Pub Date : 2026-01-07DOI: 10.1016/j.compeleceng.2026.110957
Saoueb Kerdoudi , Larbi Guezouli , Tahar Dilekh
Detecting arrhythmias via electrocardiograms (ECGs) is vital for healthcare. While deep learning has advanced classification, capturing critical patterns in complex data remains challenging. We propose Res_Bi-LSTM_MHA, a novel model integrating a multi-head self-attention (MHA) mechanism to selectively focus on relevant signal segments. This enhances the capture of subtle features often missed by conventional methods. By combining Residual Networks (ResNet) for robust feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies, our approach significantly improves accuracy. We evaluated the model at subject and record levels using the China Physiological Signal Challenge (CPSC 2018), St. Petersburg Institute of Cardiological Technics (INCART), and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) databases. The model achieved an F1 score of 98.01% and 99.42% accuracy on the MIT-BIH dataset. Our results demonstrate that effectively utilizing attention mechanisms offers a substantial improvement in arrhythmia classification.
{"title":"Enhanced ECG arrhythmia detection with deep learning and multi-head attention mechanism","authors":"Saoueb Kerdoudi , Larbi Guezouli , Tahar Dilekh","doi":"10.1016/j.compeleceng.2026.110957","DOIUrl":"10.1016/j.compeleceng.2026.110957","url":null,"abstract":"<div><div>Detecting arrhythmias via electrocardiograms (ECGs) is vital for healthcare. While deep learning has advanced classification, capturing critical patterns in complex data remains challenging. We propose Res_Bi-LSTM_MHA, a novel model integrating a multi-head self-attention (MHA) mechanism to selectively focus on relevant signal segments. This enhances the capture of subtle features often missed by conventional methods. By combining Residual Networks (ResNet) for robust feature extraction with Bidirectional Long Short-Term Memory (Bi-LSTM) for temporal dependencies, our approach significantly improves accuracy. We evaluated the model at subject and record levels using the China Physiological Signal Challenge (CPSC 2018), St. Petersburg Institute of Cardiological Technics (INCART), and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) databases. The model achieved an F1 score of 98.01% and 99.42% accuracy on the MIT-BIH dataset. Our results demonstrate that effectively utilizing attention mechanisms offers a substantial improvement in arrhythmia classification.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110957"},"PeriodicalIF":4.9,"publicationDate":"2026-01-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927375","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}
Pub Date : 2026-01-06DOI: 10.1016/j.compeleceng.2025.110928
Katroth Kalyan Singh, Kirubakaran Annamalai
This article proposes a single-phase transformerless inverter for grid-tied PV installations. At the output stage, the proposed inverter can produce five levels of voltage. It features two electrolytic switching capacitors (SCs), six power switches, and two power diodes. This architecture is lighter and less expensive due to the usage of fewer power electronic components. Because the negative DC line of the suggested inverter is directly connected to the grid neutral in PV applications, leakage current is completely minimized. Another advantage of this design is that it may easily double the output voltage without the need for a transformer or inductor. Self-balancing is achieved by symmetrically charging and discharging the SCs in parallel and in series with the input voltage over time. Therefore, a complex control technique to balance the SCs is no longer necessary with the proposed inverter. The design specifications of the proposed inverter are provided. To illustrate the benefits of the proposed inverter, including the reduction of total standing voltage and cost function, a quantitative comparison analysis with similar five-level topologies is also presented. An experimental prototype of a 1 kW grid-tied system is used to validate the topology and demonstrate the capabilities of the proposed inverter with a closed-loop PR controller. Moreover, the system dynamics are tested under different loading conditions and input voltage variations.
