Computer Science Review最新文献_第10页

A systematic review on cover selection methods for steganography: Trend analysis, novel classification and analysis of the elements 隐写术封面选择方法综述：趋势分析、新分类和要素分析

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-17 DOI: 10.1016/j.cosrev.2025.100726

Muhammad Harith Noor Azam , Farida Ridzuan , M. Norazizi Sham Mohd Sayuti , A H Azni , Nur Hafiza Zakaria , Vidyasagar Potdar

{"title":"A systematic review on cover selection methods for steganography: Trend analysis, novel classification and analysis of the elements","authors":"Muhammad Harith Noor Azam , Farida Ridzuan , M. Norazizi Sham Mohd Sayuti , A H Azni , Nur Hafiza Zakaria , Vidyasagar Potdar","doi":"10.1016/j.cosrev.2025.100726","DOIUrl":"10.1016/j.cosrev.2025.100726","url":null,"abstract":"<div><div>Cover selection is the process of selecting a suitable cover for steganography. Cover selection is crucial to maintain the steganographic characteristics performances and further avoid detection of hidden messages by eavesdroppers. Numerous existing reviews have focused mainly on the implementation and performance of steganography methods. Existing reviews have demonstrated inadequate depth of analysis and a lack of the number of articles reviewed. Thus, this article systematically reviews 34 cover selection methods for steganography in five databases including Web of Science, IEEE Xplore, ScienceDirect, Scopus, and Springer. The results include a trend analysis concerning existing cover selection algorithms for steganography. This article also establishes four novel classifications for cover selection methods. Recommendations on the implementation and design for cover selection method based on each class are provided. Analysis of the elements including cover types, datasets, searching methods, evaluation metrics for searching methods, cover selection attributes and its performance evaluations are also provided. An in-depth discussion on how cover types, searching method and evaluation metrics for searching method affects the steganography characteristics are also presented. This review offers valuable insights for researchers in developing new methods and enhance steganography systems for secure data communication.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100726"},"PeriodicalIF":13.3,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988079","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advances in attention mechanisms for medical image segmentation 医学图像分割注意机制研究进展

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-13 DOI: 10.1016/j.cosrev.2024.100721

Jianpeng Zhang , Xiaomin Chen , Bing Yang , Qingbiao Guan , Qi Chen , Jian Chen , Qi Wu , Yutong Xie , Yong Xia

{"title":"Advances in attention mechanisms for medical image segmentation","authors":"Jianpeng Zhang , Xiaomin Chen , Bing Yang , Qingbiao Guan , Qi Chen , Jian Chen , Qi Wu , Yutong Xie , Yong Xia","doi":"10.1016/j.cosrev.2024.100721","DOIUrl":"10.1016/j.cosrev.2024.100721","url":null,"abstract":"<div><div>Medical image segmentation plays an important role in computer-aided diagnosis. Attention mechanisms that distinguish important parts from irrelevant parts have been widely used in medical image segmentation tasks. This paper systematically reviews the basic principles of attention mechanisms and their applications in medical image segmentation. First, we review the basic concepts of attention mechanism and formulation. Second, we surveyed about 200 articles related to medical image segmentation, and divided them into three groups based on their attention mechanisms, Pre-Transformer attention, Transformer attention and Mamba-related attention. In each group, we deeply analyze the attention mechanisms from three aspects based on the current literature work, <span><math><mrow><mi>i</mi><mo>.</mo><mi>e</mi><mo>.</mo></mrow></math></span>, the principle of the mechanism (what to use), implementation methods (how to use), and application tasks (where to use). We also thoroughly analyzed the advantages and limitations of their applications to different tasks. Finally, we summarize the current state of research and shortcomings in the field, and discuss the potential challenges in the future, including task specificity, robustness, standard evaluation, <em>etc</em>. We hope that this review can showcase the overall research context of traditional, Transformer and Mamba attention methods, provide a clear reference for subsequent research, and inspire more advanced attention research, not only in medical image segmentation, but also in other image analysis scenarios. Finally, we maintain the paper list and open-source code at <span><span>here</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100721"},"PeriodicalIF":13.3,"publicationDate":"2025-01-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142988285","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","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 inclusive analysis for performance and efficiency of graph neural network models for node classification 图神经网络模型节点分类性能和效率的包容性分析

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-11 DOI: 10.1016/j.cosrev.2024.100722

