IEEE Open Journal of the Computer Society最新文献_第2页

Correlation-Based Knowledge Distillation in Exemplar-Free Class-Incremental Learning 无范例类增量学习中基于关联的知识提炼

IEEE Open Journal of the Computer Society Pub Date : 2025-02-28 DOI: 10.1109/OJCS.2025.3546754

Zijian Gao;Bo Liu;Kele Xu;Xinjun Mao;Huaimin Wang

{"title":"Correlation-Based Knowledge Distillation in Exemplar-Free Class-Incremental Learning","authors":"Zijian Gao;Bo Liu;Kele Xu;Xinjun Mao;Huaimin Wang","doi":"10.1109/OJCS.2025.3546754","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3546754","url":null,"abstract":"Class-incremental learning (CIL) aims to learn a family of classes incrementally with data available in order rather than training all data at once. One main drawback of CIL is that standard deep neural networks suffer from catastrophic forgetting (CF), especially when the model only has access to data from the current incremental step. Knowledge Distillation (KD) is a widely used technique that utilizes old models as the teacher model to alleviate CF. However, based on a case study, our investigation reveals that the vanilla KD is insufficient with a strict point-to-point restriction. Instead, a relaxed match between the teacher and student improves distillation performance and model stability. In this article, we propose a simple yet effective method to mitigate CF without any additional training costs or requiring any exemplars. Specifically, we apply the linear correlation between the features of the teacher and student to measure the distillation loss rather than vanilla point-to-point loss, which significantly improves the model stability. Then, we utilize label augmentation to improve feature generalization and save prototypes to alleviate classification bias further. The proposed method significantly outperforms state-of-the-art methods in the various settings of benchmarks, including CIFAR-100 and Tiny-ImageNet, demonstrating its effectiveness and robustness.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"449-459"},"PeriodicalIF":0.0,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10908063","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698225","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

Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results 用非参数 Bootstrap Likelihood 估计法研究聚类结果的机会设置

IEEE Open Journal of the Computer Society Pub Date : 2025-02-24 DOI: 10.1109/OJCS.2025.3545261

Ammar Elnour;Wencheng Yang;Yan Li

{"title":"Nonparametric Bootstrap Likelihood Estimation to Investigate the Chance Set-Up on Clustering Results","authors":"Ammar Elnour;Wencheng Yang;Yan Li","doi":"10.1109/OJCS.2025.3545261","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3545261","url":null,"abstract":"Clustering algorithms are widely used in the knowledge discovery domain, but concerns and questions about the validity of the results must be considered. The datasets commonly used for clustering tasks are often large and scale-free, making conventional statistical techniques inadequate for analyzing result uncertainty. This issue applies to most outcomes obtained from other knowledge discovery techniques, such as machine learning and statistical learning. Traditional statistical methods assume data follows standard distributions, whereas resampling and bootstrapping methods offer more accurate and reliable alternatives. This article introduces a method that employs bootstrap likelihood estimation to infer the uncertainty of generated clustering structures. We first calculated the clustering error in the original dataset and then utilized the proposed method to estimate its nonparametric bootstrapped likelihood. By comparing these two values, we can establish a nonparametric significance testing framework that directly determines the validity of the result. To evaluate the effectiveness of our method, we conducted experiments using synthetic and real datasets. The results demonstrate that our method can successfully validate clustering results.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"438-448"},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10902121","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143698199","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

FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection 基于强化学习的图神经网络自适应金融欺诈检测

IEEE Open Journal of the Computer Society Pub Date : 2025-02-18 DOI: 10.1109/OJCS.2025.3543450