{"title":"Single-phase switched-capacitor based common ground five-level inverter for grid-tied PV systems with double gain","authors":"Katroth Kalyan Singh, Kirubakaran Annamalai","doi":"10.1016/j.compeleceng.2025.110928","DOIUrl":"10.1016/j.compeleceng.2025.110928","url":null,"abstract":"<div><div>This article proposes a single-phase transformerless inverter for grid-tied PV installations. At the output stage, the proposed inverter can produce five levels of voltage. It features two electrolytic switching capacitors (SCs), six power switches, and two power diodes. This architecture is lighter and less expensive due to the usage of fewer power electronic components. Because the negative DC line of the suggested inverter is directly connected to the grid neutral in PV applications, leakage current is completely minimized. Another advantage of this design is that it may easily double the output voltage without the need for a transformer or inductor. Self-balancing is achieved by symmetrically charging and discharging the SCs in parallel and in series with the input voltage over time. Therefore, a complex control technique to balance the SCs is no longer necessary with the proposed inverter. The design specifications of the proposed inverter are provided. To illustrate the benefits of the proposed inverter, including the reduction of total standing voltage and cost function, a quantitative comparison analysis with similar five-level topologies is also presented. An experimental prototype of a 1 kW grid-tied system is used to validate the topology and demonstrate the capabilities of the proposed inverter with a closed-loop PR controller. Moreover, the system dynamics are tested under different loading conditions and input voltage variations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110928"},"PeriodicalIF":4.9,"publicationDate":"2026-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927369","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}
Self-supervised learning (SSL) models are increasingly used in speech processing tasks, where they provide powerful pretrained representations of speech. Most existing methods utilize these models by either fine-tuning them on domain-specific data or using their output representations as input features in conventional ASR systems. However, the relationship between SSL layer representations and the severity level of dysarthric speech remains poorly understood, despite the potential for different layers to capture features that vary in relevance across severity levels. Furthermore, the high dimensionality of these representations, often reaching up to 1024 dimensions, imposes a heavy computational load, highlighting the need for optimized feature representations in downstream ASR and keyword spotting (KWS) tasks. This study proposes a severity-independent approach for dysarthric speech processing using SSL features, investigating three state-of-the-art pretrained models: Wav2Vec2, HuBERT, and Data2Vec. We propose: (1) selecting SSL layers based on severity level to extract the most useful features; (2) a Kaldi-based ASR system, that uses an autoencoder to reduce the size of SSL features; and (3) validating the proposed SSL feature optimization in a KWS task. We evaluate the proposed method using a DNN–HMM model in Kaldi on two standard dysarthric speech datasets: TORGO and UAspeech. Our approach shows that selecting severity-specific SSL layers, combined with autoencoder (AE)-based feature optimization, leads to significant improvements over both zero-shot and fine-tuned SSL baselines. On TORGO, our method achieved a WER of 23.12%, outperforming zero-shot (60.35%) and fine-tuned SSL model (40.48%). On UAspeech, it reached 50.33% WER, surpassing both the fine-tuned (51.04%) and MFCC-based systems (58.67%). Layer-wise analysis revealed consistent trends: lower layers were more effective for very high-severity speech, while mid-to-upper layers performed better for low/medium-severity cases. Further, in the KWS task, later SSL layers showed the best performance, with our proposed system outperforming the MFCC baseline. These findings highlight the generalization of our proposed method, which combines layer-specific selection and autoencoder-based optimization of SSL features, for dysarthric speech processing tasks.
