WIREs Data Mining and Knowledge Discovery最新文献_第4页

A Systematic Review and Numerical Evaluation on Physics‐Informed Neural Network for Blood Flow Dynamics 血流动力学物理信息神经网络的系统回顾和数值评价

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-07-11 DOI: 10.1002/widm.70108

Niraimathi K. R., Amutha S, Dhanush R, Surendiran B

{"title":"A Systematic Review and Numerical Evaluation on Physics‐Informed Neural Network for Blood Flow Dynamics","authors":"Niraimathi K. R., Amutha S, Dhanush R, Surendiran B","doi":"10.1002/widm.70108","DOIUrl":"https://doi.org/10.1002/widm.70108","url":null,"abstract":"Physics‐Informed Neural Networks (PINNs) are deep neural networks that incorporate partial differential equations in their loss function to understand the physics of the system. Thereby enhancing the learning process of the model to achieve better predictive results than the existing models. PINNs embed governing physical principles directly into their loss functions, enabling them to solve complex problems even with limited or noisy data. This distinctive capability allows PINNs to effectively handle complex computational and engineering problems. Such problems include fluid flow prediction, heat transfer simulation, and wave propagation analysis. Hence, a comprehensive analysis of PINNs and their variations for fluid dynamics in the human biological system will be presented in this work. The findings of this numerical evaluation indicate that PINN‐based models are always in agreement with reference and in vivo data, which proves to be more accurate in predicting the major hemodynamic parameters, including velocity, pressure, and wall shear stress (WSS). However, challenges including high computational cost, convergence instability, and limitations in multi‐scale generalization remain significant. Further, the review addresses the foundational architecture of PINNs, improvements introduced through various extensions, and their relative advantages in different biofluid applications. Special emphasis will be placed on their applications in hemodynamics modeling blood flow dynamics and vascular biomechanics using PINNs. This study also discusses the major challenges of PINNs, such as high computational cost, convergence issues, multi‐scale modeling, and limited generalization. It also reviews proposed solutions and suggests future directions, including integration with other machine learning models for improved scalability and broader interdisciplinary applications.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"34 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148415576","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}

引用次数: 0

Bridging Data‐Driven Intelligence and Expert‐Driven Knowledge: A Systematic Review of the RF ‐ AHP Hybrid Paradigm 桥接数据驱动的智能和专家驱动的知识：RF - AHP混合范式的系统回顾

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-07-06 DOI: 10.1002/widm.70107

Shun Wan, Tianbin Li, Junjie Ma, Lubo Meng, Haoyu Wang, Feiyun Yuan

{"title":"Bridging Data‐Driven Intelligence and Expert‐Driven Knowledge: A Systematic Review of the RF ‐ AHP Hybrid Paradigm","authors":"Shun Wan, Tianbin Li, Junjie Ma, Lubo Meng, Haoyu Wang, Feiyun Yuan","doi":"10.1002/widm.70107","DOIUrl":"https://doi.org/10.1002/widm.70107","url":null,"abstract":"As decision‐making environments become increasingly complex, the development of human–machine collaborative systems that integrate data‐driven intelligence with expert knowledge has attracted considerable scholarly interest. Although the hybrid paradigm combining Random Forest (RF) and Analytic Hierarchy Process (AHP) presents a robust solution, a systematic review utilizing knowledge mapping remains absent in the literature. To address this deficiency, this study employs a combined bibliometric and content analysis to provide a comprehensive review of the RF‐AHP hybrid paradigm. At the macro level, the analysis reveals the distributional characteristics of global collaboration networks and identifies core research hotspots. Theoretically, the study elucidates the synergistic mechanisms between RF and AHP in terms of cognitive logic, decision robustness, and system adaptability. In practical terms, the integration of RF and AHP is categorized into five distinct approaches: result‐level fusion, mutual verification and supplementation, procedural relay, model‐level embedding, and meta‐decision selection. Each approach is analyzed with respect to specific decision‐making scenarios. The review concludes by summarizing current core challenges and outlining future research directions. This work ultimately provides a reusable technical framework and practical reference for addressing scientific problems that require integrating multi‐source data and expert experience, thereby promoting further exploration and development of this paradigm.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"78 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148409029","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}

引用次数: 0

Explainable Hallucination Mitigation in Large Language Models: A Survey 大型语言模型中可解释的幻觉缓解：调查

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-07-01 DOI: 10.1002/widm.70110

Wentao Deng, Jiao Li, Hong‐Yu Zhang, Jiuyong Li, Zhenyun Deng, Debo Cheng, Zaiwen Feng, Haoqiu Zeng

