The sudden increase in adoption of the Internet of Things (IoT) has revolutionized modern living but also brought unprecedented security challenges due to its distributed, heterogeneous, and resource‐constrained nature. This review paper offers a comprehensive examination of machine learning (ML) and deep learning (DL) approaches tailored for intrusion detection and threat mitigation in IoT ecosystems. It explores the landscape of anomaly detection and classification techniques while analyzing their suitability, limitations, and deployment feasibility across IoT layers. The study also investigates the significance of feature engineering, model selection, and system scalability. A novel addition to this review is the integration of emerging trends such as explainable AI (XAI), which enhances transparency and trust in black‐box ML/DL models, and federated learning (FL), a privacy‐preserving paradigm that allows decentralized model training without raw data sharing. The synergy between FL and Edge AI is discussed to highlight real‐time, low‐latency security analytics at the network's edge. Comparative tables, domain‐specific applications (e.g., smart homes, healthcare, and industrial IoT), and architectural illustrations support the discourse, providing readers with an up‐to‐date understanding of current capabilities and ongoing research challenges. This paper concludes with practical implications, research gaps, and future directions for building intelligent, secure, and explainable IoT security frameworks that respect user privacy and enable scalable deployment. This article is categorized under: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Internet of Things Technologies > Machine Learning
{"title":"Security Solutions for the Internet of Things Using Machine Learning and Deep Learning: Current Trends and Future Directions","authors":"Himanshu Sharma, Prabhat Kumar, Kavita Sharma","doi":"10.1002/widm.70059","DOIUrl":"https://doi.org/10.1002/widm.70059","url":null,"abstract":"The sudden increase in adoption of the Internet of Things (IoT) has revolutionized modern living but also brought unprecedented security challenges due to its distributed, heterogeneous, and resource‐constrained nature. This review paper offers a comprehensive examination of machine learning (ML) and deep learning (DL) approaches tailored for intrusion detection and threat mitigation in IoT ecosystems. It explores the landscape of anomaly detection and classification techniques while analyzing their suitability, limitations, and deployment feasibility across IoT layers. The study also investigates the significance of feature engineering, model selection, and system scalability. A novel addition to this review is the integration of emerging trends such as explainable AI (XAI), which enhances transparency and trust in black‐box ML/DL models, and federated learning (FL), a privacy‐preserving paradigm that allows decentralized model training without raw data sharing. The synergy between FL and Edge AI is discussed to highlight real‐time, low‐latency security analytics at the network's edge. Comparative tables, domain‐specific applications (e.g., smart homes, healthcare, and industrial IoT), and architectural illustrations support the discourse, providing readers with an up‐to‐date understanding of current capabilities and ongoing research challenges. This paper concludes with practical implications, research gaps, and future directions for building intelligent, secure, and explainable IoT security frameworks that respect user privacy and enable scalable deployment. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Fundamental Concepts of Data and Knowledge > Explainable AI </jats:list-item> <jats:list-item> Technologies > Internet of Things </jats:list-item> <jats:list-item> Technologies > Machine Learning </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"41 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145894662","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}
Pamela Buñay‐Guisñan, Juan A. Lara, Cristóbal Romero
Counterfactuals are a type of explanations based on hypothetical scenarios used in Explainable Artificial Intelligence (XAI), showing what changes in input variables could have led to different outcomes in predictive problems. In the field of education, counterfactuals enable educators to explore various hypothetical scenarios, facilitating informed decision‐making and the application of educational strategies for improving students' academic performance or reducing dropout rates, among others. Despite the gradual expansion of research on counterfactuals in education, systematic literature reviews on this topic remain scarce. The identification of the most relevant advancements in this field can provide a deep insight into the current state of research, highlighting the most effective areas and revealing opportunities for future studies. The objective of this research is to conduct a systematic literature review, using the PRISMA methodology, to analyze three aspects regarding the use of counterfactuals in education: the problems that counterfactuals help to address in education, the methods and/or algorithms used to generate them, and how the counterfactuals are presented in the educational context. As a result, we have identified a series of key challenges and opportunities for future research over the next few years, which constitute the main contribution of this paper. This article is categorized under: Application Areas > Education and Learning Algorithmic Development > Causality Discovery Fundamental Concepts of Data and Knowledge > Explainable AI
