Computers & Electrical Engineering最新文献

{"title":"Evaluating sustainable wind energy sources with multiple criteria decision-making (MCDM) techniques","authors":"Satyabrata Dash ,&nbsp;Sujata Chakravarty ,&nbsp;Nimay Chandra Giri ,&nbsp;Rohit Khargotra","doi":"10.1016/j.compeleceng.2025.110285","DOIUrl":"10.1016/j.compeleceng.2025.110285","url":null,"abstract":"<div><div>Rural regions with complex topographical constraints face significant challenges in implementing sustainable wind energy solutions due to variations in wind resource availability, infrastructure limitations, and policy gaps. To address this issue, this study integrates Multiple Criteria Decision Making (MCDM) techniques to systematically evaluate and prioritize various wind energy alternatives, considering technical, economic, environmental, and social factors. The PAPRIKA (Potentially All Pairwise RanKings of all possible Alternatives) method is employed to rank wind energy systems based on key criteria such as Capacity Factor, Environmental Impact, and Policy Framework. The findings indicate that Onshore Wind Turbines emerge as the most optimal solution (score: 69.9) due to superior energy production and cost-effectiveness (LCOE). Vertical Axis Wind Turbines (66.5) and Hybrid Wind Systems (60.8) follow, demonstrating balanced performance. Offshore Wind Turbines and Wind Farms with storage show promise but face grid integration and policy challenges, while Floating and Micro Wind Turbines rank lowest due to resource constraints. This research underscores the role of MCDM in integrating quantitative and qualitative assessments, providing a structured framework for energy planners and policymakers to make informed decisions. By optimizing wind energy deployment in rural settings, the study contributes to achieving Sustainable Development Goals (SDGs) 7, 9, and 13, fostering a resilient, low-carbon, and inclusive energy transition.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110285"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143737772","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":"Intelligent jamming decision-making system based on reinforcement learning","authors":"Xia Xiong ,&nbsp;Shengbo Hu ,&nbsp;Tingting Yan ,&nbsp;Zehua Xing ,&nbsp;Tianle Ma ,&nbsp;Kangjun Yin ,&nbsp;Jianbo Wang ,&nbsp;Xu Wei","doi":"10.1016/j.compeleceng.2025.110288","DOIUrl":"10.1016/j.compeleceng.2025.110288","url":null,"abstract":"<div><div>Cognitive communication countermeasures have increasingly been emphasized as an important research interests in cognitive electronic warfare. However, the low signal-to-noise ratio (SNR) and frequency hopping (FH) in communication countermeasures create significant difficulties for spectrum sensing and jamming decision-making. In this paper, an intelligent jamming decision-making system for FH communication is designed based on an improved deep Q- network (DQN). First, a spectrum sensing method utilizing a bidirectional long short-term memory (Bi-LSTM) network is introduced, which establishes the received signals as a binary hypothesis testing model and employs the Bi-LSTM network for signal classification. Second, the jamming channel selection problem is modeled as a Markov decision process (MDP), and an improved DQN algorithm is applied to facilitate intelligent decision-making for jamming channels. Finally, simulation experiments are conducted to evaluate the performance of the algorithms. The results show that the proposed Bi-LSTM network achieves a detection probability of over 88% even in low-SNR communication countermeasure environments at <span><math><mrow><mo>−</mo><mn>12</mn></mrow></math></span> dB. Furthermore, the improved DQN algorithm achieves a 100% channel jamming rate and the fastest convergence speed among the five compared algorithms, effectively learning the FH sequences and implementing jamming.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110288"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760342","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":"Three-phased multi-scale residual-dense modified-U-Net architecture for deep image steganography","authors":"Suyash Sachdeva ,&nbsp;Ujjwal Sharma ,&nbsp;Priyanshu Rajput ,&nbsp;Riya Singhal ,&nbsp;K. Madhu Kiran ,&nbsp;Rohit Dhiman","doi":"10.1016/j.compeleceng.2025.110299","DOIUrl":"10.1016/j.compeleceng.2025.110299","url":null,"abstract":"<div><div>Recent advancements in deep image steganography have shown promise, yet many existing approaches fail to address the inherently lossy nature of neural networks, limiting their effectiveness. To overcome this limitation, we propose a novel architecture that integrates residual dense multi-scale JBs and FEBs within a three-phased U-Net framework, composed of three interconnected sub-networks: the secret encoder, encoder, and decoder. This modular design enhances data encoding and facilitates easy adaptation to different datasets, offering greater flexibility. By incorporating multi-scale processing to minimize information loss and leveraging high inter-connectivity to improve feature hiding. Additionally, a custom loss function, combining seven distinct components, is employed to guide the model’s learning process. The effectiveness of this architecture is demonstrated on CIFAR-10, CIFAR-100, and CelebA datasets, where it achieves an average improvement of 4.76 in peak signal-to-noise ratio (PSNR) and 0.0292 in structural similarity index measure (SSIM), which highlight the potential proposed architecture to elevate the field of image steganography.