{"title":"Role of SSL models: Finetuning and feature optimization for dysarthric speech recognition and keyword spotting","authors":"Paban Sapkota, Hemant Kumar Kathania, Subham Kutum","doi":"10.1016/j.compeleceng.2025.110921","DOIUrl":"10.1016/j.compeleceng.2025.110921","url":null,"abstract":"<div><div>Self-supervised learning (SSL) models are increasingly used in speech processing tasks, where they provide powerful pretrained representations of speech. Most existing methods utilize these models by either fine-tuning them on domain-specific data or using their output representations as input features in conventional ASR systems. However, the relationship between SSL layer representations and the severity level of dysarthric speech remains poorly understood, despite the potential for different layers to capture features that vary in relevance across severity levels. Furthermore, the high dimensionality of these representations, often reaching up to 1024 dimensions, imposes a heavy computational load, highlighting the need for optimized feature representations in downstream ASR and keyword spotting (KWS) tasks. This study proposes a severity-independent approach for dysarthric speech processing using SSL features, investigating three state-of-the-art pretrained models: Wav2Vec2, HuBERT, and Data2Vec. We propose: (1) selecting SSL layers based on severity level to extract the most useful features; (2) a Kaldi-based ASR system, that uses an autoencoder to reduce the size of SSL features; and (3) validating the proposed SSL feature optimization in a KWS task. We evaluate the proposed method using a DNN–HMM model in Kaldi on two standard dysarthric speech datasets: TORGO and UAspeech. Our approach shows that selecting severity-specific SSL layers, combined with autoencoder (AE)-based feature optimization, leads to significant improvements over both zero-shot and fine-tuned SSL baselines. On TORGO, our method achieved a WER of 23.12%, outperforming zero-shot (60.35%) and fine-tuned SSL model (40.48%). On UAspeech, it reached 50.33% WER, surpassing both the fine-tuned (51.04%) and MFCC-based systems (58.67%). Layer-wise analysis revealed consistent trends: lower layers were more effective for very high-severity speech, while mid-to-upper layers performed better for low/medium-severity cases. Further, in the KWS task, later SSL layers showed the best performance, with our proposed system outperforming the MFCC baseline. These findings highlight the generalization of our proposed method, which combines layer-specific selection and autoencoder-based optimization of SSL features, for dysarthric speech processing tasks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110921"},"PeriodicalIF":4.9,"publicationDate":"2026-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145927370","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}
Pub Date : 2026-01-02DOI: 10.1016/j.compeleceng.2025.110926
Muhammad Farhan Khan , Sile Hu , Yuan Gao , Yu Guo , Yuan Wang , Maryam Saeed , Yucan Zhao , Jiaqiang Yang
Accurate and adaptive multi-horizon electricity load forecasting is essential for secure operation of modern power systems and for the integration of variable renewable generation. This paper proposes DWRNet, a Dynamic Weighted Residual Network that combines statistical decomposition, deep residual learning, and reinforcement learning (RL)-based adaptive fusion. A Fruit Fly Optimization-tuned Holt-Winters model first extracts the dominant seasonal-trend component, while a Long Short-Term Memory (LSTM) network learns the nonlinear residual structure. A continuous-action policy-gradient controller then produces horizon-dependent convex weights that balance the statistical and neural forecasts, enabling the ensemble to adapt to changing load regimes while remaining lightweight enough for EMS/SCADA deployment. DWRNet is evaluated on four years of hourly load data from two structurally different power systems (Inner Mongolia, China and Germany) over 24 h, 168 h, and 720 h horizons, and compared against strong baselines including SVR, LSTM, GRU, CNN, CNN-LSTM, and recent Transformer-based models (Informer, FEDformer) under a common rolling-origin protocol. Across both regions and all horizons, DWRNet consistently achieves the best or near-best MAE, RMSE, sMAPE and R² values, with particularly notable gains on weekly and monthly forecasts. Robustness is assessed through cross-validation with varying training fractions, bootstrap-based confidence intervals, ablation studies, and residual diagnostics, which collectively indicate that the improvements are stable and not attributable to overfitting. A complexity analysis and runtime benchmarks further show that the RL-based blending stage adds only modest offline training cost and negligible inference overhead. DWRNet offers a practical and scalable solution for real-time energy forecasting, with strong potential for use in energy management systems, dispatch operations, and smart grid planning.
