Expert Systems最新文献

{"title":"A code change-oriented approach to just-in-time defect prediction with multiple input semantic fusion","authors":"Teng Huang,&nbsp;Hui-Qun Yu,&nbsp;Gui-Sheng Fan,&nbsp;Zi-Jie Huang,&nbsp;Chen-Yu Wu","doi":"10.1111/exsy.13702","DOIUrl":"10.1111/exsy.13702","url":null,"abstract":"<p>Recent research found that fine-tuning pre-trained models is superior to training models from scratch in just-in-time (JIT) defect prediction. However, existing approaches using pre-trained models have their limitations. First, the input length is constrained by the pre-trained models.Secondly, the inputs are change-agnostic.To address these limitations, we propose JIT-Block, a JIT defect prediction method that combines multiple input semantics using changed block as the fundamental unit. We restructure the JIT-Defects4J dataset used in previous research. We then conducted a comprehensive comparison using eleven performance metrics, including both effort-aware and effort-agnostic measures, against six state-of-the-art baseline models. The results demonstrate that on the JIT defect prediction task, our approach outperforms the baseline models in all six metrics, showing improvements ranging from 1.5% to 800% in effort-agnostic metrics and 0.3% to 57% in effort-aware metrics. For the JIT defect code line localization task, our approach outperforms the baseline models in three out of five metrics, showing improvements of 11% to 140%.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206599","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":"Unlocking the potential: A review of artificial intelligence applications in wind energy","authors":"Safa Dörterler,&nbsp;Seyfullah Arslan,&nbsp;Durmuş Özdemir","doi":"10.1111/exsy.13716","DOIUrl":"10.1111/exsy.13716","url":null,"abstract":"<p>This paper presents a comprehensive review of the most recent papers and research trends in the fields of wind energy and artificial intelligence. Our study aims to guide future research by identifying the potential application and research areas of artificial intelligence and machine learning techniques in the wind energy sector and the knowledge gaps in this field. Artificial intelligence techniques offer significant benefits and advantages in many sub-areas, such as increasing the efficiency of wind energy facilities, estimating energy production, optimizing operation and maintenance, providing security and control, data analysis, and management. Our research focuses on studies indexed in the Web of Science library on wind energy between 2000 and 2023 using sub-branches of artificial intelligence techniques such as artificial neural networks, other machine learning methods, data mining, fuzzy logic, meta-heuristics, and statistical methods. In this way, current methods and techniques in the literature are examined to produce more efficient, sustainable, and reliable wind energy, and the findings are discussed for future studies. This comprehensive evaluation is designed to be helpful to academics and specialists interested in acquiring a current and broad perspective on the types of uses of artificial intelligence in wind energy and seeking what research subjects are needed in this field.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13716","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206860","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":"Optimizing task allocation with temporal‐spatial privacy protection in mobile crowdsensing","authors":"Yuping Liu, Honglong Chen, Xiaolong Liu, Wentao Wei, Huansheng Xue, Osama Alfarraj, Zafer Almakhadmeh","doi":"10.1111/exsy.13717","DOIUrl":"https://doi.org/10.1111/exsy.13717","url":null,"abstract":"Mobile Crowdsensing (MCS) is considered to be a key emerging example of a smart city, which combines the wisdom of dynamic people with mobile devices to provide distributed, ubiquitous services and applications. In MCS, each worker tends to complete as many tasks as possible within the limited idle time to obtain higher income, while completing a task may require the worker to move to the specific location of the task and perform continuous sensing. Thus the time and location information of each worker is necessary for an efficient task allocation mechanism. However, submitting the time and location information of the workers to the system raises several privacy concerns, making it significant to protect both the temporal and spatial privacy of workers in MCS. In this article, we propose the Task Allocation with Temporal‐Spatial Privacy Protection (TASP) problem, aiming to maximize the total worker income to further improve the workers' motivation in executing tasks and the platform's utility, which is proved to be NP‐hard. We adopt differential privacy technology to introduce Laplace noise into the location and time information of workers, after which we propose the Improved Genetic Algorithm (SPGA) and the Clone‐Enhanced Genetic Algorithm (SPCGA), to solve the TASP problem. Experimental results on two real‐world datasets verify the effectiveness of the proposed SPGA and SPCGA with the required personalized privacy protection.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"10 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206600","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":"Trust region based chaotic search for solving multi-objective optimization problems","authors":"M. A. El-Shorbagy","doi":"10.1111/exsy.13705","DOIUrl":"10.1111/exsy.13705","url":null,"abstract":"<p>A numerical optimization technique used to address nonlinear programming problems is the trust region (TR) method. TR uses a quadratic model, which may represent the function adequately, to create a neighbourhood around the current best solution as a trust region in each step, rather than searching for the original function's objective solution. This allows the method to determine the next local optimum. The TR technique has been utilized by numerous researchers to tackle multi-objective optimization problems (MOOPs). But there is not any publication that discusses the issue of applying a chaotic search (CS) with the TR algorithm for solving multi-objective (MO) problems. From this motivation, the main contribution of this study is to introduce trust-region (TR) technique based on chaotic search (CS) for solving MOOPs. First, the reference point interactive approach is used to convert MOOP to a single objective optimization problem (SOOP). The search space is then randomly initialized with a set of initial points. Second, in order to supply locations on the Pareto boundary, the TR method solves the SOOP. Finally, all points on the Pareto frontier are obtained using CS. A range of MO benchmark problems have demonstrated the efficiency of the proposed algorithm (TR based CS) in generating Pareto optimum sets for MOOPs. Furthermore, a demonstration of the suggested algorithm's ability to resolve real-world applications is provided through a practical implementation of the algorithm to improve an abrasive water-jet machining process (AWJM).