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GaitDONet: Gait Recognition Using Deep Features Optimization and Neural Network

computers, Materials & Continua 2023年第6期75卷 5087-5103页

作者： Muhammad Attique Khan Awais Khan Majed alhaisoni Abdullah alqahtani Ammar Armghan Sara A.althubiti Fayadh alenezi Senghour Mey Yunyoung Nam Department of Computer Science HITEC UniversityTaxilaPakistan Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-KharjSaudi Arabia Department of Electrical Engineering College of EngineeringJouf UniversitySakaka 72388Saudi Arabia Department of Computer Science College of Computer and Information SciencesMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of ICT Convergence Soonchunhyang UniversityKorea

Human gait recognition(HGR)is the process of identifying a sub-ject(human)based on their walking *** subject is a unique walking pattern and cannot be simulated by other ***,gait recognition is not easy and makes the system difficult if any object is carried by a subject,such as a bag or *** article proposes an automated architecture based on deep features optimization for *** our knowledge,it is the first architecture in which features are fused using multiset canonical correlation analysis(MCCA).In the proposed method,original video frames are processed for all 11 selected angles of the CASIA B dataset and utilized to train two fine-tuned deep learning models such as Squeezenet and *** transfer learning was used to train both fine-tuned models on selected angles,yielding two new targeted models that were later used for feature *** are extracted from the deep layer of both fine-tuned models and fused into one vector using *** improved manta ray foraging optimization algorithm is also proposed to select the best features from the fused feature matrix and classified using a narrow neural network *** experimental process was conducted on all 11 angles of the large multi-view gait dataset(CASIA B)dataset and obtained improved accuracy than the state-of-the-art ***,a detailed confidence interval based analysis also shows the effectiveness of the proposed architecture for HGR.

关键词： Human gait recognition biometric deep learning features fusion optimization neural network

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Enhanced Accuracy for Motor Imagery Detection Using Deep Learning for BCI

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computers, Materials & Continua 2021年第9期68卷 3825-3840页

作者： Ayesha Sarwar Kashif Javed Muhammad Jawad Khan Saddaf Rubab Oh-Young Song Usman Tariq National University of Sciences and Technology(NUST) Islamabad44000Pakistan Department of Software Sejong UniversitySeoulGwangjin-guKorea College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia

Brain-computer Interface(BCI)is a system that provides a link between the brain of humans and the hardware *** recorded brain data is converted directly to the machine that can be used to control external *** are four major components of the BCI system:acquiring signals,preprocessing of acquired signals,features extraction,and *** traditional machine learning algorithms,the accuracy is insignificant and not up to the mark for the classification of multi-class motor imagery *** major reason for this is,features are selected manually,and we are not able to get those features that give higher accuracy *** this study,motor imagery(MI)signals have been classified using different deep learning *** have explored two different methods:Artificial Neural Network(ANN)and Long Short-Term Memory(LSTM).We test the classification accuracy on two datasets:BCI competition III-dataset IIIa and BCI competition IV-dataset *** outcome proved that deep learning algorithms provide greater accuracy results than traditional machine learning *** the deep learning classifiers,LSTM outperforms the ANN and gives higher classification accuracy of 96.2%.

关键词： Brain-computer interface motor imagery artificial neural network long-short term memory classification

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A Cloud Based Sentiment Analysis through Logistic Regression in AWS Platform

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computer Systems Science & engineering 2023年第4期45卷 857-868页

作者： Mohemmed Sha Department of Software Engineering College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

The use of Amazon Web Services is growing rapidly as more users are adopting the *** has various functionalities that can be used by large corporates and individuals as *** analysis is used to build an intelligent system that can study the opinions of the people and help to classify those related *** this research work,sentiment analysis is performed on the AWS Elastic Compute Cloud(EC2)through Twitter *** data is managed to the EC2 by using elastic load *** collected data is subjected to preprocessing approaches to clean the data,and then machine learning-based logistic regression is employed to categorize the sentiments into positive and negative *** accuracy of 94.17%is obtained through the proposed machine learning model which is higher than the other models that are developed using the existing algorithms.

