Computers & Electrical Engineering最新文献

{"title":"MOTION: Multi-models correlation framework for energy-saving in wireless video sensor networks","authors":"Hassan Harb ,&nbsp;Fouad Al Tfaily ,&nbsp;Kassem Danach ,&nbsp;Hussein Hazimeh ,&nbsp;Ali Jaber","doi":"10.1016/j.compeleceng.2025.110720","DOIUrl":"10.1016/j.compeleceng.2025.110720","url":null,"abstract":"<div><div>Wireless Video Sensor Networks (WVSNs) face critical challenges in energy consumption and data bandwidth utilization due to multiple sensor nodes transmitting redundant data of the observed phenomenon. Thus, identifying and reducing such redundancies is becoming essential for extending the network lifetime and emphasizing the quality of the collected data. In this paper, we propose Multi-mOdels correlaTION framework (MOTION) that efficiently removes data duplication and conserve sensor energies in WVSNs. MOTION proposes new data reduction methods based on the temporal–spatial correlations that could be applied at different node levels, e.g. sensors and cluster-heads (CHs). At the sensor level, MOTION introduces two temporal-based correlation mechanisms to search the similarity among frames collected during each period; the first mechanism aims to detect short-term duplication, e.g. among consecutive frames, while the second one allows to detect scene changes then trigger transmissions when the variation is significant. At the second level, CH geographically groups video sensors into clusters to find the spatial correlation between them, then a scheduling strategy is applied to switch spatially-correlated ones into sleep/active modes. Extensive simulations using real-world video data sets as a benchmark are conducted to show the efficiency of the proposed framework. The results demonstrated that MOTION can reduce up to 92.4% of collected video data, leading to tremendous energy savings and network lifetime up to 74.3% compared to existing approaches.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110720"},"PeriodicalIF":4.9,"publicationDate":"2025-09-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145128273","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Revolutionizing plant disease diagnosis through vision-based intelligence and next-generation computing","authors":"Amreen Batool ,&nbsp;Yung-Cheol Byun","doi":"10.1016/j.compeleceng.2025.110695","DOIUrl":"10.1016/j.compeleceng.2025.110695","url":null,"abstract":"<div><div>Plant diseases significantly threaten global agriculture by reducing crop quality and yield. Early and accurate detection is vital to mitigate these impacts and ensure food security. This review presents a comprehensive survey of vision-based machine learning (ML) and deep learning (DL) approaches for plant disease detection. This review introduces a comparative analysis of more than 25 benchmark datasets and categorizes the progression from traditional ML methods to advanced DL models such as CNN, GAN, and Vision Transformers. This paper also addresses practical challenges such as image noise, environmental variability, and the domain gap between controlled and real-world datasets. Furthermore, the review explores the integration of Large Language Models (LLMs) into plant disease monitoring pipelines for annotation assistance, real-time farmer interaction, and multimodal reasoning. Moreover, the study emphasizes mobile and edge AI applications, including smartphone-based tools, AR interfaces, and IoT-enabled monitoring, enhancing accessibility for farmers in resource-constrained environments. A novel gap analysis and research roadmap are proposed to differentiate from existing works, outlining a future AI-driven agricultural ecosystem. This review concludes by identifying critical challenges and offering actionable research directions for robust, scalable, and interpretable plant disease detection systems in real-world agricultural settings.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110695"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118370","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multi-modal deep learning framework for power quality disturbance classification: An integration of 1D time-series signals and 2D scalograms","authors":"Mirza Ateeq Ahmed Baig ,&nbsp;Naeem Iqbal Ratyal ,&nbsp;Adil Amin ,&nbsp;Umar Jamil ,&nbsp;Haris M. Khalid ,&nbsp;Muhammad Fahad Zia","doi":"10.1016/j.compeleceng.2025.110716","DOIUrl":"10.1016/j.compeleceng.2025.110716","url":null,"abstract":"<div><div>Power quality (PQ) is crucial for the dependable functioning of electrical systems, requiring stable voltage, frequency, and waveform integrity. However, power quality disturbances (PQDs), resulting from faults, nonlinear loads, and switching events, can degrade system performance, damage equipment, and reduce operational efficiency. Accurate