clocks can result in the averaging interval being misaligned. This paper investigates the properties of the so-called time alignment error of such measurands that is caused by shifts of the averaging interval. A Markov model is derived that allows for numerically calculating the expected value and other distribution properties of this error. Actual consumption measurements of an office building are used to study the behavior of this time alignment error, and to compare the results from the trace with numerical results and simulations from a fitted Markov model. For increasing averaging interval offset, the time alignment error approaches a normal distribution, whose parameters can be calculated or approximated from the Markov model.
6 citations (Crossref) [2023-10-31] Citation Key Alias: pop00152, schwefelImpactTimeInterval2018a tex.type: [object Object]
Keywords: Alignment; Clocks; Consumption measurement; Data Quality; Data quality; Distributed measurements; Distribution Grid Measurements; Distribution grid; Distribution property; Electric power transmission networks; Errors; Markov Models; Markov model; Markov processes; Mathematical model; Measurement uncertainty; Normal distribution; Numerical models; Numerical results; Office buildings; Power measurement; Smart power grids; Time Alignment Error; Time alignment; actual consumption measurements; averaging interval offset; data quality; dept.csc; distributed measurements; distribution grids; distribution properties; electricity grids; fitted Markov model; normal distribution; parameter measurement; power grids; power supply quality; power system measurement; time alignment error; time interval alignment

An enhanced architecture for serial fusion based multi-biometric verification system - Hossain, Shafaeat; Chen, Jundong; Rahman, Khandaker Abir (International Symposium on Networks, Computers and Communications, 2018)
The traditional architecture of serial fusion based multi-biometric verification systems places an average performing or the worst performing individual verifier in the final stage. Because the final stage gives the verification decision using a single threshold and takes on the most confusing samples which are rejected by all previous stages, an average or the worst performing individual verifier may incur high verification errors in the final stage, which may negatively impact the performance of the whole system. Unfortunately, it is not possible to place a strong individual verifier in the final stage of a traditional architecture because if we place a strong individual verifier in the final stage, we will have to place a weak individual verifier in an earlier stage. Studies show that placing a weak individual verifier in an earlier stage worsens the performance of the whole system by giving more wrong decision earlier. Hence, the challenge is-how can we place the best performing individual verifier in the first stage and at the same time not place an average or the worst performing individual verifier in the final stage? In this paper, we address this challenge. We have come up with a very simple but effective solution. We have proposed a modification to the traditional architecture of serial fusion based multi-biometric verification systems. With rigorous experiments on the NIST multi-modal dataset and using three serial fusion based multi-biometric verification schemes, we demonstrated that our proposed architecture significantly improves the performance of serial fusion based multi-biometric verification systems. © 2018 IEEE.
1 citations (Crossref) [2023-10-31] tex.type: Proceedings paper
Keywords: Architecture; Biometrics; Effective solution; Multi biometrics; Multi-modal dataset; Network architecture; Proposed architectures; Serial fusion; Traditional architecture; dept.csc

A game theoretic analysis of the twitter follow-unfollow mechanism - Chen, Jundong; Hossain, Md Shafaeat; Brust, Matthias R.; Johnson, Naomi (Decision and Game Theory for Security, 2018)
Twitter users often crave more followers to increase their social popularity. While a variety of factors have been shown to attract the followers, very little work has been done to analyze the mechanism how Twitter users follow or unfollow each other. In this paper, we apply game theory to modeling the follow-unfollow mechanism on Twitter. We first present a two-player game which is based on the Prisoner’s Dilemma, and subsequently evaluate the payoffs when the two players adopt different strategies. To allow two players to play multiple rounds of the game, we propose a multi-stage game model. We design a Twitter bot analyzer which follows or unfollows other Twitter users by adopting the strategies from the multi-stage game. We develop an algorithm which enables the Twitter bot analyzer to automatically collect and analyze the data. The results from analyzing the data collected in our experiment show that the follow-back ratios for both of the Twitter bots are very low, which are 0.76%0.76%0.76\% and 0.86%0.86%0.86\%. This means that most of the Twitter users do not cooperate and only want to be followed instead of following others. Our results also exhibit the effect of different strategies on the follow-back followers and on the non-following followers as well.
2 citations (Crossref) [2023-10-31] Citation Key Alias: chenGameTheoreticAnalysis2018, pop00231 tex.type: [object Object]
Keywords: Decision theory; Game theoretic analysis; Game theory; Learning systems; Machine learning; Multi stage games; Social network; Social networking (online); Twitter bot; Twitter classification; Two-player games; dept.csc

