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

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

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

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

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

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

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

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

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

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

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