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110299"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143738040","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":"Discrete Ricci Flow: A powerful method for community detection in location-based social networks","authors":"Elahe Karampour ,&nbsp;Mohammad Reza Malek ,&nbsp;Marzieh Eidi","doi":"10.1016/j.compeleceng.2025.110302","DOIUrl":"10.1016/j.compeleceng.2025.110302","url":null,"abstract":"<div><div>Community detection is crucial to understanding behavioral patterns in location-based social networks (LBSNs) where user locations, media, and check-ins are involved. This hierarchical structure enables the formation of user communities, where a community represents a group of users sharing similar interests. In addition, selecting an appropriate community for a recommendation scenario is crucial and challenging. To address these issues, in this article, we propose a novel method to link LBSNs to the Discrete Ricci Flow (DRF) community detection algorithm. Then we use the communities formed by the Ricci curvature of the network to provide recommendations in a user-based collaborative filtering (CF) recommender system. Our evaluation method considers spatial–temporal features and user relationships. The evaluation encompasses unsupervised and supervised learning methodologies, employing the modularity evaluation index and the CF recommender system. Comparative analysis against traditional community detection algorithms, including Leiden, Infomap, Walktrap, and Fast Greedy, reveals the superior performance of our proposed method, as it achieves an impressive 0.5075% and 0.8486% modularity scores for Gowalla and Brightkite respectively that indicates the efficacy of the method in capturing the inherent structure of the data. Furthermore, when integrated into the CF recommender system, the proposed method based on DRF demonstrates superior performance compared to other community detection methods for different data sets such as Gowalla and Brightkite. In particular, for Gowalla it improves the performance of the Point Of Interest (POI) recommendation system by an average of 10.92% and 8.02% in Recall@15 and Recall@20, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110302"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143747245","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":"An effective Multi-Modality Feature Synergy and Feature Enhancer for multimodal intent recognition","authors":"Yichao Xia ,&nbsp;Jinmiao Song ,&nbsp;Shenwei Tian ,&nbsp;Qimeng Yang ,&nbsp;Xin Fan ,&nbsp;Zhezhe Zhu","doi":"10.1016/j.compeleceng.2025.110301","DOIUrl":"10.1016/j.compeleceng.2025.110301","url":null,"abstract":"<div><div>Multimodal intent recognition is a critical task that aims to accurately capture and interpret a user’s true intentions by integrating various sensory inputs such as facial expressions, body language, and vocal emotions. In complex and dynamic real-world multimodal interaction scenarios, deepening the understanding of human language and behavior becomes essential. Although multimodal data is rich in information, enhancing the representation of data features and efficiently integrating multimodal information to improve intent recognition performance remains a significant technical challenge. To address the aforementioned issue, a Video Feature Enhancer (VFE) module, combined with a Multi-Modality Feature Synergy (MFS) method, is proposed. The Video Feature Enhancer module employs a feature-weighting strategy based on energy optimization, along with an attention mechanism across channel spaces, to enhance the representational capability of video features. The Multi-Modality Feature Synergy method uses multi-level textual feature guidance and multimodal association learning to effectively integrate and optimize the feature representations of video and audio modalities. The Multi-Modality Feature Synergy method also suppresses non-essential information, facilitating the fusion of complementary information across different modalities, ultimately improving multimodal intent recognition performance. In the experimental evaluation, significant performance improvements are demonstrated over existing state-of-the-art methods on two benchmark datasets. On the MIntRec dataset, accuracy (ACC) is improved by 0.6%, weighted F1 score (WF1) by 1.21%, and weighted precision (WP) by 1.7%, while recall (R) increases by 1.8%. On the MELD-DA dataset, a 0.9% improvement in ACC is achieved, a significant increase of 1.15% in WF1 and 1.34% in WP, and also a 0.21% improvement in R is shown. Furthermore, through ablation studies, the substantial contributions of both the Video Feature Enhancer module and the Multi-Modality Feature Synergy method are validated in enhancing modality-specific feature representations and improving intent recognition accuracy.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110301"},"PeriodicalIF":4.0,"publicationDate":"2025-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143760343","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":"Intelligent assessment of power quality disturbances: A comprehensive review on machine learning and deep learning solutions","authors":"Shaurya Jain ,&nbsp;Amol Satsangi ,&nbsp;Rajat Kumar ,&nbsp;Divyani Panwar ,&nbsp;Mohammad Amir","doi":"10.1016/j.compeleceng.2025.110275","DOIUrl":"10.1016/j.compeleceng.2025.110275","url":null,"abstract":"<div><div>As the global focus on clean energy and smart grids intensifies, detecting power quality disturbances (PQDs), caused by energy instability, has become increasingly critical for achieving sustainable development goals by ensuring stable and reliable power quality. Power quality disturbances, such as voltage sags or harmonics, are disruptions in the electrical supply that can affect everything from household