{"title":"A hybrid reinforcement learning framework for adaptive multi-horizon electricity load forecasting: The DWRNet approach","authors":"Muhammad Farhan Khan , Sile Hu , Yuan Gao , Yu Guo , Yuan Wang , Maryam Saeed , Yucan Zhao , Jiaqiang Yang","doi":"10.1016/j.compeleceng.2025.110926","DOIUrl":"10.1016/j.compeleceng.2025.110926","url":null,"abstract":"<div><div>Accurate and adaptive multi-horizon electricity load forecasting is essential for secure operation of modern power systems and for the integration of variable renewable generation. This paper proposes DWRNet, a Dynamic Weighted Residual Network that combines statistical decomposition, deep residual learning, and reinforcement learning (RL)-based adaptive fusion. A Fruit Fly Optimization-tuned Holt-Winters model first extracts the dominant seasonal-trend component, while a Long Short-Term Memory (LSTM) network learns the nonlinear residual structure. A continuous-action policy-gradient controller then produces horizon-dependent convex weights that balance the statistical and neural forecasts, enabling the ensemble to adapt to changing load regimes while remaining lightweight enough for EMS/SCADA deployment. DWRNet is evaluated on four years of hourly load data from two structurally different power systems (Inner Mongolia, China and Germany) over 24 h, 168 h, and 720 h horizons, and compared against strong baselines including SVR, LSTM, GRU, CNN, CNN-LSTM, and recent Transformer-based models (Informer, FEDformer) under a common rolling-origin protocol. Across both regions and all horizons, DWRNet consistently achieves the best or near-best MAE, RMSE, sMAPE and R² values, with particularly notable gains on weekly and monthly forecasts. Robustness is assessed through cross-validation with varying training fractions, bootstrap-based confidence intervals, ablation studies, and residual diagnostics, which collectively indicate that the improvements are stable and not attributable to overfitting. A complexity analysis and runtime benchmarks further show that the RL-based blending stage adds only modest offline training cost and negligible inference overhead. DWRNet offers a practical and scalable solution for real-time energy forecasting, with strong potential for use in energy management systems, dispatch operations, and smart grid planning.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110926"},"PeriodicalIF":4.9,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885937","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}
Pub Date : 2025-12-31DOI: 10.1016/j.compeleceng.2025.110927
Alankrita, Avadh Pati, Nabanita Adhikary
This paper presents a Multi-Agent Deep Reinforcement learning (MARL) framework for distributed energy management in a DC Microgrid (DC MG) comprising Photovoltaic, Wind Turbine, and Energy Storage Systems, with the primary objective of maintaining DC link voltage stability. The decentralized control architecture employs local voltage measurements as agent state inputs and uses Deep Q-Networks to estimate individual action-value functions. Three algorithmic approaches are investigated: Independent DQN (IDQN), Value Decomposition Networks (VDN), and QMIX, each evaluated with Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) architectures. The custom reward function integrates voltage deviation penalties, power balance constraints, and battery cycling costs to achieve high renewable penetration and efficient storage dispatch. Case studies validate framework performance under diverse conditions, including variable generation and demand, network delays, false data injection attacks, ground faults, and plug-and-play topology changes. Results reveal scenario-dependent performance characteristics: RNN based VDN achieves superior voltage regulation under normal operation, IDQN demonstrates robust reward optimization during cyber-attacks, while RNN based QMIX excels in adversarial scenarios during false data injection and fastest transient response during plug-and-play events. Computational analysis identifies architecture-dependent scaling trade-offs, with QMIX requiring more compute requirements and centralized coordination overhead, while IDQN's distributed architecture and lower resource consumption suggest better scalability for multi-agent expansion. The framework demonstrates the practical viability of MARL-based distributed control for resilient energy management in DC MG with scenario-appropriate algorithm selection.