{"title":"Explainable Hallucination Mitigation in Large Language Models: A Survey","authors":"Wentao Deng, Jiao Li, Hong‐Yu Zhang, Jiuyong Li, Zhenyun Deng, Debo Cheng, Zaiwen Feng, Haoqiu Zeng","doi":"10.1002/widm.70110","DOIUrl":"https://doi.org/10.1002/widm.70110","url":null,"abstract":"Hallucinations in large language models (LLMs) present major obstacles to reliability in knowledge‐intensive and reasoning‐based tasks. While recent research has explored detection and correction techniques, a unified interpretive framework remains lacking. This survey addresses hallucination mitigation through the lens of explainability, proposing a taxonomy that distinguishes between internal explainability and post hoc explainability. We examine techniques such as attribution tracing, reasoning path construction, and prompt‐based verification, highlighting their roles in transparent diagnosis and structured control. Furthermore, we discuss the constructive role of hallucinations in creative and user‐driven applications, suggesting that context‐aware management may be more effective than blanket suppression. By synthesizing current advances, this review advocates for explainability as a foundation for trustworthy, controllable, and interpretable LLM systems. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Artificial Intelligence Application Areas > Science and Technology ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"35 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148365234","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}

引用次数: 0

Privacy‐Preserving Federated Learning in Medical Imaging: A Systematic Review 医学影像中隐私保护的联邦学习：系统综述

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-06-30 DOI: 10.1002/widm.70109

Uma Sankar Pechetti, J. Nagendra Kumar Dirisala

{"title":"Privacy‐Preserving Federated Learning in Medical Imaging: A Systematic Review","authors":"Uma Sankar Pechetti, J. Nagendra Kumar Dirisala","doi":"10.1002/widm.70109","DOIUrl":"https://doi.org/10.1002/widm.70109","url":null,"abstract":"Integrating artificial intelligence (AI) into medical imaging is often hindered by privacy regulations, fragmented data sources, and institutional reluctance to share patient information. Federated learning (FL) has emerged as a promising solution, enabling collaborative model training without the need to centralize data. This review critically examines the role of FL in medical imaging, with a particular focus on privacy‐preserving approaches. We analyze developments in FL architectures, privacy‐enhancing techniques, and challenges specific to medical imaging, such as data heterogeneity, modality‐specific constraints, optimization strategies to reduce communication overhead, and fairness issues related to modality drift. The review also provides insights into various privacy‐preserving techniques and discusses the impact of global health regulations, including the Health Insurance Portability and Accountability Act (HIPAA), the General Data Protection Regulation (GDPR), and India's Digital Personal Data Protection (DPDP) Act. Overall, this review aims to guide the development of secure, privacy‐aware, and practical FL systems tailored for real‐world medical imaging applications. This article is categorized under: Technologies > Artificial Intelligence Technologies > Machine Learning Commercial, Legal, and Ethical Issues > Security and Privacy ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"32 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148365279","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}

引用次数: 0

A Multidimensional Taxonomy of Generative Artificial Intelligence Approaches for Drug Discovery 用于药物发现的生成式人工智能方法的多维分类

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-06-16 DOI: 10.1002/widm.70104

Trinidad Crozes, Vir Sabando, Axel J. Soto, Ignacio Ponzoni

{"title":"A Multidimensional Taxonomy of Generative Artificial Intelligence Approaches for Drug Discovery","authors":"Trinidad Crozes, Vir Sabando, Axel J. Soto, Ignacio Ponzoni","doi":"10.1002/widm.70104","DOIUrl":"https://doi.org/10.1002/widm.70104","url":null,"abstract":"Generative artificial intelligence (GAI) methods have shown strong potential to accelerate drug discovery projects and de novo drug design. Yet, the fast pace of new proposals and the complexity of existing GAI methods configures a difficult scenario for professionals that aim to understand and effectively exploit these new tools. These issues are further exacerbated due to the plethora of different architectures for generative models, molecular representations, generation objectives and evaluation metrics. Moreover, other relevant aspects for understanding the inner workings of these models and their applications are scarcely addressed in the literature. For this reason, we propose different dimensions to taxonomically organize the wide range of GAI approaches used for drug discovery. These dimensions include the underlying computational model, the representation of molecules, and the building block for molecular generation. We also describe advantages and limitations of each family of methods, along with a comprehensive survey of metrics to evaluate performances from multiple perspectives. Besides, we explore how the concept of applicability domain applies to conditional generative methods, particularly in terms of quantifying the uncertainty associated with the generated molecules. As a final contribution, we describe and classify different approaches currently used in GAI‐driven drug design to enhance explainability. Finally, we discuss open challenges to strengthen the adoption of these generative models within research and industry. This article is categorized under: Technologies > Machine Learning Application Areas > Health Care ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"124 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288506","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}

引用次数: 0

A Comprehensive Review of Infant Cry Analysis and Classification: Techniques, Challenges, and Future Directions 婴儿哭声分析与分类综述：技术、挑战与未来方向

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-06-10 DOI: 10.1002/widm.70101

Ashwini K., Durai Raj Vincent P. M.

