Pub Date : 2025-07-07DOI: 10.1109/OJCS.2025.3586682
Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum
This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdependencies, which may oversimplify complex temporal dynamics. The proposed DCA model leverages discrete cosine transform (DCT) coefficients to incorporate frequency-domain information, capturing a broader spectrum of temporal features. Two selection criteria are employed to identify the most informative DCT coefficients for constructing the attention map. The first criterion utilizes the lowest frequency coefficients, whereas the second criterion selects the coefficients exhibiting the highest energy to construct the attention map. Comprehensive experiments on twelve diverse TSC datasets demonstrate that DCA consistently outperforms state-of-the-art attention mechanisms, achieving an average improvement of $text{2.2}{%}$ in classification accuracy.
{"title":"DCT-Based Channel Attention for Multivariate Time Series Classification","authors":"Amine Haboub;Hamza Baali;Abdesselam Bouzerdoum","doi":"10.1109/OJCS.2025.3586682","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3586682","url":null,"abstract":"This article introduces a novel DCT-based channel attention (DCA) mechanism for time series classification (TSC) using convolutional neural networks (CNNs). Traditional squeeze-and-excitation (SE) mechanisms rely on global average pooling to model channel-wise interdependencies, which may oversimplify complex temporal dynamics. The proposed DCA model leverages discrete cosine transform (DCT) coefficients to incorporate frequency-domain information, capturing a broader spectrum of temporal features. Two selection criteria are employed to identify the most informative DCT coefficients for constructing the attention map. The first criterion utilizes the lowest frequency coefficients, whereas the second criterion selects the coefficients exhibiting the highest energy to construct the attention map. Comprehensive experiments on twelve diverse TSC datasets demonstrate that DCA consistently outperforms state-of-the-art attention mechanisms, achieving an average improvement of <inline-formula><tex-math>$text{2.2}{%}$</tex-math></inline-formula> in classification accuracy.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1110-1120"},"PeriodicalIF":0.0,"publicationDate":"2025-07-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11072324","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716159","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}
Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (T-StructGAD), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (GConvGRUs) and Long Short-Term Memory networks (LSTMs) to jointly model both structural and temporal dynamics in graph node embeddings. Anomalies are detected using reconstruction errors generated by an AutoEncoder, enabling the framework to robustly uncover deviations across time. Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. Comprehensive evaluations on four real-world datasets demonstrate that T-StructGAD consistently outperforms 12 state-of-the-art unsupervised anomaly detection models, showcasing its superior ability to detect complex anomalies in evolving graphs. This work advances anomaly detection in dynamic graphs by integrating deep learning techniques to address structural and temporal irregularities in a more effective manner.
{"title":"Deep Temporal and Structural Embeddings for Robust Unsupervised Anomaly Detection in Dynamic Graphs","authors":"Samir Abdaljalil;Hasan Kurban;Rachad Atat;Erchin Serpedin;Khalid Qaraqe","doi":"10.1109/OJCS.2025.3584942","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584942","url":null,"abstract":"Detecting anomalies in dynamic graphs is a complex yet essential task, as existing methods often fail to capture long-term dependencies required for identifying irregularities in evolving networks. We introduce Temporal Structural Graph Anomaly Detection (<sc>T-StructGAD</small>), an unsupervised framework that leverages Graph Convolutional Gated Recurrent Units (<monospace>GConvGRU</monospace>s) and Long Short-Term Memory networks (<monospace>LSTM</monospace>s) to jointly model both structural and temporal dynamics in graph node embeddings. Anomalies are detected using reconstruction errors generated by an AutoEncoder, enabling the framework to robustly uncover deviations across time. Our method successfully captures temporal patterns, making it robust against subtle anomalies and structural changes. Comprehensive evaluations on four real-world datasets demonstrate that <sc>T-StructGAD</small> consistently outperforms 12 state-of-the-art unsupervised anomaly detection models, showcasing its superior ability to detect complex anomalies in evolving graphs. This work advances anomaly detection in dynamic graphs by integrating deep learning techniques to address structural and temporal irregularities in a more effective manner.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1100-1109"},"PeriodicalIF":0.0,"publicationDate":"2025-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11068181","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716228","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 : 2025-07-02DOI: 10.1109/OJCS.2025.3585248
Kiara Nand;Zhibo Zhang;Jiankun Hu
This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids. It introduces a novel taxonomy categorizing detection methods based on key criteria such as AC and DC systems, performance metrics, bus size, algorithm selection, and specific subcategories of detection problems. The proposed taxonomy highlights the utility of Graph Neural Networks, autoencoders, and federated learning in addressing sub-problems like privacy preservation, generalized detection, locational detection, and attack classification. The survey underscores the importance of realistic, publicly accessible datasets and enhanced attack simulation techniques. Future research directions are suggested to further the development of robust FDIA detection methods in smart grids.
