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Privacy-Aware Federated Learning Framework for IoT Security Using Chameleon Swarm Optimization and Self-Attentive Variational Autoencoder

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computer Modeling in Engineering & sciences 2025年第4期143卷 849-873页

作者： Saad Alahmari Abdulwhab Alkharashi Department of Computer Science Applied CollegeNorthern Border UniversityArar91431Saudi Arabia Department of Computer Science College of Computing and InformaticsSaudi Electronic UniversityRiyadh11673Saudi Arabia

The Internet of Things(IoT)is emerging as an innovative phenomenon concerned with the development of numerous vital *** the development of IoT devices,huge amounts of information,including users’private data,are *** systems face major security and data privacy challenges owing to their integral features such as scalability,resource constraints,and *** challenges are intensified by the fact that IoT technology frequently gathers and conveys complex data,creating an attractive opportunity for *** address these challenges,artificial intelligence(AI)techniques,such as machine learning(ML)and deep learning(DL),are utilized to build an intrusion detection system(IDS)that helps to secure IoT *** learning(FL)is a decentralized technique that can help to improve information privacy and performance by training the IDS on discrete linked *** delivers an effectual tool to defend user confidentiality,mainly in the field of IoT,where IoT devices often obtain privacy-sensitive personal *** study develops a Privacy-Enhanced Federated Learning for Intrusion Detection using the Chameleon Swarm Algorithm and Artificial Intelligence(PEFLID-CSAAI)*** main aim of the PEFLID-CSAAI method is to recognize the existence of attack behavior in IoT ***,the PEFLIDCSAAI technique involves data preprocessing using Z-score normalization to transformthe input data into a beneficial ***,the PEFLID-CSAAI method uses the Osprey Optimization Algorithm(OOA)for the feature selection(FS)*** the classification of intrusion detection attacks,the Self-Attentive Variational Autoencoder(SA-VAE)technique can be ***,the Chameleon Swarm Algorithm(CSA)is applied for the hyperparameter finetuning process that is involved in the SA-VAE model.A wide range of experiments were conducted to validate the execution of the PEFLID-CSAAI *** simulated outcomes demonstrated that the PEFLID-CSAAI

关键词： Federated learning internet of things artificial intelligence chameleon swarm algorithm intrusion detection system healthcare IoT devices

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Parameter-efficient fine-tuning of pre-trained code models for just-in-time defect prediction

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Neural computing and Applications 2024年第27期36卷 16911-16940页

作者： Abu Talib, Manar Bou Nassif, Ali Azzeh, Mohammad Alesh, Yaser Afadar, Yaman Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Computer Engineering College of Computing and Informatics Sharjah United Arab Emirates King Hussein School for Computing Sciences Princess Sumaya University for Technology Amman Jordan

Software engineering workflows use version control systems to track changes and handle merge cases from multiple contributors. This has introduced challenges to testing because it is impractical to test whole codebases to ensure each change is defect-free, and it is not enough to test changed files alone. Just-in-time software defect prediction (JIT-SDP) systems have been proposed to solve this by predicting the likelihood that a code change is defective. Numerous techniques have been studied to build such JIT software defect prediction models, but the power of pre-trained code transformer language models in this task has been underexplored. These models have achieved human-level performance in code understanding and software engineering tasks. Inspired by that, we modeled the problem of change defect prediction as a text classification task utilizing these pre-trained models. We have investigated this idea on a recently published dataset, ApacheJIT, consisting of 44k commits. We concatenated the changed lines in each commit as one string and augmented it with the commit message and static code metrics. Parameter-efficient fine-tuning was performed for 4 chosen pre-trained models, JavaBERT, CodeBERT, CodeT5, and CodeReviewer, with either partially frozen layers or low-rank adaptation (LoRA). Additionally, experiments with the Local, Sparse, and Global (LSG) attention variants were conducted to handle long commits efficiently, which reduces memory consumption. As far as the authors are aware, this is the first investigation into the abilities of pre-trained code models to detect defective changes in the ApacheJIT dataset. Our results show that proper fine-tuning improves the defect prediction performance of the chosen models in the F1 scores. CodeBERT and CodeReviewer achieved a 10% and 12% increase in the F1 score over the best baseline models, JITGNN and JITLine, when commit messages and code metrics are included. Our approach sheds more light on the abilities of l

关键词： Forecasting

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Advanced human activity recognition on wearables with multi-scale sparse attention

