WIREs Data Mining and Knowledge Discovery最新文献

{"title":"A Systematic Review of Movement Tracking for Real‐Time Monitoring of Physical Exercises in the Gym","authors":"Sahil Bhadane, Samrat Ganguly, Musaddik Karanje, Dhanush Rachaveti, S. Amutha, B. Surendiran","doi":"10.1002/widm.70057","DOIUrl":"https://doi.org/10.1002/widm.70057","url":null,"abstract":"In recent years, the amalgamation of computer vision and deep learning technologies has led to the advancement of fitness and health‐related movement tracking in gyms. Such advancements have resulted in exercise‐related analyses within the gym environment. These analyses were made possible by collecting real‐time movement data from people working in the gym, such as kinematics, kinetics, EMG, and so forth. Further, real‐time feedback was provided using movement data to avoid injuries while working in the gym. The newly emerging field of movement tracking in the gym uses technologies that could improve workout accuracy and optimization in the fitness routine. Further, a broad spectrum of recent research assesses computer vision techniques and deep learning models to evaluate physical performance and create real‐time corrective feedback and monitoring systems. The review addresses innovative noncontact and contact‐based monitoring systems that could capture movement patterns and their specific datasets. Furthermore, the article highlights the challenges in real‐world gym settings, such as lighting variations, occlusion by gym equipment or people, and the high computational requirements of real‐time processing. The article also elaborates on different methods and models used for movement tracking in the gym and their advantages and disadvantages. Hence, such a review emphasizes the emergence of transformative computer vision and deep learning technology to revolutionize the fitness domain. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Application Areas &gt; Health Care </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> <jats:list-item> Fundamental Concepts of Data and Knowledge &gt; Human Centricity and User Interaction </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"2 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145784990","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Traditional to Foundation Models: A Survey for Land Use and Land Cover Hyperspectral Image Classification","authors":"Radhesyam Vaddi, Boggavarapu Phaneendra Kumar Lakshmi Narasimha, Soma Mitra, Sushmita Mitra, Lorenzo Bruzzone, Swalpa Kumar Roy","doi":"10.1002/widm.70049","DOIUrl":"https://doi.org/10.1002/widm.70049","url":null,"abstract":"Hyperspectral remote sensing image classification is one of the key research areas of the remote sensing community. The high dimensionality, complex structure of data, and availability of fewer training samples hinder classification performance. Traditional machine learning approaches focus mainly on feature extraction for hyperspectral image classification. The complex relationships among pixels, nonlinearity, and material complexity could not be established with these approaches. This results in a suboptimal solution for fewer training samples in hyperspectral images. Recent advances in deep architectures provide means to improve performance and analyze complex patterns effectively, which were challenging with traditional approaches. The present research systematically describes deep learning models, from basic convolutional neural networks to transfer learning, ensemble learning, attention networks and graph nets. Also, advanced transformer approaches such as Mamba architectures, foundation models and vision‐language models for hyperspectral images with a specific emphasis on land use and land cover mapping. These advanced approaches provide efficient classification and real‐time processing capabilities that allow solutions to other different real‐world applications like agriculture, urban mapping, forestry, and the environment. This research also compares key state‐of‐the‐art methodologies, highlights research challenges, and offers future directions for efficient and accurate classification. This review endorses assimilating multisource data, developing lightweight models for resource‐constrained environments, and progressing explainable deep learning frameworks to improve classification performance. This research also serves as a useful reference for researchers in the hyperspectral remote sensing community, supporting the determination of the most appropriate classification technique specific to a particular remote sensing application. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Classification </jats:list-item> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145759509","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Comprehensive Survey of Deep Learning Methods in Gastro‐Intestinal Wireless Capsule Endoscopy Images","authors":"Sharmila Vijaya Pandian, Geetha Subbiah","doi":"10.1002/widm.70052","DOIUrl":"https://doi.org/10.1002/widm.70052","url":null,"abstract":"The increasing prevalence of gastrointestinal (GI) disorders necessitates the development of effective diagnostic tools. The major drawback is that it takes longer and generates a lot of images that need to be examined by a doctor. To categorize GI tract disorders and speed up processing, numerous deep‐learning (DL) models and image‐processing methods have been created recently. But, there is no research focusing on surveying the GI disorders detection in wireless capsule endoscopy (WCE) images. Hence, this survey is conducted to evaluate the role of DL techniques in improving the study of WCE images, which provide a non‐invasive means of categorizing different GI tract disorders. Together with DL‐based methods, this survey gives a detailed picture of the methods utilized to detect GI diseases. Additionally, this survey emphasizes comparative analysis to demonstrate the efficacy of different GI anomaly detecting methods under DL approaches. Moreover, surveying existing methodologies and their applications, this study aims to identify gaps in research and provide future directions to overcome the existing impact of various techniques in GI disease detection. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Application Areas &gt; Health Care </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"127 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145703913","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Conditional GAN Approaches on Regression Labels: A State‐of‐the‐Art Review","authors":"Analuz Silva‐Silverio, Pilar Gómez‐Gil, David O. Sánchez‐Argüelles","doi":"10.1002/widm.70050","DOIUrl":"https://doi.org/10.1002/widm.70050","url":null,"abstract":"This paper presents a comprehensive review of the most popular algorithms available nowadays, for the generation of synthetic data guided by continuous labeling, based on Generative Adversarial Networks (GANs). It is well known that GANs have produced an outbreak in Artificial Intelligence, particularly in deep learning (DL), where the research on models capable of generating realistic data grows daily. However, the work currently developed related to data generation driven by regression labels is rather modest, even though the number of applications is enormous, which makes it mandatory to intensify the research related to this area. Here, we classify and discuss several continuous GAN models (cGANs), methodologies, and applications currently available, showing some of their success areas, as well as the principal challenges found during their practical use. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> <jats:list-item> Application Areas &gt; Science and Technology </jats:list-item> <jats:list-item> Technologies &gt; Computational Intelligence </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145704534","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Harnessing Earth Observation and Machine Learning for Sustainable Development Goals","authors":"Touqeer Abbas, Tehseen Zahra, Kiran Shehzadi, Faisal Mehmood, Abdul Razzaq, Hui Li","doi":"10.1002/widm.70051","DOIUrl":"https://doi.org/10.1002/widm.70051","url":null,"abstract":"The integration of deep learning models with remote sensing promises significant progress in advancing sustainable development goals. New advances and a myriad of applications are already changing the way mankind will face the living planet challenges. This article reviews the current Vision‐language and foundational models for remote sensing data, along with their application toward monitoring and achieving the Sustainable Development Goals most impacted by the rapid development of deep learning in Earth observation. We systematically review case studies to (1) achieve zero hunger, (2) clean water and sanitation, and (3) mitigate and adapt to climate change. Important societal, economic, and environmental implications are of concern. Exciting times are coming where algorithms and Earth data can help in our endeavor to address the climate crisis and support more sustainable development. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Fundamental Concepts of Data and Knowledge &gt; Big Data Mining </jats:list-item> <jats:list-item> Application Areas &gt; Science and Technology </jats:list-item> <jats:list-item> Technologies &gt; Machine Learning </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"29 18 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145673570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Machine Learning Models for Schizophrenia Detection: A Systematic Review","authors":"Anushka Batte, Minirani S.","doi":"10.1002/widm.70048","DOIUrl":"https://doi.org/10.1002/widm.70048","url":null,"abstract":"Schizophrenia is a neurological disorder that is associated with several genetic, environmental, and neurobiological factors. The various prediction, detection, and classification techniques that can be used in order to catch the disease in early stages are noted in this paper. Some of the common machine learning algorithms, such as convolutional neural networks (CNNs), support vector machines (SVMs), random forest, logistic regression, and also certain models that have been developed, particularly those by the PRONIA project and ENIGMA consortium, are compared in terms of their results and accuracy in schizophrenia detection. The aim is to identify which of these machine learning models performs the best. While a lot of developments have been made, there is much more that can be done in this particular field of psychosis. Additionally, a potential new model is proposed for future work.