Expert Systems最新文献

{"title":"Lionfish Search Algorithm: A Novel Nature-Inspired Metaheuristic","authors":"Saif Mohanad Kadhim,&nbsp;Johnny Koh Siaw Paw,&nbsp;Yaw Chong Tak,&nbsp;Shahad Thamear Abd Al-Latief,&nbsp;Ahmed Alkhayyat,&nbsp;Deepak Gupta","doi":"10.1111/exsy.70016","DOIUrl":"https://doi.org/10.1111/exsy.70016","url":null,"abstract":"<div>\u0000 \u0000 <p>This study introduces an innovative optimization algorithm called Lionfish Search (LFS) technique, which is inspired by the visual predator Lionfish, in which it is specifically imitating their hunting tactics. The suggested algorithm considers several parameters that influence the hunting behaviour of lionfish, such as visual acuity, mobility, striking success, and prey swallowing potential. Furthermore, this study examines the influence of the physiological traits of the lionfish and their relationship with environmental factors. The novel search algorithm has shown enhanced performance and efficiency, particularly in scenarios where the integration of visual cues and intricate hunting strategies is vital. The suggested LFS method was evaluated using 20 well-known single-modal and multi-modal mathematical functions to analyse its different characteristics. The LFS method has shown remarkable efficacy in both exploration and exploitation, effectively reducing the likelihood of being trapped in local optima. Additionally, it has a rapid convergence capacity, particularly in the realm of large-scale global optimization. Comparisons were made between the LFS algorithm, and 10 other prominent algorithms mentioned in the literature. The proposed LFS metaheuristic algorithm outperformed the others on almost all of the examined functions, demonstrating a statistically significant advantage. Moreover, the positive results found in three practical optimization situations demonstrate the effectiveness of the LFS in accomplishing problem-solving tasks that have limited and unknown search areas.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595313","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Strategic Data-Driven Roadmap for Enhancing Energy Security in Taiwan Under Industry 5.0","authors":"Tat-Dat Bui,&nbsp;Jiun-Wei Tseng,&nbsp;Anthony S. F. Chiu,&nbsp;Kanchana Sethanan,&nbsp;Ming-Lang Tseng","doi":"10.1111/exsy.70025","DOIUrl":"https://doi.org/10.1111/exsy.70025","url":null,"abstract":"<div>\u0000 \u0000 <p>Energy security performs a decisive position in the economic sustainability and societal development. As Taiwan attempts for sustainable expansion, decoupling for energy security is fundamental and requires advanced information technologies and infrastructure application, especially in connection to the Industry 5.0 era. However, the two concepts proxy manifest the multi-dimensional nature with vast literature; there is an absence of a strategic roadmap for the implementation tactics. This study presents a systematic data-driven analysis combining text mining, the fuzzy Delphi method, interpretive structural modelling, fuzzy decision-making trial and evaluation laboratory, and analytic network process to outline a distinct energy security roadmap and unveil Industry 5.0 contributions. There are 22 valid indicators generated and allocated into five aspects. The causal interrelation model and strategic roadmap are obtained. The technological advancement and integration, environmental and climate actions, and public demand and perception are categorised as the causative aspects. The top causal indicators are indicated as climate change mitigation, cyber-physical systems, energy investment, public perception, and supply–demand side technologies. This study enriches the theoretical literature and serves as a valuable practical locus to improve energy security in the Industry 5.0.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143595314","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Artificial Orca Optimiser: Theory and Applications for Global Optimisation Problems","authors":"Lin Wang,&nbsp;Xuerui Wang,&nbsp;Yingying Pi","doi":"10.1111/exsy.70023","DOIUrl":"https://doi.org/10.1111/exsy.70023","url":null,"abstract":"<div>\u0000 \u0000 <p>With the growing complexity of real-world engineering optimisation problems, interest in meta-heuristic algorithms is increasing. However, existing meta-heuristic algorithms still suffer from several shortcomings, including a poor balance between global and local search, a tendency to converge toward the centre of the solution space, and susceptibility to getting trapped in local optima. To overcome these shortcomings, a novel meta-heuristic algorithm, called artificial orca optimiser (AOO), is proposed based on the unique behaviours of orcas in nature. Within the framework of AOO, the switching factor, guidance phase, and iterative formulas that do not converge toward the centre of the solution space, are designed to enhance the equilibrium between exploration and exploitation, ensure agents the ability to escape from the local optimum, and comprehensively explore the solution space without being limited to the centre of the solution space, thereby increasing the likelihood of finding the global optimal solution. Qualitative, quantitative, scalability, sensitivity, and practical application analyses of the experimental results demonstrate that AOO overcomes the issue of converging to the centre of the solution space, alleviates the problems of poor balance and susceptibility to the local optimum, and exhibits outstanding optimising performance, fast convergence, great scalability, high robustness, and excellent practicality.