Expert Systems最新文献

{"title":"Quantification of Data Imbalance","authors":"Jelke Wibbeke,&nbsp;Sebastian Rohjans,&nbsp;Andreas Rauh","doi":"10.1111/exsy.13840","DOIUrl":"https://doi.org/10.1111/exsy.13840","url":null,"abstract":"<p>In this article, we propose a novel approach to quantify the imbalance in data, addressing a significant gap in the field of regression analysis. Real-world datasets often exhibit an inherent imbalance in their data distribution, which adversely affects learning algorithms such as those used in neural networks. This results in less accurate learning of rare occurrences and a model bias towards more frequent cases, posing challenges in scenarios where rare events are crucial, like energy load prediction. While many solutions exist for classification problems with imbalanced data, regression problems lack adequate research. To address this, we introduce a method to quantify data imbalance by defining it as the disparity between the probability distribution of the data and a relevance-associated distribution. Our approach includes various metrics that can handle multivariate data, allowing for the identification of imbalanced samples and the abstract quantification of imbalance through the mean imbalance ratio. This method facilitates the comparison of regression datasets based on their imbalance, providing insights into dataset quality and evaluating data resampling techniques. We validate our approach using synthetic data and compare it to established metrics such as the Kullback–Leibler divergence and the Wasserstein metric. Furthermore, analysis of real datasets shows a moderate correlation between sample rarity and the approximation error of neural networks, extreme gradient boosting trees and random forests, indicating that underrepresented samples are linked to higher approximation errors.</p>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/exsy.13840","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380160","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":"Machine Learning-Driven Passenger Demand Forecasting for Autonomous Taxi Transportation Systems in Smart Cities","authors":"Adeel Munawar,&nbsp;Mongkut Piantanakulchai","doi":"10.1111/exsy.70014","DOIUrl":"https://doi.org/10.1111/exsy.70014","url":null,"abstract":"<div>\u0000 \u0000 <p>Autonomous Taxis (ATs) have seen remarkable global proliferation in recent years owing to the widespread adoption and advancements in Artificial Intelligence (AI) across various domains. ATs play a crucial role in Intelligent Transportation Systems (ITS) in smart cities. However, the effectiveness of ITS relies heavily on accurately forecasting the passenger demand for ATs, which poses a significant challenge. Precise prediction of passenger demand is essential for minimising waiting times and unnecessary cruising of ATs in metropolitan areas, which helps conserve energy. To address this issue, this study proposed an adaptive Bayesian Regularisation Backpropagation Neural Network (BRBNN) augmented with a Machine Learning (ML) model to predict passenger demand in different regions of metropolitan cities specifically for ATs. The study conducted extensive simulations using a real-world dataset collected from 4781 taxis in Bangkok, Thailand. Using MATLAB2022b, the proposed model compared various state of art methods and existing research. The results indicate that proposed model outperforms existing methods in terms of performance metrics such as Root Mean Square Error (RMSE) and <i>R</i>-squared (<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>) for passenger demand forecasting. These findings validated the effectiveness of the prediction model and its ability to accurately forecast passenger demand for ATs, thereby contributing to the advancement of efficient transportation systems in smart cities.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380159","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":"Feature Identification Using Hypotheses of Relevance and a 2D-Cascade of SEQENS Ensembles","authors":"Joaquim Arlandis,&nbsp;Rafael Llobet,&nbsp;J. Ramón Navarro Cerdán,&nbsp;Laura Arnal,&nbsp;François Signol,&nbsp;Juan-Carlos Perez-Cortes","doi":"10.1111/exsy.70002","DOIUrl":"https://doi.org/10.1111/exsy.70002","url":null,"abstract":"<div>\u0000 \u0000 <p>SEQENS is an ensemble method aimed at feature identification that has demonstrated strong performance in identifying relevant genes in high-dimensional spaces, across different synthetic tasks. In this paper, we first introduce the differences between <i>feature importance</i>, <i>feature selection (FS)</i> and <i>feature identification</i> concepts. Following this, we present a framework based on SEQENS covering the following contributions: (1) computing the hypergeometric <i>p-</i>value of the features of a SEQENS output ranking in order to be able to establish a threshold between relevant and non-relevant features; (2) extending SEQENS by introducing the use of preselected features as hypotheses of relevance in the sequential FS, which