Hybrid Evolutionary Neural Network Models for Gold Price Prediction - Sheta, Alaa; Elashmawi, Walaa H.; Aljahdali, Sultan; Rauch, Peter; Othman, Emad S. (ACIT 2025 - 26th International Arab Conference on Information Technology, Conference Proceedings, 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.
Keywords: Gold prices; dept.csc; evolutionary algorithms; financial forecasting; neural networks; prediction models

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 (2025 IEEE International Joint Conference on Biometrics, IJCB 2025, 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.
Keywords: Accelerometer; Biometrics; Capacitive sensing; Gesture; Identification (biology); Keystroke logging; Modality (human–computer interaction); SwIPe; Touchscreen; 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. (5th International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC 2025, 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.
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. (2025 Women in Bioinformatics Workshop, WIBI 2025, 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.
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.
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. (IC-AIRES 2024, 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

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