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143581822","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Classification of Damage on Wind Turbine Blades Using Automatic Machine Learning and Pressure Coefficient","authors":"Javier A. Carmona-Troyo,&nbsp;Leonardo Trujillo,&nbsp;Josué Enríquez-Zárate,&nbsp;Daniel E. Hernandez,&nbsp;Luis A. Cárdenas-Florido","doi":"10.1111/exsy.70024","DOIUrl":"https://doi.org/10.1111/exsy.70024","url":null,"abstract":"<div>\u0000 \u0000 <p>Wind turbine blades (WTB) are critical components of wind energy systems. Operating in harsh environments WTBs face significant challenges, since damage to their leading edge caused by erosion or additive surface roughness can reduce performance, and increase maintenance costs and operational downtime. One approach to detect WTB damage is to use machine learning, but properly designing a predictive system is not trivial. Auto machine learning (AutoML) can be used to simplify the design and implementation of machine learning pipelines. This work presents the first comparison of state-of-the-art AutoML methods, Auto-Sklearn, H2O-DAI and TPOT, to detect erosion and additive roughness in WTBs. The Leading-Edge Erosion Study database is used, which provides measurements of the pressure coefficient along the airfoil under different conditions. This is the first work to combine the pressure coefficient and AutoML systems to detect these types of damage. Results show the viability of using AutoML in this task, with H2O-DAI producing the best results, achieving an accuracy above <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mn>90</mn>\u0000 <mo>%</mo>\u0000 </mrow>\u0000 <annotation>$$ 90% $$</annotation>\u0000 </semantics></math> in many cases. However, statistical analysis shows that a standard classifier can achieve similar performance across all problems considered, based on the Friedman test and the Wilcoxon-Holm post hoc analysis with an <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <mi>α</mi>\u0000 <mo>=</mo>\u0000 <mn>0.05</mn>\u0000 </mrow>\u0000 <annotation>$$ alpha =0.05 $$</annotation>\u0000 </semantics></math> significance level. However, AutoML systems perform better as the complexity and difficulty of the problem increases.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143571364","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"New Technologies of Artificial Intelligence in Convergence ICT","authors":"Ji Su Park,&nbsp;Laurence T. Yang,&nbsp;Jong Hyuk Park","doi":"10.1111/exsy.70028","DOIUrl":"https://doi.org/10.1111/exsy.70028","url":null,"abstract":"<p>A total of 12 papers were accepted for the special issue on the topic of ‘New Technologies of Artificial Intelligence in Convergence ICT’. Recently, as the convergence of artificial intelligence continues to occur in various fields, various technologies are emerging. In this paper, 12 papers introduce AI utilisation technologies in various fields such as smart city, security, medical, economy, healthcare and electricity.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Lifecycle Models in Machine Learning Development","authors":"Antonio Crespí,&nbsp;Antoni-Lluís Mesquida,&nbsp;Maria Monserrat,&nbsp;Antonia Mas","doi":"10.1111/exsy.70029","DOIUrl":"https://doi.org/10.1111/exsy.70029","url":null,"abstract":"<div>\u0000 \u0000 <p>Machine Learning (ML) development introduces challenges that traditional software processes often struggle to address. As ML applications grow in complexity and adoption, various lifecycle models have been proposed to address the unique stages of ML development. This study systematically synthesises these models, mapping their stages and activities to provide an understanding of the ML development landscape. The findings highlight research gaps and opportunities, offering insights for advancing academic research and practical implementation.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554835","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Fuzzy Particle Filtering Based Approach for Battery RUL Prediction With Uncertainty Reduction Strategies","authors":"Gwanpil Kim,&nbsp;Jason J. Jung,&nbsp;Dong Kyu Kim,&nbsp;Min Koo,&nbsp;Grzegorz J. Nalepa,&nbsp;Slawomir Nowaczyk","doi":"10.1111/exsy.70027","DOIUrl":"https://doi.org/10.1111/exsy.70027","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper proposes a two-stage framework that combines uncertainty reduction and predictive modelling to enhance the accuracy of battery Remaining Useful Life (RUL) prediction. In the first stage, a simplified fuzzy optimization learning model is introduced to mitigate uncertainty caused by abnormal capacity fluctuations in battery data. The proposed fuzzy model reconstructs degradation data into a consistent downward trend based on mid- and short-term tendencies of the battery, alleviating abnormal variability and improving suitability for predictive modelling. In the second stage, uncertainty arising during the recursive prediction process of a standalone Transformer model was mitigated through the integration of a particle filter. This approach dynamically manages prediction errors using particles, effectively controlling cumulative errors and enhancing the stability and reliability of long-term predictions. This methodology can lead to extended battery life and increased operational reliability through accurate RUL prediction. The proposed methodology is validated through experiments using NASA and CALCE battery datasets, demonstrating superior prediction accuracy and stability compared to conventional approaches by systematically reducing uncertainties.