may help to attract other features that might exhibit weak correlation with the target on their own, but gain relevance when combined with the preselected ones and; (3) designing an automated process based on a 2D-cascade of SEQENS ensembles to obtain a <i>purged feature set</i>, or PFS, that is, having as many relevant features, and as few non-relevant, as possible. The framework presented, named pc–SEQENS, integrates the former techniques so that the PFS is used as a hypothesis of relevance in a SEQENS ensemble. Performance is analysed in a gene expression identification task using the E-MTAB-3732 public database and synthetic targets. pc–SEQENS is compared to other state-of-the-art methods, including SEQENS to check the effect of using hypotheses of relevance. On average, the proposed framework identifies better the relevant genes, especially in unfavourable sample-to-dimension rates, and exhibits a stronger stability.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380215","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":"Modelling Context and Content Features for Fake News Detection","authors":"Huyen Trang Phan,&nbsp;Dosam Hwang,&nbsp;Yeong-Seok Seo,&nbsp;Ngoc Thanh Nguyen","doi":"10.1111/exsy.13839","DOIUrl":"https://doi.org/10.1111/exsy.13839","url":null,"abstract":"<div>\u0000 \u0000 <p>With the emergence and rapid development of social networks, an increasing amount of news has been spreading. In addition to the benefits of factual information, there are always risks associated with the dissemination of fake news and preventing the spread of fake news has been a concern for researchers. Many methods have been proposed to detect fake news, but they do not fully extract important information related to news content and context, and rarely consider modelling the simultaneous exploitation of the news context and content in fake news detection. This study proposes a method to improve the performance of fake news detection by modelling features related to news context and content. First, we combine contextualised embeddings (e.g., BERT) and dependency-based embeddings (e.g., dependency-based GCN) to enhance the performance of the content representations of news and reviews posting them. Second, we combine all available review texts related to news belonging to the user. Third, we explore all the reviews that other users had posted about current news by clearly creating review representations posted by the same user about the same news. This leads the model to quickly memorise all reviews related to news from one user. Finally, we model the news content features and the modelled news context features to enhance the richness of the news feature representations. Experimental results on the PolitiFact and GossipCop datasets show improvement to the state-of-the-art method of more than three percentage points in the best case.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143380214","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 Novel Approach to Fire Detection With Enhanced Target Localisation and Recognition","authors":"Le Zou,&nbsp;Qiang Sun,&nbsp;Fengling Jiang,&nbsp;Zhize Wu,&nbsp;Lingma Sun,&nbsp;Xiaofeng Wang,&nbsp;Mandar Gogate,&nbsp;Kia Dashtipour,&nbsp;Amir Hussain","doi":"10.1111/exsy.70006","DOIUrl":"https://doi.org/10.1111/exsy.70006","url":null,"abstract":"<div>\u0000 \u0000 <p>Real-time monitoring of fires is crucial for safeguarding lives and property. However, current fire detection methods still suffer from issues such as redundant feature information, poor network generalisation capabilities and low perception of target location information. To address these challenges, a novel fire detection method called YOLO-FDI has been proposed. This method utilises partial convolution and coordinate convolution with attention mechanisms and Alpha loss at different stages. Specifically, to enhance target localisation accuracy, an attention mechanism is integrated into the model to autonomously focus on fire-affected areas. In terms of feature extraction, partial convolution is employed to reduce computational redundancy and memory access, improving performance and effectively extracting spatial features. During the feature fusion stage, coordinate convolution embeds feature information into coordinate data, further enhancing the coordinate perception capabilities of pixels on the feature map, thereby improving adaptability and accuracy in detecting fire targets. Additionally, the model utilises Alpha loss to enhance flexibility and robustness in fire object detection and recognition. Experimental results demonstrate the effectiveness of the proposed model based on three self-constructed datasets. Compared to the baseline YOLOv7 model, its mAP has improved by 4.5 percentage points, 1.7 percentage points and 2.6 percentage points, respectively. This method demonstrates the capability to accurately represent fire targets and exhibits better stability and reliability in fire target detection, effectively reducing false positives and missed detections.