Artificial Intelligence Review最新文献

{"title":"Musical heritage historical entity linking","authors":"Arianna Graciotti,&nbsp;Nicolas Lazzari,&nbsp;Valentina Presutti,&nbsp;Rocco Tripodi","doi":"10.1007/s10462-024-11102-9","DOIUrl":"10.1007/s10462-024-11102-9","url":null,"abstract":"<div><p>Linking named entities occurring in text to their corresponding entity in a Knowledge Base (KB) is challenging, especially when dealing with historical texts. In this work, we introduce Musical Heritage named Entities Recognition, Classification and Linking (<span>mhercl</span>), a novel benchmark consisting of manually annotated sentences extrapolated from historical periodicals of the music domain. <span>mhercl</span> contains named entities under-represented or absent in the most famous KBs. We experiment with several State-of-the-Art models on the Entity Linking (EL) task and show that <span>mhercl</span> is a challenging dataset for all of them. We propose a novel unsupervised EL model and a method to extend supervised entity linkers by using Knowledge Graphs (KGs) to tackle the main difficulties posed by historical documents. Our experiments reveal that relying on unsupervised techniques and improving models with logical constraints based on KGs and heuristics to predict <span>NIL</span> entities (entities not represented in the KB of reference) results in better EL performance on historical documents.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11102-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446433","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"An actor-critic based recommender system with context-aware user modeling","authors":"Maryam Bukhari,&nbsp;Muazzam Maqsood,&nbsp;Farhan Adil","doi":"10.1007/s10462-025-11134-9","DOIUrl":"10.1007/s10462-025-11134-9","url":null,"abstract":"<div><p>Recommendation systems empower users with tailored service assistance by learning about their interactions with systems and recommending items based on their preferences and interests. Typical recommender systems view the recommendation process as a static procedure disregarding the fact that users’ preferences are changed over time. Reinforcement learning (RL) approaches are the most advanced and recent techniques used by researchers to handle challenges where the user’s interest is captured by their most recent interactions with the system. However, most of the recent research on RL-based recommender systems focuses on simply the user’s recent interactions to generate the recommendations without taking into account the context of the user in which these interactions occur. The context has a great impact on users’ interests, behaviors, and ratings e.g., user mood, time, day type, companion, social circle, and location. In this paper, we propose a context-aware deep reinforcement learning-based recommender system focusing on context-specific state modeling methods. In this approach, states are designed based on the user’s most recent context. In parallel, a list-wise version of the context-aware recommender agent is also proposed, in which a list of items is recommended to users at each step of interaction based on their context. The findings of the study indicate that modeling users’ preferences in combination with contextual variables improves the performance of RL-based recommender systems. Furthermore, we evaluate the proposed method on context-based datasets in an offline environment. The performance in terms of evaluation measures optimally indicates the worth of the proposed method in comparison with existing studies. More precisely, the highest Presicion@5, MAP@10, and NDCG@10 of the context-aware recommender agent are 77%, 76%, and 74% respectively.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11134-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A synergetic intuitionistic fuzzy model combining AHP, entropy, and ELECTRE for data fabric solution selection","authors":"Fang Zhou,&nbsp;Ting-Yu Chen","doi":"10.1007/s10462-025-11128-7","DOIUrl":"10.1007/s10462-025-11128-7","url":null,"abstract":"<div><p>Amidst the ongoing digital transformation, enterprises face the challenge of managing ever-expanding volumes of data from multiple sources and diverse structures. Semantic data fabric emerges as a promising solution, offering an innovative approach to integrate data resources from various channels and produce meaningful insights. The selection of an appropriate data fabric solution has become a focal point amidst burgeoning data lakes and silos, garnering international attention. This research aims to precisely evaluate potential data fabric solutions using an innovative synergetic intuitionistic fuzzy evaluation model. We propose a hybrid approach, IF-AHP-Entropy-ELECTRE, which integrates the analytic hierarchy process (AHP), entropy, and elimination et choix traduisant la réalité (ELECTRE) techniques within the framework of intuitionistic fuzzy (IF) sets. This model is utilized to a data fabric solution selection (DFSS) issue for an appliance company, identifying the optimal solution based on its