Expert Systems最新文献

{"title":"A Fuzzy Decision-Making Support Model for Traffic Safety Analysis","authors":"Sarbast Moslem,&nbsp;Danish Farooq,&nbsp;Gülay Demir,&nbsp;Rana Faisal Tufail,&nbsp;Páraic Carroll,&nbsp;Domokos Esztergár-Kiss,&nbsp;Francesco Pilla","doi":"10.1111/exsy.70104","DOIUrl":"https://doi.org/10.1111/exsy.70104","url":null,"abstract":"<p>Our study delves into the crucial issue of road safety by examining the intricate dynamics of driver behaviour, often resulting in tragic accidents. The importance of comprehending these behaviours is acknowledged, leading us to propose an innovative decision-making support model that integrates the analytic hierarchy process (AHP) with the best worst method (BWM) in a fuzzy context. Our objective is to effectively evaluate the overall influence of driver behaviour on road safety while reducing ambiguity in assessments. In a practical case study involving skilled drivers in Budapest, Hungary, a thorough survey was conducted to prioritise key driving behaviour factors that impact road safety. Our findings reveal ‘errors’ as the most vital aspect, followed by specific behaviours like ‘colliding when reversing without observation’ and ‘driving under the influence of alcohol’. By simplifying the survey procedure and offering practical insights, our unified model improves decision-making for policymakers striving to tackle road safety issues efficiently. To conclude, our research showcases the effectiveness of merging AHP and BWM methodologies in a fuzzy setting to obtain valuable perspectives on road safety concerns, ultimately aiding in the advancement of sustainable transportation systems.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70104","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144740529","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":"Correction to “RETRACTION: Using Electroencephalogram Classification in a Convolutional Neural Network, Infer Privacy on Healthcare Internet of Things 5.0”","authors":"","doi":"10.1111/exsy.70107","DOIUrl":"https://doi.org/10.1111/exsy.70107","url":null,"abstract":"<p>2025. “RETRACTION: Using Electroencephalogram Classification in a Convolutional Neural Network, Infer Privacy on Healthcare Internet of Things 5.0.” <i>Expert Systems</i> 42: 13813. https://doi.org/10.1111/exsy.13813.</p><p>Kishan Bhushan Sahay reached out to the editorial office after the retraction was published to inform the journal that he had not consented to the submission or publication of this article, which the publisher has confirmed. Accordingly, the retraction text is corrected to:</p><p>The above article, published online on 03 March 2022 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. Following publication, it has come to our attention that the article was not reviewed in line with the journal's peer review standards and was solely accepted on the basis of a compromised peer review process. Furthermore, relevant information about the research concept, the authentication method, and the source or nature of the patients' data are missing. As a result, the conclusions presented are considered invalid. Kishan Bhushan Sahay stated that he did not give consent to the submission and publication of this manuscript.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":2.3,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70107","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144725604","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":"Privacy-Preserving Crowd Counting via Quantum-Enhanced Federated Learning","authors":"Chen Zhang,&nbsp;Jing-an Cheng,&nbsp;Qiang Zhou,&nbsp;Wenzhe Zhai,&nbsp;Mingliang Gao","doi":"10.1111/exsy.70098","DOIUrl":"https://doi.org/10.1111/exsy.70098","url":null,"abstract":"<div>\u0000 \u0000 <p>Crowd counting plays a crucial role in analyzing group behavior in smart cities. Traditional crowd-counting models rely on large datasets gathered from diverse individuals for training while ignoring the privacy protection for each training client. Meanwhile, the scale variation has long been a difficult problem in crowd counting and has greatly reduced model accuracy. Therefore, it is essential to achieve privacy-aware crowd counting and to solve the problem of scale variation in dense scenes. To this end, we propose a Privacy-preserving Quantum-enhanced Network (PQNet). The PQNet uses federated learning to share parameters rather than data, which ensures the privacy of each client. Subsequently, a multi-scale quantum-driven calibration module is designed to capture multi-scale information via quantum states. It enhances counting accuracy in dense crowd environments where scale varies. Experiments on four crowd counting and two vehicle counting benchmarks demonstrate that PQNet outperforms state-of-the-art methods subjectively and objectively. The code will be available at: https://github.com/sdutzhangchen/PQNet.