</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206854","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":"Underwater image enhancement using contrast correction","authors":"Nishant Singh, Aruna Bhat","doi":"10.1111/exsy.13692","DOIUrl":"https://doi.org/10.1111/exsy.13692","url":null,"abstract":"Light‐induced degeneration of underwater images occurs by physical features of seawater. According to the wavelength of the colour spectrum, light reduces intensity significantly when it moves through water. The greatest wavelength of light that is visible gets absorbed first. Red and blue absorb the most and least, respectively. Because of the reducing consequences of the light spectrum, underwater images having poor contrast can be obtained. As a result, the crucial data contained inside these images will not be effectively retrieved for later analysis. The recent research suggests a novel approach to enhance the contrast while decreasing noise in underwater images. The recommended approach involves image histogram transformation using two significant colour spaces, Red‐Green‐Blue (RGB) and Hue‐Saturation‐Value (HSV). The histogram of the dominant colour channel (blue channel) in the RGB colour model is extended towards the lower level, containing a maximum limitation of 95%, while the inferior red colour channel has been extended towards the upper side, containing a minimum limitation of 5%. During the entire dynamic range, the green colour channel having the dominant and inferior colour channels expands in each direction. The Rayleigh distribution has been utilized for developing various stretching actions within the RGB colour space. The image has been converted to the HSV colour space, having the S and V elements adjusted within 1% of their minimum and maximum values. The suggested approach is examined in both qualitative and quantitative analysis. According to qualitative analysis, the recommended approach substantially boosts image contrast, lowers its blue and green effect, and minimizes over‐enhanced and under‐enhanced sections in the final resultant underwater image. The quantitative examination of 500 large scale underwater images dataset reveals that the suggested technique generates better results. The dataset images are grouped into small fish images, blue coral images, stone wall images, and coral branch images. The quantitative examination of all these four groups have been evaluated and shown. The average mean square error, peak signal to noise ratio, underwater image quality measurement, and underwater colour image quality evaluation values of dataset images are 76.69, 31.25, 3.85, and 0.64, respectively. These values of our proposed work outperform six other previous methods.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"45 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206602","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":"Assessing interpretability of data-driven fuzzy models: Application in industrial regression problems","authors":"Jorge S. S. Júnior,&nbsp;Carlos Gaspar,&nbsp;Jérôme Mendes,&nbsp;Cristiano Premebida","doi":"10.1111/exsy.13710","DOIUrl":"10.1111/exsy.13710","url":null,"abstract":"<p>Machine Learning (ML) has attracted great interest in the modeling of systems using computational learning methods, being utilized in a wide range of advanced fields due to its ability and efficiency to process large amounts of data and to make predictions or decisions with a high degree of accuracy. However, with the increase in the complexity of the models, ML's methods have presented complex structures that are not always transparent to the users. In this sense, it is important to study how to counteract this trend and explore ways to increase the interpretability of these models, precisely where decision-making plays a central role. This work addresses this challenge by assessing the interpretability and explainability of fuzzy-based models. The structural and semantic factors that impact the interpretability of fuzzy systems are examined. Various metrics have been studied to address this topic, such as the Co-firing Based Comprehensibility Index (COFCI), Nauck Index, Similarity Index, and Membership Function Center Index. These metrics were assessed across different datasets on three fuzzy-based models: (i) a model designed with Fuzzy c-Means and Least Squares Method, (ii) Adaptive-Network-based Fuzzy Inference System (ANFIS), and (iii) Generalized Additive Model Zero-Order Takagi-Sugeno (GAM-ZOTS). The study conducted in this work culminates in a new comprehensive interpretability metric that covers different domains associated with interpretability in fuzzy-based models. When addressing interpretability, one of the challenges lies in balancing high accuracy with interpretability, as these two goals often conflict. In this context, experimental evaluations were performed in many scenarios using 4 datasets varying the model parameters in order to find a compromise between interpretability and accuracy.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206601","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":"Advancing anomaly detection in cloud environments with cutting‐edge generative AI for expert systems","authors":"Umit Demirbaga","doi":"10.1111/exsy.13722","DOIUrl":"https://doi.org/10.1111/exsy.13722","url":null,"abstract":"As artificial intelligence (AI) continues to advance, Generative AI emerges as a transformative force, capable of generating novel content and revolutionizing anomaly detection methodologies. This paper presents CloudGEN, a pioneering approach to anomaly detection in cloud environments by leveraging the potential of Generative Adversarial Networks (GANs) and Convolutional Neural Network (CNN). Our research focuses on developing a state‐of‐the‐art Generative AI‐based anomaly detection system, integrating GANs, deep learning techniques, and adversarial training. We explore unsupervised generative modelling, multi‐modal architectures, and transfer learning to enhance expert systems' anomaly detection systems. We illustrate our approach by dissecting anomalies regarding job performance, network behaviour, and resource utilization in cloud computing environments. The experimental results underscore a notable surge in anomaly detection accuracy with significant development of approximately 11%.