Computers & Electrical Engineering最新文献

{"title":"A patient-centric blockchain-assisted health information exchange framework with access control","authors":"Kausthav Pratim Kalita,&nbsp;Debojit Boro,&nbsp;Dhruba Kumar Bhattacharyya","doi":"10.1016/j.compeleceng.2025.110308","DOIUrl":"10.1016/j.compeleceng.2025.110308","url":null,"abstract":"<div><div>Electronic healthcare records have become integral to delivering patient-centric care, as they enable enhanced communication, facilitate collaborative decision-making, and improve care coordination. However, the deployment of these records necessitates meticulous management to ensure reliable access control mechanisms and comprehensive data traceability. Blockchain technology has the potential to significantly enhance patient-centric healthcare by enforcing regulatory constraints through the implementation of smart contracts. This study proposes a blockchain-powered framework, designated as PaSCon, that is specifically designed for governing sensitive medical data not suitable for exposure to external repositories. The proposed framework utilizes two interactive smart contracts to monitor and record any instances of data-sharing activities among healthcare professionals participating in the patient’s care, facilitated through a key-sharing mechanism. The interaction between the smart contracts allows secure exchange of information among trusted entities while preserving data ownership rights. The framework is implemented and evaluated in an Ethereum-based environment utilizing Solidity-based smart contracts. To assess the framework’s performance under various conditions, three prominent public cryptographic algorithms – ECC, ECIES, and RSA – were examined during the experiments. The results highlight the execution time and associated costs of the specific activities permitted within PaSCon when applying each of these three algorithms individually.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110308"},"PeriodicalIF":4.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839757","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 new parallel PQDs classification method based on an optimized NLM and an improved DRSN-TCN model","authors":"Zhicheng Ren ,&nbsp;Dapeng Liu ,&nbsp;Yong Liu ,&nbsp;Shuqing Zhang ,&nbsp;Hao Hu ,&nbsp;Anqi Jiang","doi":"10.1016/j.compeleceng.2025.110326","DOIUrl":"10.1016/j.compeleceng.2025.110326","url":null,"abstract":"<div><div>Considering the multidimensional characteristics of single and composite Power Quality Disturbances (PQDs), this article proposes a new parallel PQDs classification method based on an optimized NLM and an improved DRSN-TCN model. The method effectively solves the problems of high computational complexity, overfitting, and gradient explosion in existing serial method. Meanwhile, it could restrain the interference of noise on the inherent characteristics of PQDs in actual power grids, effectively extracts PQDs features, and thus improves classification accuracy, with strong noise robustness and generalization ability. Firstly, an optimized non-local means (NLM) denoising method is employed to process the noisy PQDs signals. The Greater cane rat algorithm (GCRA) is utilized to adaptively determine the optimal parameters for NLM. By estimating and performing weighted averaging for each sample point in the noisy signal, the method effectively preserves signal detail features, thereby achieving an accurate reconstruction of the original signal. Secondly, to overcome the poor anti-noise capability of the Deep Residual Shrinkage Network (DRSN) model, a new threshold function is proposed to replace the original soft threshold function, enhancing its anti-noise interference capability; to address the issue of the Temporal Convolutional Network (TCN) model's complex structure leading to prolonged training times, a reverse TCN structure is proposed. This structure decreases the receptive field layer by layer as the network depth increases, reducing training parameters and improving training efficiency. Finally, the high-dimensional PQDs features extracted from DRSN and TCN are fed into a feature fusion module for classification. To verify the effectiveness of this method, a parallel classification model is built based on the PyTorch framework, and simulation and comparative experiments on single and composite PQDs are conducted. The results show that the proposed method can effectively classify PQDs, under no noise, 40 dB, 30 dB, and 20 dB noise conditions, the classification accuracies for 16 types of single and composite PQDs are 99.34 %, 98.89 %, 98.12 %, and 96.28 %, respectively, demonstrating the model's strong noise robustness and generalization capability. To further validate the superiority of this method, comparative experiments were conducted among the proposed model and other models such as EWT+SVM, CNN, CNN-LSTM, Transformer, TCN, and DRSN. The results indicate that the improved DRSN-TCN model converges smoothly without oscillation and achieves better classification accuracy. Therefore, the proposed model demonstrates certain advantages over other models.