{"title":"A Comprehensive Survey on the Usage of Machine Learning to Detect False Data Injection Attacks in Smart Grids","authors":"Kiara Nand;Zhibo Zhang;Jiankun Hu","doi":"10.1109/OJCS.2025.3585248","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3585248","url":null,"abstract":"This article provides a comprehensive survey on the application of machine learning techniques for detecting False Data Injection Attacks (FDIA) in smart grids. It introduces a novel taxonomy categorizing detection methods based on key criteria such as AC and DC systems, performance metrics, bus size, algorithm selection, and specific subcategories of detection problems. The proposed taxonomy highlights the utility of Graph Neural Networks, autoencoders, and federated learning in addressing sub-problems like privacy preservation, generalized detection, locational detection, and attack classification. The survey underscores the importance of realistic, publicly accessible datasets and enhanced attack simulation techniques. Future research directions are suggested to further the development of robust FDIA detection methods in smart grids.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1121-1132"},"PeriodicalIF":0.0,"publicationDate":"2025-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11063250","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716206","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 : 2025-06-30DOI: 10.1109/OJCS.2025.3584205
Chih-Lyang Hwang;Felix Gunawan;Chih-Han Chen
Attributable to the random features of wireless signal, different environments, user areas, and variabilities in user gestures, wireless gesture recognition becomes more formidable. In this work, a continuous wireless gesture recognition developed by integrating distributed ultrawideband network (DUWBN) and squeeze-excitation transformer with residual bi-gate recurrent unit (SE-T-RB-GRU) model can tackle the above difficulties. It presents distinguished improvements in processing continuous data streams for real-time applications. The details of model training, optimization strategies, and data preprocessing techniques are presented to improve the performance. From the viewpoint of accuracy and training time, the best sequence length from 3 anchors with different heights is achieved. Furthermore, only one subarea including wireless localization is needed for the modeling and the other extended subareas is achieved by coordinate transformationation. A mode filter trigger is also designed to prevent noisy commands. Finally, extensively experimental comparisons with the state-of-the-art methods have average accuracy of 96.31% and an application to human-UAV interactions is implemented. The proposed approach becomes a plug-in module for similar tasks, e.g., a warehouse management system, home appliances.
{"title":"Squeeze-Excitation Transformer With Residual Bi-GRU Model for Distributed UWB Based Continuous Gesture Recognition and its Application to Human-UAV Interactions","authors":"Chih-Lyang Hwang;Felix Gunawan;Chih-Han Chen","doi":"10.1109/OJCS.2025.3584205","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584205","url":null,"abstract":"Attributable to the random features of wireless signal, different environments, user areas, and variabilities in user gestures, wireless gesture recognition becomes more formidable. In this work, a continuous wireless gesture recognition developed by integrating distributed ultrawideband network (DUWBN) and squeeze-excitation transformer with residual bi-gate recurrent unit (SE-T-RB-GRU) model can tackle the above difficulties. It presents distinguished improvements in processing continuous data streams for real-time applications. The details of model training, optimization strategies, and data preprocessing techniques are presented to improve the performance. From the viewpoint of accuracy and training time, the best sequence length from 3 anchors with different heights is achieved. Furthermore, only one subarea including wireless localization is needed for the modeling and the other extended subareas is achieved by coordinate transformationation. A mode filter trigger is also designed to prevent noisy commands. Finally, extensively experimental comparisons with the state-of-the-art methods have average accuracy of 96.31% and an application to human-UAV interactions is implemented. The proposed approach becomes a plug-in module for similar tasks, e.g., a warehouse management system, home appliances.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1077-1089"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11059322","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144657375","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 : 2025-06-30DOI: 10.1109/OJCS.2025.3584297
Leandro Honorato de S. Silva;Agostinho Freire;George O. A. Azevedo;Sérgio Campello Oliveira;Carlo M. R. da Silva;Bruno J. T. Fernandes
