Computer Science Review最新文献

{"title":"Cognitive mapping variants and their training algorithms","authors":"Maikel Leon","doi":"10.1016/j.cosrev.2025.100862","DOIUrl":"10.1016/j.cosrev.2025.100862","url":null,"abstract":"<div><div>Cognitive modeling has been crucial in understanding and simulating complex systems, especially for decision-making processes. From Tolman’s cognitive maps of rats to Kosko’s fuzzy cognitive maps and modern quantum cognitive models, researchers have sought representations that combine human expertise with computational rigor. This survey offers a comprehensive, historical, and mathematical treatment of concept maps, cognitive maps, and a rich family of fuzzy and hybrid cognitive map models. We discuss rule-based, crisp, gray, rough, neutrosophic, and quantum cognitive maps, and we review machine learning algorithms used to train them, including Hebbian and differential Hebbian learning, genetic and evolutionary algorithms, particle swarm optimization, reinforcement learning, and other hybrid techniques. The aim is to present a unified theoretical framework that traces the development of cognitive mapping from its origins to the latest advances, providing clear pathways for future research.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100862"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145598635","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}

{"title":"Information-theoretic reduction of Markov chains","authors":"Bernhard C. Geiger","doi":"10.1016/j.cosrev.2025.100802","DOIUrl":"10.1016/j.cosrev.2025.100802","url":null,"abstract":"<div><div>We survey information-theoretic approaches to the reduction of Markov chains. Our survey is structured in two parts: The first part considers Markov chain coarse graining, which focuses on projecting the Markov chain to a process on a smaller state space that is <em>informative</em> about certain quantities of interest. The second part considers Markov chain model reduction, which focuses on replacing the original Markov model by a simplified one that yields <em>similar</em> behavior as the original Markov model. We discuss the practical relevance of both approaches in the field of knowledge discovery and data mining by formulating problems of unsupervised machine learning as reduction problems of Markov chains. Finally, we briefly discuss the concept of lumpability, the phenomenon when a coarse graining yields a reduced Markov model.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100802"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145119248","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}

{"title":"Explainable AI for the diagnosis of neurodegenerative diseases: Unveiling methods, opportunities, and challenges","authors":"Alden Jenish S ,&nbsp;Karthik R ,&nbsp;Suganthi K","doi":"10.1016/j.cosrev.2025.100821","DOIUrl":"10.1016/j.cosrev.2025.100821","url":null,"abstract":"<div><div>Artificial Intelligence (AI) has exhibited significant potential in diagnosis and operational efficiency across medical domains. Nevertheless, the opacity of the AI-driven diagnostic models creates a major roadblock to clinical deployment. Explainable Artificial Intelligence (XAI) techniques have emerged to improve physician trust and transparency in AI-based predictions by addressing interpretability and explainability. This review aims to explore and analyze recent advancements in XAI techniques applied to the diagnosis of Neurodegenerative Diseases (NDs). Based on their approaches toward interpretability, the included studies were categorized into model-agnostic and model-specific techniques. These interpretability techniques provide deeper insights into the factors influencing clinical diagnoses. The review examines various interpretative methods that enhance the transparency of AI-driven models, ensuring alignment with clinical decision-making. This summary reflects all major findings and critical analysis of the responses to the research questions posed. The next stage of analysis describes how XAI enhances model reliability and eases the clinical decision-making process. This review presents a cross-disease comparative evaluation of XAI techniques applied to major NDs such as Alzheimer’s Disease (AD), Parkinson’s Disease (PD), and Multiple Sclerosis (MS), offering a unified perspective on interpretability across modalities and disorders. This study explores existing approaches, highlights their strengths and limitations, and discusses future research directions in this domain.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100821"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145093960","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}