appliances to industrial machinery, making their detection and management essential for a stable power system. They can cause significant damage to power grid infrastructure, leading to energy inefficiency, restricted electricity generation and consumption, equipment malfunction, and industrial process failures. The incorporation of artificial intelligence (AI) has transformed PQD classification, providing substantial advancements in monitoring and managing electrical systems. This paper presents a systematic review of the existing literature, focusing on the integration of machine learning and deep learning techniques for PQD detection. It analyzes high-quality studies on PQDs detection and classification, categorizing them based on the AI techniques employed. Additionally, it emphasizes the role of digital signal processing (DSP) techniques in extracting features, with studies segregated based on the incorporation of DSP and non-DSP approaches. A case study demonstrates the practical application and effectiveness of AI techniques in real-world contexts, with the Bagged Trees classifier achieving the highest testing accuracy of 96.6 %. The insights provided aim to support researchers and practitioners in navigating the evolving landscape of power quality assessment, ultimately improving the reliability and accuracy of PQD detection systems.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110275"},"PeriodicalIF":4.0,"publicationDate":"2025-03-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143734961","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":"Modeling the future: Mathematical insights for smart grid 3.0","authors":"Muhammad Adnan ,&nbsp;Muhammad Sajid Iqbal ,&nbsp;Sadia Jabeen Siddiqi ,&nbsp;Ijaz Ahmed ,&nbsp;Anwar Shah ,&nbsp;Inam Ullah ,&nbsp;Muhammad Tariq","doi":"10.1016/j.compeleceng.2025.110283","DOIUrl":"10.1016/j.compeleceng.2025.110283","url":null,"abstract":"<div><div>The synergistic integration of Metaverse, Digital Twin (DT), and Blockchain technologies is redefining the framework of smart grids (SGs) and establishing the foundation for the revolutionary phase of Smart Grid 3.0 (SG 3.0). This advancement offers unique opportunity to create robust convergence models that include the Metaverse, Digital Twins, and Blockchain, allowing an authentic depiction of the SG 3.0 environment in the complex interaction between customers and utilities. This advanced convergence model functions as an exact platform for technical experts, attracting growing interest from both academic and industrial sectors. In this swiftly advancing domain, it is essential to tackle the task of adaptively modifying the SG 3.0 architecture in response to multiple disruptions. The choice of an appropriate convergence model for specific type of disruptions is a critical research concern. Numerous convergence models have been developed in recent literature to address these problems. However, the extensive variety and characteristics of these mathematical models complicate the assessment of their realism and compliance with the SG 3.0 framework. This research presents an innovative mathematical modeling strategy to address these challenges, facilitating the evolution of the SG framework into SG 3.0. Our methodology integrates Metaverse, Digital Twin, and Blockchain technologies, providing a distinctive viewpoint that surpasses existing frameworks in the literature. Finally, this study addresses the essential requirement for clarity on the realism and attributes of mathematical models tailored for SG 3.0, hence pioneering new avenues and advancing knowledge and innovation in the growth of Smart Grids.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110283"},"PeriodicalIF":4.0,"publicationDate":"2025-03-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725009","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":"Improving privacy in peer-to-peer energy-sharing systems: A data-centric architectural approach","authors":"Farhad Rahmanifard,&nbsp;Masoud Barati","doi":"10.1016/j.compeleceng.2025.110294","DOIUrl":"10.1016/j.compeleceng.2025.110294","url":null,"abstract":"<div><div>Energy-sharing systems increasingly leverage decentralized infrastructures and peer-to-peer networks to minimize transmission and distribution losses of renewable energy sources. In these systems, prosumers’ personal data may be shared with suppliers or subcontractors without their knowledge during energy transactions. Despite employing intelligent and tamper-proof technologies to enhance traceability of energy production and consumption, mechanisms for secure and transparent personal data management remain lacking—a core concept in privacy regulations. This problem includes the safe storage and transfer of data within the network and the absence of automated methods allowing consumers to control their personal information during the data processing life-cycle. To address these challenges, this paper proposes a new privacy-preserving platform designed to secure prosumers’ data collection and processing. A key component is a monitoring tool that enhances transparency by automatically logging and immutably recording all data accesses. This tool enables both prosumers and trusted arbiters to be informed about any access to personal data and to verify or detect privacy violations. The proposed platform offers fundamental privacy rights mandated by privacy regulations such as the General Data Protection Regulation (GDPR) in an adaptable design. Its performance and overhead are evaluated through a prototype implementation tested in a simulated energy-sharing system environment.