S. Ratna , Sukhdeep Singh , Anuj Sharma

引用次数: 0

Artificial intelligence based classification for waste management: A survey based on taxonomy, classification & future direction 基于人工智能的废物管理分类：基于分类法、分类及未来发展方向的综述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-09 DOI: 10.1016/j.cosrev.2024.100723

Dhanashree Vipul Yevle , Palvinder Singh Mann

{"title":"Artificial intelligence based classification for waste management: A survey based on taxonomy, classification & future direction","authors":"Dhanashree Vipul Yevle , Palvinder Singh Mann","doi":"10.1016/j.cosrev.2024.100723","DOIUrl":"10.1016/j.cosrev.2024.100723","url":null,"abstract":"<div><div>Waste management has grown to become one of the leading global challenges due to the massive generation of thousands of tons of waste that is produced daily, leading to severe environmental degradation, the risk of public health, and resource depletion. Despite efforts directed towards solving these problems, traditional methods of sorting and categorizing waste are inefficient and unsustainable, thus requiring the conceptualization of innovative AI-based solutions for more effective waste management. This review presents, a comprehensive review of all the strategies which are critical for AI based techniques, thus improve productivity and sustainability in operations. Diverse datasets used to train AI models along with performance evaluation metrics, and discusses challenges of AI assimilation in waste management systems, most fundamentally the issue of data privacy and concern of bias in the algorithms. Additionally, the role of loss functions and optimizers in enhancing AI model performance and suggests future research opportunities for sustainable resource recovery, recycling, and reuse based on AI.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100723"},"PeriodicalIF":13.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939697","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Multimodal missing data in healthcare: A comprehensive review and future directions 医疗保健中的多模式缺失数据：全面回顾和未来方向

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-09 DOI: 10.1016/j.cosrev.2024.100720

Lien P. Le , Thu Nguyen , Michael A. Riegler , Pål Halvorsen , Binh T. Nguyen

{"title":"Multimodal missing data in healthcare: A comprehensive review and future directions","authors":"Lien P. Le , Thu Nguyen , Michael A. Riegler , Pål Halvorsen , Binh T. Nguyen","doi":"10.1016/j.cosrev.2024.100720","DOIUrl":"10.1016/j.cosrev.2024.100720","url":null,"abstract":"<div><div>The rapid advancement in healthcare data collection technologies and the importance of using multimodal data for accurate diagnosis leads to a surge in multimodal data characterized by different types, structures, and missing values. Machine learning algorithms for predicting or analyzing usually demand the completeness of data. As a result, handling missing data has become a critical concern in the healthcare sector. This survey paper comprehensively reviews recent works on handling multimodal missing data in healthcare. We emphasize methods for synthesizing data from various modalities or multiple sources in imputing missing data, including early fusion, late fusion, and intermediate fusion methods for missing data imputation. The main objective of this study is to identify gaps in the surveyed area and list future tasks and challenges in handling multimodal missing data in healthcare. This review is valuable for researchers and practitioners in healthcare data analysis. It provides insights into using fusion methods to improve data quality and healthcare outcomes.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100720"},"PeriodicalIF":13.3,"publicationDate":"2025-01-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939737","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications 人工智能在自闭症谱系障碍研究中的出现：神经成像和行为应用综述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-06 DOI: 10.1016/j.cosrev.2024.100718

Indra Devi K.B., Durai Raj Vincent P.M.