Yiwen Cui;Xu Han;Jiaying Chen;Xinguang Zhang;Jingyun Yang;Xuguang Zhang

{"title":"FraudGNN-RL: A Graph Neural Network With Reinforcement Learning for Adaptive Financial Fraud Detection","authors":"Yiwen Cui;Xu Han;Jiaying Chen;Xinguang Zhang;Jingyun Yang;Xuguang Zhang","doi":"10.1109/OJCS.2025.3543450","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3543450","url":null,"abstract":"As financial systems become increasingly complex and interconnected, traditional fraud detection methods struggle to keep pace with sophisticated fraudulent activities. This article introduces FraudGNN-RL, an innovative framework that combines Graph Neural Networks (GNNs) with Reinforcement Learning (RL) for adaptive and context-aware financial fraud detection. Our approach models financial transactions as a dynamic graph, where entities (e.g., users, merchants) are nodes and transactions form edges. We propose a novel GNN architecture, Temporal-Spatial-Semantic Graph Convolution (TSSGC), which simultaneously captures temporal patterns, spatial relationships, and semantic information in transaction data. The RL component, implemented as a Deep Q-Network (DQN), dynamically adjusts the fraud detection threshold and feature importance, allowing the model to adapt to evolving fraud patterns and minimize detection costs. We further introduce a Federated Learning scheme to enable collaborative model training across multiple financial institutions while preserving data privacy. Extensive experiments on a large-scale, real-world financial dataset demonstrate that FraudGNN-RL outperforms state-of-the-art baselines, achieving a 97.3% F1-score and reducing false positives by 31% compared to the best-performing baseline. Our framework also shows remarkable resilience to concept drift and adversarial attacks, maintaining high performance over extended periods. These results suggest that FraudGNN-RL offers a robust, adaptive, and privacy-preserving solution for financial fraud detection in the era of Big Data and interconnected financial ecosystems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"426-437"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892045","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143792930","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

Threats, Attacks, and Defenses in Machine Unlearning: A Survey 机器学习中的威胁、攻击和防御：综述

IEEE Open Journal of the Computer Society Pub Date : 2025-02-18 DOI: 10.1109/OJCS.2025.3543483

Ziyao Liu;Huanyi Ye;Chen Chen;Yongsen Zheng;Kwok-Yan Lam

{"title":"Threats, Attacks, and Defenses in Machine Unlearning: A Survey","authors":"Ziyao Liu;Huanyi Ye;Chen Chen;Yongsen Zheng;Kwok-Yan Lam","doi":"10.1109/OJCS.2025.3543483","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3543483","url":null,"abstract":"Machine Unlearning (MU) has recently gained considerable attention due to its potential to achieve Safe AI by removing the influence of specific data from trained Machine Learning (ML) models. This process, known as knowledge removal, addresses AI governance concerns of training data such as quality, sensitivity, copyright restrictions, obsolescence, and compliance with privacy regulations. Efforts have been made to design efficient unlearning approaches, with MU services being examined for integration with existing machine learning as a service (MLaaS), allowing users to submit requests to remove specific data from the training corpus. However, recent research highlights vulnerabilities in machine unlearning systems that can lead to significant security and privacy concerns. Moreover, extensive research indicates that unlearning methods and prevalent attacks fulfill diverse roles within MU systems. This underscores the intricate relationship and complex interplay among these mechanisms in maintaining system functionality and safety. This survey aims to fill the gap between the extensive number of studies on threats, attacks, and defenses in machine unlearning and the absence of a comprehensive review that categorizes their taxonomy, methods, and solutions, thus offering valuable insights for future research directions and practical implementations.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"413-425"},"PeriodicalIF":0.0,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10892039","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143645299","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

Enhancing Cloud Security: A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques 增强云安全：使用机器学习技术的多因素认证和自适应加密方法

IEEE Open Journal of the Computer Society Pub Date : 2025-02-04 DOI: 10.1109/OJCS.2025.3538557