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110294"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725008","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":"IoT-based intrusion detection system using explainable multi-class deep learning approaches","authors":"Sapna Sadhwani,&nbsp;Ameya Navare,&nbsp;Alan Mohan,&nbsp;Raja Muthalagu,&nbsp;Pranav M. Pawar","doi":"10.1016/j.compeleceng.2025.110256","DOIUrl":"10.1016/j.compeleceng.2025.110256","url":null,"abstract":"<div><div>With the surge in Internet of Things (IoT) across various domains and the rise in security threats, researchers have developed Intrusion Detection Systems (IDS) attacks in networks. These Machine Learning (ML) and Deep Learning (DL) models are powerful in detecting and classifying attacks; however, they have a black-box nature and lack interpretability. Explainable Artificial Intelligence (XAI) works towards this and improves the model's transparency and trustworthiness with research in XAI increasing significantly. However, its application within cybersecurity and IoT Intrusion Detection, particularly, requires more work to interpret various IDS models and provide explanations on how various cyber-attacks occur. This work proposes various DL-based IDS trained on four datasets: NSL-KDD, UNSW-NB15, TON-IoT and X-IIoTID and applies XAI using Shapely Additive Explanations (SHAP) to interpret these models. Utilizing four datasets captures diverse network environments to thoroughly evaluate and interpret the model. Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) based models were trained for multi-class classification. The best model (based on performance and training time) was chosen for each dataset and SHAP was applied to it. Furthermore, a novel set of 15 features, which impacted the model's decisions the most, were extracted using explanations generated from SHAP. The models trained on these reduced features required less training time without significant impact on training time and achieving a higher performance in comparison to peer models. This work achieves model accuracies of 98.21 % in NSL-KDD, 97.80 % in TON-IoT, 92.9 % in UNSW-NB15 and 98.09 % in X-IIoTID dataset using a CNN-based model, CNN-X and using a subset of only 15 features in each dataset. This work achieves high model performances, while improving the efficiency and interpretability of IDS.</div><div>© 2017 Elsevier Inc. All rights reserved.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110256"},"PeriodicalIF":4.0,"publicationDate":"2025-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143725176","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":"Smart offloading for IoT application: Building a fog-cloud based context aware offloading framework and exploring potential for integration with blockchain","authors":"Karan Bajaj ,&nbsp;Shaily Jain ,&nbsp;Raman Singh ,&nbsp;Chander Prabha ,&nbsp;Md. Mehedi Hassan ,&nbsp;Anupam Kumar Bairagi ,&nbsp;Sheikh Mohammed Shariful Islam","doi":"10.1016/j.compeleceng.2025.110292","DOIUrl":"10.1016/j.compeleceng.2025.110292","url":null,"abstract":"<div><div>In the world of interconnected devices also referred to as the Internet of Things (IoT) in the modern era, it's important to ensure that computing resources are allocated efficiently to nearby devices such as edge, fog, or cloud systems to meet resource needs. However, problems such as delays in data transmission, high energy consumption, and slow response times can negatively impact the performance of time-sensitive applications in cloud-based environments.</div><div>This paper presents the Context-Aware Offloading Framework (CAOF) for resource-constrained IoT applications. CAOF leverages contextual information to identify scenarios where offloading tasks to the cloud or to the local instances are beneficial. The framework aims to make optimal offloading decisions to improve system performance and minimize energy consumption. The effectiveness of CAOF is evaluated through simulations, comparing its performance against established offloading frameworks. CAOF is implemented as a middleware solution within an Amazon Web Services (AWS) ecosystem. This middleware integrates a Greengrass intelligent gateway that dynamically determines how to handle incoming data based on contextual information. The intelligent gateway can either process the data on local Elastic Cloud Compute (EC2) instances, effectively creating a fog layer, or send it directly to the cloud for further processing.</div><div>Experimental results demonstrate that CAOF achieves an energy consumption of 0.0011 joules approximately, with an memory utilization of 3.46 MB calculated as and average over all the EC2 machines. The framework execution time, averaging 4.07 s on edge, 5.41 s on cloud, and only 0.56 s when leveraging EC2 instances specifically, including an 80.4% accuracy in multi-class classification tasks. The CAOF systematically selects the most suitable alternatives for each offloading scenario to optimize efficiency in terms of time, memory, CPU, and energy consumption. The proposed smart gateway framework utilizes a hybrid approach to make optimal offloading decisions by considering contextual data. The research concludes with the design and development of an edge or fog-based framework that uses smart computing to make decisions using machine learning reasoning. The proposed framework architecture incorporates feature selection, classification, and hybrid logistic regression-based learning for the most effective offloading solution.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"123 ","pages":"Article 110292"},"PeriodicalIF":4.0,"publicationDate":"2025-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143714543","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}