Computer systems: Digital design, fundamentals of computer architecture and assembly language - Elahi, Ata (Springer International Publishing, 2018)
This textbook covers digital design, fundamentals of computer architecture, and assembly language. The book starts by introducing basic number systems, character coding, basic knowledge in digital design, and components of a computer. The book goes on to discuss information representation in computing; Boolean algebra and logic gates; sequential logic; input/output; and CPU performance. The author also covers ARM architecture, ARM instructions and ARM assembly language which is used in a variety of devices such as cell phones, digital TV, automobiles, routers, and switches. The book contains a set of laboratory experiments related to digital design using Logisim software; in addition, each chapter features objectives, summaries, key terms, review questions and problems. The book is targeted to students majoring Computer Science, Information System and IT and follows the ACM/IEEE 2013 guidelines. • Comprehensive textbook covering digital design, computer architecture, and ARM architecture and assembly • Covers basic number system and coding, basic knowledge in digital design, and components of a computer • Features laboratory exercises in addition to objectives, summaries, key terms, review questions, and problems in each chapter
Citation Key Alias: elahiComputerSystemsDigital2017
Keywords: ARM architecture; ARM processors; Assembly language; Boolean algebra; Computation theory; Computer architecture; Digital designs; Digital devices; Digital television; Information representation; Laboratory exercise; Laboratory experiments; Number system; Numbering systems; Reviews; Sequential logic; Textbooks; dept.csc

Understanding the relationship between network traffic correlation and queueing behavior: A review based on the N-Burst ON/OFF model - Schwefel, Hans-Peter; Antonios, Imad; Lipsky, Lester (Performance Evaluation, 2017)
Understanding the impact of network traffic properties on performance behavior in bottleneck links or larger networks is of primary interest to traffic analysts and network designers. Among the contributing factors, variance and correlation properties have been thoroughly studied and a large set of individual results have been obtained. However, these individual contributing factors are not sufficient to predict performance behavior. In this paper we review a unifying and versatile class of ON/OFF models through which the relationship among these parameters can be characterized and their influence on network performance be understood. The analytic performance results from the model show that there is a radically different queueing behavior when the ON period duration follows truncated power-tail distributions (even if truncated), as opposed to model variants where these distribution types are used for the OFF periods. All these models create correlation functions that only decay slowly. This motivates the development of a simple data analysis scheme to distinguish performance relevant correlation. The scheme is described both for interarrival and count processes of traffic data and its effectiveness is shown using real data traces.
9 citations (Crossref) [2023-10-31] Citation Key Alias: lens.org/016-979-057-202-359, pop00159 tex.type: [object Object]
Keywords: Contributing factor; Correlation function; Correlation properties; Long range dependence; Long-range dependence; Network designer; Network traffic; ON/OFF models; Power-tail distributions; Queueing model; Queueing models; Queueing networks; Queueing theory; Tail distribution; dept.csc

Sentiment analysis of the correlation between regular tweets and retweets - Chen, Jundong; Li, He; Wu, Zeju; Hossain, Md Shafaeat (2017 IEEE 16th International Symposium on Network Computing and Applications (NCA), 2017)
In this paper, we study the influence from the sentiment of regular tweets on retweeting. We propose a method to calculate the sentiment score for each tweet and each Twitter user. This method enables us to place the tweets and retweets into the same time period to explore the sentiment factor. We adopt the correlation coefficient between the sentiment scores of regular tweets and those of retweets to measure the influence. We categorize the Twitter users in three different ways to investigate three factors, which are the number of followers, betweenness centrality and the types of accounts. Community detection and machine learning are integrated into our approach. We find that the difference for correlation coefficients exists between different levels of the number of followers, and different types of users. Our method sheds a light on better predicting the dynamics of tweets diffusion by including the sentiment factor into the prediction model. © 2017 IEEE.
6 citations (Crossref) [2023-10-31] Citation Key Alias: pop00210 tex.type: [object Object]
Keywords: Artificial intelligence; Betweenness centrality; Community detection; Correlation coefficient; Data mining; Learning systems; Prediction model; Sentiment analysis; Sentiment scores; Social networking (online); Time-periods; Twitter classification; dept.csc; machine learning; sentiment analysis; social network

An empirical study on verifier order selection in serial fusion based multi-biometric verification system - Hossain, Md Shafaeat; Rahman, Khandaker Abir (Advances in Artificial Intelligence: From Theory to Practice, 2017)
Selecting the order of verifier in a serial fusion based multi-biometric system is a crucial parameter to fix because of its high impact on verification errors. A wrong choice of verifier order might lead to tremendous user inconvenience by denying a large number of genuine users and might cause severe security breach by accepting impostors frequently. Unfortunately, this design issue has been poorly investigated in multi-biometric literature. In this paper, we address this design issue by performing experiments using three different serial fusion based multi-biometric verification schemes, in particular (1) symmetric scheme, (2) SPRT-based scheme, and (3) Marcialis et al.’s scheme. We experimented on publicly available NIST-BSSR1 multi-modal database. We tested 24 orders—all possible orders originated from four individual verifiers—on a four-stage biometric verification system. Our experimental results show that the verifier order “best-to-worst”, where the best performing individual verifier is placed in the first stage, the next best performing individual verifier is placed in the second stage, and so on, is the top performing order for all three serial fusion schemes mentioned above.
2 citations (Crossref) [2023-10-31] Citation Key Alias: hossainEmpiricalStudyVerifier2017, pop00277 tex.type: [object Object]
Keywords: Biometric verification; Biometrics; Crucial parameters; Intelligent systems; Multi biometrics; Multi-biometric verification; Multibiometric systems; Multimodal database; Order selection; Security breaches; Serial fusion; Verifier order selection; dept.csc