superior performance in foundational technology, real-time analytics, and customizable features. The effectiveness and adaptability of this approach stem from a novel hierarchical evaluative criteria system encompassing technology, capability, cost, and security. The criteria weights, derived from IF-AHP-Entropy, reflect both subjective and objective judgments of decision-makers, while the ranking generated by IF-ELECTRE employs a piecewise scoring function and a unique distance measure, factoring in optimistic perspectives and cross-information. Through sensitivity and comparative analyses, our approach demonstrates enhanced robustness, precision, and adaptability in dynamic DFSS contexts when compared to traditional multicriteria decision-making methods, such as IF-WSM, IF-TOPSIS, and IF-ELECTRE. Specifically, our model provides a decision support system that combines extensive functionality with a user-friendly design, making it highly effective for DFSS challenges. This approach not only establishes a solid foundation for data integration in data management but also enhances business competitiveness and supports sustained growth in the digital economy.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11128-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143446491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Psychological and physiological computing based on multi-dimensional foot information","authors":"Shengyang Li,&nbsp;Huilin Yao,&nbsp;Ruotian Peng,&nbsp;Yuanjun Ma,&nbsp;Bowen Zhang,&nbsp;Zhiyao Zhao,&nbsp;Jincheng Zhang,&nbsp;Siyuan Chen,&nbsp;Shibin Wu,&nbsp;Lin Shu","doi":"10.1007/s10462-024-11087-5","DOIUrl":"10.1007/s10462-024-11087-5","url":null,"abstract":"<div><p>As the population ages, utilizing foot information to continuously monitor the physiological and psychological health status of the elderly is emerging as a pivotal tool for meeting this crucial societal demand. However, few reviews explored how multi-dimensional foot data has been integrated into physiological and psychological computing. This review is essential as it fills a critical knowledge gap in understanding the connections between physiological and psychological disorders and various components of foot information. To identify relevant literature, a thorough search was conducted across IEEE, DBLP, Elsevier, Springer, Google Scholar, and PubMed, initially yielding 2386 publications. After multiple rounds of systematic filtering, 404 publications were selected for in-depth analysis. This review examines (1) the mechanisms linking foot information to human physiological and psychological conditions, (2) the monitoring devices that collect diverse foot-based data, (3) the datasets correlating diseases with multiple foot data, (4) the prevalent feature engineering of different foot data, and (5) the cutting-edge machine and deep learning algorithms for diseases analysis. It also provides insights into future developments in foot information health monitoring for psychological and physiological computing.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11087-5.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Neural combinatorial optimization with reinforcement learning in industrial engineering: a survey","authors":"K. T. Chung,&nbsp;C. K. M. Lee,&nbsp;Y. P. Tsang","doi":"10.1007/s10462-024-11045-1","DOIUrl":"10.1007/s10462-024-11045-1","url":null,"abstract":"<div><p>In recent trends, machine learning is widely used to support decision-making in various domains and industrial operations. Because of the increasing complexity of modern industries, industrial engineering aims not only to increase cost-effectiveness and productivity but also to consider sustainability, resilience, and human centricity, resulting in many-objective, constrained, and stochastic operations research. Based on the above stringent requirements, combinatorial optimization (CO) problems are thus developed to support the complicated decision-making process in operations research. Due to the computational complexity of exact algorithms and the uncertain solution quality of heuristic methods, there is a growing trend to leverage the power of machine learning in solving CO problems, known as neural combinatorial optimization (NCO), where reinforcement learning (RL) is the core to achieve the sequential decision support. This survey study provides a comprehensive investigation of the theories and recent advancements in applying RL to solve hard CO problems, such as vehicle routing, bin packing, assignment, scheduling, and planning problems, and, in addition, summarizes the applications of neural combinatorial optimization with reinforcement learning (NCO-RL). The detailed review found that although the research domain of NCO-RL is still under-explored, its research potential has been proven to address environmental sustainability, adaptability, and human factors. Last but not least, the technical challenges and opportunities of the NCO-RL to embrace the industry 5.0 paradigm are discussed.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-024-11045-1.