In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections. Accordingly, we start by generating reference embeddings using a graph neural network (GNN) trained on full graph knowledge. Afterward, we train the generative models, specifically an autoencoder and a generative adversarial network (GAN), which use only the initial node features to generate close and almost indistinguishable embeddings to those generated by the GNN. To this end, we use a customized loss function acting as a strong regularization for our models. It compels them to generate only embeddings with small error values from those generated by the fully fledged model. Using real-world graph datasets, we evaluate the quality of the generated embeddings for different similarity metrics such as the mean-squared error (MSE) and cosine similarity. We also assess their ability in reconstructing an initial graph and predicting the neighborhood of each newly added node. Results show the superiority of the proposed generative models over the conventional ones and that the proposed GAN model outperforms the proposed autoencoder with an efficiency in graph reconstruction exceeding 85% for different datasets.
{"title":"Deep Generative Models for Node Embedding and Neighborhood Prediction in Dynamic Graphs of Recommendation Systems","authors":"Mohamed Darghouthi;Aymen Hamrouni;Hakim Ghazzai;Lokman Sboui","doi":"10.1109/ICJECE.2025.3650740","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3650740","url":null,"abstract":"In this article, we develop generative models that generate embeddings for graph nodes while using only their initial features without any knowledge about their neighborhoods and connections. Accordingly, we start by generating reference embeddings using a graph neural network (GNN) trained on full graph knowledge. Afterward, we train the generative models, specifically an autoencoder and a generative adversarial network (GAN), which use only the initial node features to generate close and almost indistinguishable embeddings to those generated by the GNN. To this end, we use a customized loss function acting as a strong regularization for our models. It compels them to generate only embeddings with small error values from those generated by the fully fledged model. Using real-world graph datasets, we evaluate the quality of the generated embeddings for different similarity metrics such as the mean-squared error (MSE) and cosine similarity. We also assess their ability in reconstructing an initial graph and predicting the neighborhood of each newly added node. Results show the superiority of the proposed generative models over the conventional ones and that the proposed GAN model outperforms the proposed autoencoder with an efficiency in graph reconstruction exceeding 85% for different datasets.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"105-117"},"PeriodicalIF":1.9,"publicationDate":"2026-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175732","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/ICJECE.2026.3650868
Elhamsadat Hejazi;Majid Ahmadi;Arash Ahmadi
Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model—channel-spatial faster vision transformer (CSFVIT)—that integrates local and global feature processing to enhance recognition performance under diverse occlusion scenarios. The local branch refines facial features using a parallel channel-spatial attention (PCSA) module based on ResNet-18, while the global branch leverages a faster vision Transformer (FasterViT) to capture long-range dependencies. A dynamic attention fusion (DAF) module adaptively balances these features based on occlusion severity. We validate our model on five benchmark datasets: CASIA-WebFace, LFW, Extended Yale B, ORL, and AR. The model achieves 97.46% accuracy on CASIA-WebFace, 97.62% on LFW, 99.39% on Extended Yale B, 98.78% on ORL, and 98.50% on AR (sunglasses)/97.50% (scarf), consistently outperforming state-of-the-art baselines. CSFVIT achieves consistently high recognition accuracy under both synthetic and real-world occlusions, outperforming several attention- and transformer-based baselines. This practical and efficient architecture demonstrates strong potential for real-world face recognition applications in unconstrained environments.