identification and classification of PQDs are therefore critical for effective mitigation. Traditional methods that rely solely on either one-dimensional (1D) time-series signals or two-dimensional (2D) waveform images often fail to capture the full characteristics of disturbances, leading to reduced accuracy. To address this limitation, a multi-modal deep learning framework is proposed that integrates 1D time-series data with corresponding 2D scalogram images. The proposed model employs parallel 1D and 2D convolutional neural networks (CNNs), each enhanced with attention mechanisms to enhance feature extraction by focusing on modality-specific salient information. The proposed model is evaluated on a comprehensive synthetic dataset of sixteen PQD types. Experimental results demonstrate that the proposed approach achieves an average classification accuracy of 99.99%, a sensitivity of 99.98%, and a specificity of 99.99%, outperforming existing methods. These results demonstrate the framework’s robustness and its potential as an effective solution for PQD monitoring and classification in smart grid environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110716"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118369","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Corrigendum to “Mitigating Dimension Constraints: A Novel Sliding Attack Strategy for Robust Synthetic Voice Detection” [Computers and Electrical Engineering Volume 118(2024), 1414/109355]","authors":"Jun Li ,&nbsp;Zijian Cui ,&nbsp;Yi Zhou","doi":"10.1016/j.compeleceng.2025.110719","DOIUrl":"10.1016/j.compeleceng.2025.110719","url":null,"abstract":"","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110719"},"PeriodicalIF":4.9,"publicationDate":"2025-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145362788","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SAGANConvLSTM: A novel spatio-temporal forecasting approach combining semivariogram-enhanced GAN and ConvLSTM for power load forecasting","authors":"Rasoul Jalalifar ,&nbsp;Mahmoud Reza Delavar ,&nbsp;Seyed Farid Ghaderi ,&nbsp;Seyedeh Leyla Mansouri Alehashem","doi":"10.1016/j.compeleceng.2025.110718","DOIUrl":"10.1016/j.compeleceng.2025.110718","url":null,"abstract":"<div><div>In power distribution networks, spatio-temporal load forecasting plays a crucial role in decision-making and the development of distribution networks. Accurate forecasting models are essential to handle the complex dependencies in power consumption across different districts of megacities. However, the presence of missing values in smart meter data often caused by device malfunctions or communication failures, can significantly degrade model performance by increasing complexity and reducing forecasting accuracy. To address this challenge, this paper presents a novel forecasting approach named SAGAN<img>ConvLSTM based on Spatial Autocorrelation (SA) utilizing spatio-temporal semivarigram and Generative Adversarial Network (GAN) to combine spatial statistics and deep learning to impute missing values in power load time series. The semivariogram quantifies spatial and temporal dependencies among substations and guides the GAN to reconstruct missing data while preserving realistic spatio-temporal structures. After imputation, the refined time series is processed by a Convolutional Long Short-Term Memory (ConvLSTM) network to generate short-term power load forecasts.The proposed model was evaluated using data from Tehran's power distribution network, achieving a mean absolute error (MAE) of 10.05 % and root mean square error (RMSE) of 15.46 %, outperforming other models like GRU, LSTM, ConvLSTM, and SA-ConvLSTM in forecasting power load over a 20-day period.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110718"},"PeriodicalIF":4.9,"publicationDate":"2025-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145118368","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost–benefit evaluation of computer network systems with warm standby units and general repair times","authors":"Jones Edward Chiwinga,&nbsp;Muhammad Salihu Isa,&nbsp;Jinbiao Wu","doi":"10.1016/j.compeleceng.2025.110724","DOIUrl":"10.1016/j.compeleceng.2025.110724","url":null,"abstract":"<div><div>The rapid growth of artificial intelligence (AI) and internet of things (IoT) across several industries emphasizes on the necessity for reliable and strong network infrastructure. In our increasingly interconnected world, network availability and reliability are critical for the success of diverse sectors such as businesses, academic institutions, communication, and healthcare systems. However, issues like software bugs, hardware malfunctions, and human error pose serious threats to the stability of the network. Addressing these problems is becoming more and more crucial as the need