Expert Systems最新文献

{"title":"ST-IDS: Spatio-Temporal Feature-Based Multi-Tier Intrusion Detection System for Artificial Intelligence-Powered Connected Autonomous Vehicles","authors":"Amol Ghanshyam Bhatkar,&nbsp;Shashank Gupta,&nbsp;Priyansh Patel","doi":"10.1111/exsy.70026","DOIUrl":"https://doi.org/10.1111/exsy.70026","url":null,"abstract":"<div>\u0000 \u0000 <p>Advancements in 3GPP specifications and the extensive deployment of 5G networks have driven significant growth in the Internet of Vehicles (IoVs). This development has led to an increase in Connected and Autonomous Vehicles (CAVs), which provide capabilities such as automated navigation, ADAS, cruise control, and environmentally sustainable transportation in real-time. Additionally, the widespread adoption of CAVs has also escalated vulnerabilities within the IoV ecosystem, exposing it to potential cyberattacks. The integration of various functional interfaces has enlarged its attack surface, thereby increasing the risk of vehicle infiltration. Researchers have proposed various Intrusion Detection Systems (IDS) to address the ongoing risk of vehicle attacks, without applying encryption and related authentication methods for intra-and inter-vehicular communications. However, a significant limitation of many IDSs is their dependency on characteristics specific to a particular category of vehicles, which limits their adaptability. Additionally, current IDSs frequently rely on one-dimensional features such as traffic, time, etc., which limits their capability of detecting attacks in adverse scenarios. Moreover, incorporating machine learning algorithms into IDSs deployed in automated automobiles causes an increase in computational demands. We propose to develop a collaborative IDS specifically designed for cloud-based vehicle environments. We aim to improve our capabilities of identifying intrusion detection and differentiate which are malicious by using multidimensional features. A customised Convolutional Neural Network (CNN), optimised through hyperparameter tuning, is also developed for detecting the malicious vehicles and enhancing the overall IDS. To address the challenge of data diversity, we integrate various vehicular datasets into a unified feature space. This integration allows a single model to efficiently perform multi-classification tasks without frequent adjustments. Our feature space integrates dimensions such as traffic, time and so forth, levels, thereby expanding the spectrum of detectable attack scenarios. By identifying abnormal data points within this comprehensive feature framework, our system effectively identifies intrusions across a diverse range of vehicle types. As a result, our methodology supports robust intrusion detection through comprehensive multiclass vehicle classification.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143689319","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":"G-WVDTW: A Generalised Weighted Variance Dynamic Time Warping Algorithm for Subsequence Matching in Multivariate Time Series","authors":"Danyang Cao,&nbsp;ZiFeng Lin,&nbsp;Di Liu,&nbsp;Xiaoyuan Chai","doi":"10.1111/exsy.70036","DOIUrl":"https://doi.org/10.1111/exsy.70036","url":null,"abstract":"<div>\u0000 \u0000 <p>Dynamic time warping (DTW) is an algorithm used to measure the similarity between sequences, with widespread applications in domains such as speech recognition, image processing and video synchronisation. However, when matching a shorter multivariate time subsequence to a longer time series containing a similar subsequence, existing DTW variants struggle to accurately determine the matching path. To address this issue, we propose an improved algorithm, generalised weighted variance DTW (G-WVDTW). We extend the DTW algorithm to multivariate time series and introduce a weighted variance-based approach to calculate local distances. This allows the algorithm to better assess the distance between different time points in multivariate time series. Additionally, we modify the algorithm's boundary conditions, enabling it to handle subsequence matching tasks in multivariate time series. We conducted similarity retrieval experiments using public datasets and evaluated the algorithm's performance with the AUC metric, achieving up to a 19% improvement on certain datasets. Furthermore, we performed alignment experiments on industrial data, where we artificially generated aligned sequences and quantitatively assessed the alignment errors, which were lower than those produced by other DTW variants. Finally, we validated the algorithm's superior performance in multivariate time series subsequence matching tasks using a synthetic dataset and showcased its use in motif detection using a wind power generation dataset. The algorithm can be applied in fields such as industrial, meteorological and electrocardiogram (ECG) signal analysis for tasks like time series retrieval, matching and data labelling.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688902","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":"Automatic Speech Recognition: Comparisons Between Convolutional Neural Networks, Hidden Markov Model and Hybrid Architecture","authors":"Lyndainês Santos,&nbsp;Nícolas de Araújo Moreira,&nbsp;Robson Sampaio,&nbsp;Raizielle Lima,&nbsp;Francisco Carlos Mattos Brito Oliveira","doi":"10.1111/exsy.70032","DOIUrl":"https://doi.org/10.1111/exsy.70032","url":null,"abstract":"<div>\u0000 \u0000 <p>Automatic Speech Recognition (ASR) systems have been widely used as a practical method of interaction between humans and devices. They are typically employed to enhance the accessibility of devices and to improve the security of systems, among other purposes. However, the design of speech-based systems imposes many challenges due to their particularities. Currently, the majority of ASR systems is based on the Hidden Markov Model (HMM), and, more recently, on Convolutional Neural Networks (CNN). The present research evaluates the performance of Hidden Markov Model (HMM) and Convolutional Neural Network (CNN) algorithms in speech recognition and proposes a novel hybrid approach that combines both methods. The study assesses various performance metrics, including accuracy, precision, recall, F1-score, response time, and computational cost. The experimental tests show that the integration between HMM and CNN increased the accuracy by 6% and 8% when compared to HMM and CNN isolated, respectively, in accordance with results presented in previous papers. However, the results of the ANOVA test revealed that the difference in question is not statistically significant, and the HMM-only approach still being an interesting option for embedded systems due to its lesser demanded computational effort.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 5","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143688954","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":"Generalised Entropies for Decision Trees in Classification Under Monotonicity Constraints","authors":"Oumaima Khalaf,&nbsp;Salvador Garcia,&nbsp;Anis Ben Ishak","doi":"10.1111/exsy.70035","DOIUrl":"https://doi.org/10.1111/exsy.70035","url":null,"abstract":"<div>\u0000 \u0000 <p>Several decision-making approaches involve ordinal labelling between feature values and decision outcomes. These issues refer to ordinal classification under monotonicity constraints. Recently, some machine learning approaches have been designed to deal with these kinds of problems. Indeed, numerous experiments have shown that these algorithms are widely used in real-life applications because of their flexibility and efficiency in terms of interpretation and predictions. In this paper, we introduce novel approaches for measuring feature quality and information quantity, called Rényi-Tsallis Monotonic Tree (RTMT), which uses the advantages of Rényi and Tsallis entropies while incorporating monotonicity constraints through an optimisation framework. Moreover, we introduce Mono-CART, a variant of the CART approach adapted for monotonic classification. New decision tree algorithms are designed on the basis of aforementioned entropies while considering the monotonicity constraints within an optimisation system. The experiments conducted using some benchmark datasets demonstrate the superiority of the proposed approaches compared to existing methods.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 4","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143602517","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":"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}