{"title":"Counterfactual Explanations in Education: A Systematic Review","authors":"Pamela Buñay‐Guisñan, Juan A. Lara, Cristóbal Romero","doi":"10.1002/widm.70060","DOIUrl":"https://doi.org/10.1002/widm.70060","url":null,"abstract":"Counterfactuals are a type of explanations based on hypothetical scenarios used in Explainable Artificial Intelligence (XAI), showing what changes in input variables could have led to different outcomes in predictive problems. In the field of education, counterfactuals enable educators to explore various hypothetical scenarios, facilitating informed decision‐making and the application of educational strategies for improving students' academic performance or reducing dropout rates, among others. Despite the gradual expansion of research on counterfactuals in education, systematic literature reviews on this topic remain scarce. The identification of the most relevant advancements in this field can provide a deep insight into the current state of research, highlighting the most effective areas and revealing opportunities for future studies. The objective of this research is to conduct a systematic literature review, using the PRISMA methodology, to analyze three aspects regarding the use of counterfactuals in education: the problems that counterfactuals help to address in education, the methods and/or algorithms used to generate them, and how the counterfactuals are presented in the educational context. As a result, we have identified a series of key challenges and opportunities for future research over the next few years, which constitute the main contribution of this paper. This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item> Application Areas > Education and Learning </jats:list-item> <jats:list-item> Algorithmic Development > Causality Discovery </jats:list-item> <jats:list-item> Fundamental Concepts of Data and Knowledge > Explainable AI </jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"30 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145847529","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}
Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as extracellular recording, which can offer scientists valuable information about diseases and neuron activities. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are significant components of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is essential. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough, as it involved extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. Recognizing noises from spikes produced by neural activity causes the spike classification task to bear a significant demand. Classifying spikes accurately and quickly reveals the role of AI in the scope of spike classification. This review provides an in‐depth discussion of the importance and use of AI in spike classification. This work organizes materials in the spike classification field for future studies and fully describes how spikes are recognized. Therefore, the existing datasets are described first. The topic of spike classification is then separated into three major components: preprocessing, classification, and evaluation. Each of these sections introduces existing methods and determines their importance. Having been summarized and compared, more efficient algorithms are highlighted. The primary goal of this work is to provide a perspective on spike classification for future research, as well as a thorough grasp of the methodologies and issues involved. In this work, numerous studies were extracted from various databases. The PRISMA‐related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. Although there are research papers on spike sorting using the keyword spike, the primary focus of this study is on spike classification. Finally, 47 papers were selected for in‐depth review. First, useful information on the datasets for these papers is supplied. In addition, preprocessing approaches, classification methods, and ultimate performance are investigated in each of these studies. The material is then summarized. Furthermore, the fundamental concerns regarding spike classification raised in the opening of this paper are thoroughly addressed throughout the review. Our reviewing outcomes illustrate that support vector machine and clustering‐based algorithms drastically influence machine learning methods in terms of high accuracy and many uses. Moreover, convolutional neural networks, spiky neural networks, and atten
{"title":"Functional Classification of Spiking Signal Data Using Artificial Intelligence Techniques: A Systematic Review","authors":"Danial Sharifrazi, Nouman Javed, Javad Hassannataj Joloudari, Roohallah Alizadehsani, Saadat Behzadi, Prasad N. Paradkar, Ru‐San Tan, U. Rajendra Acharya, Asim Bhatti","doi":"10.1002/widm.70053","DOIUrl":"https://doi.org/10.1002/widm.70053","url":null,"abstract":"Human brain neuron activities are incredibly significant nowadays. Neuronal behavior is assessed by analyzing signal data such as extracellular recording, which can offer scientists valuable information about diseases and neuron activities. One of the difficulties researchers confront while evaluating these signals is the existence of large volumes of spike data. Spikes are significant components of signal data that can happen as a consequence of vital biomarkers or physical issues such as electrode movements. Hence, distinguishing types of spikes is essential. From this spot, the spike classification concept commences. Previously, researchers