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Multimedia Tools and Applications 2025年 1-30页

作者： Muniasamy, Anandhavalli Asiri, Fatima College of Computer Science Department of Informatics and Computer Systems King Khalid University Abha Saudi Arabia College of Computer Science Informatics and Computer Systems Department King Khalid University Abha Saudi Arabia

Maintaining a regular daily activity routine is essential for overall health and well-being. Wearable sensors offer a convenient way to track daily activities, but accurately identifying a wide range of activities remains a challenge for existing methods. These methods relied heavily on feature selection approaches in CNNs, which were not only tedious but also struggled to accurately identify activities with similar patterns and, also fails to find the long-range dependencies. The introduction of transformers in CNNs addresses these limitations, enabling more efficient and precise recognition of activities even when patterns are similar. In this paper, a novel Human activity recognition model that combines CNN with a Residual Transformer Network (RTN) is proposed. This model incorporates two key improvements: a sparse attention technique applied at multiple scales to capture complex spatial patterns, and an adaptive fusion. Sparse attention allows the model to concentrate on the most critical parts of the spectrogram at different scales and directions. This approach aids the model in distinguishing between activities with similar overall patterns by capturing subtle differences. Additionally, it enhances the model’s ability to recognize long-range dependencies. An adaptive fusion technique is introduced to combine features from CNN and RTN, with a dynamic scoring function optimally weighting each feature stream. This ensures balanced contributions from spatial patterns and temporal dependencies for accurate activity classification. Experiments conducted on the HARTH, HU-HAR, and HuGaDB datasets demonstrate impressive results with higher accuracies of 94.82%, 98.94%, and 96.33%, respectively. These findings highlight the effectiveness of the proposed method for accurate activity detection and continuous health monitoring applications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2025.

关键词： Wearable sensors

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MH-HMR:Human mesh recovery from monocular images via multi-hypothesis learning

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CAAI Transactions on Intelligence Technology 2024年第5期9卷 1263-1274页

作者： Haibiao Xuan Jinsong Zhang Yu-Kun Lai Kun Li College of Intelligence and Computing Tianjin UniversityTianjinChina School of Computer Science and Informatics Cardiff UniversityCardiffUK

Recovering 3D human meshes from monocular images is an inherently ill-posed and challenging task due to depth ambiguity,joint occlusion,and ***,most existing approaches do not model such uncertainties,typically yielding a single reconstruction for one *** contrast,the ambiguity of the reconstruction is embraced and the problem is considered as an inverse problem for which multiple feasible solutions *** address these issues,the authors propose a multi-hypothesis approach,multi-hypothesis human mesh recovery(MH-HMR),to efficiently model the multi-hypothesis representation and build strong relationships among the hypothetical ***,the task is decomposed into three stages:(1)generating a reasonable set of initial recovery results(i.e.,multiple hypotheses)given a single colour image;(2)modelling intra-hypothesis refinement to enhance every single-hypothesis feature;and(3)establishing inter-hypothesis communication and regressing the final human ***,the authors take further advantage of multiple hypotheses and the recovery process to achieve human mesh recovery from multiple uncalibrated *** with state-of-the-art methods,the MH-HMR approach achieves superior performance and recovers more accurate human meshes on challenging benchmark datasets,such as Human3.6M and 3DPW,while demonstrating the effectiveness across a variety of *** code will be publicly available at https://***/faculty/likun/projects/MH-HMR.

关键词： 3-D computer vision human reconstraction

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Different Methods to Estimate Stress-Strength Reliability Function for Modified Exponentiated Lomax Distribution

Iraqi Journal for Computer Science and Mathematics

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Iraqi Journal for computer science and Mathematics 2025年第2期6卷 99-106页

作者： Al-Rassam, Raya Salim Mohammed, Khalida Ahmed Rashed, Safwan Nathem Department of Statistics & Informatics College of Computer Science & Mathematics University of Mosul Iraq