{"title":"Robust Face Recognition and Classification Under Occlusion Using a Refined Transformer-Based Attention Mechanism","authors":"Elhamsadat Hejazi;Majid Ahmadi;Arash Ahmadi","doi":"10.1109/ICJECE.2026.3650868","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3650868","url":null,"abstract":"Robust face recognition under partial occlusions remains a key challenge in real-world biometric and surveillance systems. In this article, we propose a hybrid dual-branch model—channel-spatial faster vision transformer (CSFVIT)—that integrates local and global feature processing to enhance recognition performance under diverse occlusion scenarios. The local branch refines facial features using a parallel channel-spatial attention (PCSA) module based on ResNet-18, while the global branch leverages a faster vision Transformer (FasterViT) to capture long-range dependencies. A dynamic attention fusion (DAF) module adaptively balances these features based on occlusion severity. We validate our model on five benchmark datasets: CASIA-WebFace, LFW, Extended Yale B, ORL, and AR. The model achieves 97.46% accuracy on CASIA-WebFace, 97.62% on LFW, 99.39% on Extended Yale B, 98.78% on ORL, and 98.50% on AR (sunglasses)/97.50% (scarf), consistently outperforming state-of-the-art baselines. CSFVIT achieves consistently high recognition accuracy under both synthetic and real-world occlusions, outperforming several attention- and transformer-based baselines. This practical and efficient architecture demonstrates strong potential for real-world face recognition applications in unconstrained environments.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"93-104"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175779","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-28DOI: 10.1109/ICJECE.2025.3635704
Kaligotla Ravikumar;C. Sivakumar
The proliferation of interconnected mobile devices within densely packed cloud networks necessitates sophisticated frameworks for capacity optimization to ensure efficiency, reliability, and data security. This study explores the challenges posed by user mobility, dynamic calculations, and increasing service demands in edge computing environments. We propose a novel capacity optimization algorithm (COA) that leverages a deep autoencoder-based binary bat algorithm to improve resource allocation. The system uses the SHA- 512 cryptographic hash function for capacity requests (CRs), facilitating seamless user access to resources while quickly detecting and revoking access for unauthorized users. The system employs a selective routing mechanism that considers specific service requirements, allowing it to prioritize user demands and maximize resource utilization. The quality of service (QoS) integration ensures consistent, high-quality performance for mobile nodes, leading to an improved user experience. The framework’s effectiveness is evaluated through experiments, demonstrating its ability to optimize throughput and reduce interference in multinode networks.
在密集的云网络中，互连移动设备的激增需要复杂的容量优化框架，以确保效率、可靠性和数据安全性。本研究探讨了边缘计算环境中用户移动性、动态计算和不断增长的服务需求所带来的挑战。我们提出了一种新的容量优化算法（COA），该算法利用基于深度自编码器的二进制bat算法来改善资源分配。系统对cr （capacity request）请求采用SHA- 512加密哈希函数，实现用户对资源的无缝访问，同时快速发现并撤销对未授权用户的访问。该系统采用选择性路由机制，考虑特定的业务需求，使其能够优先考虑用户需求并最大限度地利用资源。QoS （quality of service）集成保证了移动节点一致的高质量性能，从而提升用户体验。通过实验评估了该框架的有效性，证明了其在多节点网络中优化吞吐量和减少干扰的能力。
{"title":"Efficient Resource Allocation in Edge Networks Using Autoencoder-Based Capacity Optimization and SHA-512 Security","authors":"Kaligotla Ravikumar;C. Sivakumar","doi":"10.1109/ICJECE.2025.3635704","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3635704","url":null,"abstract":"The proliferation of interconnected mobile devices within densely packed cloud networks necessitates sophisticated frameworks for capacity optimization to ensure efficiency, reliability, and data security. This study explores the challenges posed by user mobility, dynamic calculations, and increasing service demands in edge computing environments. We propose a novel capacity optimization algorithm (COA) that leverages a deep autoencoder-based binary bat algorithm to improve resource allocation. The system uses the SHA- 512 cryptographic hash function for capacity requests (CRs), facilitating seamless user access to resources while quickly detecting and revoking access for unauthorized users. The system employs a selective routing mechanism that considers specific service requirements, allowing it to prioritize user demands and maximize resource utilization. The quality of service (QoS) integration ensures consistent, high-quality performance for mobile nodes, leading to an improved user experience. The framework’s effectiveness is evaluated through experiments, demonstrating its ability to optimize throughput and reduce interference in multinode networks.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"83-92"},"PeriodicalIF":1.9,"publicationDate":"2026-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146175782","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-26DOI: 10.1109/ICJECE.2026.3651551