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362665","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":"No-Reference Image Quality Assessment: Past, Present, and Future","authors":"Qingyu Mao,&nbsp;Shuai Liu,&nbsp;Qilei Li,&nbsp;Gwanggil Jeon,&nbsp;Hyunbum Kim,&nbsp;David Camacho","doi":"10.1111/exsy.13842","DOIUrl":"https://doi.org/10.1111/exsy.13842","url":null,"abstract":"<div>\u0000 \u0000 <p>No-reference image quality assessment (NR-IQA) has garnered significant attention due to its critical role in various image processing applications. This survey provides a comprehensive and systematic review of NR-IQA methods, datasets, and challenges, offering new perspectives and insights for the field. Specifically, we propose a novel taxonomy for NR-IQA methods based on distortion scenarios and design principles, which distinguishes this work from previous surveys. Representative methods within each category are thoroughly examined, with a focus on their strengths, limitations, and performance characteristics. Additionally, we review 20 widely used NR-IQA datasets that serve as benchmarks for evaluating these methods, providing detailed information on the number of images, distortion types, and distortion levels for each dataset. Furthermore, we identify and discuss key challenges currently faced by NR-IQA methods, such as handling diverse and complex distortions, ensuring generalisation across datasets and devices, and achieving real-time performance. We also suggest potential future research directions to address these issues. In summary, this survey offers a comprehensive and systematic examination of NR-IQA methods, datasets, and challenges, offering valuable insights and guidance for researchers and practitioners working in the NR-IQA domain.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143362664","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":"WoodAD: A New Dataset and a Comparison of Deep Learning Approaches for Wood Anomaly Detection","authors":"Omar del-Tejo-Catala,&nbsp;Javier Perez,&nbsp;Nicolas Garcia,&nbsp;Juan-Carlos Perez-Cortes,&nbsp;Javier Del Ser","doi":"10.1111/exsy.13834","DOIUrl":"https://doi.org/10.1111/exsy.13834","url":null,"abstract":"<div>\u0000 \u0000 <p>Anomaly detection is a crucial task in computer vision, with applications ranging from quality control to security monitoring, among many others. Recent technological advancements have enabled near-perfect solutions on benchmark datasets like MVTec, raising the need for novel datasets that pose new challenges for this modelling task. This work presents a novel Wood Anomaly Detection (WoodAD) dataset, which includes defects in wooden pieces that result in challenges for the most advanced techniques applied to other established datasets. This article evaluates such challenges posed by WoodAD with one-class and few-shot supervised learning approaches. Our experiments herein reveal that EfficientAD, a state-of-the-art method previously excelling on the MVTec dataset, outperforms all other one-class learning approaches. Nevertheless, there is room for improvement, as EfficientAD achieves a 0.535 pixel/segmentation average precision (AP) over the complete test set. UNet, a well-known pixel-level classification architecture, leveraged few-shot supervised learning to enhance the pixel AP score, achieving 0.862 pixel/segmentation AP over the entire test set. Our WoodAD dataset represents a valuable contribution to the field of anomaly detection, offering complex image textures and challenging defects. Researchers and practitioners are encouraged to leverage this dataset to push the boundaries of anomaly detection and develop more robust and effective solutions for more complex real-world applications. The WoodAD dataset has been made publicly available in Kaggle (https://www.kaggle.com/datasets/itiresearch/wood-anomaly-detection-one-class-classification).</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248580","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":"Improving Paragraph Similarity by Sentence Interaction With BERT","authors":"Xi Jin","doi":"10.1111/exsy.70003","DOIUrl":"https://doi.org/10.1111/exsy.70003","url":null,"abstract":"<div>\u0000 \u0000 <p>Research on semantic similarity between relatively short texts, for example, at word- and sentence-level, has progressed significantly in recent years. However, paragraph-level similarity has not been researched in as much detail owing to the challenges associated with embedding representations, despite its utility in numerous applications. A rudimentary approach to paragraph-level similarity involves treating each paragraph as an elongated sentence, thereby encoding the entire paragraph into a single vector. However, this results in the loss of long-distance dependency information, ignoring interactions between sentences belonging to different paragraphs. In this paper, we propose a simple yet efficient method for estimating paragraph similarity. Given two paragraphs, it first obtains a vector for each sentence by leveraging advanced