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110326"},"PeriodicalIF":4.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843014","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":"Hybrid twin attention based convolutional stacked sparse autoencoder for classification of defected weld images","authors":"T. Srikanth ,&nbsp;M. Radhika Mani","doi":"10.1016/j.compeleceng.2025.110328","DOIUrl":"10.1016/j.compeleceng.2025.110328","url":null,"abstract":"<div><div>Welding is an essential joining process in industrial manufacturing. Many deep learning models are introduced to detect welding errors. However, with a shortage of training data samples, most existing models take longer and are less accurate because of limited learning ability and increased computational complexity problems. To address issues with existing methods, this research presents an efficient deep learning model for accurately classifying multiple welding flaws in minimal time. The most crucial steps that are carried out in the proposed welding error detection framework are pre-processing feature extraction and classification. Initially, the input images are collected from the welding defects dataset. To increase the quality of the obtained raw input images, different pre-processing techniques, such as image scaling, image denoising, and image enhancement are applied. After pre-processing, feature extraction is carried out with the help of the discrete wavelet transform (DWT) and the grey-level run length matrix (GLRLM), which helps to reduce the complexity problems. Finally, a Hybrid twin attention-based Convolutional Stacked Sparse Autoencoder (HAT_CS2E) is used to classify multiple weld defects accurately from the given images. The proposed model combines a convolutional neural network (CNN) and a stacked sparse autoencoder network. The integration of these networks helps to learn more spatial and local features that generate high quality feature maps and produce accurate classification outcomes. For simulation, the Welding Defects Dataset is utilized, and several existing approaches are compared with the proposed model in terms of accuracy, precision, recall, F1-score, and calculation time. The proposed model attained an accuracy of 97.01 %, precision of 96.98 %, recall of 95.76 %, F1-score of 95.12 %, and computation time of 0.021 s by altering frame level welding defect recognition. Also, the proposed model achieved superior results in pixel level welding defect detection process compared with existing studies in terms of accuracy at 99.23 %, recall value at 80.3 %, precision value at 68.78 %, and F1-score at 75.91 %.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110328"},"PeriodicalIF":4.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143839717","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":"The role of machine and deep learning in modern intrusion detection systems: A comprehensive review","authors":"Uday Chandra Akuthota,&nbsp;Lava Bhargava","doi":"10.1016/j.compeleceng.2025.110318","DOIUrl":"10.1016/j.compeleceng.2025.110318","url":null,"abstract":"<div><div>Network intrusion benchmark datasets serve an essential role in improving the advancement of research in cybersecurity because they provide standardized resources for assessing the effectiveness of intrusion detection systems and associated cybersecurity solutions. This review article provides a detailed examination of the cutting-edge in network intrusion benchmark datasets, concentrating on their features, content, utilization, and implications for cybersecurity research. We systematically review a wide variety of benchmark datasets that are often utilized in the industry, which include the DARPA, KDDcup99, NSL-KDD, Kyoto, UNSW-NB15, and CICIDS-17 datasets. We analyzed each dataset, including its performance based on machine learning and deep learning models, by critically synthesizing existing literature. Additionally, we discussed the common challenges existing in intrusion detection systems. Furthermore, we provided a description of various machine learning and deep learning algorithms used for intrusion detection applications. This study aims to assist researchers in choosing suitable datasets and techniques for evaluating and benchmarking intrusion detection systems, ultimately advancing cybersecurity research and the development of reliable and efficient cybersecurity solutions.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110318"},"PeriodicalIF":4.0,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143843013","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 non-isolated buck-boost converter based on SEPIC topology for renewable energy applications","authors":"Priyanshu Kumar,&nbsp;Moina Ajmeri","doi":"10.1016/j.compeleceng.2025.110325","DOIUrl":"10.1016/j.compeleceng.2025.110325","url":null,"abstract":"<div><div>In this manuscript, a novel non-isolated buck-boost converter topology based on SEPIC converter and switched capacitor circuit is proposed to meet the demand for high voltage gain in renewable energy applications. The operation of the proposed converter is investigated using a single-duty ratio in continuous conduction mode. Voltage gain of the proposed converter is found to be double of the conventional SEPIC’s gain. Additionally, the voltage stresses on the switches and diodes of the proposed circuit are lower than or comparable to other related existing converters. Further to prove the circuit’s feasibility, the performance of the proposed circuit is examined both in the boost and buck modes under the dynamic changes in duty ratios and load resistances. A prototype of the developed converter with a power rating of 200 W is designed and tested in the laboratory. With the proposed converter a maximum theoretical efficiency of 93.46% and the maximum experimental efficiency of approximately 91.81% are obtained.