Song Liu;Yun Wang
The cyberthreats faced by power cyber–physical systems (CPSs) have become increasingly serious. However, existing cyberattack detectors still cannot resist them effectively due to the data imbalance, the high false alarm rate (FAR), and highly covert cyberattacks. To address the issues, this article proposes a novel data-driven cyberattack detector based on deep learning for power CPSs. The proposed detector is equipped with two Wasserstein generative adversarial networks (WGANs), which overcome the data imbalance issue in existing detectors by synthesizing adequate abnormal samples involving cyberattacks. Moreover, a novel substation-level detector with a modified light gradient boosting machine (LightGBM) and a maximal information coefficient (MIC) unit is introduced into the proposed detector. It captures differences between abnormal sampled values caused by cyberattacks and natural faults, thus reducing the FAR. Furthermore, a novel overalllevel detector based on an improved graph convolutional neural network (IGCNN) is built for the proposed detector. It performs spatial–temporal topology mining on complete power CPS graphs to fully extract more comprehensive attack-related features than existing detectors, thus realizing exhaustive detection sensitive enough to highly covert cyberattacks. Finally, the effectiveness and superiority of the proposed detector are verified by experimental research on actual power data from China.
{"title":"Data-Driven Cyberattack Detection Based on Deep Learning for Power Cyber–Physical Systems","authors":"Song Liu;Yun Wang","doi":"10.1109/ICJECE.2026.3651551","DOIUrl":"https://doi.org/10.1109/ICJECE.2026.3651551","url":null,"abstract":"The cyberthreats faced by power cyber–physical systems (CPSs) have become increasingly serious. However, existing cyberattack detectors still cannot resist them effectively due to the data imbalance, the high false alarm rate (FAR), and highly covert cyberattacks. To address the issues, this article proposes a novel data-driven cyberattack detector based on deep learning for power CPSs. The proposed detector is equipped with two Wasserstein generative adversarial networks (WGANs), which overcome the data imbalance issue in existing detectors by synthesizing adequate abnormal samples involving cyberattacks. Moreover, a novel substation-level detector with a modified light gradient boosting machine (LightGBM) and a maximal information coefficient (MIC) unit is introduced into the proposed detector. It captures differences between abnormal sampled values caused by cyberattacks and natural faults, thus reducing the FAR. Furthermore, a novel overalllevel detector based on an improved graph convolutional neural network (IGCNN) is built for the proposed detector. It performs spatial–temporal topology mining on complete power CPS graphs to fully extract more comprehensive attack-related features than existing detectors, thus realizing exhaustive detection sensitive enough to highly covert cyberattacks. Finally, the effectiveness and superiority of the proposed detector are verified by experimental research on actual power data from China.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"69-82"},"PeriodicalIF":1.9,"publicationDate":"2026-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082225","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-23DOI: 10.1109/ICJECE.2025.3638759
N. Cabanos;W. Le;Abolfazl Ghassemi
This article presents an empirical study on the integration of natural language processing (NLP) into inventory management systems to improve operational efficiency within e-commerce and supply chain contexts. Traditional inventory systems often face limitations in handling unstructured data and providing timely decision support. To address these challenges, a modular framework incorporating NLP, machine learning, and a hybrid database architecture is proposed and evaluated. The system enables users to interact through natural language queries, which are translated into improved SQL commands using semantic parsing and Transformer models. Performance evaluation using real-world and synthetic datasets demonstrates significant improvements in query execution time, demand prediction accuracy, and inventory optimization. Comparative results indicate that the NLP-based system outperforms conventional systems in both cost-efficiency and responsiveness. The findings demonstrate the potential of NLP-based inventory systems to improve data interaction and predictive analytics across supply chain operations.