for uninterrupted connectivity keeps rising. To address these challenges this paper compares three retrial computer network systems comprising warm standby units, a single repairman with planned vacations, and general repair time distributions. The steady state probabilities and related availabilities for three retrial systems are systematically derived by employing supplementary variable technique and recursive analytical method. Additionally, comparison of the cost–benefit ratios and availabilities for these systems is presented. The best retrial system is obtained by sorting the efficiency of the three retrial systems under consideration. The adopted technique outperforms typical models by a significant margin, according to numerical results, this enhances efficiency of system availability even in case of unit failures. Based on the calculated numerical outcomes, more strategies for decision making management are also suggested for practical implications.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110724"},"PeriodicalIF":4.9,"publicationDate":"2025-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive dynamic K-nearest neighbors and context-aware similarity optimization for microbe-disease association prediction","authors":"Bo Wang ,&nbsp;Peilong Wu ,&nbsp;Xiaoxin Du ,&nbsp;JianFei Zhang ,&nbsp;Chunyu Zhang","doi":"10.1016/j.compeleceng.2025.110725","DOIUrl":"10.1016/j.compeleceng.2025.110725","url":null,"abstract":"<div><div>Microbes play a crucial role in disease occurrence, progression, and treatment. Traditional experimental methods are time-consuming, prompting researchers to turn to computational models. However, existing models often suffer from limited data adaptability and improper feature selection, making them prone to noise interference. To address these limitations, we propose ADKNN-KFGCN, a novel adaptive framework that integrates dynamic K-nearest neighbors, graph convolutional networks, and context-aware similarity optimization. The model constructs multi-source similarity networks by integrating various similarity measures between microbes and diseases, forming a comprehensive foundation for association inference. To better capture complex local patterns, it employs adaptive dynamic K-nearest neighbors to adjust the number of neighbors based on local structure, enhancing the accuracy of network construction. This is followed by context-aware similarity optimization, which filters out low-similarity nodes to suppress noise and emphasize the most informative connections. On this refined graph, graph convolutional networks are used to extract high-level representations, effectively capturing intricate topological relationships. These features are then fused through kernel-based strategies, combining multiple similarity sources via averaging and weighted integration to form a unified representation. Finally, Laplacian Regularized Least Squares leverages the global graph structure during prediction, improving generalization and ensuring robust performance. Experimental results show that ADKNN-KFGCN outperforms seven state-of-the-art methods, achieving an AUC of 0.9851±0.0025 and AUPR of 0.9587±0.0032 on the HMDAD dataset. Case studies on rheumatoid arthritis and inflammatory bowel disease further demonstrate its potential to uncover novel associations, provide insights into disease mechanisms, and support therapeutic target discovery.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110725"},"PeriodicalIF":4.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"TwinLiteNet+: An enhanced multi-task segmentation model for autonomous driving","authors":"Quang-Huy Che,&nbsp;Duc-Tri Le,&nbsp;Minh-Quan Pham,&nbsp;Vinh-Tiep Nguyen,&nbsp;Duc-Khai Lam","doi":"10.1016/j.compeleceng.2025.110694","DOIUrl":"10.1016/j.compeleceng.2025.110694","url":null,"abstract":"<div><div>Semantic segmentation is a fundamental perception task in autonomous driving, particularly for identifying drivable areas and lane markings to enable safe navigation. However, most state-of-the-art (SOTA) models are computationally intensive and unsuitable for real-time deployment on resource-constrained embedded devices. In this paper, we introduce TwinLiteNet<span><math><msup><mrow></mrow><mrow><mo>+</mo></mrow></msup></math></span>, an enhanced multi-task segmentation model designed for real-time drivable area and lane segmentation with high efficiency. TwinLiteNet<span><math><msup><mrow></mrow><mrow><mo>+</mo></mrow></msup></math></span> employs a hybrid encoder architecture that integrates stride-based dilated convolutions and depthwise separable dilated convolutions, balancing representational capacity and computational cost. To improve task-specific decoding, we propose two lightweight upsampling modules-Upper Convolution Block (UCB) and Upper Simple Block (USB)-alongside a Partial Class Activation Attention (PCAA) mechanism that enhances segmentation precision. The model is available in four configurations, ranging from the ultra-compact TwinLiteNet<span><math><msubsup><mrow></mrow><mrow><mtext>Nano</mtext></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> (34K parameters) to the high-performance TwinLiteNet<span><math><msubsup><mrow></mrow><mrow><mtext>Large</mtext></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> (1.94M parameters). On the BDD100K dataset (Yu et al. (2020)), TwinLiteNet<span><math><msubsup><mrow></mrow><mrow><mtext>Large</mtext></mrow><mrow><mo>+</mo></mrow></msubsup></math></span> achieves 92.9% mIoU for drivable area segmentation and 34.2% IoU for lane segmentation-surpassing existing state-of-the-art models while requiring 11<span><math><mo>×</mo></math></span> fewer floating-point operations (FLOPs) for computation. The results compared with other models are shown in <span><span>Fig. 1</span></span>. Extensive evaluations on embedded devices demonstrate superior inference speed, quantization robustness (INT8/FP16), and energy efficiency, validating TwinLiteNet<span><math><msup><mrow></mrow><mrow><mo>+</mo></mrow></msup></math></span> as a compelling solution for real-world autonomous driving systems. Code is available at <span><span>https://github.com/chequanghuy/TwinLiteNetPlus</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110694"},"PeriodicalIF":4.9,"publicationDate":"2025-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Penetration testing: Taxonomies, trade-offs, and adaptive strategies","authors":"Sitanshu Kapur,&nbsp;Praneet Saurabh","doi":"10.1016/j.compeleceng.2025.110686","DOIUrl":"10.1016/j.compeleceng.2025.110686","url":null,"abstract":"<div><div>Modern cybersecurity faces increasing complexity due to the growth of cloud-native platforms, legacy systems, and the proliferation of IoT devices. Traditional penetration testing methods, such as manual exploits and signature-based scanners, offer precision, but lack scalability and adaptability. Conversely, AI-based approaches, which employ techniques such as machine learning, reinforcement learning, and large language models to automate specific phases of the penetration testing workflow, introduce adaptability but also face significant challenges, including data dependency, limited interpretability, and high computational cost. This review focuses on three core questions: the comparative strengths and weaknesses of conventional and AI-based penetration testing, the influence of deployment contexts such as cloud and IoT, and how hybrid strategies can balance automation with human oversight. In this review, we focus mainly on the literature from 2010 to 2025, with inclusion criteria based on empirical validation, relevance, and impact. We conclude by proposing a research agenda focused on explainable AI, efficient model deployment, and standardized evaluation benchmarks for next-generation penetration testing systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110686"},"PeriodicalIF":4.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A hybrid deep learning framework using DT-FLBP and entropy features for stroke detection in MRI images","authors":"S․E Viswapriya,&nbsp;D Rajeswari","doi":"10.1016/j.compeleceng.2025.110711","DOIUrl":"10.1016/j.compeleceng.2025.110711","url":null,"abstract":"<div><div>Cerebrovascular diseases such as strokes seriously affect a person's life and good health. The diagnosis and treatment of stroke are significantly aided by the quantitative analysis of the brain using Magnetic Resonance Imaging (MRI) images. The prime intention of this research is to design an effective Hybrid Xception-ShuffleNet (HX-ShuffleNet) for detecting stroke disease. Initially, an MRI image is acquired from the database. Then, the acquired MRI image is fed into the image denoising module, where image denoising is performed using a median filter. Later, the stroke lesion segmentation is done based on the U-Net to isolate the stroke lesions from the entire image. After stroke lesion segmentation, image augmentation (random rotation, shifting, shearing, flipping) is done. Features are extracted using Dual-Tree-Fuzzy Local Binary Pattern (DT-FLBP), which combines Dual-Tree Complex Wavelet Transform (DTCWT), Fuzzy Local Binary Pattern (FLBP), and entropy. For stroke detection, HX-ShuffleNet, a fusion of Xception and ShuffleNet models, is used, achieving a True Positive Rate (TPR) of 0.928, accuracy of 0.935, True Negative Rate (TNR) of 0.929, Precision of 0.922, and F1-score of 0.928.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110711"},"PeriodicalIF":4.9,"publicationDate":"2025-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096389","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}