{"title":"The emergence of artificial intelligence in autism spectrum disorder research: A review of neuro imaging and behavioral applications","authors":"Indra Devi K.B., Durai Raj Vincent P.M.","doi":"10.1016/j.cosrev.2024.100718","DOIUrl":"10.1016/j.cosrev.2024.100718","url":null,"abstract":"<div><div>The quest to find reliable biomarkers in autism spectrum disorders (ASD) is an ongoing endeavour to identify both underlying causes and measurable indicators of this neurodevelopmental condition. Machine learning (ML) and advanced deep learning (DL) techniques have enhanced biomarker identification in neuroimaging and behavioral studies, aiding in diagnostic accuracy and early detection. This review paper examines the transformative impact of applying machine learning (ML), particularly deep learning (DL) techniques such as transfer learning and transformer architectures, in advancing ASD diagnosis. The review begins by critically assessing existing literature utilizing ML techniques like logistic regression, random forest, and support vector machines in identifying biomarkers that could potentially aid in the diagnosis of ASD and differentiate between ASD and neurotypical individuals. The focus then shifts to DL models, including Multilayer Perceptrons, Convolutional Neural Networks, Graph Neural Networks, and Long Short-Term Memory networks, to evaluate their suitability for identifying complex patterns linked to ASD. Addressing limited datasets, the review examines transfer learning with pre-trained models, including VGG, ResNet, DenseNet, MobileNet, Inception, and Xception architectures. Additionally, using the ABIDE-I dataset, VGG19, MobileNet, InceptionV3, and DenseNet121 were applied, evaluating their performance through accuracy, sensitivity, specificity, and F1 score. The review further considers transformer architectures, such as Vision Transformers, Swin Transformers, Spatial Temporal Transformers, BolT Transformer, and Convolutional Network Transformer for capturing long-range dependencies in ASD diagnosis. This review aims to be an essential reference for researchers exploring the field of AI-powered ASD diagnosis and classification, by offering analysis of various approaches and highlighting recent advancements.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100718"},"PeriodicalIF":13.3,"publicationDate":"2025-01-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142939739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Advancements in AI for cardiac arrhythmia detection: A comprehensive overview 人工智能在心律失常检测中的进展：全面概述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2025-01-03 DOI: 10.1016/j.cosrev.2024.100719

Jagdeep Rahul , Lakhan Dev Sharma

{"title":"Advancements in AI for cardiac arrhythmia detection: A comprehensive overview","authors":"Jagdeep Rahul , Lakhan Dev Sharma","doi":"10.1016/j.cosrev.2024.100719","DOIUrl":"10.1016/j.cosrev.2024.100719","url":null,"abstract":"<div><div>Cardiovascular diseases (CVDs) are a global health concern, demanding advanced healthcare solutions. Accurate identification of CVDs via electrocardiogram (ECG) analysis is complex. Artificial Intelligence (AI) offers potential in improving diagnostic accuracy and uncovering new associations between ECG patterns and heart health risks. This paper reviews AI's historical evolution in CVD diagnosis, focusing on recent ECG analysis advancements and discussing societal implications and future research directions. AI has transformed medical decision-making, progressing from rule-based systems to modern machine learning (ML) and deep learning (DL) methods. By utilizing extensive datasets and advanced neural networks, AI models excel in detecting and categorizing cardiac arrhythmias. However, AI's effectiveness depends on access to large labeled datasets and collaboration within the biomedical community. AI-driven ECG analysis holds promise for revolutionizing cardiovascular care, enabling faster, more accurate diagnostics, and personalized medicine. Key challenges in cardiac arrhythmia classification with AI encompass data quality, class imbalance, and seamless integration with clinical workflows. Addressing these challenges is imperative for realizing the full potential of AI in cardiac care and ensuring accurate diagnosis.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100719"},"PeriodicalIF":13.3,"publicationDate":"2025-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142918041","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","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 comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions 联邦入侵检测系统综合调查：技术、挑战和解决方案

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-20 DOI: 10.1016/j.cosrev.2024.100717

Ioannis Makris , Aikaterini Karampasi , Panagiotis Radoglou-Grammatikis , Nikolaos Episkopos , Eider Iturbe , Erkuden Rios , Nikos Piperigkos , Aris Lalos , Christos Xenakis , Thomas Lagkas , Vasileios Argyriou , Panagiotis Sarigiannidis

