Expert Systems最新文献

{"title":"RETRACTION: Consumer Sentiment Analysis With Aspect Fusion and GAN-BERT Aided Adversarial Learning","authors":"","doi":"10.1111/exsy.70048","DOIUrl":"https://doi.org/10.1111/exsy.70048","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>P. K. Jain</span>, <span>W. Quamer</span>, and <span>R. Pamula</span>, “ <span>Consumer Sentiment Analysis With Aspect Fusion and GAN-BERT Aided Adversarial Learning</span>,” <i>Expert Systems</i> <span>40</span>, no. <span>4</span> (<span>2023</span>): e13247, https://doi.org/10.1111/exsy.13247.\u0000 </p><p>The above article, published online on 14 February 2023 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Experiments and analysis do not support the research objectives sufficiently. Accordingly, the conclusions of this manuscript are considered unreliable.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70048","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909414","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":"Design, Implementation and Validation of a Level 2 Automated Driving Vehicle Reference Architecture","authors":"Javier Saez-Perez,&nbsp;Julio Diez-Tomillo,&nbsp;David Tena-Gago,&nbsp;Jose M. Alcaraz-Calero,&nbsp;Qi Wang","doi":"10.1111/exsy.70050","DOIUrl":"https://doi.org/10.1111/exsy.70050","url":null,"abstract":"<p>Automated vehicles represent a rapidly expanding global market, drawing significant attention from both industry and academia. However, existing solutions often lack transparency, particularly in the disclosure of architectural designs, resulting in fragmented development approaches. To address these gaps, this paper introduces a novel, modular reference architecture tailored for Level 2 Automated Driving Systems (ADS). The proposed architecture ensures safety, scalability, and adaptability across diverse vehicle platforms. A comprehensive validation is conducted using OpenPilot, an open-source Level 2 ADS implementation, demonstrating the architecture's practical feasibility in achieving reliable control tasks under real-time constraints. This work bridges the gap between industrial and academic contributions, offering actionable insights and a robust foundation for future advancements in ADS development.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70050","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909415","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":"RETRACTION: Investigations on Optimization Techniques for Stabilized Clinical Images","authors":"","doi":"10.1111/exsy.70047","DOIUrl":"https://doi.org/10.1111/exsy.70047","url":null,"abstract":"<p>\u0000 <b>RETRACTION</b>: <span>D. R. J. Dolly</span>, <span>J. D. Peter</span>, and <span>D. J. Jagannath</span>, “ <span>Investigations on Optimization Techniques for Stabilized Clinical Images</span>,” <i>Expert Systems</i> (Early View): https://doi.org/10.1111/exsy.12901.\u0000 </p><p>The above article, published online on 29 November 2021 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor-in-Chief, David Camacho; and John Wiley &amp; Sons Ltd. The article was submitted as part of a guest-edited special issue. Discussion, analysis, and research conducted in this manuscript are insufficiently described. Accordingly, the results are considered insufficiently supported and not reproducible.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70047","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143909416","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":"Using Deep Neural Networks Architectures to Identify Narcissistic Personality Traits","authors":"Lidice Haz,&nbsp;Miguel Ángel Rodríguez-García,&nbsp;Alberto Fernández","doi":"10.1111/exsy.70056","DOIUrl":"https://doi.org/10.1111/exsy.70056","url":null,"abstract":"<div>\u0000 \u0000 <p>Personality is the characteristics of a person represented by thoughts, feelings and behaviours in a certain way. Knowing the personality characteristics of an individual can help improve interpersonal relationships, regardless of their type. Virtual media of social interaction is a rich source of information where online users share and post comments, and express their feelings of likes or dislikes. This information reveals traits about the personality and behaviour of users. In this sense, it is possible to identify personality traits of the dark triad through computational models. In this area, research has found correlations between personality traits and users' online behaviour. In this study, we propose a computational model that uses Neural Network Architectures and Transformer models to identify narcissistic personality traits in Spanish-language text based on the Narcissistic Personality Inventory (NPI) test. Specifically, we leverage the ability of the pre-trained Transformers models BERT, RoBERTa and DistilBERT, to capture the semantic context and structural features of text using sentence-level embeddings. These attributes make them suitable for multi-class classification tasks, such as identifying personality traits from reviews. Furthermore, the model utilises the algorithms Glove, FastText, and Word2Vec to generate embedding, which are used to represent vectors of semantic and syntactic features of words in narcissistic expressions. The semantic information is then used by several neural network architectures—namely SimpleRNN, LSTM, GRU, BiLSTM, CNN + BiLSTM, and CNN + GRU—to construct a multi-class model for automatically identifying narcissistic personality traits. The model's performance is assessed using a Twitter dataset that has been annotated by psychology experts and increased using augmentation techniques such as Back Translation, Paraphrasing, and substituting words with their synonyms. Ultimately, the results indicate that BERT and RoBERTa Transformers yield better accuracy and precision compared to Neural Network Architectures.