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554853","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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 Survey on Multi-Facet Fog-Computing Resource Management Techniques, Trends, Applications and Future Directions","authors":"Salman Khan,&nbsp;Ibrar Ali Shah,&nbsp;Shabir Ahmad,&nbsp;Javed Ali Khan,&nbsp;Muhammad Shahid Anwar,&nbsp;Khursheed Aurangzeb","doi":"10.1111/exsy.70019","DOIUrl":"https://doi.org/10.1111/exsy.70019","url":null,"abstract":"<div>\u0000 \u0000 <p>Due to the recent advancements in high-speed networks, underlying hardware computing resources and resource scheduling algorithms, Cloud computing has emerged as a popular computing paradigm globally providing end-user services such as infrastructure, hardware platforms and application tools. Subsequently, the researchers across various domains have integrated different services to facilitate the end users. However, the real issue faced by the cloud infrastructure is the network latency due to the physical dispersion between clients and cloud data centers. According to an estimate, billions of internet of things (IoT) devices are sharing approximately two exabytes of data daily. Such a huge amount of data can affect network performance if the underlying physical system does not expand up to the required levels, leading to performance degradation. To overcome these issues, a new computing paradigm called Fog Computing has emerged in recent years. In this paper, we discuss the recent developments in fog computing with the integration of real-time Healthcare 5.0 technology. Furthermore, we describe the proposed layered architecture and taxonomy of resource management (RM) techniques in fog computing, which consists of energy awareness, scheduling, reliability and scalability. Besides that, our survey covers the three-tier layered architecture, evaluation metrics, real-time application aspects of fog computing and tools providing the implementation of RM techniques in fog computing. Furthermore, the proposed layered architecture of the standard fog framework and different state-of-the-art techniques for utilising the computing resources of fog networks have been covered in this study. Moreover, we include various sensors to demonstrate the fog data offloading example in healthcare 5.0 applications. We also present a thorough discussion on various current and future real-time applications of fog computing. Finally, open challenges and promising future research directions have been identified and discussed in the area of fog-based real-time applications.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143564989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Helping to Choose a Robust Alternative: A Sensitivity Analysis and a Software Tool for Multi-Criteria Decision-Making","authors":"Pavel Novoa-Hernández,&nbsp;David A. Pelta,&nbsp;Carlos Cruz Corona","doi":"10.1111/exsy.70031","DOIUrl":"https://doi.org/10.1111/exsy.70031","url":null,"abstract":"<div>\u0000 \u0000 <p>Multicriteria decision-making (MCDM) often involves evaluating or ranking alternatives on multiple attributes, a process that is far from trivial due to flexible preferences and uncertainty in the criteria importance. The recently proposed <b>W</b>eightless, <b>I</b>nterval-<b>B</b>ased <b>A</b>pproach (WIBA) tackles these issues by relying on an ordering of the criteria (according to their relevance) instead of explicit weights and using interval scores to evaluate alternatives. Although originally proposed for selecting solutions of interest in the context of multi-objective and many objective optimization problems, it can be adapted to rank such solutions. However, the robustness of WIBA rankings has not been studied, and sensitivity analysis approaches based on perturbations of the weights cannot be applied. Furthermore, there is no friendly environment for exploring WIBA properties. This paper addresses these gaps by (1) introducing a novel local sensitivity analysis technique to explore how small perturbations in the order of criteria affect rankings, and (2) presenting WIBApp, a freely available visual software tool that implements WIBA features, including the proposed sensitivity analysis. Using a case study on the selection of technical universities, the paper first illustrates WIBA's flexibility and utility in real-world decision scenarios, enabling decision makers to effectively deal with uncertainty and complexity, and second shows how WIBApp simplifies data management, enhances analysis and facilitates comparisons among rankings. By advancing the theoretical foundations of WIBA and providing a practical implementation, this work contributes to providing decision makers with a robust framework for handling multi-criteria problems, enhancing the reliability of rankings and supporting informed decisions.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554268","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"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":"Semantic Role Labelling: A Systematic Review of Approaches, Challenges, and Trends for English and Indian Languages","authors":"Kunal Chakma,&nbsp;Sima Datta,&nbsp;Anupam Jamatia,&nbsp;Dwijen Rudrapal","doi":"10.1111/exsy.13838","DOIUrl":"https://doi.org/10.1111/exsy.13838","url":null,"abstract":"<div>\u0000 \u0000 <p>This systematic review looks at the advances, trends, and challenges within semantic role labelling (SRL) for both English and Indian languages. SRL stands as a pivotal undertaking in the realm of natural language processing (NLP), entailing the identification of semantic connections between predicates and their corresponding arguments in a given sentence. The synthesis of findings from publicly available NLP repositories in this review sheds light on the progression of SRL methodologies and their use across various linguistic contexts. The investigation examines the distinct hurdles presented by Indian languages, which are characterised by their morphological complexity and syntactic variability, juxtaposed with the more widely studied English language. Furthermore, we perform an analysis of the impact of sophisticated machine learning algorithms, particularly deep learning, on enhancing SRL efficacy across these languages. The review identifies key research gaps and proposes future research pathways to address the complex nature of SRL in multilingual environments. By offering a comprehensive overview of the evolutionary trajectory of SRL research, the primary objective of this article is to contribute to the advancement of more resilient and adaptable NLP systems capable of accommodating a myriad of languages.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143554252","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}