关键词： Sentiment analysis AWS cloud data analytics machine learning

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Performance Comparison of Deep and Machine Learning Approaches Toward COVID-19 Detection

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Intelligent Automation & Soft Computing 2023年第8期37卷 2247-2261页

作者： Amani Yahyaoui Jawad Rasheed Shtwai alsubai Raed M.Shubair Abdullah alqahtani Buket Isler Rana Zeeshan Haider Department of Software Engineering Istanbul Sabahattin Zaim UniversityIstanbul34303Turkey Department of Software Engineering Nisantasi UniversityIstanbul34398Turkey Department of Computer Science College of Computer Engineering and SciencesPrince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Electrical and Computer Engineering New York University(NYU)Abu Dhabi129188United Arab Emirates College of Computer Engineering and Sciences Prince Sattam bin Abdulaziz UniversityAl-Kharj11942Saudi Arabia Department of Computer Engineering Istanbul Topkapi UniversityIstanbul34087Turkey Baqai Institute of Hematology Baqai Medical UniversityKarachi75340Pakistan

The coronavirus(COVID-19)is a disease declared a global pan-demic that threatens the whole *** then,research has accelerated and varied to find practical solutions for the early detection and correct identification of this *** researchers have focused on using the potential of Artificial Intelligence(AI)techniques in disease diagnosis to diagnose and detect the *** paper developed deep learning(DL)and machine learning(ML)-based models using laboratory findings to diagnose *** different methods are used in this study:K-nearest neighbor(KNN),Decision Tree(DT)and Naive Bayes(NB)as a machine learning method,and Deep Neural Network(DNN),Convolutional Neural Network(CNN),and Long-term memory(LSTM)as DL *** approaches are evaluated using a dataset obtained from the Israelita albert Einstein Hospital in Sao Paulo,*** data consists of 5644 laboratory results from different patients,with 10%being Covid-19 positive *** dataset includes 18 attributes that characterize *** used accuracy,f1-score,recall and precision to evaluate the different developed *** obtained results confirmed these approaches’effectiveness in identifying COVID-19,However,ML-based classifiers couldn’t perform up to the standards achieved by DL-based *** all,NB performed worst by hardly achieving accuracy above 76%,Whereas KNN and DT compete by securing 84.56%and 85%accuracies,*** these,DL models attained better performance as CNN,DNN and LSTM secured more than 90%*** LTSM outperformed all by achieving an accuracy of 96.78%and an F1-score of 96.58%.

关键词： Artificial intelligence COVID-19 deep learning diagnosis machine learning

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Assessing synergies between thermal energy and torrefaction severity index of wood spruce sawdust via machine learning algorithms

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Journal of Analytical and Applied Pyrolysis 2025年 190卷

作者： Šafář, Michal Raclavska, Helena Růžičková, Jana ali, Imtiaz Naqvi, Salman Raza Scala, Fabrizio CEET/ENET Centre VŠB – Technical University of Ostrava Ostrava-Poruba708 00 Czech Republic Department of Chemical Engineering College of Engineering Prince Mohammad Bin Fahd University Al Khobar Saudi Arabia Department of Engineering and Chemical Sciences Karlstad University Karlstad Sweden Faculty of Electrical Engineering and Computer Science VŠB-Technical University of Ostrava 17. Listopadu 2172/15 Poruba Ostrava708 00 Czech Republic DICMaPI Università di Napoli Federico II P.le Tecchio 80 Napoli80125 Italy STEMS CNR P.le Tecchio 80 Napoli80125 Italy