When ensuring the reliability of device or the suitability of a material, it is necessary to take into consideration the stress cases in the operating environment. This means that the uncertainty about the reality environmental stress must be taken into as random. The stress-strength (S-S) model treated the stress and strength variables as random. In the simplest form of stress-strength model, y represents the stress put on the unit by the operating environment, and the strength of the unit represented by x. A unit is able to perform its required function if its stress imposed on it is less than the strength of the unit. In this paper, the stress-strength reliability estimation for the modified exponentiated Lomax distribution, which is generalization of the Lomax distribution, with an unknown shape parameter and a known scale parameters is studied using different methods. These methods include the maximum likelihood method, Bayesian estimation method under a quadratic loss function, and the least squares method for complete data. The estimators are compared based on Markov Chain Monte Carlo (MCMC) simulations using R-Studio, evaluated by the mean square error (MSE) criteria. The simulation results show that the maximum likelihood estimators are the best in two cases: the first is when the sample sizes are equal and the second is when the shape parameter of the strength variable is greater than the shape parameter of the stress variable. While least squares estimators are the beast if the strength sample size is smaller than the stress sample size. Finally when the strength sample size is greater than the stress sample size, then the best estimators differ between the maximum likelihood estimators and Bayesian estimators. Bayesian estimators become the best when the shape parameter of stress variable is larger than the shape parameter of the strength variable. © 2025 The Author(s).

关键词： Maximum likelihood estimation

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Contemporary Study for Detection of COVID-19 Using Machine Learning with Explainable AI

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computers, Materials & Continua 2024年第7期80卷 1075-1104页

作者： Saad Akbar Humera Azam Sulaiman Sulmi Almutairi Omar Alqahtani Habib Shah Aliya Aleryani Department of Computer Science University of KarachiKarachi75270Pakistan Department of Computing Faculty of Engineering Science and TechnologyHamdard UniversityKarachi75540Pakistan Department of Health Informatics College of Applied Medical SciencesQassim UniversityQassim51452Saudi Arabia Department of Computer Science College of Computer ScienceKing Khalid UniversityAbha61421Saudi Arabia

The prompt spread of COVID-19 has emphasized the necessity for effective and precise diagnostic *** this article,a hybrid approach in terms of datasets as well as the methodology by utilizing a previously unexplored dataset obtained from a private hospital for detecting COVID-19,pneumonia,and normal conditions in chest X-ray images(CXIs)is proposed coupled with Explainable Artificial Intelligence(XAI).Our study leverages less preprocessing with pre-trained cutting-edge models like InceptionV3,VGG16,and VGG19 that excel in the task of feature *** methodology is further enhanced by the inclusion of the t-SNE(t-Distributed Stochastic Neighbor Embedding)technique for visualizing the extracted image features and Contrast Limited Adaptive Histogram Equalization(CLAHE)to improve images before extraction of ***,an AttentionMechanism is utilized,which helps clarify how the modelmakes decisions,which builds trust in artificial intelligence(AI)*** evaluate the effectiveness of the proposed approach,both benchmark datasets and a private dataset obtained with permissions from Jinnah PostgraduateMedical Center(JPMC)in Karachi,Pakistan,are *** 12 experiments,VGG19 showcased remarkable performance in the hybrid dataset approach,achieving 100%accuracy in COVID-19 *** classification and 97%in distinguishing normal ***,across all classes,the approach achieved 98%accuracy,demonstrating its efficiency in detecting COVID-19 and differentiating it fromother chest disorders(Pneumonia and healthy)while also providing insights into the decision-making process of the models.

关键词： COVID-19 detection deep neural networks support vector machine CXIs InceptionV3 VGG16 VGG19 t-SNE embedding CLAHE attention mechanism XAI

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Ensemble Deep Learning Approaches in Health Care:A Review

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computers, Materials & Continua 2025年第3期82卷 3741-3771页

作者： Aziz Alotaibi Department of Computer Science College of Computing and Information TechnologyTaif UniversityTaif21974Saudi Arabia

Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of ***,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/*** the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big *** deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning *** ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble *** deep learning has been successfully used in several areas,such as bioinformatics,finance,and health *** this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug *** cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also ***,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and ***,future directions and opportunities for enhancing healthcare model performance are discussed.