Waste Printed Circuit Boards (WPCBs) contain many valuable and rare metals found in electronic waste, and recycling these boards can help recover these metals and prevent hazardous elements from harming the environment. However, the diverse composition of PCBs makes it challenging to automate the recycling process, which should ideally be tailored to each PCB’s composition. Computer vision is a possible solution to evaluate WPCBs, but most state-of-the-art models depend on labeled datasets unavailable in the WPCB domain. Building a large and fully labeled WPCB dataset is expensive and time-consuming. In addition, the presence of long-tailed class imbalance, where specific electronic components are significantly more prevalent than others, further complicates the development of accurate detection and classification models. To address this, we propose a new method called GEN Self-Labeling Electronic Component Detector, which utilizes a domain adaptation strategy to train semi-supervised teacher-student models that can handle the lack of fully labeled datasets while mitigating the effects of class imbalance. We also introduce a new version of the Waste Printed Circuit Board Economic Feasibility Assessment (WPCB-EFAv2), which characterizes the PCB’s composition by identifying hazardous components, calculating the density of each component type, and estimating the metals that could be recovered from recycling electrolytic capacitors and integrated circuits. Finally, we present a case study involving six PCBs with different characteristics, from which we estimated that 121 g of metals could be recovered. The most recovered metal (108 g) was aluminum from electrolytic capacitors. This information can help reduce the PCB’s composition uncertainty, leading to more efficient dismantling and cost-effective recycling processes.
{"title":"GEN Self-Labeling Object Detector for PCB Recycling Evaluation","authors":"Leandro Honorato de S. Silva;Agostinho Freire;George O. A. Azevedo;Sérgio Campello Oliveira;Carlo M. R. da Silva;Bruno J. T. Fernandes","doi":"10.1109/OJCS.2025.3584297","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3584297","url":null,"abstract":"Waste Printed Circuit Boards (WPCBs) contain many valuable and rare metals found in electronic waste, and recycling these boards can help recover these metals and prevent hazardous elements from harming the environment. However, the diverse composition of PCBs makes it challenging to automate the recycling process, which should ideally be tailored to each PCB’s composition. Computer vision is a possible solution to evaluate WPCBs, but most state-of-the-art models depend on labeled datasets unavailable in the WPCB domain. Building a large and fully labeled WPCB dataset is expensive and time-consuming. In addition, the presence of long-tailed class imbalance, where specific electronic components are significantly more prevalent than others, further complicates the development of accurate detection and classification models. To address this, we propose a new method called GEN Self-Labeling Electronic Component Detector, which utilizes a domain adaptation strategy to train semi-supervised teacher-student models that can handle the lack of fully labeled datasets while mitigating the effects of class imbalance. We also introduce a new version of the Waste Printed Circuit Board Economic Feasibility Assessment (WPCB-EFAv2), which characterizes the PCB’s composition by identifying hazardous components, calculating the density of each component type, and estimating the metals that could be recovered from recycling electrolytic capacitors and integrated circuits. Finally, we present a case study involving six PCBs with different characteristics, from which we estimated that 121 g of metals could be recovered. The most recovered metal (108 g) was aluminum from electrolytic capacitors. This information can help reduce the PCB’s composition uncertainty, leading to more efficient dismantling and cost-effective recycling processes.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1041-1052"},"PeriodicalIF":0.0,"publicationDate":"2025-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11058390","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634817","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 : 2025-06-27DOI: 10.1109/OJCS.2025.3583945
Isaac Ampratwum;Amiya Nayak
Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. The proposed method leverages the decision-making capabilities of DRL and the graph-structured learning potential of GNN to dynamically adapt to network disruptions. By modeling network topology as a graph, the GNN extracts structural features, while the DRL agent intelligently selects restoration paths, balancing network load and minimizing restoration time. Experimental evaluations across multiple network topologies and failure scenarios reveal that the hybrid DRL+GNN approach outperforms conventional restoration techniques in terms of restoration success rate, resource utilization, and scalability. The framework’s ability to generalize across diverse network configurations highlights its adaptability and potential for deployment in real-world optical communication systems. This study underscores the transformative impact of combining AI techniques on advancing WDM network resilience and recovery capabilities.