{"title":"Multiagent deep reinforcement learning-based distributed control strategy for energy management in DC Microgrid","authors":"Alankrita, Avadh Pati, Nabanita Adhikary","doi":"10.1016/j.compeleceng.2025.110927","DOIUrl":"10.1016/j.compeleceng.2025.110927","url":null,"abstract":"<div><div>This paper presents a Multi-Agent Deep Reinforcement learning (MARL) framework for distributed energy management in a DC Microgrid (DC MG) comprising Photovoltaic, Wind Turbine, and Energy Storage Systems, with the primary objective of maintaining DC link voltage stability. The decentralized control architecture employs local voltage measurements as agent state inputs and uses Deep Q-Networks to estimate individual action-value functions. Three algorithmic approaches are investigated: Independent DQN (IDQN), Value Decomposition Networks (VDN), and QMIX, each evaluated with Multilayer Perceptron (MLP) and Recurrent Neural Network (RNN) architectures. The custom reward function integrates voltage deviation penalties, power balance constraints, and battery cycling costs to achieve high renewable penetration and efficient storage dispatch. Case studies validate framework performance under diverse conditions, including variable generation and demand, network delays, false data injection attacks, ground faults, and plug-and-play topology changes. Results reveal scenario-dependent performance characteristics: RNN based VDN achieves superior voltage regulation under normal operation, IDQN demonstrates robust reward optimization during cyber-attacks, while RNN based QMIX excels in adversarial scenarios during false data injection and fastest transient response during plug-and-play events. Computational analysis identifies architecture-dependent scaling trade-offs, with QMIX requiring more compute requirements and centralized coordination overhead, while IDQN's distributed architecture and lower resource consumption suggest better scalability for multi-agent expansion. The framework demonstrates the practical viability of MARL-based distributed control for resilient energy management in DC MG with scenario-appropriate algorithm selection.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110927"},"PeriodicalIF":4.9,"publicationDate":"2025-12-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885936","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}
Pub Date : 2025-12-29DOI: 10.1016/j.compeleceng.2025.110922
Mohamed Lahdeb , Ali Hennache , Bachir Bentouati , M.M.R. Ahmed , Ragab A. El-Sehiemy , M. Elzalik
The optimal power flow (OPF) problem is a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.
{"title":"Hybrid Brown-Bear and Hippopotamus Optimization with Quasi-Opposition-Based Learning for Optimal Power Flow with Renewable Energy Integration","authors":"Mohamed Lahdeb , Ali Hennache , Bachir Bentouati , M.M.R. Ahmed , Ragab A. El-Sehiemy , M. Elzalik","doi":"10.1016/j.compeleceng.2025.110922","DOIUrl":"10.1016/j.compeleceng.2025.110922","url":null,"abstract":"<div><div>The optimal power flow (OPF) problem <strong>is</strong> a highly nonlinear and complex multi-dimension optimization problem, especially with the increased penetration of uncertain renewable energies (RES). In this line, this paper presents the Hybrid Brown-Bear and Hippopotamus Optimization Algorithms with Quasi-Opposition-Based Learning (HBOA-QOBL) to enhance multi-dimension OPF solution. The algorithm combines the strengths of Brown-Bear optimizer, which excels in exploration and adaptive search mechanisms, and the Hippopotamus optimizer, known for its social behavior modeling and localized search strategies. By integrating QOBL, the HBOA-QOBL improves exploration through the generation of quasi-opposite solutions, allowing for a wider search of the solution space and reducing the risk of premature convergence. Adaptive search mechanisms embedded in HBOA-QOBL enhance exploitation by dynamically adjusting search behaviors during iterative power dispatch tuning, enabling improved fine-tuning of generation schedules and voltage profiles. The effectiveness of the proposed method is evaluated on the IEEE 30-bus, 57-bus, and 118-bus test systems for multiple dimension OPF objectives, including fuel cost minimization, emission reduction, power loss reduction, voltage deviation minimization, reactive power loss reduction and the voltage stability indicator (L-index). Simulation results indicate faster convergence compared to conventional techniques, achieving near-optimal solutions within 200 iterations, with a standard deviation of 63.8%, demonstrating superior technical and economic performance relative to previous research. Key convergence parameters such as population size, maximum iterations, and learning factor are explicitly tuned to enhance both exploration and exploitation. Simulation results confirm that HBOA-QOBL outperforms conventional optimization techniques in terms of solution quality, convergence speed, and stability, establishing significant improvement in the technical and economic issues.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110922"},"PeriodicalIF":4.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885934","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}
Pub Date : 2025-12-29DOI: 10.1016/j.compeleceng.2025.110918
Ande Bhargav, Mohamed Asan Basiri M.
