Expert Systems最新文献

{"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}

{"title":"A Review of Abnormal Behaviour Detection in Crowd for Video Surveillance: Advances and Trends, Datasets, Opportunities and Prospects","authors":"A. Jency,&nbsp;K. Ramar","doi":"10.1111/exsy.70013","DOIUrl":"https://doi.org/10.1111/exsy.70013","url":null,"abstract":"<div>\u0000 \u0000 <p>The detection of abnormal behaviours with fast and automatic recognising is significant in crowded areas to provide higher security to the public. The adoption of deep learning and machine learning-based abnormal behaviour detection models enhances the influential detection and real-time security monitoring in crowds. The researchers have remotely evaluated the heart rate based on physiological information to detect abnormal activities in various years. Over the past few years, several progress have been made, and there are still some issues concerning processing time, accuracy, and computational complexity. The developed approaches detects the activities of anomalies like traffic rule violations, riots, fighting, and stampede, in addition, several anomalous entities such as abandoned luggage and weapons at the sensitive place automatically in time. However, the identification of video anomalies methods poses several challenges because of various environmental conditions, the ambiguous nature of the anomaly, lack of proper datasets, and the complex nature of human characteristics. In recent days, there have been only a few devoted surveys associated with deep learning related video anomaly identification as the research domain is in its initial stages. In this review work, the abnormal behaviour analysis models using deep learning are reviewed in depth in for security applications. Based on the traditionally used abnormal behaviour analysis models in crowded scenes, we widely categorised the methods into classification using object tracking, classification using handcrafted extracted features, classification using non-contact heart rate variability and blood pressure, analysing motion patterns from the visual frames, and classification using face images. We also discuss the comparative analysis of the previous methods with respect to datasets, computational infrastructure, and performance measures for both qualitative and quantitative analysis. In addition, the open and trending research challenges are analysed for future research.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143533512","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":"Cutting-Edge Deep Learning Methods for Image-Based Object Detection in Autonomous Driving: In-Depth Survey","authors":"Narges Saeedizadeh,&nbsp;Seyed Mohammad Jafar Jalali,&nbsp;Burhan Khan,&nbsp;Shady Mohamed","doi":"10.1111/exsy.70020","DOIUrl":"https://doi.org/10.1111/exsy.70020","url":null,"abstract":"<p>Object detection is a critical aspect of computer vision (CV) applications, especially within autonomous driving systems (AVs), where it is fundamental to ensuring safety and reducing traffic accidents. Recent advancements in computational resources have enabled the widespread adoption of Deep Learning (DL) techniques, significantly enhancing the efficiency and accuracy of object detection tasks. However, the technology for autonomous driving has yet to reach a level of maturity that guarantees consistent performance, reliability, and safety, with several challenges remaining unresolved. This study specifically focuses on 2D image-based object detection methods, which offer several advantages over other modalities, such as cost-effectiveness and the ability to capture visual features like colour and texture that are not detectable by LiDAR. We provide a comprehensive survey of DL-based strategies for detecting vehicles and pedestrians using 2D images, analysing both one-stage and two-stage detection frameworks. Additionally, we review the most commonly used publicly available datasets in autonomous driving research and highlight their relevance to 2D detection tasks. The paper concludes by discussing the current challenges in this domain and proposing potential future directions, aiming to bridge the gap between the capabilities of 2D image-based models and the requirements of real-world autonomous driving applications. Comparative tables are included to facilitate a clear understanding of the different approaches and datasets.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513481","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":"Q-GEV Based Novel Trainable Clustering Scheme for Reducing Complexity of Data Clustering","authors":"Mohamed Abd Elaziz,&nbsp;Esraa Osama Abo Zaid,&nbsp;Mohammed A. A. Al-qaness,&nbsp;Amjad Ali,&nbsp;Ali Kashif Bashir,&nbsp;Ahmed A. Ewees,&nbsp;Yasser D. Al-Otaibi,&nbsp;Ala Al-Fuqaha","doi":"10.1111/exsy.70011","DOIUrl":"https://doi.org/10.1111/exsy.70011","url":null,"abstract":"<div>\u0000 \u0000 <p>This paper presents a new data clustering technique aimed at enhancing the performance of the trainable path-cost algorithm and reducing the computational complexity of data clustering models. The proposed method facilitates the discovery of natural groupings and behaviours, which is crucial for effective coordination in complex environments. It identifies natural groupings within a set of features and detects the best clusters with similar behaviour in the data, overcoming the limitations of traditional state-of-the-art methods. The algorithm utilises a density peak clustering method to determine cluster centers and then extracts features from paths passing through these peak points (centers). These features are used to train the support vector machine (SVM) to predict the labels of other points. The proposed algorithm is enhanced using two key concepts: first, it employs Q-Generalised Extreme Value (Q-GEV) under power normalisation instead of traditional generalised extreme value distributions, thereby increasing modelling flexibility; second, it utilises the random vector functional link (RVFL) network rather than the SVM, which helps avoid overfitting and improves label prediction accuracy. The effectiveness of the proposed clustering algorithm is evaluated through various experiments, including those on UCI benchmark datasets and real-world data, demonstrating significant improvements across multiple performance metrics, including F1 measure, Jaccard index, purity, and accuracy, highlighting its capability in accurately identifying paths between similar clusters. Its average F1 measure, Jaccard index, purity, and accuracy is measured 76.87%, 56.29%, 80.29%, and 79.64%, respectively.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143513480","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}