Computers & Electrical Engineering最新文献

{"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}

{"title":"Fuzzy Cost-risk management of HEV-penetrated power-to-ammonia multi-energy buildings considering the limited production flexibility","authors":"Yujie Qin ,&nbsp;Da Xu ,&nbsp;Ziyi Bai ,&nbsp;Dongjie Shi","doi":"10.1016/j.compeleceng.2025.110717","DOIUrl":"10.1016/j.compeleceng.2025.110717","url":null,"abstract":"<div><div>The traditional multi-energy building generally focuses on economically optimizing the energy cost, whereas the increasing complexity of energy couplings and conflicting energy cost/risk are often overlooked. This paper proposes fuzzy cost-risk management of a power-to-ammonia (P2A) multi-energy building considering the limited production flexibility. In this model, the solar-wind multi-energy complementarities are exploited on the basis of P2A electrolytic thermo-electrochemical effects, which are converted and regulated via an energy hub. Hybrid electric vehicles (HEVs) are envisioned as multi-energy load and managed through a novel real-time multi-energy supply–demand pricing mechanism. The limited ammonia production flexibility is considered and modeled as a first-order transition to capture the dynamic regulation process of P2A. The formulated cost-risk management problem is captured using the mean-variance method to articulate the risk linked to forecasting uncertainties, which is subsequently addressed through fuzzy optimization to achieve a trade-off between two conflicting objectives. Simulation analyses implemented in a community building environment validate the superiority and effectiveness of the proposed approach, achieving 12.03 % cost reduction and 19.25 % improvement of production flexibility modeling.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110717"},"PeriodicalIF":4.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096386","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":"Optimal placement and sizing of distributed energy resources in active distribution networks under uncertainty: A multi-objective approach using electric eel foraging optimization","authors":"A. Elsawy Khalil ,&nbsp;Joseph S. Sedky ,&nbsp;Ahmed M. Ibrahim ,&nbsp;Tarek A. Boghdady","doi":"10.1016/j.compeleceng.2025.110715","DOIUrl":"10.1016/j.compeleceng.2025.110715","url":null,"abstract":"<div><div>The integration of Renewable Energy Sources (RESs) into active distribution networks presents a trade-off between loss reduction and operational uncertainties, as well as challenges to network stability. This paper proposes a novel techno-economic multi-objective function (MOF) to optimize the sizing and placement of RESs, fuel cells, and shunt capacitors. The proposed MOF maximizes the hosting capacity and voltage stability while minimizing power losses, voltage deviation, and costs. The proposed MOF utilizes the electric eel foraging optimization (EEFO) algorithm, which was benchmarked against seven metaheuristic algorithms (GA, PSO, GWO, SMA, MPA, AHA, and ARO) and tested on the modified IEEE 69-bus system under uncertainty. Simulation results demonstrate the superior performance of the EEFO-based MOF, achieving a 69.48% reduction in power losses. Under uncertain conditions, it attained the best overall performance: the lowest loss (9.46 kW), minimal cost ($226/h), least land use (5604.3 m²), and highest hosting capacity (55.1%).</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110715"},"PeriodicalIF":4.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096388","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":"Design and deployment of an integrated network architecture leveraging RabbitMQ for optimizing automation systems in smart logistics","authors":"Jihyeok Ryu ,&nbsp;Seonghoon Jang ,&nbsp;Byeongjun Park ,&nbsp;Chaegyu Lee ,&nbsp;Jongpil Jeong","doi":"10.1016/j.compeleceng.2025.110721","DOIUrl":"10.1016/j.compeleceng.2025.110721","url":null,"abstract":"<div><div>Modern smart factories exhibit significant complexity due to the interaction of multiple subsystems and intricate network topologies. Such complexity often results in redundant communication processes between subsystems, which hinders communication efficiency, limits real-time responsiveness, and reduces overall system reliability. This study proposes an integrated network architecture utilizing RabbitMQ to eliminate redundant communication paths, thereby improving transfer speeds, enhancing real-time functionality, and strengthening system dependability. Experimental evaluation demonstrated that, under identical conditions, the RabbitMQ-based direct transmission method reduced average delay by approximately 30.9 % compared to the legacy interface, while the RabbitMQ-based Message Queuing Telemetry Transport (MQTT) method achieved an 81.4 % improvement. The architecture is designed to facilitate streamlined maintenance and to simplify future upgrades and expansions, ensuring adaptability to evolving operational requirements. By implementing this integrated framework, transportation subsystems within smart factories can be tangibly optimized, contributing to higher productivity and operational stability. This paper presents a comprehensive examination of the design principles, implementation procedures, and practical benefits of the proposed architecture, providing insights into its applicability in Industry 4.0 environments.