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423100","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"A review on EEG-based multimodal learning for emotion recognition","authors":"Rajasekhar Pillalamarri,&nbsp;Udhayakumar Shanmugam","doi":"10.1007/s10462-025-11126-9","DOIUrl":"10.1007/s10462-025-11126-9","url":null,"abstract":"<div><p>Emotion recognition from electroencephalography (EEG) signals is crucial for human–computer interaction yet poses significant challenges. While various techniques exist for detecting emotions through EEG signals, contemporary studies have explored the combination of EEG signals with other modalities. However, the field is still rapidly evolving, and new advancements are constantly being made. Comprehensive research is essential by distilling all factors in one manuscript to stay up-to-date with the latest research findings. This review offers an overview of multimodal leaning in EEG-based emotion recognition and discusses current literature in this domain from 2017 to 2024. Three primary challenges addressed are the fusion algorithm, representation of different modalities, and classification scheme. The review thoroughly explores the challenges of fusion algorithms, representation of different modalities, and classification schemes through empirical studies, offering a detailed analysis of their effectiveness. The approach of fusion algorithms is compared and evaluated based on convention and deep learning fusion methods. The research results show that poor performance is attributed to a lack of rigor and inadequate methods to identify correlated patterns across modalities to create a unified representation for experiments. This indicates a need for more thorough analysis and integration of data in future studies. When more than two modalities are involved, it becomes increasingly important to consider different aspects of classification schemes, such as the number of features and model selection. However, designing a classification scheme without considering the number of parameters and emotional categories may compromise the accuracy of classification. To aid readers in understanding the findings better, the results of different classification schemes and their corresponding accuracies are summarized. The tables in this draft display the fusion algorithms researchers utilize and evaluate the effectiveness of selected modalities, providing valuable insights for decision-making. Key contributions include a systematic survey of EEG features, an exploration of EEG integration with behavioral modalities, an investigation of fusion methods, and an overview of key challenges and future research directions in implementing multimodal emotion recognition systems.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11126-9.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"AI-based bridge maintenance management: a comprehensive review","authors":"Farham Shahrivar,&nbsp;Amir Sidiq,&nbsp;Mojtaba Mahmoodian,&nbsp;Sanduni Jayasinghe,&nbsp;Zhiyan Sun,&nbsp;Sujeeva Setunge","doi":"10.1007/s10462-025-11144-7","DOIUrl":"10.1007/s10462-025-11144-7","url":null,"abstract":"<div><p>Over recent decades, the implementation of Artificial Intelligence (AI) across various industrial fields from automation to cybersecurity has been transformative. Whilst the implementations of linking AI and data sciences remain complex and thus limited, they both aim to harness data for actionable insights and future predictions. A research focal point in the application of AI in maintenance is crucial for the sustainability and efficiency of assets. Typically, in the civil infrastructure, there are significant benefits to be gained from AI-driven applications. This study reviews the implementation of the AI in bridge maintenance decision-making by conducting a review of literature on major works undertaken by researchers and analysing 102 scientific articles published from 2010 to 2023. Our literature review revealed an emerging trend in recent studies, focusing on the exploration of defect prognosis in bridge maintenance. However, upon further analysis, it becomes evident that there is a notable gap in the existing literature, in the studies related to performance-based prognostic maintenance strategies for bridges. This gap presents an opportunity for future research, one that could yield valuable insights in the field of bridge maintenance and asset management. The review also reveals the focus of the existing literature on defect identification by using the bridge imagery processing. While the AI’s potential in damage detection using bridge imagery is evident, challenges persist including the computational processing and data availability. This review of the literature includes a comprehensive overview of the current implementation of AI in bridge maintenance, highlighting limitations, challenges, and prospective directions.