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892762","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":"Facial Expression Recognition by Multi-Scale Local Binary Patterns (MLBP) and Convolutional Neural Network (CNN) Features","authors":"Haoyu Yang,&nbsp;Entesar Gemeay,&nbsp;Abdullah Alqahtani,&nbsp;Abed Alanazi,&nbsp;Shtwai Alsubai,&nbsp;Sangkeum Lee","doi":"10.1111/exsy.70044","DOIUrl":"https://doi.org/10.1111/exsy.70044","url":null,"abstract":"<div>\u0000 \u0000 <p>The quality of human-computer interactions (HCI) has increased recently because of developments in artificial intelligence (AI) and machine learning methods, but there are still numerous obstacles to overcome. One of these difficulties that has been taken into account by several academics in recent years is the recognition of emotions via the processing of facial pictures. Most of the previously suggested solutions have drawbacks like poor accuracy and restrictions on the amount of emotions detected. On the other hand, researchers need to focus more on identifying the ideal feature set that results in maximum detection accuracy. This work addresses these issues by outlining a novel method for extracting the best face characteristics and their improved categorisation. Pre-processing, feature extraction, feature selection and classification are the four phases of the suggested technique. Image normalisation and face recognition are steps in the pre-processing stage. The ideal features are chosen using a black hole optimisation approach in the proposed method, which combines a Convolutional Neural Network (CNN) and Multi-scale Local Binary Patterns (MLBP) to extract the feature. The next step is to categorise certain characteristics and identify facial emotions in the photos using Error Correcting Output Codes (ECOC). To lessen the issue's complexity, the suggested ECOC model combines a number of Support Vector Machine (SVM) classifiers. Results reveal that the proposed model has average accuracies of 98.9% and 79.82%, respectively, for the Yale and FER-2013 datasets in recognising facial expressions, which shows an increase of at least 1% over the prior approaches.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143892761","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 Particle Swarm Optimization-Based Approach Coupled With Large Language Models for Prompt Optimization","authors":"Po-Cheng Hsieh,&nbsp;Wei-Po Lee","doi":"10.1111/exsy.70049","DOIUrl":"https://doi.org/10.1111/exsy.70049","url":null,"abstract":"<div>\u0000 \u0000 <p>Large language models (LLMs) have been developing rapidly to attract significant attention these days. These models have exhibited remarkable abilities in achieving various natural language processing (NLP) tasks, but the performance depends highly on the quality of prompting. Prompt engineering methods have been promoted for further extending the models' abilities to perform different applications. However, prompt engineering involves crafting input prompts for better accuracy and efficiency, demanding substantial expertise with trial-and-error effort. Automating the prompting process is important and can largely reduce human efforts in building suitable prompts. In this work, we develop a new metaheuristic algorithm to couple the Particle Swarm Optimization (PSO) technique and LLMs for prompt optimization. Our approach has some unique features: it can converge within only a small number of iterations (i.e., typically 10–20 iterations) to vastly reduce the expensive LLM usage cost; it can easily be applied to conduct many kinds of tasks owing to its simplicity and efficiency; and most importantly, it does not need to depend so much on the quality of initial prompts, because it can improve the prompts through learning more effectively based on enormous existing data. To evaluate the proposed approach, we conducted a series of experiments with several types of NLP datasets and compared them to others. The results highlight the importance of coupling metaheuristic search algorithms and LLMs for prompt optimization, proving that the presented approach can be adopted to enhance the performance of LLMs.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889182","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":"On the Robustness of Transformer-Based Models to Different Linguistic Perturbations: A Case of Study in Irony Detection","authors":"Reynier Ortega-Bueno,&nbsp;Elisabetta Fersini,&nbsp;Paolo Rosso","doi":"10.1111/exsy.70062","DOIUrl":"https://doi.org/10.1111/exsy.70062","url":null,"abstract":"<div>\u0000 \u0000 <p>This study investigates the robustness of Transformer models in irony detection addressing various textual perturbations, revealing potential biases in training data concerning ironic and non-ironic classes. The perturbations involve three distinct approaches, each progressively increasing in complexity. The first approach is word masking, which employs wild-card characters or utilises BERT-specific masking through the mask token provided by BERT models. The second approach is word substitution, replacing the bias word with a contextually appropriate alternative. Lastly, paraphrasing generates a new phrase while preserving the original semantic meaning. We leverage Large Language Models (GPT 3.5 Turbo) and human inspection to ensure linguistic correctness and contextual coherence for word substitutions and paraphrasing. The results indicate that models are susceptible to these perturbations, and paraphrasing and word substitution demonstrate the most significant impact on model predictions. The irony class appears to be particularly challenging for models when subjected to these perturbations. The SHAP and LIME methods are used to correlate variations in attribution scores with prediction errors. A notable difference in the Total Variation of attribution scores is observed between original examples and cases involving bias word substitution or masking. Among the corpora used, <i>TwSemEval2018</i> emerges as the most challenging. Regarding model performance, Transformer-based models such as RoBERTa and BERTweet demonstrate superior overall performance addressing these perturbations. This research contributes to understanding the robustness and limitations of irony detection models, highlighting areas for improvement in model design and training data curation.