IEEE Open Journal of the Computer Society最新文献_第8页

Innovative Evaluation Framework for Consumer Electronics-Enabled Intelligent Transportation Systems: Leveraging $q$-Rung Picture Fuzzy Hypersoft Schweizer-Sklar Aggregation Operators 消费类电子智能交通系统的创新评估框架：利用$q$阶图像模糊超软Schweizer-Sklar聚合算子

IEEE Open Journal of the Computer Society Pub Date : 2025-03-19 DOI: 10.1109/OJCS.2025.3571815

Badiea Abdulkarem Mohammed;Himanshu Dhumras;Varun Shukla;Rakesh Kumar Bajaj;Zeyad Ghaleb Al-Mekhlafi

{"title":"Innovative Evaluation Framework for Consumer Electronics-Enabled Intelligent Transportation Systems: Leveraging $q$-Rung Picture Fuzzy Hypersoft Schweizer-Sklar Aggregation Operators","authors":"Badiea Abdulkarem Mohammed;Himanshu Dhumras;Varun Shukla;Rakesh Kumar Bajaj;Zeyad Ghaleb Al-Mekhlafi","doi":"10.1109/OJCS.2025.3571815","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3571815","url":null,"abstract":"The development of intelligent transportation systems (ITS), supported by consumer electronics, is important for modern urban mobility. However, to evaluate these systems, complex, uncertain and interrelated factors, such as technological feasibility, economic viability, and risk assessment, must be addressed. Traditional multi-criteria decision-making (MCDM) approaches do not manage these uncertainties well. This article presents a novel assessment framework based on new Schweizer-Sklar (SS) aggregation operations and the q-rung picture fuzzy hypersoft set (<inline-formula><tex-math>$q$</tex-math></inline-formula>-RPFHSS) to tackle this problem. These operators enhance decision-making accuracy by modeling sub-parameterized uncertainties. The proposed method is applied to rank five cities for the deployment of ITS, showing excellent flexibility and precision compared to existing methods. Comparative analyses illustrate the robustness of the proposed approach in strategic decision-making for ITS, ensuring a more reliable and adaptive evaluation process.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"834-845"},"PeriodicalIF":0.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11007259","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264271","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}

引用次数: 0

An Enhanced Deep Learning Approach to Potential Purchaser Prediction: AutoGluon Ensembles for Cross-Industry Profit Maximization 潜在购买者预测的增强深度学习方法：跨行业利润最大化的AutoGluon集成

IEEE Open Journal of the Computer Society Pub Date : 2025-03-18 DOI: 10.1109/OJCS.2025.3552376

Hashibul Ahsan Shoaib;Md Anisur Rahman;Jannatul Maua;Ashifur Rahman;M. F. Mridha;Pankoo Kim;Jungpil Shin

{"title":"An Enhanced Deep Learning Approach to Potential Purchaser Prediction: AutoGluon Ensembles for Cross-Industry Profit Maximization","authors":"Hashibul Ahsan Shoaib;Md Anisur Rahman;Jannatul Maua;Ashifur Rahman;M. F. Mridha;Pankoo Kim;Jungpil Shin","doi":"10.1109/OJCS.2025.3552376","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3552376","url":null,"abstract":"Accurately identifying potential purchasers is critical for maximizing profitability in highly competitive markets, spanning industries from finance and insurance to telecommunications. This article presents an enhanced deep learning approach for potential purchaser prediction, leveraging an AutoGluon ensemble framework to optimize accuracy and profitability across diverse datasets, including time deposits, health insurance, 5G packages, and credit cards. The proposed AutoGluon-based ensemble integrates neural networks with boosted trees, stacking, and bagging to maximize the Expected Maximum Profit Criterion (EMPC) and deliver consistent predictive performance across datasets. Our model demonstrates superior performance in terms of Area Under the Curve (AUC), EMPC, and top decile lift (TDL) relative to benchmark classifiers. Specifically, for the credit card dataset, the model achieved an AUC of 0.8856, an EMPC of 13.8453, and a TDL of 3.80, marking significant improvements over prior results. Bayesian A/B testing, based on 40 EMPC ranks, further confirms the robustness of our model, with a 98.5% probability of being the best-performing model across datasets. The AutoGluon ensemble consistently outperforms traditional ensemble models, achieving an average rank-adjusted p-value below 0.015 in the Holm post-hoc test, validating its statistical significance. This study underscores the efficacy of deep learning ensembles in cross-industry potential purchaser prediction, providing a scalable, profit-driven approach for enhanced marketing and customer acquisition strategies.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"468-479"},"PeriodicalIF":0.0,"publicationDate":"2025-03-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10930799","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143817949","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}