K. Sasikumar;Sivakumar Nagarajan

{"title":"Enhancing Cloud Security: A Multi-Factor Authentication and Adaptive Cryptography Approach Using Machine Learning Techniques","authors":"K. Sasikumar;Sivakumar Nagarajan","doi":"10.1109/OJCS.2025.3538557","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3538557","url":null,"abstract":"The rapid expansion of cloud computing underscores the critical need for advanced security measures to protect sensitive data on remote servers. Authentication is crucial for safeguarding these data. Despite various proposed methods, vulnerabilities persist. This article introduces a novel multi-factor authentication system integrated with a hybrid cryptographic framework that dynamically changes encryption algorithms using machine learning techniques based on an intrusion-detection system. The proposed system employs passwords, conditional attributes, and fingerprint authentication to derive the encryption key from fingerprint data. It uses a dual-encryption strategy that combines five algorithm pairs: AES + HMAC (SHA-256), ECC + HMAC (SHA-512), HMAC-MD5 + PBKDF2, Twofish + Argon2, and Blowfish + HMAC SHA3-256. A Hybrid CNN-transformer model predicts and classifies attacks by dynamically adjusting an encryption algorithm to secure the data. The framework exhibited strong resilience against brute force, spoofing, phishing, guessing, and impersonation attacks. The proposed model achieved a commendable accuracy of 96.8%, outperforming other models. Implementing this framework in a cloud authentication environment significantly enhances data confidentiality and protects against unauthorized access. This study highlights the potential of combining multi-factor authentication and adaptive cryptography to obtain robust cloud security solutions.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"392-402"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10872830","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143535549","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

A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting 一种用于精确和可扩展销售预测的混合时间卷积网络和变压器模型

IEEE Open Journal of the Computer Society Pub Date : 2025-02-04 DOI: 10.1109/OJCS.2025.3538579

MD AL Rafi;Gourab Nicholas Rodrigues;MD Nazmul Hossain Mir;MD Shahriar Mahmud Bhuiyan;M. F. Mridha;MD Rashedul Islam;Yutaka Watanobe

{"title":"A Hybrid Temporal Convolutional Network and Transformer Model for Accurate and Scalable Sales Forecasting","authors":"MD AL Rafi;Gourab Nicholas Rodrigues;MD Nazmul Hossain Mir;MD Shahriar Mahmud Bhuiyan;M. F. Mridha;MD Rashedul Islam;Yutaka Watanobe","doi":"10.1109/OJCS.2025.3538579","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3538579","url":null,"abstract":"Accurate product sales forecasting is critical for inventory management, pricing strategies, and supply chain optimization in the retail industry. This article proposes a novel deep learning architecture that integrates Temporal Convolutional Networks (TCNs) with Transformer-based attention mechanisms to capture both short-term and long-term dependencies in time-series sales data. Utilizing the Favorita Grocery Sales Forecasting dataset, our hybrid TCN Transformer model demonstrates superior performance over existing models by incorporating external factors such as holidays, promotions, oil prices, and transaction data. The model achieves state-of-the-art results with a Mean Absolute Error (MAE) of 2.01, Root Mean Squared Error (RMSE) of 2.81, and a Weighted Mean Absolute Percentage Error (wMAPE) of 4.22%, significantly outperforming other leading models such as LSTM, GRU, and TFT. Extensive cross-validation confirms the robustness of our model, achieving consistently high performance across multiple folds.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"380-391"},"PeriodicalIF":0.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10870315","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143521504","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 Efficient and Privacy-Preserving Federated Learning Approach Based on Homomorphic Encryption 一种高效且保护隐私的同态加密联邦学习方法

IEEE Open Journal of the Computer Society Pub Date : 2025-01-30 DOI: 10.1109/OJCS.2025.3536562