sentence-embedding techniques. Next, the similarity between each sentence in the first paragraph and the second paragraph is estimated as the maximum cosine similarity value between the sentence and each sentence in the second paragraph. This process is repeated for all sentences in the first paragraph to determine the maximum similarity of each sentence with the second paragraph. Finally, overall paragraph similarity is computed by averaging the maximum cosine similarity values. This method alleviates long-range dependency by embedding sentences individually. In addition, it accounts for sentence-level interactions between the two paragraphs. Experiments conducted on two benchmark data sets demonstrate that the proposed method outperforms the baseline approach that encodes entire paragraphs into single vectors.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248541","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":"Medical Data Classification Using Genetic Programming: A Systematic Literature Review","authors":"Pratibha Maurya,&nbsp;Arati Kushwaha,&nbsp;Om Prakash","doi":"10.1111/exsy.70007","DOIUrl":"https://doi.org/10.1111/exsy.70007","url":null,"abstract":"<div>\u0000 \u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Medical data classification has always been a growing area of research. While machine learning techniques have been successfully applied in this field, the vast amount of data generated and the complexity of applications necessitate more robust and powerful methods, especially in the absence of domain expertise. Genetic programming (GP) being a flexible evolutionary approach can autonomously craft efficient classification programs merely from example data and has thus gained significant attention across various classification domains.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Content</h3>\u0000 \u0000 <p>This article presents a literature survey on the application of genetic programming to medical data classification. Reported studies are evaluated based on the examination of datasets, classifier architecture, and achieved classification accuracy. Additionally, we also discuss the strengths and weaknesses of genetic programming with other algorithms, covering aspects like classification accuracy, computational efficiency, interpretability, and resource consumption. The limitations of existing GP techniques and future directions are also presented in this study.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The study presented in this article indicates that GP-based classifiers perform better than other classifiers in the medical domain. To the best of our knowledge, this article is the first of its kind which discusses the application of GP explicitly in medical data classification. Through this article, we aim to enlighten the readers on key concepts of GP and encourage them to build new classifiers by exploring the potential and limitations of genetic programming.</p>\u0000 </section>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143248579","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":"Utilising Explainable AI to Enhance Real-Time Student Performance Prediction in Educational Serious Games","authors":"Manuel J. Gomez,&nbsp;Álvaro Armada Sánchez,&nbsp;Mariano Albaladejo-González,&nbsp;Félix J. García Clemente,&nbsp;José A. Ruipérez-Valiente","doi":"10.1111/exsy.70008","DOIUrl":"https://doi.org/10.1111/exsy.70008","url":null,"abstract":"<div>\u0000 \u0000 <p>In recent years, serious games (SGs) have emerged as a powerful tool in education by combining pedagogy and entertainment, facilitating the acquisition of knowledge and skills in engaging environments. SGs enable the collection of valuable interaction data from students, allowing for the analysis of student performance, with artificial intelligence (AI) playing a key role in processing this data to make informed inferences about their knowledge and skills. However, the lack of explainability in AI models represents a significant challenge. This research aims to develop an interpretable model for predicting students' performance in real-time while playing an SG by: (1) calculating the performance of an interpretable prediction model of task completion in an SG and (2) demonstrating the application of the interpretable model for just-in-time (JIT) classroom interventions. Our results show that we are able to predict students' task completion in real-time with a balanced accuracy result of 77.21% after a short playtime has elapsed. In addition, an explainable artificial intelligence (XAI) approach has been applied to ensure the interpretability of the developed models. This approach supports personalised learning experiences, unlocks AI benefits for non-technical users, and maintains transparency in education.</p>\u0000 </div>","PeriodicalId":51053,"journal":{"name":"Expert Systems","volume":"42 3","pages":""},"PeriodicalIF":3.0,"publicationDate":"2025-02-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143111541","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}