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110325"},"PeriodicalIF":4.0,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143833826","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":"SAOA: Skill archimedes optimization algorithm based privacy enhancement for blockchain storage optimization in medical IoT environment","authors":"Kavita R․ Shelke ,&nbsp;Subhash K․ Shinde","doi":"10.1016/j.compeleceng.2025.110270","DOIUrl":"10.1016/j.compeleceng.2025.110270","url":null,"abstract":"<div><div>This paper develops an efficient technique for storage optimization in medical IoT based on privacy enhancement. Initially, in the medical IoT devices, the transactions are generated and sent them to the base station (BS), where the data sensing is performed and the IoT devices collect the data. After that, the data from BS is transferred to peers in the blockchain (BC). Before storing the data in the cloud, adaptive segmentation is performed using a fuzzy clustering-based time series approach. Subsequently, during the encryption process, the data blocks are encrypted with a privacy protection model employing the Advanced Encryption Standard (AES) algorithm. The Deep Kronecker Network (DKN) is utilized for key generation. Finally, the blocks are selected optimally for each peer by using the Skill Archimedes Optimization Algorithm (SAOA). Here, SAOA is the combination of the Skill Optimization Algorithm (SOA) and Archimedes Optimization Algorithm (AOA). The performance of the developed SAOA model is evaluated based on metrics, such as transmission time, query probability, storage cost, local space occupancy, sensitivity level, trust level, and transmission time and achieved maximum values of 0.392, 19.672, 51.7 MB, 0.925, 0.866 and 0.610 s, respectively.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110270"},"PeriodicalIF":4.0,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143828573","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":"Energy management of networked energy hub considering risk assessment and cyber security: A deep reinforcement learning approach","authors":"Abbas Dalimi-Asl,&nbsp;Shahram Javadi,&nbsp;Amir Ahmarinejad,&nbsp;Payam Rabbanifar","doi":"10.1016/j.compeleceng.2025.110262","DOIUrl":"10.1016/j.compeleceng.2025.110262","url":null,"abstract":"<div><div>This research presents a comprehensive analysis of data-driven energy management within the framework of a networked energy hub (NEH), focusing on three objective functions that account for various uncertainties, alongside risk assessment and cybersecurity considerations. The primary objectives of the initial phase encompass the optimal operation of the NEH, which entails maximizing subscriber engagement through integrated demand response initiatives that respond to wholesale and retail market price signals, thereby altering subscriber consumption behaviors, and enhancing the operational efficiency of energy storage systems (ESS) to mitigate operational expenses. The subsequent phase aims to minimize environmental pollution costs, while the final phase is dedicated to evaluating risk costs and conducting a cybersecurity assessment. The model put forward, which utilizes the K-means clustering methodology alongside a probabilistic framework for power generation sources, ESS units, and loads, is articulated through the application of time sequence matrices, auto-correlation matrices, and cross-correlation matrices. This model is constructed using the deep reinforcement learning algorithm, with the soft actor-critic architecture employed to ascertain optimal control strategies. This investigation has been assessed across three scenarios. The first scenario shows the optimal operation of NEH, the second scenario is the same as the first scenario plus IDR performance, and the third scenario is the same as the second scenario with the optimal participation of ESSs. The outcomes show a 15.14 % reduction in total operation costs in the second scenario and 19.49 % in the third scenario.