Reversible digital image watermarking methods are crucial for embedding authentication information in medical imaging, military communication, and etc. The reversible data hiding (RDH) techniques embed auxiliary data or necessitate separate transmission of location maps to recover the data. These practices reduce the imperceptibility of the stegano image and demand higher bandwidth. To overcome these limitations, this paper proposes histogram-based pixel sorting (HBPS) in Algorithm-I, which directly embeds data into the least significant bits (LSBs), improving the Peak Signal-to-Noise Ratio (PSNR) by 22.29%. The experimental results validate the superior visual quality of the recovered cover image with average PSNR exceeding 50 dB. Algorithms-II and III incorporate preprocessing of the cover image using Laplacian kernel and the proposed triplet linear pixel transformation (TLPT), respectively to preserve the visual integrity of the cover image. The observed PSNR and latency gains compared to existing methods are statistically significant at the 95% confidence level using t-tests with Bonferroni correction. The preprocessing technique in Algorithm-IV refines the pixel value search algorithm (PVSA) with a sharpening filter to reduce latency by 52.82%. The multi-core implementation of PVSA to reduce the latency is shown in Algorithm-V.
{"title":"Digital image watermarking using histogram based pixel sorting and pixel value search techniques","authors":"Ande Bhargav, Mohamed Asan Basiri M.","doi":"10.1016/j.compeleceng.2025.110918","DOIUrl":"10.1016/j.compeleceng.2025.110918","url":null,"abstract":"<div><div>Reversible digital image watermarking methods are crucial for embedding authentication information in medical imaging, military communication, and etc. The reversible data hiding (RDH) techniques embed auxiliary data or necessitate separate transmission of location maps to recover the data. These practices reduce the imperceptibility of the stegano image and demand higher bandwidth. To overcome these limitations, this paper proposes histogram-based pixel sorting (HBPS) in Algorithm-I, which directly embeds data into the least significant bits (LSBs), improving the Peak Signal-to-Noise Ratio (PSNR) by 22.29%. The experimental results validate the superior visual quality of the recovered cover image with average PSNR exceeding 50 dB. Algorithms-II and III incorporate preprocessing of the cover image using Laplacian kernel and the proposed triplet linear pixel transformation (TLPT), respectively to preserve the visual integrity of the cover image. The observed PSNR and latency gains compared to existing methods are statistically significant at the 95% confidence level using t-tests with Bonferroni correction. The preprocessing technique in Algorithm-IV refines the pixel value search algorithm (PVSA) with a sharpening filter to reduce latency by 52.82%. The multi-core implementation of PVSA to reduce the latency is shown in Algorithm-V.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110918"},"PeriodicalIF":4.9,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145885935","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}
Pub Date : 2025-12-27DOI: 10.1016/j.compeleceng.2025.110916
Barrister R , Ambeth Kumar V. D , Ashok Kumar V. D
Sign language is the primary means of communication for those who are hard of hearing or speaking. In daily lives, people rely on visual signals to express their thoughts and emotions because of deafness or being dumb. Most commonly, sign language is communicated through hand gestures and is analyzed in the present research, but it faces the problem of inaccurate detection of poses due to improper extraction of features. Also, the study and detection concerning Mizo sign language are very rarely seen in the literature. Hence, the proposed study presents a novel hybrid model that combines machine learning and deep learning to detect Mizo hand sign language. The Mizo hand sign language datasets are used in the first phase of the system evaluation process to assess its effectiveness. The next step involves pre-processing to remove extraneous background from photos. Next, a hybrid feature extraction is carried out using a depth-wise convolutional network (DCN) and a spatial-frequency multi-scale dilated transformer (SF-MSDT) in order to extract the significant features. The output of the hybrid feature extractor is fed independently over the feature fusion module to generate a single dimensional feature vector. In order to detect the Mizo sign language, classification is finally performed using three classifiers named support vector machine (SVM), random forest classifier, and Residual network (ResNet). The experimental analysis demonstrates the most feasible ResNet classifier with an accuracy of 98.23 %, precision of 92.36 %, recall of 88.52 %, and F1-score of 85.77 %. The proposed model using a ResNet classifier possesses 1.25 % improved accuracy when compared with recurrent networks and 4.3 % with convolutional networks.