{"title":"A comprehensive survey of Federated Intrusion Detection Systems: Techniques, challenges and solutions","authors":"Ioannis Makris , Aikaterini Karampasi , Panagiotis Radoglou-Grammatikis , Nikolaos Episkopos , Eider Iturbe , Erkuden Rios , Nikos Piperigkos , Aris Lalos , Christos Xenakis , Thomas Lagkas , Vasileios Argyriou , Panagiotis Sarigiannidis","doi":"10.1016/j.cosrev.2024.100717","DOIUrl":"10.1016/j.cosrev.2024.100717","url":null,"abstract":"<div><div>Cyberattacks have increased radically over the last years, while the exploitation of Artificial Intelligence (AI) leads to the implementation of even smarter attacks which subsequently require solutions that will efficiently confront them. This need is indulged by incorporating Federated Intrusion Detection Systems (FIDS), which have been widely employed in multiple scenarios involving communication in cyber–physical systems. These include, but are not limited to, the Internet of Things (IoT) devices, Industrial IoT (IIoT), healthcare systems (Internet of Medical Things/IoMT), Internet of Vehicles (IoV), Smart Manufacturing (SM), Supervisory Control and Data Acquisition (SCADA) systems, Multi-access Edge Computing (MEC) devices, among others. Tackling the challenge of cyberthreats in all the aforementioned scenarios is of utmost importance for assuring the safety and continuous functionality of the operations, crucial for maintaining proper procedures in all Critical Infrastructures (CIs). For this purpose, pertinent knowledge of the current status in state-of-the-art (SOTA) federated intrusion detection methods is mandatory, towards encompassing while simultaneously evolving them in order to timely detect and mitigate cyberattack incidents. In this study, we address this challenge and provide the readers with an overview of FL implementations regarding Intrusion Detection in several CIs. Additionally, the distinct communication protocols, attack types and datasets utilized are thoroughly discussed. Finally, the latest Machine Learning (ML) and Deep Learning (DL) frameworks and libraries to implement such methods are also provided.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100717"},"PeriodicalIF":13.3,"publicationDate":"2024-12-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867649","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Knowledge graph representation learning: A comprehensive and experimental overview 知识图表示学习：一个全面的和实验性的概述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-19 DOI: 10.1016/j.cosrev.2024.100716

Dorsaf Sellami , Wissem Inoubli , Imed Riadh Farah , Sabeur Aridhi

{"title":"Knowledge graph representation learning: A comprehensive and experimental overview","authors":"Dorsaf Sellami , Wissem Inoubli , Imed Riadh Farah , Sabeur Aridhi","doi":"10.1016/j.cosrev.2024.100716","DOIUrl":"10.1016/j.cosrev.2024.100716","url":null,"abstract":"<div><div>Knowledge graph embedding (KGE) is a hot topic in the field of Knowledge graphs (KG). It aims to transform KG entities and relations into vector representations, facilitating their manipulation in various application tasks and real-world scenarios. So far, numerous models have been developed in KGE to perform KG embedding. However, several challenges must be addressed when designing effective KGE models. The most discussed challenges in the literature include scalability (KGs contain millions of entities and relations), incompleteness (missing links), the complexity of relations (symmetries, inversion, composition, etc.), and the sparsity of some entities and relations. The purpose of this paper is to provide a comprehensive overview of KGE models. We begin with a theoretical analysis and comparison of the existing methods proposed so far for generating KGE, which we have classified into four categories. We then conducted experiments using four benchmark datasets to compare the efficacy, efficiency, inductiveness, the electricity and the CO<span><math><msub><mrow></mrow><mrow><mn>2</mn></mrow></msub></math></span> emission of five state-of-the-art methods in the link prediction task, providing a comprehensive analysis of the most commonly used benchmarks in the literature.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100716"},"PeriodicalIF":13.3,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142867650","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A comprehensive review of usage control frameworks 对使用控制框架的全面回顾

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-09 DOI: 10.1016/j.cosrev.2024.100698

Ines Akaichi, Sabrina Kirrane

{"title":"A comprehensive review of usage control frameworks","authors":"Ines Akaichi, Sabrina Kirrane","doi":"10.1016/j.cosrev.2024.100698","DOIUrl":"10.1016/j.cosrev.2024.100698","url":null,"abstract":"<div><div>The sharing of data and digital assets in a decentralized settling is entangled with various legislative challenges, including, but not limited to, the need to adhere to legal requirements with respect to privacy and copyright. In order to provide more control to data and digital asset owners, usage control could be used to make sure that consumers handle data according to privacy, licenses, regulatory requirements, among others. However, considering that many of the existing usage control frameworks were designed to cater for different use cases (e.g., networking, operating systems, and industry 4.0), there is a need to better understand the existing proposals and how they compare to one another. In this paper, we provide a holistic overview of existing usage control frameworks and their support for a broad set of requirements. We systematically collect requirements that are routinely used to guide the development of usage control solutions, which are classified according to three broad dimensions: <em>specification</em>, <em>enforcement</em>, and <em>system</em>. We use these requirements to conduct a qualitative comparison of the most prominent usage control frameworks found in the literature. Finally, we identify existing gaps, challenges, and opportunities in the field of usage control in general, and in decentralized environments in particular.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100698"},"PeriodicalIF":13.3,"publicationDate":"2024-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142816523","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0