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716552","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":"Cognitive Based Detection of Anomalous Sequences Using Bayesian Surprise","authors":"Ken McGarry,&nbsp;David Nelson","doi":"10.1111/exsy.70106","DOIUrl":"https://doi.org/10.1111/exsy.70106","url":null,"abstract":"<p>In this work we implement Bayesian surprise as a method to sift through sequences of discrete patterns and identify any unusual or interesting patterns that deviate from known sequences. Surprise is a biological trait inherent in humans and animals and is essential for many creative acts and efforts of discovery. Numerous technical domains are comprised of discrete elements in sequences such as e-commerce transactions, genome data searching, online financial transactions of many types, criminal cyber-attacks and life-course data from sociology. In addition to the complexity and computational burden of this type of problem is the issue of their rarity. Many anomalies are infrequent and may defy categorisation; therefore, they are not suited to classification solutions. We test our methods on four discrete datasets (Hospital Sepsis patients, Chess Moves, the Wisconsin Card Sorting Task and BioFamilies) consisting of discrete sequences. Probabilistic Suffix Trees are trained on this data which maintain each discrete symbol's location and position in a given sequence. The trained models are exposed to “new” data where any deviations from learned patterns either in location on the sequence or frequency of occurrence will denote patterns that are unusual compared with the original training data. To assist in the identification of new patterns and to avoid confusing old patterns as new or novel we use Bayesian surprise to detect the discrepancies between what we are expecting and actual results. We can assign the degree of surprise or unexpectedness to any new pattern and provide an indication of why certain patterns are deemed novel or surprising and why others are not.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70106","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716553","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":"Artificial Intelligent Application in Project Management: An Algorithm Comparison for Solar Plants Planning Construction","authors":"Manuel Ángel López Ferreiro,&nbsp;Jesús Gil Ruiz,&nbsp;Óscar García,&nbsp;Luis De La Fuente Valentín","doi":"10.1111/exsy.70105","DOIUrl":"https://doi.org/10.1111/exsy.70105","url":null,"abstract":"<p>Construction planning is a critical and complex phase in the deployment of large-scale renewable energy infrastructure. This study applies artificial intelligence techniques to a domain-specific problem that has traditionally relied on expert judgement: the generation of detailed construction schedules for photovoltaic power plants. As renewable generation is a key part to meet the challenges of energy transition, the implementation of large projects has increased in recent years and this trend is expected to continue in the future. The main difficulty in meeting construction deadlines is the elaboration of an adequate planning. A tool that automatically generates schedules can be of great help to set up an initial baseline planning. To this end, this work compares five artificial intelligence techniques, on a data set consisting of real examples of successfully completed projects. The evaluation of the results obtained on test data shows that Adaptive Neuro-Fuzzy Inference System (ANFIS) is the technique that obtains the best performance in all error metrics, although it entails a high computational cost. The model thus obtained manages to generate a complete construction schedule with an error of 8% of the total duration. The use of metrics as MAE, MSE and <span></span><math>\u0000 <semantics>\u0000 <mrow>\u0000 <msup>\u0000 <mi>R</mi>\u0000 <mn>2</mn>\u0000 </msup>\u0000 </mrow>\u0000 <annotation>$$ {R}^2 $$</annotation>\u0000 </semantics></math> provides a robust understanding of prediction accuracy, variability, and fit. These metrics are commonly used in project planning evaluations and help interpret model behaviour under different error profiles. Additionally, the resulting 8% total duration error implies a deviation of around 24 days in a 300-day project, which is highly actionable in real-world solar project management. The findings not only demonstrate the feasibility of using AI for solar construction planning, but also lay the groundwork for the development of intelligent software tools or platforms that could support planners in the renewable energy sector. While this study focuses on photovoltaic plants, the approach is extendable to other power plants as wind farms, combined-cycle or nuclear plants, or even to other construction projects.