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

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

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