引用次数: 0

Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning 连字符：基于自监督表学习的无卡欺诈检测系统

IEEE Open Journal of the Computer Society Pub Date : 2025-03-15 DOI: 10.1109/OJCS.2025.3570600

Josue Genaro Almaraz-Rivera;Jose Antonio Cantoral-Ceballos;Juan Felipe Botero;Francisco Javier MuñOz;Brian David Martinez

{"title":"Hyphatia: A Card-Not-Present Fraud Detection System Based on Self-Supervised Tabular Learning","authors":"Josue Genaro Almaraz-Rivera;Jose Antonio Cantoral-Ceballos;Juan Felipe Botero;Francisco Javier MuñOz;Brian David Martinez","doi":"10.1109/OJCS.2025.3570600","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3570600","url":null,"abstract":"In order to conduct credit card fraud, having only the payment card information of the victim it is possible to fake its identity and buy on e-commerce platforms. This type of fraud is known as Card-Not-Present and shows in the form of chargebacks, projecting billion-dollar losses worldwide in the coming years. The IEEE-CIS dataset has emerged as a strong option for creating and validating smart detection systems against this problem. In this work, we propose a solution, Hyphatia, where Self-Supervised Learning is implemented for tabular data based on SubTab. Our model outperforms XGBoost by 2.14% AUROC, detecting 67.44% of the fraud cases in the IEEE-CIS. This pioneering experimentation prioritizes those features that are not obfuscated. Furthermore, beyond providing just classification metrics, we also share time performance and feature importance calculations for explainability. To the best of our knowledge, this is one of the first works in the literature using Self-Supervised Learning for the problem of credit card fraud detection, specifically using the Self-Supervised Tabular Learning approach.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"812-821"},"PeriodicalIF":0.0,"publicationDate":"2025-03-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11004629","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144264200","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}

引用次数: 0

SSL-XIoMT: Secure, Scalable, and Lightweight Cross-Domain IoMT Sharing With SSI and ZKP Authentication SSL-XIoMT：安全，可扩展，轻量级跨域IoMT共享与SSI和ZKP认证

IEEE Open Journal of the Computer Society Pub Date : 2025-03-14 DOI: 10.1109/OJCS.2025.3570087

Lyhour Hak;Somchart Fugkeaw

{"title":"SSL-XIoMT: Secure, Scalable, and Lightweight Cross-Domain IoMT Sharing With SSI and ZKP Authentication","authors":"Lyhour Hak;Somchart Fugkeaw","doi":"10.1109/OJCS.2025.3570087","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3570087","url":null,"abstract":"The Internet of Medical Things (IoMT) is transforming healthcare by enabling devices to generate and share critical patient data. However, securely sharing this data across different healthcare entities remains a significant challenge due to concerns over privacy and security. Traditional solutions using Ciphertext Policy Attribute-Based Encryption (CP-ABE), Self-Sovereign Identity (SSI), and Zero-Knowledge Proofs (ZKPs) offer secure and anonymous data access, but they often fall short in scalability and integration, particularly in cross domain environments. To address these limitations, we introduce SSL-XIoMT, an optimized SSI and ZKP authentication framework within a consortium Hyperledger-based environment. This innovative system integrates SSI under advanced Zero-Knowledge Scalable Transparent Argument of Knowledge (ZK-STARK) and Plonk protocols within a consortium Hyperledger framework for privacy-preserving identity verification. We enhance identity privacy by integrating Multi-Party Computation (MPC), ensuring that identity credentials and ZKP proofs are securely shared and reconstructed without exposing sensitive information. Additionally, we optimize CP-ABE by offloading complex computations to fog nodes, which pre-compute attributes and logical operations. This approach significantly reduces computational overhead and enhances both privacy and efficiency. Our extensive analysis shows that SSL-XIoMT dramatically improves the performance of processing time for CP-ABE encryption and decryption compared to current methods. Moreover, our hybrid ZKPs based authentication approach outperforms the existing schemes regarding processing time and flexibility. The throughput test also demonstrates that SSL-XIoMT is practical for large scale cross-domain data sharing implementation.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"714-725"},"PeriodicalIF":0.0,"publicationDate":"2025-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11003572","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144205884","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}