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144705647","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":"A Large-Scale Dataset and Robust Multifeature Representation With Maximum Correlation-Based Feature Fusion and Matching for Apparel Image Retrieval","authors":"Marryam Murtaza,&nbsp;Muhammad Fayyaz,&nbsp;Mussarat Yasmin,&nbsp;Muhammad Anwar,&nbsp;Kashif Naseer Qureshi,&nbsp;Usman Ahmed Raza","doi":"10.1111/exsy.70097","DOIUrl":"https://doi.org/10.1111/exsy.70097","url":null,"abstract":"<p>Finding the correct match to a probe image from a vast amount of data is critical for the online retrieval of apparel images. These images are captured under an uncontrolled environment (e.g., viewpoint and illumination changes); therefore, such type of data is extremely challenging in Content-Based Image Retrieval (CBIR) research. Even in Google searches, most of the time the query results are provided with inaccurate results or duplicate results due to the minor variations between apparel. Another major challenge is that the extracted feature vector dimensions are too high and difficult to handle. In this paper, a method named Multifeature Representation with Maximum Correlation-based Feature Fusion, and Matching (MFR-MCF²M) is proposed for apparel retrieval. This method consists of three modules: (1) Multifeature Representation Module (MFR-M), (2) Maximum Correlation-based Feature Fusion Module (MCF²-M) and (3) Multifeature Matching Module (MFM-M). In the MFR module, the shape, texture and deep features of apparel images are extracted using a Histogram of Oriented Gradients (HOG), Local Binary Patterns (LBP) and a pretrained deep CNN model, respectively. Also, the dimensionality of extracted features is reduced using the proposed Feature Subselection (FSS) method. The MCF module is implemented to measure the maximum correlation between reduced feature vectors. Finally, MCF² is performed using Euclidean distance and a generated Feature Correlation Vector (FCV) to improve the retrieval accuracy and as the benchmark to assess the efficacy of the proposed method. In addition, a new large-scale dataset named Apparel Images Gallery (AIG), which consists of 130,000 images, has been provided to the community. The performance of the proposed MFR-MCF²M method is evaluated on three datasets, including two publicly available datasets and the proposed AIG dataset. The retrieval results are obtained after passing through the threshold function of both the Euclidean distance and the computed FCV. The proposed method achieved an accuracy of 78.3% on the clothing dataset, 94.8% on the CR dataset and 89.1% on the proposed AIG dataset. Consequently, the MFR-MCF²M outperformed state-of-the-art (SOTA) apparel retrieval methods.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.70097","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144673198","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":"A Comprehensive Review of Unimodal and Multimodal Emotion Detection: Datasets, Approaches, and Limitations","authors":"Priyanka Thakur,&nbsp;Nirmal Kaur,&nbsp;Naveen Aggarwal,&nbsp;Sarbjeet Singh","doi":"10.1111/exsy.70103","DOIUrl":"https://doi.org/10.1111/exsy.70103","url":null,"abstract":"<div>\u0000 \u0000 <p>Emotion detection from face and speech is inherent for human–computer interaction, mental health assessment, social robotics, and emotional intelligence. Traditional machine learning methods typically depend on handcrafted features and are primarily centred on unimodal systems. However, the unique characteristics of facial expressions and the variability in speech features present challenges in capturing complex emotional states. Accordingly, deep learning models have been substantial in automatically extracting intrinsic emotional features with greater accuracy across multiple modalities. The proposed article presents a comprehensive review of recent progress in emotion detection, spanning from unimodal to multimodal systems, with a focus on facial and speech modalities. It examines state-of-the-art machine learning, deep learning, and the latest transformer-based approaches for emotion detection. The review aims to provide an in-depth analysis of both unimodal and multimodal emotion detection techniques, highlighting their limitations, popular datasets, challenges, and the best-performing models. Such analysis aids researchers in judicious selection of the most appropriate dataset and audio-visual emotion detection models. Key findings suggest that integrating multimodal data significantly improves emotion recognition, particularly when utilising deep learning methods trained on synchronised audio and video datasets. By assessing recent advancements and current challenges, this article serves as a fundamental resource for researchers and practitioners in the field of emotional AI, thereby aiding in the creation of more intuitive and empathetic technologies.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 9","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672905","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":"Securing Autonomous Vehicles: An In-Depth Review of Cyber Attacks and Anomaly Detection Challenges","authors":"Ratnapal Kumarswami Mane,&nbsp;Poonam Sharma","doi":"10.1111/exsy.70100","DOIUrl":"https://doi.org/10.1111/exsy.