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11144-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Quantum deep learning in neuroinformatics: a systematic review","authors":"Nabil Anan Orka,&nbsp;Md. Abdul Awal,&nbsp;Pietro Liò,&nbsp;Ganna Pogrebna,&nbsp;Allen G. Ross,&nbsp;Mohammad Ali Moni","doi":"10.1007/s10462-025-11136-7","DOIUrl":"10.1007/s10462-025-11136-7","url":null,"abstract":"<div><p>Neuroinformatics involves replicating and detecting intricate brain activities through computational models, where deep learning plays a foundational role. Our systematic review explores quantum deep learning (QDL), an emerging deep learning sub-field, to assess whether quantum-based approaches outperform classical approaches in brain data learning tasks. This review is a pioneering effort to compare these deep learning domains. In addition, we survey neuroinformatics and its various subdomains to understand the current state of the field and where QDL stands relative to recent advancements. Our statistical analysis of tumor classification studies (n = 16) reveals that QDL models achieved a mean accuracy of 0.9701 (95% CI 0.9533–0.9868), slightly outperforming classical models with a mean accuracy of 0.9650 (95% CI 0.9475–0.9825). We observed similar trends across Alzheimer’s diagnosis, stroke lesion detection, cognitive state monitoring, and brain age prediction, with QDL demonstrating better performance in metrics such as F1-score, dice coefficient, and RMSE. Our findings, paired with prior documented quantum advantages, highlight QDL’s promise in healthcare applications as quantum technology evolves. Our discussion outlines existing research gaps with the intent of encouraging further investigation in this developing field.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11136-7.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Metaheuristic optimization algorithms for multi-area economic dispatch of power systems: part II—a comparative study","authors":"Yang Wang,&nbsp;Guojiang Xiong","doi":"10.1007/s10462-025-11125-w","DOIUrl":"10.1007/s10462-025-11125-w","url":null,"abstract":"<div><p>Multi-Area Economic Dispatch (MAED) plays an important role in the operation and planning of power systems. In Part I of this series, we have summarized various optimization techniques to the MAED problem comprehensively, showing clearly that metaheuristic optimization algorithms (MOAs) have become the dominant approach for solving this problem due to their ease of application and powerful search capability. Although many different types of MOAs have been proposed, there is no study on the comprehensive evaluation, comparison and recommendation of different MOAs for the MAED problem. In this part, we selected 32 algorithms including differential evolution, particle swarm optimization, teaching–learning based algorithm, JAYA algorithm, and their advanced variants to evaluate and compare their performance on the eleven reported MAED cases summarized in Part I of this series. The comparative study was comprehensively conducted based on various performance criteria including solution quality, convergence, robustness, computational efficiency, and statistical analysis. The comparisons reveal that the DE series is the most competitive overall. Nevertheless, there is no single algorithm that ranks in the top three on all cases. This study can provide a practical reference and applicability recommendation for the selection of MOAs for solving the MAED problem.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11125-w.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423102","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}

{"title":"Evaluation of belief entropies: from the perspective of evidential neural network","authors":"Kun Mao,&nbsp;Yanni Wang,&nbsp;Wen Zhou,&nbsp;Jiangang Ye,&nbsp;Bin Fang","doi":"10.1007/s10462-025-11130-z","DOIUrl":"10.1007/s10462-025-11130-z","url":null,"abstract":"<div><p>In Dempster-Shafer’s theory, the belief entropy for total uncertainty measure of mass function has attracted the interest of many researchers in recent years. Although various belief entropies can meet some basic requirements, how to judge the performance of belief entropies is still an open issue. This paper proposes a novel evidential neural network (ENN) classifier to evaluate different belief entropies in practical application. Driven by the least commitment principle (LCP), the maximum entropy is integrated into the traditional divergence-based loss function. The proposed loss function consists of divergence and maximum entropy parts, which considers not only the distribution difference but also the degree of approaching the maximum entropy. Some classification experiments are conducted in 7 real-world datasets to validate the effectiveness of the proposed evaluation method.</p></div>","PeriodicalId":8449,"journal":{"name":"Artificial Intelligence Review","volume":"58 5","pages":""},"PeriodicalIF":10.7,"publicationDate":"2025-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://link.springer.com/content/pdf/10.1007/s10462-025-11130-z.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143423099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"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}