{"title":"An Empirical Analysis of NLP-Based Databases for Inventory Management","authors":"N. Cabanos;W. Le;Abolfazl Ghassemi","doi":"10.1109/ICJECE.2025.3638759","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3638759","url":null,"abstract":"This article presents an empirical study on the integration of natural language processing (NLP) into inventory management systems to improve operational efficiency within e-commerce and supply chain contexts. Traditional inventory systems often face limitations in handling unstructured data and providing timely decision support. To address these challenges, a modular framework incorporating NLP, machine learning, and a hybrid database architecture is proposed and evaluated. The system enables users to interact through natural language queries, which are translated into improved SQL commands using semantic parsing and Transformer models. Performance evaluation using real-world and synthetic datasets demonstrates significant improvements in query execution time, demand prediction accuracy, and inventory optimization. Comparative results indicate that the NLP-based system outperforms conventional systems in both cost-efficiency and responsiveness. The findings demonstrate the potential of NLP-based inventory systems to improve data interaction and predictive analytics across supply chain operations.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"60-68"},"PeriodicalIF":1.9,"publicationDate":"2026-01-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-22DOI: 10.1109/ICJECE.2025.3638784
Seong Tak Woo
This study presents the design and evaluation results of a dual-sensing angle detection module that integrates inductive and Hall effect sensors to improve the accuracy and reliability of steering angle detection in automobiles. Unlike dual Hall implementations, the proposed architecture leverages the complementary properties of the two sensing principles. The Hall channel provides high resolution and fast response, while the inductive channel contributes robustness against stray magnetic fields and mechanical tolerances. A compact prototype module was fabricated and tested on a laboratory test stand and in a real vehicle equipped with a steering robot. The results show that the Hall sensor achieved a maximum absolute angular error of 0.8° and the inductive sensor 0.5° over a rotation range of −720° to +720° and speeds up to 2000°/s. Vehiclebased evaluations confirmed consistent performance, though errors increased up to 1.5° due to installation misalignment and gear backlash (∼0.135°). These findings highlight not only the benefits but also the practical limitations of the dual-sensing design; they provide valuable insights into the practical application of the module beyond simple module-level verification.
{"title":"Dual-Sensing Hall Effect and Inductive Steering Angle Module","authors":"Seong Tak Woo","doi":"10.1109/ICJECE.2025.3638784","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3638784","url":null,"abstract":"This study presents the design and evaluation results of a dual-sensing angle detection module that integrates inductive and Hall effect sensors to improve the accuracy and reliability of steering angle detection in automobiles. Unlike dual Hall implementations, the proposed architecture leverages the complementary properties of the two sensing principles. The Hall channel provides high resolution and fast response, while the inductive channel contributes robustness against stray magnetic fields and mechanical tolerances. A compact prototype module was fabricated and tested on a laboratory test stand and in a real vehicle equipped with a steering robot. The results show that the Hall sensor achieved a maximum absolute angular error of 0.8° and the inductive sensor 0.5° over a rotation range of −720° to +720° and speeds up to 2000°/s. Vehiclebased evaluations confirmed consistent performance, though errors increased up to 1.5° due to installation misalignment and gear backlash (∼0.135°). These findings highlight not only the benefits but also the practical limitations of the dual-sensing design; they provide valuable insights into the practical application of the module beyond simple module-level verification.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"50-59"},"PeriodicalIF":1.9,"publicationDate":"2026-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146082200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2026-01-13DOI: 10.1109/ICJECE.2025.3641939
Supakan Janthong;Pornchai Phukpattaranont
This article presents a robust triple power quality disturbance (PQD) classification framework integrating spectral analysis, a denoising autoencoder (DAE), and a gated recurrent unit (GRU) network. The system is designed to detect and classify 16 classes of triple PQDs under various noise conditions. Synthetic PQD signals were generated per IEEE 1159 standards and subjected to additive white Gaussian noise (AWGN) at signal-to-noise ratio (SNR) levels of 5–20 dB. The spectral analysis transforms time-domain signals into the frequency domain to enhance class separability, while the DAE effectively denoises and compresses spectral data. The GRU network then models temporal dependencies for final classification. Extensive experiments reveal that the proposed model outperforms traditional baselines across all noise levels, achieving a peak accuracy of 99.7% in noise-free conditions and maintaining 85.6% at 5-dB SNR. Visual analyses, including power spectrum comparisons, t-distributed stochastic neighbor embedding (t-SNE), and DAE reconstructions, validate the model’s discriminative power and noise resilience. Benchmarking against recent methods confirms state-of-the-art performance, while validation on IEEE PES datasets verifies high accuracy and robustness under real-world conditions. These results demonstrate the framework’s strong generalization capability and practical utility for PQD monitoring applications.