{"title":"Intra-node transaction parallelism in blockchains: Models, solutions, and trends","authors":"Bin Yu ,&nbsp;Tong Zhou ,&nbsp;He Zhao ,&nbsp;Xiaoyan Li ,&nbsp;Yuhui Fan ,&nbsp;Lei Chen","doi":"10.1016/j.cosrev.2025.100853","DOIUrl":"10.1016/j.cosrev.2025.100853","url":null,"abstract":"<div><div>Blockchain technology has been widely adopted across diverse domains, yet its scalability remains a critical bottleneck. Traditional serial transaction execution limits throughput to a low level, failing to meet the demands of high-frequency trading. While parallelization solutions exist, existing literature predominantly focus on “broad parallelism” like sharding and cross-chain. But they overlook “intra-node parallelism”, which is a lightweight approach to optimize transaction execution within single nodes without altering core protocols. We aim to fill this gap by conducting a focused, systematic analysis of intra-node transaction parallelism in blockchains. The research methods include: (1) Categorizing intra-node parallelism into three core models (deterministic, optimistic, emerging) based on conflict-handling mechanisms and architectural paradigms. (2) Analyzing ∼20 representative solutions to evaluate their core mechanisms, performance trade-offs, and applicable scenarios. (3) Investigating critical practical considerations, including conflict density-based applicability, overhead trade-offs, and synergy with consensus/network layers. (4) Comparing models across dimensions (conflict handling, performance, complexity) to identify strengths and limitations. Key results show that: (1) Deterministic models achieve ∼2–3x serial throughput with negligible rollbacks, making them ideal for high-conflict environments. (2) Optimistic models reach ∼5–10x serial throughput in low-conflict scenarios but suffer from rollback overhead in high-conflict settings. (3) Emerging models offer breakthrough scalability but require ecosystem changes. (4) No single model dominates. Optimal selection depends on conflict density, contract complexity, and compatibility needs. This study provides a foundational framework for researchers to navigate intra-node parallelism and for practitioners to select or design solutions balancing scalability, consistency, and decentralization. It advances blockchain scalability research by highlighting lightweight, backward-compatible optimizations that complement broad parallelism, enabling the development of high-performance blockchain systems for real-world applications.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100853"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145473227","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}

{"title":"A survey of AI-supported materials informatics","authors":"Sanjay Chakraborty ,&nbsp;Jonas Björk ,&nbsp;Martin Dahlqvist ,&nbsp;Johanna Rosen ,&nbsp;Fredrik Heintz","doi":"10.1016/j.cosrev.2025.100845","DOIUrl":"10.1016/j.cosrev.2025.100845","url":null,"abstract":"<div><div>The evolution from traditional artificial intelligence (AI) to advanced AI is explored in the predictive and structural analysis in materials informatics, highlighting how advancements in machine learning have revolutionised the discovery and design of new materials and molecular structures. It examines how traditional AI, with its reliance on heuristic models and empirical data, has paved the way for the emergence of generative AI, which leverages advanced machine learning frameworks to predict material properties, structural design and analysis and synthesise new materials. The work highlights key developments, compares the effectiveness of various approaches, relevant databases and software frameworks in material informatics, and discusses the transformative impact of traditional and advanced AI in accelerating materials discovery and innovation. Through a detailed analysis of recent advancements, challenges, and future prospects, this paper aims to offer valuable insights into the evolving landscape of AI-driven materials informatics.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100845"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314957","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}

{"title":"A comprehensive review on the white shark optimizer, its variants, statistical analysis and performance evaluation","authors":"Vimal Kumar Pathak","doi":"10.1016/j.cosrev.2025.100848","DOIUrl":"10.1016/j.cosrev.2025.100848","url":null,"abstract":"<div><div>The development of novel nature inspired algorithms and their enhancements based on intelligent strategies have seen rapid growth but with similar intrinsic disadvantages. The probable reason being limited investigations were performed on specific algorithms prior to its improvement. To this end, this paper presents a comprehensive review and statistical evaluation of newly developed white shark optimizer (WSO) algorithm to justify the need of fundamental reviewing, performance assessment and statistical analysis before its improvement. The WSO algorithm is one of the recent nature inspired algorithms introduced in 2022, based on exceptional navigation and foraging behaviour of white sharks, and has attracted many researchers in solving complex optimization problems owing to simplistic concept, ease of implementation and optimistic features. The WSO algorithm’s performance was statistically evaluated on twenty unimodal and multimodal benchmark functions, confirming its stability, optimization quality, exploration-exploitation equilibrium and convergence, thus shows its efficiency in solving real-life practical problems with notable usage in solving optimization problems on renewable energy conservation (18 %), image processing (18 %) and engineering (16 %) applications. It was also demonstrated that Springer and Elsevier emerged as the top publisher of WSO-related articles, contributing 28 % and 25 % of the papers, respectively. In this paper, it is demonstrated that the WSO procedure is significantly improved by mostly hybridizing with other metaheuristic algorithms (23 %), incorporating chaotic mapping (10 %), introducing binary versions (6 %) and other refreshing strategies (6 %), respectively. Finally, comparative evaluation of WSO with other metaheuristic algorithms were reported outlining its advantages and disadvantages with probable recommendation for future research directions in improvements of WSO.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100848"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382989","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}