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"59 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"XAI ‐Guided Continual Learning: Rationale, Methods, and Future Directions","authors":"Michela Proietti, Alessio Ragno, Roberto Capobianco","doi":"10.1002/widm.70046","DOIUrl":"https://doi.org/10.1002/widm.70046","url":null,"abstract":"Providing neural networks with the ability to learn new tasks sequentially represents one of the main challenges in artificial intelligence. Unlike humans, neural networks are prone to losing previously acquired knowledge upon learning new information, a phenomenon known as catastrophic forgetting. Continual learning proposes diverse solutions to mitigate this problem, but only a few leverage explainable artificial intelligence. This work justifies using explainability techniques in continual learning, emphasizing the need for greater transparency and trustworthiness in these systems and grounding our approach in empirical findings from neuroscience that highlight parallels between forgetting in biological and artificial neural networks. Finally, we review existing work applying explainability methods to address catastrophic forgetting and propose potential avenues for future research. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Fundamental Concepts of Data and Knowledge &gt; Explainable AI </jats:list-item> <jats:list-item> Technologies &gt; Artificial Intelligence </jats:list-item> <jats:list-item> Technologies &gt; Cognitive Computing </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145396366","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Systematic Review of Image-Based Emotion Recognition Datasets: A Comprehensive Analysis","authors":"Michał Tomaszewski, Zineb Bougriche, Jakub Osuchowski, Rafał Gasz","doi":"10.1002/widm.70047","DOIUrl":"https://doi.org/10.1002/widm.70047","url":null,"abstract":"The present systematic review gives a broad overview of the image-based datasets on which emotion has been classified. These datasets are categorized into three groups: (1) those containing only facial images, (2) those incorporating facial images with additional contextual body features, and (3) those based on visual components with physiological or audio signals. This paper discusses datasets in each category, focusing on their structure, emotion categories, emotion sources, anonymization level, and suitability for machine learning applications. A key aspect of this study is mapping emotions in the datasets analyzed to Plutchik's Wheel of Emotions, allowing standardization based on a consistent psychological model. This alignment demonstrates the need for more thorough and balanced datasets encompassing a wider range of affective states and exposes discrepancies in the representation of emotions. Comprehensive statistics on each dataset are also provided in our study, allowing for comparisons on sample size, distribution of emotions, anonymization, and sources of emotion. To conclude, this study discusses future directions, for which multimodal datasets that incorporate images, audio, and biosignals are of high importance as are databases containing a larger variety of emotions mapped more evenly across Plutchik's model.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145332055","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey on Medical Document Summarization: From Machine Learning Techniques to Large Language Models","authors":"Akash Ghosh, Raghav Jain, Anubhav Jhangra, Sriparna Saha, Adam Jatowt","doi":"10.1002/widm.70045","DOIUrl":"https://doi.org/10.1002/widm.70045","url":null,"abstract":"The widespread adoption of the Internet has transformed healthcare by enabling the digital storage, sharing, and management of medical documents. This shift has improved information access, enhanced patient care, and opened new avenues for research and innovation. As the volume of medical data available to clinicians and patients continues to grow, the need for effective summarization methods becomes increasingly critical. Recent breakthroughs in deep learning—particularly the emergence of Large Language Models (LLMs)—have further accelerated progress in this area. This paper provides a comprehensive survey of current techniques and emerging trends in medical document summarization.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"12 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145247627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring the Application of the Internet of Things in Precision Machining by Comparative Text Mining","authors":"Edward Hengzhou Yan, Feng Guo, Baolong Zhang, Muhammad Rehan, Delei Wang, Zhicheng Xu, Chi Ho Wong, Long Teng, Wai Sze Yip, Suet To","doi":"10.1002/widm.70042","DOIUrl":"https://doi.org/10.1002/widm.70042","url":null,"abstract":"Precision machining, manufacturing components with superior surface quality and dimensional accuracy, increasingly leverages Internet of Things (IoT) technologies. This study employs a novel comparative text mining approach by systematically integrating tree maps, word clouds, keyword network analysis, and Pearson correlation to identify critical linkages between IoT and precision machining. By analyzing a scientific research database (2019–2023), this study highlights IoT's core competencies in enhancing precision machining, including real‐time monitoring, predictive maintenance, and data‐driven optimization. Furthermore, this study proposes actionable strategies, including neural network‐based cyber production systems, blockchain‐integrated IIoT platforms, and machine learning‐driven predictive models, for precision machining. These recommendations empower academia and industry to harness IoT to improve product quality and reduce costs in precision machining.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Algorithmic Development &gt; Text Mining</jats:list-item> <jats:list-item>Fundamental Concepts of Data and Knowledge &gt; Knowledge Representation</jats:list-item> <jats:list-item>Technologies &gt; Data Preprocessing</jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-09-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144995182","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}