{"title":"Hybrid Approach for WDM Network Restoration: Deep Reinforcement Learning and Graph Neural Networks","authors":"Isaac Ampratwum;Amiya Nayak","doi":"10.1109/OJCS.2025.3583945","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3583945","url":null,"abstract":"Ensuring robust and efficient service restoration in Wavelength Division Multiplexing (WDM) networks is crucial for maintaining network reliability amidst failures caused by disasters, equipment malfunctions, or power outages. This article presents a hybrid framework that integrates Deep Reinforcement Learning (DRL) and Graph Neural Networks (GNN) to optimize WDM network restoration. The proposed method leverages the decision-making capabilities of DRL and the graph-structured learning potential of GNN to dynamically adapt to network disruptions. By modeling network topology as a graph, the GNN extracts structural features, while the DRL agent intelligently selects restoration paths, balancing network load and minimizing restoration time. Experimental evaluations across multiple network topologies and failure scenarios reveal that the hybrid DRL+GNN approach outperforms conventional restoration techniques in terms of restoration success rate, resource utilization, and scalability. The framework’s ability to generalize across diverse network configurations highlights its adaptability and potential for deployment in real-world optical communication systems. This study underscores the transformative impact of combining AI techniques on advancing WDM network resilience and recovery capabilities.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1012-1026"},"PeriodicalIF":0.0,"publicationDate":"2025-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11054280","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606288","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 : 2025-06-25DOI: 10.1109/OJCS.2025.3583271
Hanyue Xu;Kah Phooi Seng;Li-Minn Ang;Wei Wang;Jeremy Smith
The rise of smart cities has leveraged the power of Internet of Things devices to transform urban services. A key element of this transformation is the widespread deployment of IoT devices for data collection, which feeds into machine learning algorithms to improve city services. However, the centralization of sensitive IoT data for ML raises privacy and efficiency concerns. Distributed collaborative machine learning, particularly split federated learning, has emerged as a solution, enabling privacy-preserving, resource-efficient training on IoT devices. This article introduces a novel SFL-based framework for graph convolutional neural networks, SFLGCN, which includes two variants SFLGCN (general) and SFLGCN-PP (Privacy Preservation), specifically designed for resource-constrained IoT systems in smart cities. SFLGCN-PP, an enhanced version of the framework, focuses on privacy preservation and is capable of handling graph-structured data, which is common in smart city scenarios, without requiring pre-defined adjacency matrices, thus enhancing data privacy. The framework’s efficacy is validated through predictive modeling of autonomous vehicle passenger demand using real-world IoT data. Additionally, the generalization capability of our framework is demonstrated on public graph datasets, where it outperforms traditional federated learning in graph neural network tasks, particularly in large-scale IoT environments with varying data distributions and client capacities.
{"title":"Graph Split Federated Learning for Distributed Large-Scale AIoT in Smart Cities","authors":"Hanyue Xu;Kah Phooi Seng;Li-Minn Ang;Wei Wang;Jeremy Smith","doi":"10.1109/OJCS.2025.3583271","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3583271","url":null,"abstract":"The rise of smart cities has leveraged the power of Internet of Things devices to transform urban services. A key element of this transformation is the widespread deployment of IoT devices for data collection, which feeds into machine learning algorithms to improve city services. However, the centralization of sensitive IoT data for ML raises privacy and efficiency concerns. Distributed collaborative machine learning, particularly split federated learning, has emerged as a solution, enabling privacy-preserving, resource-efficient training on IoT devices. This article introduces a novel SFL-based framework for graph convolutional neural networks, SFLGCN, which includes two variants SFLGCN (general) and SFLGCN-PP (Privacy Preservation), specifically designed for resource-constrained IoT systems in smart cities. SFLGCN-PP, an enhanced version of the framework, focuses on privacy preservation and is capable of handling graph-structured data, which is common in smart city scenarios, without requiring pre-defined adjacency matrices, thus enhancing data privacy. The framework’s efficacy is validated through predictive modeling of autonomous vehicle passenger demand using real-world IoT data. Additionally, the generalization capability of our framework is demonstrated on public graph datasets, where it outperforms traditional federated learning in graph neural network tasks, particularly in large-scale IoT environments with varying data distributions and client capacities.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1027-1040"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11050992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144634647","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 : 2025-06-25DOI: 10.1109/OJCS.2025.3582726