{"title":"Unlocking the potential of news: A systematic review of advantages and challenges for event detection and analysis","authors":"Klaifer Garcia,&nbsp;Lilian Berton","doi":"10.1016/j.cosrev.2025.100838","DOIUrl":"10.1016/j.cosrev.2025.100838","url":null,"abstract":"<div><div>Social media platforms, including both social networks and news outlets, have been widely utilized for event detection and analysis tasks. While social networks constitute the most commonly used data source due to their high volume and immediacy, news articles offer distinctive advantages such as access to well-structured historical archives and the availability of more coherent, detailed narratives, which can enhance the reliability and interpretability of event-related insights. In this study, we conduct a review and highlight key considerations that should be addressed when developing event detection applications based on news data sources. In our systematic review, we retrieved 654 papers from 2019 until 2024, covering four digital libraries (Springer Link, Science Direct from Elsevier, ACM, IEEE Explore). After applying exclusion criteria, we analyzed 79 papers qualitatively and quantitatively. We aimed to answer the following research questions: What is the motivation for using news data? What is the time span of the analyzed events? How detailed can the information be extracted? What are the most commonly used techniques and evaluation metrics? Based on the results, we identified several use cases where news is the most effective source of data in terms of the amount of information that can be retrieved, the quality of the content, and the response time, which can be as fast as social networks in some situations. Finally, we presented some challenges and opportunities in the area.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100838"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145261668","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}

{"title":"Random spanning trees and forests: a geometric focus","authors":"Lyuben Lichev ,&nbsp;Dieter Mitsche ,&nbsp;Xavier Pérez-Giménez","doi":"10.1016/j.cosrev.2025.100857","DOIUrl":"10.1016/j.cosrev.2025.100857","url":null,"abstract":"<div><div>The current article surveys the rich literature on spanning trees with a special focus on geometric graph models.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100857"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145567470","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}

{"title":"Homomorphic cryptography: Challenges and perspectives","authors":"Khalil Hariss ,&nbsp;Jean Paul A. Yaacoub ,&nbsp;Hassan N. Noura","doi":"10.1016/j.cosrev.2025.100815","DOIUrl":"10.1016/j.cosrev.2025.100815","url":null,"abstract":"<div><div>The study proposes practical recommendations and future directions to improve HE’s relevance in the real world, especially in healthcare, digital forensics, Machine Learning (ML), and smart infrastructure. The primary product of this study is a cohesive framework that unifies mathematical foundations with real-world applications to direct the implementation and development of Homomorphic Cryptography (HC) in privacy-preserving computing. Homomorphic Encryption (HE), which is an innovative cryptographic technique, protects confidentiality, integrity, and authenticity in untrusted computing environments such as cloud infrastructure and Internet of Things (IoT) ecosystems by allowing computations on encrypted data without the need for decryption. The present study provides an in-depth investigation of HC, identifying symmetric and asymmetric methods and assigning them to the appropriate security services. The mathematical complexities of well-known HE algorithms like BGV, BFV, DGHV, and CKKS are further explained, and experimental performance evaluations are provided using the Python-SEAL and Microsoft SEAL libraries. The paper examines contemporary attacks and defences while highlighting significant drawbacks, such as computational cost and security flaws.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100815"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145007708","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}

{"title":"Distributed edge artificial intelligence empowered data trading: Theories, algorithms, and applications","authors":"Bing Mi ,&nbsp;Kongyang Chen","doi":"10.1016/j.cosrev.2025.100844","DOIUrl":"10.1016/j.cosrev.2025.100844","url":null,"abstract":"<div><div>As data emerges as a crucial production factor in the digital economy, data trading has become a key innovation across industrial applications. However, traditional data trading models face significant challenges, including privacy concerns, pricing mechanisms, transaction efficiency, and decentralization. Edge Artificial Intelligence (AI) offers a transformative approach to data trading by enabling local intelligence, privacy-preserving computation, and decentralized architectures. This paper explores the integration of Edge AI with data trading, analyzing its technical foundations, key challenges, and diverse applications. Edge AI enhances data trading by reducing data transmission costs, enabling privacy-preserving federated learning, and facilitating decentralized trust mechanisms through blockchain. Compared to centralized models, Edge AI-based data trading supports secure, efficient, and transparent transactions while mitigating the data misuse. This paradigm benefits various domains, including IoT-enabled smart factories and connected vehicles, financial risk assessment and credit scoring, secure electronic health record sharing, supply chain optimization, and personalized advertising. Despite its advantages, Edge AI-driven data trading still faces challenges in standardization, security, computational efficiency, data quality assessment, and regulatory compliance. Future research should focus on establishing global data trading protocols, optimizing privacy-preserving mechanisms, and improving decentralized transaction models to enhance security and efficiency. Edge AI-enabled data trading will play an increasingly vital role in facilitating trustworthy and efficient data markets across digital industries.</div></div>","PeriodicalId":48633,"journal":{"name":"Computer Science Review","volume":"59 ","pages":"Article 100844"},"PeriodicalIF":12.7,"publicationDate":"2026-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145314960","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}