Computer Science Review最新文献_第4页

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

Cloud continuum testbeds and next-generation ICTs: Trends, challenges, and perspectives 云连续测试平台和下一代信息通信技术：趋势、挑战和前景

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-06 DOI: 10.1016/j.cosrev.2024.100696

Fran Casino , Peio Lopez-Iturri , Constantinos Patsakis

{"title":"Cloud continuum testbeds and next-generation ICTs: Trends, challenges, and perspectives","authors":"Fran Casino , Peio Lopez-Iturri , Constantinos Patsakis","doi":"10.1016/j.cosrev.2024.100696","DOIUrl":"10.1016/j.cosrev.2024.100696","url":null,"abstract":"<div><div>As society’s dependence on Information and Communication Technologies (ICTs) grows, providing efficient and resourceful services entails many complexities that require, among others, scalable systems that are provided with intelligent and automated management. In parallel, the different components of cloud computing are continuously evolving to enhance their capabilities towards leveraging the next generation of ICTs. Due to the substantial investment in resources required to provide efficient services, suitable research and experimentation platforms to test and validate cloud technologies before releasing them into operational versions are crucial to delivering sound systems with sustainable cost/benefit ratios. In this article, we review the current state of the art by analysing cloud testbeds devoted to studying the capabilities of the cloud continuum. Instead of recalling a component-wise or architectural discussion of these systems, this article explores the full spectrum of the cloud continuum testbeds and their features, providing a taxonomy that can be practically used as an entry point to identify each testbed’s scope. Moreover, we extract the challenges found in the literature to deliver a profound discussion, correlating the analysed testbeds and their features. Our findings highlight the main gaps and potential roadmaps to provide effective testbeds considering the next generation of ICTs.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100696"},"PeriodicalIF":13.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788849","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

Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights 将可解释的人工智能与下一代物联网的联邦学习相结合：全面回顾和前瞻性见解

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-06 DOI: 10.1016/j.cosrev.2024.100697

Praveer Dubey, Mohit Kumar

{"title":"Integrating Explainable AI with Federated Learning for Next-Generation IoT: A comprehensive review and prospective insights","authors":"Praveer Dubey, Mohit Kumar","doi":"10.1016/j.cosrev.2024.100697","DOIUrl":"10.1016/j.cosrev.2024.100697","url":null,"abstract":"<div><div>The emergence of the Internet of Things (IoT) signifies a transformative wave of innovation, establishing a network of devices designed to enrich everyday experiences. Developing intelligent and secure IoT applications without compromising user privacy and the transparency of model decisions causes a significant challenge. Federated Learning (FL) serves as a innovative solution, encouraging collaborative learning across a wide range of devices and ensures the protection of user data and builds trust in the process. However, challenges remain, including data variability, potential security vulnerabilities within FL, and the necessity for transparency in decentralized models. Moreover, the lack of clarity associated with traditional AI models raises issues regarding transparency, trust and fairness in IoT applications. The survey examines the integration of Explainable AI (XAI) and FL within the Next Generation IoT framework. It provides a thorough analysis of how XAI techniques can elucidate the mechanisms of FL models, addressing challenges such as communication overhead, data heterogeneity and privacy-preserving explanation methods. The survey brings attention to the benefits of FL, including secure data sharing, effective modeling of heterogeneous data and improved communication and interoperability. Additionally, it presents mathematical formulations of the challenges in FL and discusses potential solutions aimed at enhancing the resilience and scalability of IoT implementations. Eventually, convergence of XAI and FL enhances interpretability and promotes the development of trustworthy and transparent AI systems, establishing a strong foundation for impactful applications in the ever evolving Next-Generation IoT landscape.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"56 ","pages":"Article 100697"},"PeriodicalIF":13.3,"publicationDate":"2024-12-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142788848","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

Ontology learning towards expressiveness: A survey 面向表达的本体学习：综述

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-05 DOI: 10.1016/j.cosrev.2024.100693

Pauline Armary , Cheikh Brahim El-Vaigh , Ouassila Labbani Narsis , Christophe Nicolle

引用次数: 0

Non-square grids: A new trend in imaging and modeling? 非方形网格：成像和建模的新趋势？

IF 13.3 1区计算机科学

Computer Science Review Pub Date : 2024-12-04 DOI: 10.1016/j.cosrev.2024.100695

Paola Magillo

引用次数: 0