Mohammad Ennab;Hamid Mcheick
Pneumonia is a leading cause of morbidity and mortality worldwide, necessitating timely and precise diagnosis for effective treatment. Chest X-rays are the primary diagnostic tool, but their interpretation demands substantial expertise. Recent advancements in AI have shown promise in enhancing pneumonia detection from X-ray images, yet the opacity of deep learning models raises concerns about their clinical adoption. Interpretability in AI models is vital for fostering trust among healthcare professionals by providing transparency in decision-making processes. This study conducts a comparative analysis of two interpretability methods, Pixel Level Interpretability (PLI) and Gradient-weighted Class Activation Mapping (Grad-CAM), in the context of pneumonia classification using VGG19 on X-ray datasets. The research includes an experiment involving three distinct X-ray datasets. VGG19 is applied to classify a query image, and both PLI and Grad-CAM are used to interpret the classification decisions. The study evaluates these interpretability methods across multiple dimensions: computational efficiency, diagnostic performance, explanation continuity, calibration accuracy, robustness to training parameters, and feedback from medical experts. Our findings aim to determine which interpretability technique offers a more clinically meaningful explanation, balancing computational feasibility and diagnostic reliability. This study contributes to the development of explainable AI in healthcare, supporting the integration of trustworthy AI systems in clinical environments for enhanced pneumonia diagnosis.
{"title":"Enhancing Pneumonia Diagnosis Through AI Interpretability: Comparative Analysis of Pixel-Level Interpretability and Grad-CAM on X-ray Imaging With VGG19","authors":"Mohammad Ennab;Hamid Mcheick","doi":"10.1109/OJCS.2025.3582726","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3582726","url":null,"abstract":"Pneumonia is a leading cause of morbidity and mortality worldwide, necessitating timely and precise diagnosis for effective treatment. Chest X-rays are the primary diagnostic tool, but their interpretation demands substantial expertise. Recent advancements in AI have shown promise in enhancing pneumonia detection from X-ray images, yet the opacity of deep learning models raises concerns about their clinical adoption. Interpretability in AI models is vital for fostering trust among healthcare professionals by providing transparency in decision-making processes. This study conducts a comparative analysis of two interpretability methods, Pixel Level Interpretability (PLI) and Gradient-weighted Class Activation Mapping (Grad-CAM), in the context of pneumonia classification using VGG19 on X-ray datasets. The research includes an experiment involving three distinct X-ray datasets. VGG19 is applied to classify a query image, and both PLI and Grad-CAM are used to interpret the classification decisions. The study evaluates these interpretability methods across multiple dimensions: computational efficiency, diagnostic performance, explanation continuity, calibration accuracy, robustness to training parameters, and feedback from medical experts. Our findings aim to determine which interpretability technique offers a more clinically meaningful explanation, balancing computational feasibility and diagnostic reliability. This study contributes to the development of explainable AI in healthcare, supporting the integration of trustworthy AI systems in clinical environments for enhanced pneumonia diagnosis.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1155-1165"},"PeriodicalIF":0.0,"publicationDate":"2025-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11049939","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716205","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 : 2025-06-23DOI: 10.1109/OJCS.2025.3581950
Md Shahin Alam Mozumder;Mohammad Balayet Hossain Sakil;Md Rokibul Hasan;Md Amit Hasan;K. M Nafiur Rahman Fuad;M. F. Mridha;Md Rashedul Islam;Yutaka Watanobe
Fraud detection in digital payment systems is a critical challenge due to the growing complexity of transaction patterns and the inherent class imbalance in datasets. This article proposes a novel Hybrid Contrastive Learning framework integrating Siamese Networks with Attention-Based Neural Networks to effectively distinguish fraudulent from legitimate transactions. The proposed model achieves state-of-the-art results, surpassing 10 recent methods in key metrics, with a recall of 95.42%, precision of 97.35%, and ROC-AUC of 98.78% on the Credit Card Fraud Detection dataset. Cross-dataset evaluations using a simulated transaction dataset demonstrate consistent generalization, achieving a recall of 95.12% and ROC-AUC of 98.60%. An ablation study underscores the impact of attention mechanisms and contrastive learning, with the combined approach improving F1-score by up to 2.64%. Additionally, SHAP-based analysis reveals the importance of key features such as transaction amount and PCA-derived components in model decisions, enhancing explainability. The results establish the proposed framework as a robust, interpretable, and scalable solution for fraud prevention in digital payment systems.