classified spikes manually. The manual classification was not precise enough, as it involved extensive analysis. Consequently, Artificial Intelligence (AI) was introduced into neuroscience to assist clinicians in classifying spikes correctly. Recognizing noises from spikes produced by neural activity causes the spike classification task to bear a significant demand. Classifying spikes accurately and quickly reveals the role of AI in the scope of spike classification. This review provides an in‐depth discussion of the importance and use of AI in spike classification. This work organizes materials in the spike classification field for future studies and fully describes how spikes are recognized. Therefore, the existing datasets are described first. The topic of spike classification is then separated into three major components: preprocessing, classification, and evaluation. Each of these sections introduces existing methods and determines their importance. Having been summarized and compared, more efficient algorithms are highlighted. The primary goal of this work is to provide a perspective on spike classification for future research, as well as a thorough grasp of the methodologies and issues involved. In this work, numerous studies were extracted from various databases. The PRISMA‐related research guidelines were then used to choose papers. Then, research studies based on spike classification using machine learning and deep learning approaches with effective preprocessing were selected. Although there are research papers on spike sorting using the keyword spike, the primary focus of this study is on spike classification. Finally, 47 papers were selected for in‐depth review. First, useful information on the datasets for these papers is supplied. In addition, preprocessing approaches, classification methods, and ultimate performance are investigated in each of these studies. The material is then summarized. Furthermore, the fundamental concerns regarding spike classification raised in the opening of this paper are thoroughly addressed throughout the review. Our reviewing outcomes illustrate that support vector machine and clustering‐based algorithms drastically influence machine learning methods in terms of high accuracy and many uses. Moreover, convolutional neural networks, spiky neural networks, and atten","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"17 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-12-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844735","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}
Sahil Bhadane, Samrat Ganguly, Musaddik Karanje, Dhanush Rachaveti, S. Amutha, B. Surendiran
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: Application Areas > Health Care Technologies > Artificial Intelligence Fundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
{"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 > Health Care </jats:list-item> <jats:list-item> Technologies > Artificial Intelligence </jats:list-item> <jats:list-item> Fundamental Concepts of Data and Knowledge > 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}
Radhesyam Vaddi, Boggavarapu Phaneendra Kumar Lakshmi Narasimha, Soma Mitra, Sushmita Mitra, Lorenzo Bruzzone, Swalpa Kumar Roy
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: Technologies > Classification Technologies > Machine Learning Technologies > Artificial Intelligence
{"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 > Classification </jats:list-item> <jats:list-item> Technologies > Machine Learning </jats:list-item> <jats:list-item> Technologies > 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}
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: Application Areas > Health Care Technologies > Artificial Intelligence
{"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 > Health Care </jats:list-item> <jats:list-item> Technologies > 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}
Analuz Silva‐Silverio, Pilar Gómez‐Gil, David O. Sánchez‐Argüelles
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: Technologies > Machine Learning Application Areas > Science and Technology Technologies > Computational Intelligence
{"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 > Machine Learning </jats:list-item> <jats:list-item> Application Areas > Science and Technology </jats:list-item> <jats:list-item> Technologies > 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}
Touqeer Abbas, Tehseen Zahra, Kiran Shehzadi, Faisal Mehmood, Abdul Razzaq, Hui Li
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: Fundamental Concepts of Data and Knowledge > Big Data Mining Application Areas > Science and Technology Technologies > Machine Learning
{"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 > Big Data Mining </jats:list-item> <jats:list-item> Application Areas > Science and Technology </jats:list-item> <jats:list-item> Technologies > 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}
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.
{"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}
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: Fundamental Concepts of Data and Knowledge > Explainable AI Technologies > Artificial Intelligence Technologies > Cognitive Computing
{"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 > Explainable AI </jats:list-item> <jats:list-item> Technologies > Artificial Intelligence </jats:list-item> <jats:list-item> Technologies > 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}
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