引用次数: 0

Editorial: Gratitude, Reflection, and Celebration: My Tenure as EiC Comes to a Close 社论：感恩，反思和庆祝：我作为EiC的任期即将结束

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3525990

Song Guo

引用次数: 0

TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation 基于变压器和注意力增强的脑肿瘤分割架构

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569758

Mir Nafiul Nagib;Rahat Pervez;Afsana Alam Nova;Hadiur Rahman Nabil;Zeyar Aung;M. F. Mridha

{"title":"TuSegNet: A Transformer-Based and Attention-Enhanced Architecture for Brain Tumor Segmentation","authors":"Mir Nafiul Nagib;Rahat Pervez;Afsana Alam Nova;Hadiur Rahman Nabil;Zeyar Aung;M. F. Mridha","doi":"10.1109/OJCS.2025.3569758","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3569758","url":null,"abstract":"Brain tumor segmentation is crucial in medical imaging, allowing informed diagnosis and treatment planning. In this study, we propose TuSegNet, a new transformer-based and attention-enhanced architecture for robust brain tumor segmentation. The model combines convolutional layers with transformer blocks for global context awareness, incorporates Atrous Spatial Pyramid Pooling (ASPP) for multi-scale feature extraction, and employs channel attention mechanisms to concentrate on tumor-relevant parts. Evaluated on three datasets—Dataset A, Dataset B, and a combined dataset—TuSegNet achieves state-of-the-art performance with a Dice Similarity Coefficient (DSC) of 0.895, 0.910, and 0.930, respectively, and an Intersection over Union (IoU) of 0.820, 0.835, and 0.860. Ablation studies validate the importance of ASPP and attention mechanisms, while comparative analysis demonstrates outstanding performance over existing SOTA models such as Swin UNet and TransUNet. The proposed methodology improves segmentation accuracy and highlights the importance of hybrid architectures in handling complex medical imaging tasks. These developments underscore the potential of TuSegNet for real-world healthcare applications in brain tumor diagnosis.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"750-761"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11002687","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232131","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}

引用次数: 0

WiFi-Based Location Tracking: A Still Open Door on Laptops 基于wifi的位置跟踪：笔记本电脑的一扇尚未打开的大门

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569437

Mariana Cunha;Ricardo Mendes;Yves-Alexandre de Montjoye;João P. Vilela

引用次数: 0

Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification 基于对立的白鲨优化器优化改进的EfficientNetV2道路裂缝分类

IEEE Open Journal of the Computer Society Pub Date : 2025-03-12 DOI: 10.1109/OJCS.2025.3569208

Mohammed Al-Shalabi;Mohammed A. Mahdi;Malik Braik;Mohammed Azmi Al-Betar;Shahanawaj Ahamad;Sawsan A. Saad

{"title":"Opposition-Based White Shark Optimizer for Optimizing Modified EfficientNetV2 in Road Crack Classification","authors":"Mohammed Al-Shalabi;Mohammed A. Mahdi;Malik Braik;Mohammed Azmi Al-Betar;Shahanawaj Ahamad;Sawsan A. Saad","doi":"10.1109/OJCS.2025.3569208","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3569208","url":null,"abstract":"Maintaining reliable and long-lasting road infrastructure requires accurate identification and management of pavement cracks, as these cracks can significantly weaken asphalt and concrete surfaces over time. Although Convolutional Neural Networks (CNNs) and meta-heuristic algorithms have proven effective in solving real-world problems, their use in low-contrast pavement crack images is worth investigating. This study proposes an automated crack detection framework that integrates three key components: (1) a new variant of a pre-trained CNN architecture, referred to as Modified EfficientNetV2 (MEfficientNetV2) for pavement crack classification; (2) a combination of opposition-based learning with White Shark Optimizer (WSO), known as Opposition WSO (OWSO), to improve the balance between exploration and exploitation; and (3) Principal Component Analysis (PCA) for efficient dimensionality reduction and feature selection. This method is validated on various publicly available asphalt crack datasets that contain low-contrast natural images. Preprocessing techniques are first applied to eliminate noise and enhance image quality. The OWSO algorithm is then integrated to optimize the classification performance of MEfficientNetV2, while PCA accelerates the learning process by retaining critical features in the thresholds of the varying components. Comparative evaluations with state-of-the-art methods demonstrate that the proposed model excels in terms of precision, robustness, and generalizability. The outcome emphasizes its ability to identify the most effective solution for crack detection in practical scenarios, where PCA-based feature selection improves computational efficiency without compromising performance. This study focuses on the potential of hybrid deep learning and bio-inspired optimization strategies to improve automated pavement maintenance systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"762-775"},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10999102","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144206021","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}