Assessing the synergies between thermal energy and the torrefaction severity index by elucidating the effects of biomass torrefaction conditions on product characteristics is still a relevant research question. This study explores the optimization of torrefaction of spruce wood sawdust by analyzing the chemical and physical features of the resultant material. The study employs thermogravimetric analysis and Thermal Desorption-Gas Chromatography/Mass Spectrometry (TD-GC/MS) to examine the changes in the components during torrefaction. The torrefaction process has a profound effect on the composition, causing conversion of up to 30 % of the initial mass to volatile organic compounds and incondensable gases when subjected to a temperature of 300 °C. The correlation matrix highlights the relationships between variables, including time, temperature, heating value, mass yield, energy yield, and ratios of C, H, and O. The matrix visually represents the interplay between these factors during the torrefaction process. Principal component analysis (PCA) and hierarchical cluster analysis (HCA) revealed that torrefaction severity index, cellulose, lignin, torrefaction time, and temperature correlate positively, while H/C, O/C, and hemicellulose content correlate negatively. The ANN model exhibited superior predictive accuracy (R² = 0.99982, RMSE = 0.00359), surpassing C&RT (R² = 0.99483, RMSE = 0.01943), KNN (R² = 0.99467, RMSE = 0.01974), and SVM (R² = 0.99022, RMSE = 0.02674), thus validating the efficacy of machine learning for precise torrefaction severity index (TSI) prediction. This finding enhances the efficiency of biomass processing and provides reliable tools for future research in the field, thereby informing and guiding future studies and industrial applications. © 2025 The Authors

关键词： Hierarchical clustering

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Design and fabrication of a compact microstrip hybrid ring coupler with harmonic reduction for L band radar system

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Scientific Reports 2025年第1期15卷 1-12页

作者： Roshani, Sobhan Yahya, Salah I. Moradi, Milad Chaudhary, Muhammad Akmal Assaad, Maher Hazzazi, Fawwaz Hussin, Fawnizu Azmadi Karaaslan, Muharrem Roshani, Saeed Department of Electrical Engineering Ker.C Islamic Azad University Kermanshah Iran Department of Computer Technology Engineering College of Technical Engineering Al-Hadba University Mosul Iraq Department of Software Engineering Faculty of Engineering Koya University Koya 46017 Iraq Department of Electrical and Computer Engineering College of Engineering and Information Technology Ajman University 346 Ajman United Arab Emirates Department of Electrical Engineering College of Engineering Prince Sattam bin Abdulaziz University Al-Kharj 11492 Saudi Arabia Department of Electrical and Electronic Engineering Universiti Teknologi PETRONAS Seri Iskandar 32610 Malaysia Department of Electrical and Electronics Engineering Iskenderun Technical University Hatay Iskenderun 31200 Turkey

In this paper, a compact hybrid ring coupler is designed, simulated, and fabricated to achieve both size reduction and unwanted harmonic suppression in the L-band frequency range. As the demand for miniaturized and high-performance microwave components increases, reducing the footprint of passive devices such as couplers has become a key objective in modern electronic design. The proposed coupler integrates a specially designed resonator between its ports to effectively suppress unwanted harmonics, ensuring improved signal integrity and enhanced operational efficiency. Through this approach, 2nd to 10th harmonics are successfully eliminated, minimizing interference and improving overall system performance. The proposed design operates at a center frequency of 1.4 GHz, achieving an exceptionally low insertion loss of 0.3 dB. Additionally, it demonstrates a remarkable 70% reduction in size compared to conventional couplers while maintaining excellent isolation and phase balance. The design and simulation of the rat-race coupler were conducted using Advanced Design System (ADS) software, and the device was fabricated using a Rogers RT/duroid 5880 substrate with a thickness of 20 mil and a dielectric constant of 2.2. Experimental validation confirms strong agreement between simulated and measured results, highlighting the effectiveness of the proposed structure in achieving compactness and superior performance. This novel design presents a promising solution for modern microwave applications requiring highly efficient, compact, and low-loss couplers. © The Author(s) 2025.