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous Vehicle (AV) initiatives have rapidly grown in recent years, significantly impacting daily life and enhancing transportation safety and efficiency. Autonomous driving technology promises a future with fully self-driving vehicles while presenting new challenges in safety assurance. In this review, the evolution, obstacles, and methodologies of the statistical approaches for defining cyber threats and discovering anomalies in AVs, specifically under negative conditions and different datasets, are discussed. More critically, this survey assesses the strengths and weaknesses of these methods for their current and future directions. Discussing anomaly detection in AVs under adverse conditions through Federated Learning (FL) and Deep Learning (DL) techniques enhances threat detection capabilities. Additionally, the review explores security vulnerabilities of intra-vehicle and inter-vehicle communication systems concerning various sensors and perception systems, and examines possible attacks on AV software and hardware, emphasising their effects. In addition, the research proposes defensive schemes, founded on statistical methods, DL, optimisations, FL, and blockchain, for strengthening AVs' security. The review aims to improve AVs' resilience against cyberattacks in reconstructing the weaknesses in sensor and perception systems, thereby resulting in the growth and safety of self-driving technology. Furthermore, the review covers anomaly detection in various scenarios, examining advancements in methodologies for better detection performance. Performance evaluations using publicly available datasets are thoroughly analysed, offering a comprehensive overview of current research trends and suggesting pathways for future improvements in AV detection technology.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144624898","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":"Cloud Layer and Precipitation Forecasting via Multi-Scale Gated Temporal and Spatial Attention Network","authors":"Jiabing Liu,&nbsp;Jianhao Sun,&nbsp;Haiwen Wei,&nbsp;Junzhi Shi,&nbsp;Mingliang Gao","doi":"10.1111/exsy.70099","DOIUrl":"https://doi.org/10.1111/exsy.70099","url":null,"abstract":"<div>\u0000 \u0000 <p>Cloud layer and precipitation forecasting play a crucial role in daily life and decision-making. Most existing deep learning models extract features at a single scale and ignore the correlation between features at different scales in the cloud layer and precipitation data. This hinders the ability to extract multi-scale cloud layer features and precipitation features and further constrains the predictive accuracy of the model. To address these challenges, we propose the multi-scale gated temporal and spatial attention network (MGTSA-Net). This network is designed to capture multi-scale spatiotemporal features in the cloud layer and precipitation data more effectively. As a result, it can improve the accuracy of cloud layer and precipitation forecasting. The core component is the multi-scale temporal gated (MTG) module, which integrates multi-scale convolutions and gated recurrent unit (GRU). To further enhance the model's capability of spatial feature extraction, we integrate a multi-scale spatial attention (MSA) module into the encoder. Experimental evaluations on the WeatherBench dataset demonstrate that the MGTSA-Net outperforms state-of-the-art models in predictive accuracy and computational efficiency.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606503","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":"Transformer-Based Contrastive Learning With Dynamic Masking and Adaptive Pathways for Time Series Anomaly Detection","authors":"Qian Liang,&nbsp;Xiang Yin","doi":"10.1111/exsy.70102","DOIUrl":"https://doi.org/10.1111/exsy.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>Time Series Anomaly Detection (TSAD) has demonstrated broad applicability across various industries, including manufacturing, healthcare, and finance. Its primary objective is to identify unusual deviations in the test set by capturing the typical behavioral patterns of timing data. Despite their strong detection capabilities when labeled data is not available, current reconstruction-based approaches still struggle with anomalous interference and inadequate semantic information extraction at higher time series levels. To tackle these problems, we provide a multi-scale dual-domain patch attention contrast learning model (DMAP-DDCL) that incorporates adaptive path selection and adaptive dynamic context-aware masking. Dynamic context-aware masks are specifically used by DMAP-DDCL to improve the model's generalization ability and mitigate bias resulting from the influence of anomalous data during training. Multi-scale patch segmentation and dual attention to the segmented patches are introduced to capture local details and global correlations as time dependencies. By enlarging the contrast between the two data perspectives, global and local, DMAP-DDCL improves the capacity to differentiate between normal and abnormal patterns. In addition, we enhance the adaptive path of the multi-scale bi-domain attention network, which adapts the multi-scale modeling process to the temporal dynamics of the inputs and enhances the model's accuracy. According to experimental results, DMAP-DDCL performs better on five real datasets from various domains than eight state-of-the-art baselines. Specifically, our model enhances F1 and R_AUC_ROC by an average of 7.5% and 16.67%.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 8","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-07-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144606509","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}