引用次数: 0

MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer MEViT：基于元学习effentnet视觉转换器的深度假检测推广

IEEE Open Journal of the Computer Society Pub Date : 2025-03-07 DOI: 10.1109/OJCS.2025.3568044

Van-Nhan Tran;Hoanh-Su Le;Piljoo Choi;Suk-Hwan Lee;Ki-Ryong Kwon

{"title":"MEViT: Generalization of Deepfake Detection With Meta-Learning EfficientNet Vision Transformer","authors":"Van-Nhan Tran;Hoanh-Su Le;Piljoo Choi;Suk-Hwan Lee;Ki-Ryong Kwon","doi":"10.1109/OJCS.2025.3568044","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3568044","url":null,"abstract":"Deepfakes are digitally manipulated videos that appear realistic but are actually fake. With the rapid advances in deep generative models, the accessibility and sophistication of such manipulation technologies are increasing, making it more challenging to detect fake content. Different facial forgery techniques result in complex data distributions, and most existing deepfake detection approaches rely on convolutional neural networks (CNNs) that treat the task as a binary classification problem. While these methods achieve high accuracy on specific datasets, their generalization performance across datasets is often poor due to overfitting to manipulation techniques seen during training. In this study, we propose a model called MEViT, which integrates the EfficientNet Vision Transformer with a meta-learning framework to enhance generalization in deepfake detection. Furthermore, we introduce a pair-discrimination loss to push the feature representations of fake samples away from those of real samples, and a domain adjustment loss to reduce domain shifts across different manipulation methods. The MEViT model is trained on a specific manipulation method in the FaceForensics++ dataset and evaluated on other unseen methods from the same dataset. Additionally, we conduct extensive experiments on multiple deepfake benchmarks, including FaceForensics++ and CelebDF-v2, and compare our method with various state-of-the-art approaches to demonstrate its effectiveness.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"789-800"},"PeriodicalIF":0.0,"publicationDate":"2025-03-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10992261","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144232130","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}

引用次数: 0

FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking 软件定义网络中网络威胁情报的综合平衡联邦一类分类

IEEE Open Journal of the Computer Society Pub Date : 2025-03-06 DOI: 10.1109/OJCS.2025.3567386

Syed Hussain Ali Kazmi;Faizan Qamar;Rosilah Hassan;Kashif Nisar

{"title":"FOCC: A Synthetically Balanced Federated One-Class-Classification for Cyber Threat Intelligence in Software Defined Networking","authors":"Syed Hussain Ali Kazmi;Faizan Qamar;Rosilah Hassan;Kashif Nisar","doi":"10.1109/OJCS.2025.3567386","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3567386","url":null,"abstract":"Federated Learning offers a promising approach for building Cyber Threat Intelligence (CTI) by utilizing cross-domain data in Software Defined Networking (SDN) while addressing privacy concerns. However, as sixth-generation (6G) systems evolve with heterogeneous characteristics, the training data across individual SDN domains is likely to be highly Non-Independent and Identically Distributed (Non-IID), which significantly impairs the performance of Artificial Intelligence (AI) based Intrusion Detection Systems (IDSs). Therefore, this study proposes a novel framework called Federated One Class Classification (FOCC), which contains parallel inference with threat-specific independent autoencoders as local model at each domain and empowered with Variational Auto Encoders (VAEs). Firstly, the relation between weight divergence and multi-classification in Non-IID data is derived using mathematical analysis. Secondly, the threat specific data is generated by VAEs at each domain with latent space aggregation, which achieved the reduced validation loss in Federated Learning by synthetically balancing threat-specific data. Finally, the proposed FOCC framework depicts substantial improvement in threat specific multiclassification on InSDN dataset as compared to the existing state-of-the-art solutions for performance parameters; including accuracy, precision, recall and F1 score. Moreover, the integration of parallel processing in the proposed FOCC framework significantly minimizes computational delays.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"701-713"},"PeriodicalIF":0.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10989587","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144178955","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}

引用次数: 0