关键词： Compact rat race coupler Elimination of unwanted harmonics Insertion loss Microstrip Resonator Size reduction

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Federated learning with Blockchain on Denial-of-Service attacks detection and classification of edge IIoT networks using Deep Transfer Learning model

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computers and Electrical engineering 2025年 124卷

作者： Abdullah, Monir Mengash, Hanan Abdullah Maray, Mohammed alrslani, Faheed A.F. alkhudhayr, Hanadi alghanmi, Nouf Atiahallah Subahi, alanoud Majdoubi, Jihen Department of Computer Science and Artificial Intelligence College of Computing and Information Technology University of Bisha Bisha67714 Saudi Arabia Department of Information Systems College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Information Systems College of Computer Science King Khalid University Abha Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology Northern Border University Rafha Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Department of Information Technology Faculty of Computing and Information Technology King Abdulaziz University Rabigh25732 Saudi Arabia Engineering and Technical Programs Unit Applied College Majmaah University Al-Majmaah11952 Saudi Arabia

With the rapid evolution of Internet of Things (IoT) and artificial intelligence (AI), the industry 4.0 era has arisen. As per the IBM prediction, by the constant spread of 5G technology, the IoT intend to be more extensively utilized in industries. Recently, federated learning (FL) turned out to be significant focus among Industrial IoT (IIoT) scholars. Conversely, numerous devices in IIoT presently hold an issue of low computational power, so these devices can't able to function well while challenging the updating and training method tasks in FL. The latest development of machine learning (ML) and deep learning (DL) approaches will help to reinforce these IDSs. The secrecy behaviour of these databases and extensive advent of combative outbreaks makes it challenging for main institutions to transmit their fragile data. Therefore, the current study designs a Federated Learning on Denial-of-Service Attacks Detection and Classification using Deep Transfer Learning (FLDoSADC-DTL) model for BC-supported IIoT environment. The aim of the presented FLDoSADC-DTL approach is to recognize the presence of DoS attacks in the BC-based IIoT environment. To enable secure communication in the IIoT networks, BC technology is used. To accomplish this, the FLDoSADC-DTL technique performs sand cat swarm algorithm (SCSA) based feature subset selection. For the DoS attack detection process, a stacked auto-encoder (SAE) model is utilized in this study. Finally, the black widow optimization algorithm (BWOA) can be implemented for hyperparameter tuning of the SAE technique. A widespread of experiments were performed to emphasize the higher performance of FLDoSADC-DTL method. The complete experimentation outcomes indicated the enhanced performance of FLDoSADC-DTL approach over other recent methods. © 2025 Elsevier Ltd

关键词： Federated learning

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Compact and energy efficient design of LIF neuron leveraging Zener breakdown for neuromorphic computing

Micro and Nanostructures

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Micro and Nanostructures 2025年 205卷

作者： Faisal Bashir Department of Computer Engineering College of Computer Sciences and Information Technology King Faisal University Al-Ahsa Eastern Province Saudi Arabia

In this paper, a single two terminal device (Zener diode) is used to construct a Leaky Integrate and Fire (LIF) neuron with significant improvement in energy efficiency, area and reduction in cost. Using calibrated 2D simulation, it has been confirmed that Zener diode based LIF neuron is able to imitate the neuron actions accurately. The Zener diode shows extremely sharper reverse breakdown voltage, the sharpness in the characteristics is responsible for achieving ultra-low energy per spike. The proposed Zener diode based LIF neuron needs only 86fJ/spike, which is 500 lower compared to recently reported Silicon on Insulator (SOI) based MOSFET, which needs 45pJ/spike of energy. Besides this, the proposed neuron design can be used to obtain neuron oscillation with different frequencies using an input current or capacitor values.