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143889181","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":"Guide in Designing an Asynchronous Performance-Centric Framework for Heterogeneous Microservices in Time-Critical Cybersecurity Applications. The BIECO Use Case","authors":"Daniela Delinschi,&nbsp;Rudolf Erdei,&nbsp;Emil Marian Pasca,&nbsp;Iulia Bărăian,&nbsp;Oliviu Matei","doi":"10.1111/exsy.70064","DOIUrl":"https://doi.org/10.1111/exsy.70064","url":null,"abstract":"<p>This article presents the architecture, design and validation of a microservice orchestration approach that improves the flexibility of heterogeneous microservice-based platforms. Improving user experience and interaction for time-critical applications are aspects that were primary objectives for the design of the architecture. Each microservice can provide its own embedded user interface component, also decentralising it and, in consequence, improving the loosely coupled approach to the architecture. Obtained results are promising, with high throughput and low response times. Also, a key finding was the introduction of benchmarking as a new step in the development lifecycle of performance-critical software components, with an example of how it can be applied within an Agile methodology. Further research is proposed to improve the results and raise the final technology readiness level of the system. Obtained results already make the approach a candidate and viable alternative to classical service composers.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70064","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888866","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":"Customs Commodity Classification Method Based on the Fusion of Text Sequence and Graph Information","authors":"Haichao Sun,&nbsp;Chengjie Zhou,&nbsp;Chao Che","doi":"10.1111/exsy.70057","DOIUrl":"https://doi.org/10.1111/exsy.70057","url":null,"abstract":"<div>\u0000 \u0000 <p>In today's prevalent international trade, the customs clearance and flow of massive import and export commodities bring enormous audit and regulatory pressure to ports of entry. With the rise of artificial intelligence, many researchers have explored deep learning technology to assist import and export commodity classification and audit. However, the text of the commodity declaration needs to be structured and arranged according to the customs audit rules, resulting in its lack of continuous context, and the elements in the text present complex joint discriminative relationships; it is difficult for existing algorithms to classify commodities accurately based on the unprocessed commodity declaration text. In order to solve the above problems, this paper proposes a fusing text sequence and graph information (FTSGI) neural network. The model comprises the following components: (a) The sequence learning module identifies sequential features and filters out irrelevant details. (b) The key element identification mechanism (KEIM) distinguishes between ordinary and key declaration elements. (c) The graph learning module introduces graph features by modeling the relationships between crucial declaration elements, capturing the interdependencies between textual elements. Compared to other models that have achieved state-of-the-art performance on text classification tasks, FTSGI demonstrates superior performance on real customs datasets.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888865","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":"From AI to the Era of Explainable AI in Healthcare 5.0: Current State and Future Outlook","authors":"Anichur Rahman,&nbsp;Dipanjali Kundu,&nbsp;Tanoy Debnath,&nbsp;Muaz Rahman,&nbsp;Utpol Kanti Das,&nbsp;Abu Saleh Musa Miah,&nbsp;Ghulam Muhammad","doi":"10.1111/exsy.70060","DOIUrl":"https://doi.org/10.1111/exsy.70060","url":null,"abstract":"<div>\u0000 \u0000 <p>Artificial intelligence (AI) and explainable artificial intelligence (XAI) are advancing rapidly, with the potential to deliver significant benefits to modern society. The healthcare sector, in particular, has experienced transformative changes; overall, these technologies are helping to address numerous challenges, such as cancer cell detection, tumour zone identification in animal bodies, predictions of major and minor diseases, diagnosis, and more. This article provides an in-depth and detailed overview of AI and XAI, focusing on recent trends and their implications for advancing Healthcare 5.0 applications. Initially, the study examines the key concepts and exceptional features of AI, XAI, and Healthcare 5.0. Additional emphasis is placed on state-of-the-art practices currently being implemented in healthcare, particularly those involving AI and XAI. Subsequently, it establishes a coherent link between AI and XAI in Healthcare 5.0, grounded in contemporary advancements. Based on the findings, algorithms are recommended to address initial obstacles to integrating AI into the Healthcare 5.0 framework. Proposals for further enhancing Healthcare 5.0 performance through the integration of XAI and its unique features are discussed in detail. The work also provides in-depth implementation strategies and highlights model-specific trends within AI and XAI frameworks in Healthcare 5.0. Particular attention is given to AI model predictions in healthcare settings, emphasising their contributions to improved patient feedback and the delivery of more sophisticated care. Most importantly, this research highlights the potential for AI and XAI to support sustainable advancements in Healthcare 5.0 applications. Finally, significant issues are analysed, and an open discussion is presented on future guidelines for the blending of AI with XAI, and Healthcare 5.0 applications.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 6","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143888864","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}