{"title":"A Comprehensive Review of Infant Cry Analysis and Classification: Techniques, Challenges, and Future Directions","authors":"Ashwini K., Durai Raj Vincent P. M.","doi":"10.1002/widm.70101","DOIUrl":"https://doi.org/10.1002/widm.70101","url":null,"abstract":"Crying is a newborn's natural and spontaneous form of communication, expressing their needs and discomforts. Variations in the acoustic properties of cries can serve as early indicators of medical conditions such as asphyxia, hypothyroidism, or hearing impairments. Recognizing these variations is essential for timely and appropriate medical intervention. This comprehensive review traces the evolution of infant cry analysis and classification, from manual observation to automatic recognition systems. It examines key phases of the analysis process, including data collection, signal preprocessing, feature extraction, feature selection and dimensionality reduction, classification techniques, and performance evaluation. Artificial intelligence‐based approaches are examined, with a focus on machine learning (ML) algorithms, neural network architectures, and deep learning (DL) frameworks. In addition, this work identifies persistent challenges such as dataset scarcity, lack of standardized benchmarks, and limited cross‐dataset generalization, and outlines future research priorities, including explainable AI, robust modeling in noisy environments, and ethical deployment in clinical care. Complementing the review, the study empirically benchmarks the Baby Chillanto dataset, using established CNNs as baselines for future research. Overall, it provides a foundational reference for developing accurate, interpretable, and practical cry recognition systems to support infant care and clinical applications. This article is categorized under: Application Areas > Health Care Technologies > Artificial Intelligence Technologies > Machine Learning ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"80 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288537","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}

引用次数: 0

Mental Fatigue in the Modern Era: Neurobiological Mechanisms, Sociocultural Influence, and Translational AI Application 现代精神疲劳：神经生物学机制、社会文化影响和翻译人工智能应用

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-06-10 DOI: 10.1002/widm.70102

Saba Parveen, Md Belal Bin Heyat, Attiq Ur Rehman, Faijan Akhtar, Umair Tariq, Hafiz Muhammad Zeeshan, Saima Parveen, Huang Lei

{"title":"Mental Fatigue in the Modern Era: Neurobiological Mechanisms, Sociocultural Influence, and Translational AI Application","authors":"Saba Parveen, Md Belal Bin Heyat, Attiq Ur Rehman, Faijan Akhtar, Umair Tariq, Hafiz Muhammad Zeeshan, Saima Parveen, Huang Lei","doi":"10.1002/widm.70102","DOIUrl":"https://doi.org/10.1002/widm.70102","url":null,"abstract":"The multifaceted cognitive state of mental fatigue caused by chronic mental effort, maintained workload, and constant psychological stress has been found to impair memory, attention, and emotional control, posing significant challenges across occupational, clinical, and everyday settings. This review presents a comprehensive framework for understanding mental fatigue by integrating insights into neurochemical and brain network dynamics. It is correlated with stress, affects daily functioning, and is influenced by socioeconomic and cultural factors. The article also assesses the importance of artificial intelligence (AI) in the tracking of mental fatigue. The Web of Science and Scopus were used to conduct this review based on research citations for 429 subjects (99.00% human and 1.00% animal). The notable ones are the examination of neuro‐mechanism, behavioral influence, sociocultural determinants, and AI‐based solutions for real‐time detection, clinical, and occupational use. The review suggests that significant research and methodological discrepancies exist; therefore, standardization and interdisciplinary research are necessary. Combining neuroscience, psychology, AI, and sociocultural perspectives, this study is the first one to provide a comprehensive vision of mental fatigue. It predetermines the priorities of translational research and future development. This article is categorized under: Application Areas > Health Care Technologies > Machine Learning Algorithmic Development > Biological Data Mining ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"15 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288544","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}

引用次数: 0

An Overview on Regularized Regression for Multiple Imputation in High‐Dimensional Data: Concepts, Methods, and Practical Guidance 高维数据中多元归算的正则化回归综述：概念、方法和实践指导

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-06-04 DOI: 10.1002/widm.70100