Francesco Castro;Donato Impedovo;Giuseppe Pirlo

{"title":"An Efficient and Privacy-Preserving Federated Learning Approach Based on Homomorphic Encryption","authors":"Francesco Castro;Donato Impedovo;Giuseppe Pirlo","doi":"10.1109/OJCS.2025.3536562","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536562","url":null,"abstract":"Federated Learning (FL) is a decentralized and collaborative learning approach that ensures the data privacy of each participant. However, recent studies have shown that the private data of each participant can be obtained from shared parameters of local models through reversal model and membership inference attacks leading to privacy leakage. Privacy-preserving federated learning strategies based on Homomorphic Encryption (PPFL-HE) have been developed to solve this issue. PPFL-HE methods require high communication and computational overheads, which are impractical for resource-limited devices. This work proposes an efficient PPFL-HE method to reduce communication and computational overheads. The proposed method is based on an innovative quantization process that introduces a dynamic range evaluation layer-for-layer (DREL) to encode the weights of the local models into long-signed integers. Compared to standard quantization approaches, the proposed method reduces the quantization errors and the communication overhead. Moreover, it enables the encryption of local weights with the Brakerski/Fan-Vercauteren Homomorphic Encryption scheme (BFV-HE), which is highly efficient on integers, reducing encryption, aggregation, decryption time, and ciphertext size. The experiments conducted with five popular datasets and four different Machine Learning (ML) models (three CNN models and a feedforward neural network) show that the proposed method is more efficient in communication and computational overheads than other PPFL-HE methods. Specifically, the proposed method requires fewer FL rounds to achieve global model convergence and leads to an average reduction in encryption time of 99.95% and 73.79%, in decryption time of 99.90% and 55.13%, and in ciphertext size of 5.78% and 75.17% compared to PPFL-HE methods based on Paillier and CKKS schemes, respectively.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"336-347"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858339","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480785","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

The Rise of Cognitive SOCs: A Systematic Literature Review on AI Approaches 认知soc的兴起：关于人工智能方法的系统文献综述

IEEE Open Journal of the Computer Society Pub Date : 2025-01-30 DOI: 10.1109/OJCS.2025.3536800

Farid Binbeshr;Muhammad Imam;Mustafa Ghaleb;Mosab Hamdan;Mussadiq Abdul Rahim;Mohammad Hammoudeh

{"title":"The Rise of Cognitive SOCs: A Systematic Literature Review on AI Approaches","authors":"Farid Binbeshr;Muhammad Imam;Mustafa Ghaleb;Mosab Hamdan;Mussadiq Abdul Rahim;Mohammad Hammoudeh","doi":"10.1109/OJCS.2025.3536800","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536800","url":null,"abstract":"The increasing sophistication of cyber threats has led to the evolution of Security Operations Centers (SOCs) towards more intelligent and adaptive systems. This review explores the integration of Artificial Intelligence (AI) in SOCs, focusing on their current state, challenges, opportunities, and advantages over traditional methods. We address three key questions: (1) What are the current AI approaches in SOCs? (2) What challenges and opportunities exist with these approaches? (3) What benefits do AI models offer in SOC environments compared to traditional methods? We analyzed 38 studies using a structured methodology involving database searches, quality checks, and data extraction. Our findings show that Machine Learning (ML) techniques dominate SOC research, with a trend towards multi-approach AI methods. We classified these into ML, Natural Language Processing, multi-approach, and others, forming a detailed taxonomy of AI applications in SOCs. Challenges include data quality, model interpretability, legacy system integration, and the need for constant adaptation. Opportunities involve task automation, enhanced threat detection, real-time analysis, and adaptive learning. AI-driven SOCs show better accuracy, reduced false positives, greater scalability, and predictive capabilities than traditional approaches. This review defines Cognitive SOCs, emphasizing their ability to mimic human-like processes. We offer practical insights for SOC designers and managers on implementing AI to improve security operations. Finally, we suggest future research directions in explainable AI, human-AI collaboration, and privacy-preserving AI for SOCs.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"360-379"},"PeriodicalIF":0.0,"publicationDate":"2025-01-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10858372","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480811","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

Generative AI and the Metaverse: A Scoping Review of Ethical and Legal Challenges 生成人工智能和虚拟世界：伦理和法律挑战的范围审查