{"title":"Hybrid Denoising Autoencoder–GRU Architecture for Robust Power Quality Disturbance Detection","authors":"Supakan Janthong;Pornchai Phukpattaranont","doi":"10.1109/ICJECE.2025.3641939","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3641939","url":null,"abstract":"This article presents a robust triple power quality disturbance (PQD) classification framework integrating spectral analysis, a denoising autoencoder (DAE), and a gated recurrent unit (GRU) network. The system is designed to detect and classify 16 classes of triple PQDs under various noise conditions. Synthetic PQD signals were generated per IEEE 1159 standards and subjected to additive white Gaussian noise (AWGN) at signal-to-noise ratio (SNR) levels of 5–20 dB. The spectral analysis transforms time-domain signals into the frequency domain to enhance class separability, while the DAE effectively denoises and compresses spectral data. The GRU network then models temporal dependencies for final classification. Extensive experiments reveal that the proposed model outperforms traditional baselines across all noise levels, achieving a peak accuracy of 99.7% in noise-free conditions and maintaining 85.6% at 5-dB SNR. Visual analyses, including power spectrum comparisons, t-distributed stochastic neighbor embedding (t-SNE), and DAE reconstructions, validate the model’s discriminative power and noise resilience. Benchmarking against recent methods confirms state-of-the-art performance, while validation on IEEE PES datasets verifies high accuracy and robustness under real-world conditions. These results demonstrate the framework’s strong generalization capability and practical utility for PQD monitoring applications.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"39-49"},"PeriodicalIF":1.9,"publicationDate":"2026-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146026428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-17DOI: 10.1109/ICJECE.2025.3606705
{"title":"IEEE Canadian Journal of Electrical and Computer Engineering","authors":"","doi":"10.1109/ICJECE.2025.3606705","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3606705","url":null,"abstract":"","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"48 4","pages":"C2-C2"},"PeriodicalIF":1.9,"publicationDate":"2025-12-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11301995","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145778381","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/ICJECE.2025.3618647
Deepak Mishra;Anil Kumar;Girish Kumar Singh
Nowadays, the application of crop images for sharing crop information is perpetually increasing. As a result, image datasets need more storage space and channel bandwidth, leading to higher costs. Therefore, reducing image data size is essential. This article, therefore, introduces a compression method based on the discrete wavelet transform (DWT) and the modified singular vector sparse reconstruction (MSVSR) approaches. It gives good reconstruction quality and compression characteristics. In the first stage, input images are decomposed using DWT into frequency subbands. In addition, a modified sparse representation of singular vectors based on the singular value decomposition (SVD) approach is applied in detailed subbands to improve the compression efficiency. At the reconstruction stage, piecewise linear interpolation (PLI) and inverse DWT are used to retrieve a good-quality image. The performance of the proposed method has been evaluated based on various fidelity parameters, including bit-per-pixel (BPP), peak signal-to-noise ratio (PSNR), mean square error, and structural-similarity index. Moreover, the experimental results illustrate that the proposed DWT-MSVSR technique with Daubechies 4 wavelet has achieved significantly higher compression (67.27%), and structural similarity index measure (SSIM) (36.27%), as compared with SVSR with similar image quality, as well as other SVD-based existing methods. From the evaluated results, it is observed that this method has proven to be efficient in compressing different types of crop images with acceptable reconstruction quality.