Bugra Varol, Fulden Cantas Turkis

{"title":"An Overview on Regularized Regression for Multiple Imputation in High‐Dimensional Data: Concepts, Methods, and Practical Guidance","authors":"Bugra Varol, Fulden Cantas Turkis","doi":"10.1002/widm.70100","DOIUrl":"https://doi.org/10.1002/widm.70100","url":null,"abstract":"High‐dimensional datasets with missing values present substantial challenges for statistical modeling and inference because conventional multiple imputation (MI) methods may become unstable or nonidentifiable under severe dimensional imbalance. Regularized regression techniques, including lasso, ridge, and related Bayesian formulations, offer a promising way to improve feasibility by enabling variable selection and stabilizing estimation in high‐dimensional settings. This overview provides a structured synthesis of regularized regression‐based MI methods, organized into direct and indirect integration strategies, Bayesian sparsity‐inducing approaches, optimization‐based and sequential penalization methods, and post‐imputation resampling or adjustment techniques. For each category, we summarize the underlying methodological logic, review empirical findings from simulation studies, and highlight practical considerations for implementation. Particular attention is given to methods developed for missing not at random (MNAR) and to the inferential challenges that arise after variable selection. Overall, this overview aims to help researchers choose regularized imputation strategies that are methodologically appropriate, practically feasible, and aligned with the analytical goals of high‐dimensional incomplete‐data studies. This article is categorized under: Technologies > Machine Learning Algorithmic Development > Statistics ","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"16 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-06-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148288548","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}

引用次数: 0

A Review of EEG ‐Based Driver Fatigue Detection: Nonlinear Dynamics, Brain Networks, and Deep Learning Advances 基于脑电图的驾驶员疲劳检测：非线性动力学、脑网络和深度学习进展

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-05-20 DOI: 10.1002/widm.70099

Jichi Chen, Wenhao Zhao, Yuguo Cui, Chunfeng Wei, Kemal Polat, Fayadh Alenezi

{"title":"A Review of EEG ‐Based Driver Fatigue Detection: Nonlinear Dynamics, Brain Networks, and Deep Learning Advances","authors":"Jichi Chen, Wenhao Zhao, Yuguo Cui, Chunfeng Wei, Kemal Polat, Fayadh Alenezi","doi":"10.1002/widm.70099","DOIUrl":"https://doi.org/10.1002/widm.70099","url":null,"abstract":"Driver fatigue has been identified as one of the primary causes of traffic accidents. As long‐duration and high‐load driving becomes increasingly common, the risks of delayed reactions and impaired distance judgment continue to rise. Traditional behavior‐based methods for detecting driver fatigue often exhibit limited stability in complex driving environments. In contrast, electroencephalography (EEG) offers a more reliable detecting method by directly capturing central nervous system activity. This work focuses on fatigue driving detection based on deep learning and EEG, which outlines commonly used public datasets, key preprocessing techniques, feature extraction techniques, performance evaluation metrics, and mainstream deep learning architectures. Based on research progress over the past three years, the use of datasets, published journals, research challenges, and limitations of current methods were analyzed. Future research should improve the model's generalization ability and robustness, introduce richer brain network features, and construct a larger‐scale, high‐quality dataset that closely resembles the real driving environment.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"19 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148290574","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}

引用次数: 0

Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges 基于卫星的森林扰动监测的深度学习：最新进展和挑战

WIREs Data Mining and Knowledge Discovery Pub Date : 2026-05-07 DOI: 10.1002/widm.70096

Carolina Natel, Christoph Molnar, Ricardo Dalagnol, Peer Nowack

{"title":"Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges","authors":"Carolina Natel, Christoph Molnar, Ricardo Dalagnol, Peer Nowack","doi":"10.1002/widm.70096","DOIUrl":"https://doi.org/10.1002/widm.70096","url":null,"abstract":"Climate change and land use pressures are intensifying forest disturbances in many world regions, as reflected in the increasing frequency and severity of wildfires, widespread drought‐induced tree mortality, and extensive forest degradation. Hence, spatially explicit and timely information on disturbances is essential for safeguarding the ecological integrity and societal value of both managed and natural forest ecosystems. Satellite‐based remote sensing has long been central to forest monitoring, and recent advances in deep learning (DL) are further enhancing the extraction of information from remotely sensed data, thereby improving the accuracy and scalability in detecting, mapping, and attributing disturbance events. Such applications range from mapping logging activities and delineating burned areas to the complex task of classifying disturbances into different agents such as pests, fire, and logging. Despite this progress, DL‐based approaches also face significant challenges, including the demand for large annotated training datasets and limited generalization, which might hinder their integration into operational monitoring frameworks. Addressing these barriers will require interdisciplinary collaboration that bridges algorithmic innovation with domain knowledge. This review synthesizes recent advances in DL‐based forest disturbance research, situates them within the broader landscape of traditional remote sensing methods, and highlights emerging innovations with the potential to overcome current limitations. We conclude with perspectives on key research priorities for advancing the role of DL in global forest disturbance monitoring.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148290381","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}

引用次数: 0