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110721"},"PeriodicalIF":4.9,"publicationDate":"2025-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096387","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":"Millimeter-wave radar for intelligent sensing: A comprehensive review of techniques, applications, and challenges","authors":"Yash Soni ,&nbsp;Malhaar Goswami ,&nbsp;Nishit Prabhakar Shetty ,&nbsp;Dhiraj","doi":"10.1016/j.compeleceng.2025.110696","DOIUrl":"10.1016/j.compeleceng.2025.110696","url":null,"abstract":"<div><div>Millimeter-wave (mmWave) radar sensing has established itself as a robust technology across diverse applications, such as automotive, healthcare, security, and smart homes. Its exceptional capacity to function effectively in varying environmental conditions, detect concealed objects, sense physiological signals, and facilitate precise target detection positions it as a pivotal enabler for next-generation sensing solutions. The survey employs bibliometric analysis to critically evaluate the existing literature surrounding mmWave radar, highlighting key research trends, notable publications, and the challenges faced within the field. This work presents a comprehensive examination of mmWave radar-based sensing, detailing its fundamental operating principles, signal processing methodologies, advancements in hardware, and the latest developments in machine learning applications. It also addreses the key challenges in signal processing, including resolution enhancement, environmental adaptability, and data fusion with complementary sensors such as LiDAR and cameras. Furthermore, explored the potential of deep learning techniques to enhance target classification, activity recognition, gesture identification, and healthcare applications while addressing concerns related to accuracy and precision. This survey also sheds light on emerging trends by assessing the strengths, limitations, and prospects of mmWave radar technology. This review aims to provide insightful guidance for researchers and practitioners committed to advancing radar-based sensing and its real-world implementations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110696"},"PeriodicalIF":4.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096356","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":"Performance improvement and power management based arithmetic optimization algorithm in grid-integrated photovoltaic with electric vehicle batteries systems","authors":"AL-Wesabi Ibrahim ,&nbsp;Abdullrahman A. Al-Shamma'a ,&nbsp;Hassan M. Hussein Farh ,&nbsp;Yuqing Yang ,&nbsp;Jiazhu Xu","doi":"10.1016/j.compeleceng.2025.110707","DOIUrl":"10.1016/j.compeleceng.2025.110707","url":null,"abstract":"<div><div>Power quality is paramount for ensuring reliable, stable, and environmentally sustainable electricity supply from distributed renewable energy sources (DRESs). However, conventional controllers in hybrid Photovoltaic–Electric Vehicle Battery (PV–EVB) systems typically suffer from limitations such as steady-state error, harmonic distortion, suboptimal transient response, and voltage overshoot. Addressing these issues, this paper proposes a novel arithmetic optimization algorithm (AOA) to enhance performance and power quality in PV–EVB systems subject to load and environmental variability. The proposed methodology consists of two primary components. First, an AOA-based global maximum power point tracking (GMPPT) controller dynamically adjusts PV output to suppress upward frequency oscillations. Second, AOA is employed to optimize the proportional-integral (PI) controller gains for both the bidirectional DC/DC converter and the single-phase inverter of the EVB system, thereby reducing downward frequency fluctuations. These coordinated strategies effectively stabilize DC link voltage (DLV), control grid frequency, and minimize total harmonic distortion (THD) in the grid current. Quantitative results demonstrate that, AOA-based approach achieves a rapid settling time of 0.3 s, low overshoot (3%), and minimal steady-state error (0.2%), while maintaining high PV power and system efficiency (99%). Thus, the AOA-based control strategy significantly improves the grid-integration of hybrid PV–EVB systems and supports more robust, efficient, and sustainable energy infrastructure.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"128 ","pages":"Article 110707"},"PeriodicalIF":4.9,"publicationDate":"2025-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145096384","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}