{"title":"Hybrid Contrastive Learning With Attention-Based Neural Networks for Robust Fraud Detection in Digital Payment Systems","authors":"Md Shahin Alam Mozumder;Mohammad Balayet Hossain Sakil;Md Rokibul Hasan;Md Amit Hasan;K. M Nafiur Rahman Fuad;M. F. Mridha;Md Rashedul Islam;Yutaka Watanobe","doi":"10.1109/OJCS.2025.3581950","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3581950","url":null,"abstract":"Fraud detection in digital payment systems is a critical challenge due to the growing complexity of transaction patterns and the inherent class imbalance in datasets. This article proposes a novel Hybrid Contrastive Learning framework integrating Siamese Networks with Attention-Based Neural Networks to effectively distinguish fraudulent from legitimate transactions. The proposed model achieves state-of-the-art results, surpassing 10 recent methods in key metrics, with a recall of 95.42%, precision of 97.35%, and ROC-AUC of 98.78% on the Credit Card Fraud Detection dataset. Cross-dataset evaluations using a simulated transaction dataset demonstrate consistent generalization, achieving a recall of 95.12% and ROC-AUC of 98.60%. An ablation study underscores the impact of attention mechanisms and contrastive learning, with the combined approach improving F1-score by up to 2.64%. Additionally, SHAP-based analysis reveals the importance of key features such as transaction amount and PCA-derived components in model decisions, enhancing explainability. The results establish the proposed framework as a robust, interpretable, and scalable solution for fraud prevention in digital payment systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"1053-1064"},"PeriodicalIF":0.0,"publicationDate":"2025-06-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045880","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144640947","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}
The need to deploy a network intrusion detection system (NIDS) is essential and has become increasingly necessary for every network, regardless whether it is wired, wireless, or hybrid, and its purpose is commercial, medical, defense, or social. Since the amount of data transfer over the Internet increases every year, using a single model as an IDS to secure the network cannot be considered enough as it may have many problems like high bias or high variance, which lead to high rates of false negatives and false positives. In this article, we propose an ensemble learning-based NIDS (DSEM-NIDS); this system is a deep-stacking model with a nested structure that has the ability to score a high performance with low false positive and low false negative rates. Four datasets are used as a benchmark to evaluate the proposed model: The 5G-NIDD, UNR-IDD, N-BaIoT, and NSL-KDD datasets. The results show that the proposed deep stacking model is robust, has good scalability, has the ability to distinguish between classes, and has the flexibility to adapt to different input data. It also performs better than other used models.
{"title":"DSEM-NIDS: Enhanced Network Intrusion Detection System Using Deep Stacking Ensemble Model","authors":"Loreen Mahmoud;Madhusanka Liyanage;Jitin Singla;Sugata Gangopadhyay","doi":"10.1109/OJCS.2025.3581036","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3581036","url":null,"abstract":"The need to deploy a network intrusion detection system (NIDS) is essential and has become increasingly necessary for every network, regardless whether it is wired, wireless, or hybrid, and its purpose is commercial, medical, defense, or social. Since the amount of data transfer over the Internet increases every year, using a single model as an IDS to secure the network cannot be considered enough as it may have many problems like high bias or high variance, which lead to high rates of false negatives and false positives. In this article, we propose an ensemble learning-based NIDS (DSEM-NIDS); this system is a deep-stacking model with a nested structure that has the ability to score a high performance with low false positive and low false negative rates. Four datasets are used as a benchmark to evaluate the proposed model: The 5G-NIDD, UNR-IDD, N-BaIoT, and NSL-KDD datasets. The results show that the proposed deep stacking model is robust, has good scalability, has the ability to distinguish between classes, and has the flexibility to adapt to different input data. It also performs better than other used models.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"955-967"},"PeriodicalIF":0.0,"publicationDate":"2025-06-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11045004","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144598035","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}
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