关键词： Electron tunneling

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SmartEdge: Smart Healthcare End-to-End Integrated Edge and Cloud Computing System for Diabetes Prediction Enabled by Ensemble Machine Learning

arXiv

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arXiv 2025年

作者： Hennebelle, alain Dieng, Qifan Ismail, Leila Buyya, Rajkumar Abu Dhabi United Arab Emirates Lab School of Computing and Information Systems University of Melbourne Melbourne Australia Lab Department of Computer Science and Software Engineering College of Information Technology United Arab Emirates Emirates Center for Mobility Research United Arab Emirates National Water and Energy Center UAE University Abu Dhabi Al-Ain United Arab Emirates

The Internet of Things (IoT) revolutionizes smart city domains such as healthcare, transportation, industry, and education. The Internet of Medical Things (IoMT) is gaining prominence, particularly in smart hospitals and Remote Patient Monitoring (RPM). The vast volume of data generated by IoMT devices should be analyzed in real-time for health surveillance, prognosis, and prediction of diseases. Current approaches relying on Cloud computing to provide the necessary computing and storage capabilities do not scale for these latency-sensitive applications. Edge computing emerges as a solution by bringing cloud services closer to IoMT devices. This paper introduces SmartEdge, an AI-powered smart healthcare end-to-end integrated edge and cloud computing system for diabetes prediction. This work addresses latency concerns and demonstrates the efficacy of edge resources in healthcare applications within an end-to-end system. The system leverages various risk factors for diabetes prediction. We propose an Edge and Cloud-enabled framework to deploy the proposed diabetes prediction models on various configurations using edge nodes and main cloud servers. Performance metrics are evaluated using, latency, accuracy, and response time. By using ensemble machine learning voting algorithms we can improve the prediction accuracy by 5% versus a single model *** Codes 68T01, 68T09, 68M14, 68W10, 68W15 © 2025, CC BY.

关键词： Electronic health record

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Consumer Technology in Task Offloading and Edge Resource allocation: AIoT and Edge Computing for Next-Generation Communication

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IEEE Transactions on Consumer Electronics 2025年

作者： Byeon, Haewon alsaadi, Mahmood Quraishi, Aadam alghamdi, Azzah Ahanger, Tariq Ahamed Keshta, Ismail Xalikovich, Pardayev Abdunabi Soni, Mukesh Bhatt, Mohammed Wasim Convergence Department Korea University of Technology and Education Cheonan Korea Republic of Department of Computer Sciences College of Science University of Al Maarif Al Anbar31001 Iraq M.D. Research Intervention Treatment Institute HoustonTX United States Computer Information Systems Department College of Computer Science and Information Technology Imam Abdalrhman Bin Faisal University Khobar Saudi Arabia Department of Management Information Systems CoBA Prince Sattam Bin Abdulaziz University Saudi Arabia Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia The Department of Accounting and Reporting Tashkent State University of Economics Tashkent Uzbekistan Division of Research and Development Lovely Professional University Phagwara India Dr. D. Y. Patil Vidyapeeth Dr. D. Y. Patil School of Science & Technology Pune Tathawade Pune India Model Institute of Engineering and Technology J&K Jammu India

Artificial Intelligence of Things (AIoT) edge computing has emerged as a critical enabler for consumer technology, easing the load on distribution grid networks through enhanced data transmission and processing capabilities. The Internet of Things (IoT), a modern network paradigm, connects devices using sensors and communication mediums to enable seamless information exchange. However, the limited computational capacity of edge nodes poses challenges in optimizing AIoT edge resources compared to cloud computing. To address this, a cloud-edge three-layer framework for task offloading and edge resource allocation is proposed in the context of consumer technology and distribution grids. This model incorporates random tasks, limited resources, unequal processing power, and high latency requirements. It features two key phases: a resource auction using a multi-round iterative process and compute offloading managed by a Deep Reinforcement Learning (DRL) approach. Enhanced algorithms such as Double DQN and Dueling DQN are integrated into the job offloading process to improve performance. Simulations validate the convergence of the task offloading algorithm and demonstrate that the proposed methods significantly enhance computational efficiency and resource utilization for edge nodes. These advancements offer promising solutions for the dynamic needs of consumer technology and future communication networks. © 2025 IEEE.

关键词： Computation offloading

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