关键词： Deep learning ensemble learning deep ensemble learning deep learning approaches for health care health care

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Integrating Bayesian and Convolution Neural Network for Uncertainty Estimation of Cataract from Fundus Images

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computer Modeling in Engineering & sciences 2025年第4期143卷 569-592页

作者： Anandhavalli Muniasamy Ashwag Alasmari Department of Informatics and Computer Systems College of Computer ScienceKing KhalidUniversityAbha61421Saudi Arabia Department of Computer Science King Khalid UniversityAbha61421Saudi Arabia

The effective and timely diagnosis and treatment of ocular diseases are key to the rapid recovery of ***,the mass disease that needs attention in this context is *** deep learning has significantly advanced the analysis of ocular disease images,there is a need for a probabilistic model to generate the distributions of potential outcomes and thusmake decisions related to uncertainty ***,this study implements a Bayesian Convolutional Neural Networks(BCNN)model for predicting cataracts by assigning probability values to the *** prepares convolutional neural network(CNN)and BCNN *** proposed BCNN model is CNN-based in which reparameterization is in the first and last layers of the CNN *** study then trains them on a dataset of cataract images filtered from the ocular disease fundus images *** deep CNN model has an accuracy of 95%,while the BCNN model has an accuracy of 93.75% along with information on uncertainty estimation of cataracts and normal eye *** compared with other methods,the proposed work reveals that it can be a promising solution for cataract prediction with uncertainty estimation.

关键词： Bayesian neural networks(BNNs) convolution neural networks(CNN) Bayesian convolution neural networks(BCNNs) predictive modeling precision medicine uncertainty quantification

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E-CommerceShield: A Framework for Enhanced Security in E-Commerce with Awareness, DNS Matching, and Blockchain Integration 30

E-CommerceShield: A Framework for Enhanced Security in E-Com...

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30th IEEE International Conference on Telecommunications, ICT 2024

作者： Belghith, Aymen College of Informatics and Computing Saudi Electronic University Computer Science Department Saudi Arabia

ISBN: (纸本)9798350356694

Security attacks on e-commerce platforms are increasingly prevalent, posing significant risks to both users and organizations. These attacks aim to cause financial harm or disrupt e-commerce services, highlighting the critical need for robust security measures. This study investigates key security attacks targeting e-commerce platforms and evaluates the efficacy of various attack detection methods. Through a comprehensive literature review and descriptive analysis, the study identifies financial frauds, phishing, and Denial-of-Service (DoS) and Distributed Denial-of-Service (DDoS) attacks as the major threats to e-commerce systems. Furthermore, the study examines different detection methods, revealing that blockchain-based security systems emerge as the most effective solution against a range of identified security attacks. Building upon these findings, this paper proposes a novel framework, dubbed e-CommerceShield, which integrates user awareness, DNS matching, and blockchain technology to enhance security in e-commerce environments. The framework prioritizes proactive security measures, empowers users with awareness and education, and leverages advanced technologies to detect and prevent security threats effectively. By adopting the e-CommerceShield framework, e-commerce platforms can fortify their security posture and safeguard the integrity of online transactions, thereby fostering trust and confidence among users in the digital marketplace. © 2024 IEEE.

关键词： Blockchain

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Hybrid Deep Learning Approach for Automating App Review Classification:Advancing Usability Metrics Classification with an Aspect-Based Sentiment Analysis Framework

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computers, Materials & Continua 2025年第1期82卷 949-976页

作者： Nahed Alsaleh Reem Alnanih Nahed Alowidi Department of Computer Science Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddah21589Saudi Arabia Department of Computer Science College of Computer Science and EngineeringUniversity of HailHail81451Saudi Arabia

App reviews are crucial in influencing user decisions and providing essential feedback for developers to improve their *** the analysis of these reviews is vital for efficient review *** traditional machine learning(ML)models rely on basic word-based feature extraction,deep learning(DL)methods,enhanced with advanced word embeddings,have shown superior *** research introduces a novel aspectbased sentiment analysis(ABSA)framework to classify app reviews based on key non-functional requirements,focusing on usability factors:effectiveness,efficiency,and *** propose a hybrid DL model,combining BERT(Bidirectional Encoder Representations from Transformers)with BiLSTM(Bidirectional Long Short-Term Memory)and CNN(Convolutional Neural Networks)layers,to enhance classification *** analysis against state-of-the-art models demonstrates that our BERT-BiLSTM-CNN model achieves exceptional performance,with precision,recall,F1-score,and accuracy of 96%,87%,91%,and 94%,*** contributions of this work include a refined ABSA-based relabeling framework,the development of a highperformance classifier,and the comprehensive relabeling of the Instagram App Reviews *** advancements provide valuable insights for software developers to enhance usability and drive user-centric application development.

关键词： Requirements Engineering(RE) app review analysis usabilitymetrics hybrid deep learning BERT-BiLSTM-CNN

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