{"title":"Mizo hand sign language detection using a multi-scale transformer-based hybrid feature extractor and fusion network","authors":"Barrister R , Ambeth Kumar V. D , Ashok Kumar V. D","doi":"10.1016/j.compeleceng.2025.110916","DOIUrl":"10.1016/j.compeleceng.2025.110916","url":null,"abstract":"<div><div>Sign language is the primary means of communication for those who are hard of hearing or speaking. In daily lives, people rely on visual signals to express their thoughts and emotions because of deafness or being dumb. Most commonly, sign language is communicated through hand gestures and is analyzed in the present research, but it faces the problem of inaccurate detection of poses due to improper extraction of features. Also, the study and detection concerning Mizo sign language are very rarely seen in the literature. Hence, the proposed study presents a novel hybrid model that combines machine learning and deep learning to detect Mizo hand sign language. The Mizo hand sign language datasets are used in the first phase of the system evaluation process to assess its effectiveness. The next step involves pre-processing to remove extraneous background from photos. Next, a hybrid feature extraction is carried out using a depth-wise convolutional network (DCN) and a spatial-frequency multi-scale dilated transformer (SF-MSDT) in order to extract the significant features. The output of the hybrid feature extractor is fed independently over the feature fusion module to generate a single dimensional feature vector. In order to detect the Mizo sign language, classification is finally performed using three classifiers named support vector machine (SVM), random forest classifier, and Residual network (ResNet). The experimental analysis demonstrates the most feasible ResNet classifier with an accuracy of 98.23 %, precision of 92.36 %, recall of 88.52 %, and F1-score of 85.77 %. The proposed model using a ResNet classifier possesses 1.25 % improved accuracy when compared with recurrent networks and 4.3 % with convolutional networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110916"},"PeriodicalIF":4.9,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842637","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}
Pub Date : 2025-12-27DOI: 10.1016/j.compeleceng.2025.110919
Himanshu Nandanwar , Rahul Katarya
The security and sustainability of Industrial Internet of Things (IIoT) systems are paramount to ensuring the safety of human lives during critical operations. Modern IIoT networks require robust security mechanisms encompassing safety, trust, privacy, reliability, and resilience to address the inadequacies of traditional security approaches, which are hindered by protocol incompatibilities, limited update capabilities, and outdated measures. These challenges are exacerbated in heterogeneous IoT environments, where intrusion detection systems (IDS) face significant obstacles in accuracy, scalability, and efficiency. This paper presents Alpha-Net, a unique and trustworthy Deep Learning (DL)-based IDS framework enhanced by a Quantum-Inspired Genetic Algorithm (QIGA) for optimized feature selection. By differentiating between benign and attack scenarios effectively, QIGA ensures superior feature representation, improving the model's transparency and reliability. The proposed Alpha-Net is evaluated on real-world IoT datasets, attaining an exceptional accuracy of 99.97 %, a true negative rate (TNR) of 99 %, and a recall of 99.94 %. Additionally, it achieves an accuracy of 99.75 % across ten classes, outperforming state-of-the-art techniques (SOTA) by a edge of 5 % to 15.93 %. Alpha-Net demonstrates remarkable efficiency in detecting and classifying botnet attacks in IIoT environments, showcasing its ability to address critical security challenges and establish itself as a dependable solution for anomaly detection in Industrial Internet of Things networks.