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110262"},"PeriodicalIF":4.0,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143823900","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":"Fractional-order gradient based local binary pattern for texture classification","authors":"Nuh Alpaslan ,&nbsp;Kazım Hanbay","doi":"10.1016/j.compeleceng.2025.110316","DOIUrl":"10.1016/j.compeleceng.2025.110316","url":null,"abstract":"<div><div>The local binary patterns method plays an efficient role in texture classification and feature extraction. These approaches extract textural features by using the neighboring pixel values. The single or joint histogram of the texture image is constructed from the LBP features obtained from local relationships. In this study, a method of utilizing fractional derivative information effectively has been proposed for classifying color texture images. The magnitude of the fractional horizontal and vertical derivatives obtained with Gaussian derivative filters are integrated into the ACS-LBP method. The magnitude information of the fractional derivatives of local texture patterns has been modeled according to the relationship between neighboring pixels. The computed derivative information has been incorporated into the ACS-LBP model to effectively encode the local pixel relationship. In order to maintain, these fractional-order edge and texture transition detection operators provide both high robustness and continue to detect small textural details. To accomplish these capabilities, the fractional-order parameter is tuned to target particular pixel transition frequencies. This gives the proposed LBP method greater latitude in selecting the fractional-order mask. An additional degree of freedom in designing various masks is provided by the fractional-order parameter. The developed model has been evaluated on widely used texture databases. It also has been compared with existing LBP and deep learning models in terms of different performance metrics. The proposed method has shown significant advantages over up to date methods in both classification accuracy and execution time.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110316"},"PeriodicalIF":4.0,"publicationDate":"2025-04-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143820744","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":"Facial expression recognition in the wild: A new Adaptive Attention-Modulated Contextual Spatial Information network","authors":"Xue Li ,&nbsp;Chunhua Zhu ,&nbsp;Shuzhi Yang","doi":"10.1016/j.compeleceng.2025.110258","DOIUrl":"10.1016/j.compeleceng.2025.110258","url":null,"abstract":"<div><div>Facial expression recognition (FER) is an important and widely applied task. This paper proposes an Adaptive Attention-modulated Contextual Spatial Information (Ad-ACSI) model to improve FER in uncontrolled environments. The proposed Ad-ACSI model incorporates an Attention-modulated Contextual Spatial Information Network (ACSI-Net), a joint loss, and an adaptive attention modulator (AAM). The ACSI-Net, built on ResNet with contextual convolution (CoConv) and coordinated attention (CA), effectively captures global and local contextual features. The adaptive attention modulator (AAM) refines the features and generates dynamic weights for the center loss. The cross-entropy (CE) loss is refined into an equilibrium loss and combined with a sparse center loss to improve inter-class discrimination and intra-class clustering. Experiments on the RAF-DB and AffectNet datasets show that the proposed method achieves results comparable to state-of-the-art methods of FER in the wild, with it promising for integration into popular architectures.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110258"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817400","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":"Active implementation of Chua’s diode employing MOSFET","authors":"Vivek Bhatt,&nbsp;Ashish Ranjan","doi":"10.1016/j.compeleceng.2025.110306","DOIUrl":"10.1016/j.compeleceng.2025.110306","url":null,"abstract":"<div><div>This research work contributes a Chua’s circuit design using only two Metal-Oxide- Semiconductor-Field-Effect-Transistor (MOSFET) based Chua’s diode. This design enables the following advantages like: <span><math><mrow><mo>(</mo><mi>i</mi><mo>)</mo></mrow></math></span> Use of a minimum number of transistors, <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> Absence of passive components in Chua’s diode, <span><math><mrow><mo>(</mo><mi>i</mi><mi>i</mi><mi>i</mi><mo>)</mo></mrow></math></span> High packaging density with 108.9 <span><math><mi>μ</mi></math></span>m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> chip area, <span><math><mrow><mo>(</mo><mi>i</mi><mi>v</mi><mo>)</mo></mrow></math></span> Suitable for the generation of all fundamental bifurcation sequences. The workability of the circuit is verified using Cadence’s Virtuoso tool with gpdk 180 nm technology parameters, along with the process, biasing (bias current), and temperature variation analysis. Moreover, an experimental validation is performed using commercially available N-MOSFET (2N7000). The proposed design is suitable for Chaotic-Amplitude-Shift-Keying (CASK) transmission for secure communication. At end, a comparative analysis is done with the available Chua’s circuit.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"124 ","pages":"Article 110306"},"PeriodicalIF":4.0,"publicationDate":"2025-04-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817406","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}