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"18 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206852","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":"Intent detection for task‐oriented conversational agents: A comparative study of recurrent neural networks and transformer models","authors":"Mourad Jbene, Abdellah Chehri, Rachid Saadane, Smail Tigani, Gwanggil Jeon","doi":"10.1111/exsy.13712","DOIUrl":"https://doi.org/10.1111/exsy.13712","url":null,"abstract":"Conversational assistants (CAs) and Task‐oriented ones, in particular, are designed to interact with users in a natural language manner, assisting them in completing specific tasks or providing relevant information. These systems employ advanced natural language understanding (NLU) and dialogue management techniques to comprehend user inputs, infer their intentions, and generate appropriate responses or actions. Over time, the CAs have gradually diversified to today touch various fields such as e‐commerce, healthcare, tourism, fashion, travel, and many other sectors. NLU is fundamental in the natural language processing (NLP) field. Identifying user intents from natural language utterances is a sub‐task of NLU that is crucial for conversational systems. The diversity in user utterances makes intent detection (ID) even a challenging problem. Recently, with the emergence of Deep Neural Networks. New State of the Art (SOA) results have been achieved for different NLP tasks. Recurrent neural networks (RNNs) and Transformer architectures are two major players in those improvements. RNNs have significantly contributed to sequence modelling across various application areas. Conversely, Transformer models represent a newer architecture leveraging attention mechanisms, extensive training data sets, and computational power. This review paper begins with a detailed exploration of RNN and Transformer models. Subsequently, it conducts a comparative analysis of their performance in intent recognition for Task‐oriented (CAs). Finally, it concludes by addressing the main challenges and outlining future research directions.","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"75 1","pages":""},"PeriodicalIF":3.3,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206604","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":"Multi-Pop: Enhancing user engagement with content-based multimodal popularity prediction in social media","authors":"Jiyoon Kim,&nbsp;Hyeongjin Ahn,&nbsp;Eunil Park","doi":"10.1111/exsy.13707","DOIUrl":"10.1111/exsy.13707","url":null,"abstract":"<p>Social media has entrenched itself as an indispensable marketing tool. We introduce a quantitative approach to predicting the popularity of social media posts within the café and bakery sector. Employing <i>Multi-Pop</i>, a multimodal popularity prediction model that harnesses both images and text from post content, it utilizes the features of posts that significantly influence their popularity on one of the most widely used platforms, Instagram. By focusing solely on post-content features and excluding user information, we analysed 8765 Instagram posts from the cafe and bakery domain, revealing that our model attains a superior accuracy rate of 82.0% compared with existing popularity prediction methods. Furthermore, the study identifies hashtags and post captions as exerting a greater impact on post popularity than images. This research furnishes valuable insights, particularly for small business owners and individual entrepreneurs, by introducing novel computational and empirical methodologies for Instagram marketing strategy and post popularity prediction, thereby enhancing the comprehension of social media marketing dynamics.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206605","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":"Multi agent collaborative search algorithm with adaptive weights","authors":"Li Cao,&nbsp;Maocai Wang,&nbsp;Massimiliano Vasile,&nbsp;Guangming Dai","doi":"10.1111/exsy.13709","DOIUrl":"10.1111/exsy.13709","url":null,"abstract":"<p>This paper presents a new version of Multi Agent Collaborative Search (MACS) with Adaptive Weights (named MACS-AW). MACS is a multi-agent memetic scheme for multi-objective optimization originally developed to mix local and population-based search. MACS was proven to perform well on a number of test cases but had three limitations: (i) the amount of computational resources allocated to each agent was not proportional to the difficulty of the sub-problem the agent had to solve; (ii) the population-based search (called social actions in the following) was using only one differential evolution (DE) operator with fixed parameters; (iii) the descent directions were not adapted during convergence, leading to a loss of diversity. In this paper, we propose an improved version of MACS, that implements: (i) a new utility function to better manage computational resources; (ii) new social actions with multiple adaptive DE operators; (iii) an automatic adaptation of the descent directions with an innovative trigger to initiate adaptation. First, MACS-AW is compared against some state-of-art algorithms and its predecessor MACS2.1 on some standard benchmarks. Then, MACS-AW is applied to the solution of two real-life optimization problems and compared against MACS2.1. It will be shown that MACS-AW produces competitive results on most test cases analysed in this paper. On the standard benchmark test set, MACS-AW outperforms all other algorithms in 11 out of 30 cases and comes second in other 8 cases. On the two real engineering test set, MACS-AW and its predecessor obtain same results.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"41 12","pages":""},"PeriodicalIF":3.0,"publicationDate":"2024-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142206606","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}