IEEE Open Journal of the Computer Society Pub Date : 2025-01-29 DOI: 10.1109/OJCS.2025.3536082

Aliya Tabassum;Ezieddin Elmahjub;Aasim I. Padela;Andrej Zwitter;Junaid Qadir

{"title":"Generative AI and the Metaverse: A Scoping Review of Ethical and Legal Challenges","authors":"Aliya Tabassum;Ezieddin Elmahjub;Aasim I. Padela;Andrej Zwitter;Junaid Qadir","doi":"10.1109/OJCS.2025.3536082","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3536082","url":null,"abstract":"The metaverse, a pioneering digital realm merging virtual and augmented realities with Artificial Intelligence (AI), represents a transformative environment where digital and physical realities converge seamlessly. Generative AI (GenAI) is indispensable in powering the metaverse's dynamic and immersive experiences, enabling the autonomous generation of diverse digital content. Large Language Models (LLMs), as a component of GenAI, play a critical role by facilitating real-time communication, multilingual translation, and personalized interactions, enhancing user engagement in shared virtual spaces. This scoping review explores the interdependence between GenAI and the metaverse and the unique ethical and legal challenges that emerge from their integration. It identifies key ethical and legal issues, such as bias in AI-generated content, misinformation, and data privacy concerns, related to the deployment of GenAI and LLMs, and offers strategic recommendations for addressing these challenges responsibly. Emphasizing the transformative potential of these technologies, this review highlights the necessity of developing tailored ethical and legal frameworks to manage their convergence responsibly, ensuring equitable and sustainable growth within the metaverse.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"348-359"},"PeriodicalIF":0.0,"publicationDate":"2025-01-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10857306","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143512793","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

Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions 混合POF-VLC系统：最新进展、挑战、机遇和未来方向

IEEE Open Journal of the Computer Society Pub Date : 2025-01-28 DOI: 10.1109/OJCS.2025.3535663

Rola Abdallah;Mohamed Atef;Nasir Saeed

{"title":"Hybrid POF-VLC Systems: Recent Advances, Challenges, Opportunities, and Future Directions","authors":"Rola Abdallah;Mohamed Atef;Nasir Saeed","doi":"10.1109/OJCS.2025.3535663","DOIUrl":"https://doi.org/10.1109/OJCS.2025.3535663","url":null,"abstract":"Hybrid Polymer Optical Fiber and Visible Light Communication (POF-VLC) systems are emerging as a promising solution for high-speed, interference-free connectivity, especially in environments where traditional RF communication is constrained. This paper investigates key nonlinear impairments in POF-VLC systems, such as chromatic dispersion (CD), self-phase modulation (SPM), cross-phase modulation (XPM), four-wave mixing (FWM), and stimulated scattering, which severely degrade signal quality and limit transmission range. We review advanced modulation techniques like Orthogonal Frequency Division Multiplexing (OFDM) and Discrete Multitone Modulation (DMT), alongside traditional methods like Non-Return-to-Zero (NRZ) and On-Off Keying (OOK), evaluating their effectiveness in overcoming these challenges. Furthermore, the application of machine learning, particularly neural network-based equalizers like Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), is highlighted for their potential to enhance signal quality and system performance. This review emphasizes the transformative role these advanced strategies can play in optimizing hybrid POF-VLC systems, paving the way for their integration into high-demand communication environments. Moreover, this paper presents several promising research directions, such as optimizing training algorithms, exploring deeper neural network architectures, and integrating POF-VLC systems with emerging technologies like beyond 5G, improving energy efficiency, and addressing scalability and complexity in real-time adaptive POF-VLC systems.","PeriodicalId":13205,"journal":{"name":"IEEE Open Journal of the Computer Society","volume":"6 ","pages":"317-335"},"PeriodicalIF":0.0,"publicationDate":"2025-01-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10856321","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143480810","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