{"title":"Alpha-Net: A dependable and trustworthy deep learning framework for securing industrial internet of things networks against botnet attacks","authors":"Himanshu Nandanwar , Rahul Katarya","doi":"10.1016/j.compeleceng.2025.110919","DOIUrl":"10.1016/j.compeleceng.2025.110919","url":null,"abstract":"<div><div>The security and sustainability of Industrial Internet of Things (IIoT) systems are paramount to ensuring the safety of human lives during critical operations. Modern IIoT networks require robust security mechanisms encompassing safety, trust, privacy, reliability, and resilience to address the inadequacies of traditional security approaches, which are hindered by protocol incompatibilities, limited update capabilities, and outdated measures. These challenges are exacerbated in heterogeneous IoT environments, where intrusion detection systems (IDS) face significant obstacles in accuracy, scalability, and efficiency. This paper presents Alpha-Net, a unique and trustworthy Deep Learning (DL)-based IDS framework enhanced by a Quantum-Inspired Genetic Algorithm (QIGA) for optimized feature selection. By differentiating between benign and attack scenarios effectively, QIGA ensures superior feature representation, improving the model's transparency and reliability. The proposed Alpha-Net is evaluated on real-world IoT datasets, attaining an exceptional accuracy of 99.97 %, a true negative rate (TNR) of 99 %, and a recall of 99.94 %. Additionally, it achieves an accuracy of 99.75 % across ten classes, outperforming state-of-the-art techniques (SOTA) by a edge of 5 % to 15.93 %. Alpha-Net demonstrates remarkable efficiency in detecting and classifying botnet attacks in IIoT environments, showcasing its ability to address critical security challenges and establish itself as a dependable solution for anomaly detection in Industrial Internet of Things networks.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110919"},"PeriodicalIF":4.9,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842638","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}
Pub Date : 2025-12-26DOI: 10.1016/j.compeleceng.2025.110920
Akash Kumar Deep , G. Lloyds Raja , Gagan Deep Meena
Communication delays severely impair frequency regulation in cyber–physical power systems under false-data-injection attacks by inducing abrupt frequency oscillations that threaten grid stability. Existing mitigation strategies are largely scenario-specific, limiting their scalability and robustness. This paper proposes a robust Direct Synthesis-based Proportional–Integral–Derivative with Filter (DS-PIDF) controller that aligns desired and actual closed-loop dynamics. The single tuning parameter of DS-PIDF controller and the setpoint weighting factor are jointly optimized using a Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCFOADE) to minimize the Integral Time-weighted Absolute Error (ITAE). The proposed approach is validated under communication delay, load perturbations, hybrid cyberattacks, nonlinearities, renewable penetration and hybrid energy storage integration. The HCFOADE-tuned DS-PIDF achieves up to 52.11% and 52.02% faster settling in Areas 1 and 2, respectively, compared to the Proportional–Integral–Double-Derivative (PIDD2) controller, and 8.66–16.30% faster than the Indirect-Internal-Model-Control Proportional–Integral–Derivative (IIMC-PID). Robustness analysis confirms stable operation under ±30% parameter variations.
{"title":"Robust single-parameter frequency controller tuned with Artificial Intelligence-driven hybrid optimization for modern power systems amid cyber threats and latency","authors":"Akash Kumar Deep , G. Lloyds Raja , Gagan Deep Meena","doi":"10.1016/j.compeleceng.2025.110920","DOIUrl":"10.1016/j.compeleceng.2025.110920","url":null,"abstract":"<div><div>Communication delays severely impair frequency regulation in cyber–physical power systems under false-data-injection attacks by inducing abrupt frequency oscillations that threaten grid stability. Existing mitigation strategies are largely scenario-specific, limiting their scalability and robustness. This paper proposes a robust Direct Synthesis-based Proportional–Integral–Derivative with Filter (DS-PIDF) controller that aligns desired and actual closed-loop dynamics. The single tuning parameter of DS-PIDF controller and the setpoint weighting factor are jointly optimized using a Hybrid Crayfish Optimization Algorithm with Differential Evolution (HCFOADE) to minimize the Integral Time-weighted Absolute Error (ITAE). The proposed approach is validated under communication delay, load perturbations, hybrid cyberattacks, nonlinearities, renewable penetration and hybrid energy storage integration. The HCFOADE-tuned DS-PIDF achieves up to 52.11% and 52.02% faster settling in Areas 1 and 2, respectively, compared to the Proportional–Integral–Double-Derivative (PIDD2) controller, and 8.66–16.30% faster than the Indirect-Internal-Model-Control Proportional–Integral–Derivative (IIMC-PID). Robustness analysis confirms stable operation under ±30% parameter variations.</div></div>","PeriodicalId":50630,"journal":{"name":"Computers & Electrical Engineering","volume":"131 ","pages":"Article 110920"},"PeriodicalIF":4.9,"publicationDate":"2025-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145842707","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}
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