{"title":"A New Singular Vector Sparse Representation Technique for Crop Image Compression","authors":"Deepak Mishra;Anil Kumar;Girish Kumar Singh","doi":"10.1109/ICJECE.2025.3618647","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3618647","url":null,"abstract":"Nowadays, the application of crop images for sharing crop information is perpetually increasing. As a result, image datasets need more storage space and channel bandwidth, leading to higher costs. Therefore, reducing image data size is essential. This article, therefore, introduces a compression method based on the discrete wavelet transform (DWT) and the modified singular vector sparse reconstruction (MSVSR) approaches. It gives good reconstruction quality and compression characteristics. In the first stage, input images are decomposed using DWT into frequency subbands. In addition, a modified sparse representation of singular vectors based on the singular value decomposition (SVD) approach is applied in detailed subbands to improve the compression efficiency. At the reconstruction stage, piecewise linear interpolation (PLI) and inverse DWT are used to retrieve a good-quality image. The performance of the proposed method has been evaluated based on various fidelity parameters, including bit-per-pixel (BPP), peak signal-to-noise ratio (PSNR), mean square error, and structural-similarity index. Moreover, the experimental results illustrate that the proposed DWT-MSVSR technique with Daubechies 4 wavelet has achieved significantly higher compression (67.27%), and structural similarity index measure (SSIM) (36.27%), as compared with SVSR with similar image quality, as well as other SVD-based existing methods. From the evaluated results, it is observed that this method has proven to be efficient in compressing different types of crop images with acceptable reconstruction quality.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"1-11"},"PeriodicalIF":1.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-12-09DOI: 10.1109/ICJECE.2025.3625985
Nirma Peter;Nidhi Goel;Pankaj Gupta
Fault detection and protection is one of the challenging tasks in a power system, especially when integrated with microgrids. This is due to frequent changes in topology and variations in the short-circuit level, which affect the overcurrent grading of the relays. However, machine learning (ML) has been found to be effective in such scenarios. This article proposes an adaptive intelligent fault detection and classification method that dynamically integrates three learning models, adjusting their contributions based on performance under various conditions. This approach simplifies the system by utilizing novel data labeling for fault line detection and localization with a light gradient boosting machine (LightGBM) model, thus reducing complexity and response time. The current, measured as data input, is decomposed using wavelet packet decomposition (WPD). The standard deviation and energy are calculated from the wavelet coefficients, which serve as features for training the models. The proposed method effectively addresses challenges in hybrid microgrids, achieving: 1) 99.35% accuracy in fault detection and classification and 2) 99.99% accuracy in identifying faulty lines and their locations. It offers a precise and adaptable solution for simulated data, outperforming conventional protection strategies.
{"title":"An Adaptive Intelligent Strategy for Efficient Fault Detection and Localization in Hybrid Microgrid","authors":"Nirma Peter;Nidhi Goel;Pankaj Gupta","doi":"10.1109/ICJECE.2025.3625985","DOIUrl":"https://doi.org/10.1109/ICJECE.2025.3625985","url":null,"abstract":"Fault detection and protection is one of the challenging tasks in a power system, especially when integrated with microgrids. This is due to frequent changes in topology and variations in the short-circuit level, which affect the overcurrent grading of the relays. However, machine learning (ML) has been found to be effective in such scenarios. This article proposes an adaptive intelligent fault detection and classification method that dynamically integrates three learning models, adjusting their contributions based on performance under various conditions. This approach simplifies the system by utilizing novel data labeling for fault line detection and localization with a light gradient boosting machine (LightGBM) model, thus reducing complexity and response time. The current, measured as data input, is decomposed using wavelet packet decomposition (WPD). The standard deviation and energy are calculated from the wavelet coefficients, which serve as features for training the models. The proposed method effectively addresses challenges in hybrid microgrids, achieving: 1) 99.35% accuracy in fault detection and classification and 2) 99.99% accuracy in identifying faulty lines and their locations. It offers a precise and adaptable solution for simulated data, outperforming conventional protection strategies.","PeriodicalId":100619,"journal":{"name":"IEEE Canadian Journal of Electrical and Computer Engineering","volume":"49 1","pages":"12-24"},"PeriodicalIF":1.9,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145754197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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