The application of argument mining (AM) in the educational domain is a tool for identifying text structures that express an argument. AM can help evaluate the quality of students' assignments, generate insights into their perspectives, and understand their stance on certain topics. This article examines various aspects of AM in education, including techniques, models, approaches, data representation, language resources, and target artifacts. The findings suggest that AM can enhance learning and teaching processes. However, the study highlights gaps in the literature, particularly in exploring educational artifacts like debates and a lack of research on AM in languages other than English. This paper calls for further research to improve educational outcomes through AM in the educational domain.This article is categorized under: Application Areas > Education and LearningTechnologies > Artificial IntelligenceTechnologies > Machine Learning
{"title":"A Comprehensive Survey of Argument Mining in the Educational Domain: Techniques, Applications, and Future Directions","authors":"David Eduardo Pereira, Daniela Thuaslar Simão Gomes, Larissa Lucena Vasconcelos, Claudio Elizio Calazans Campelo","doi":"10.1002/widm.70041","DOIUrl":"https://doi.org/10.1002/widm.70041","url":null,"abstract":"The application of argument mining (AM) in the educational domain is a tool for identifying text structures that express an argument. AM can help evaluate the quality of students' assignments, generate insights into their perspectives, and understand their stance on certain topics. This article examines various aspects of AM in education, including techniques, models, approaches, data representation, language resources, and target artifacts. The findings suggest that AM can enhance learning and teaching processes. However, the study highlights gaps in the literature, particularly in exploring educational artifacts like debates and a lack of research on AM in languages other than English. This paper calls for further research to improve educational outcomes through AM in the educational domain.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Application Areas > Education and Learning</jats:list-item> <jats:list-item>Technologies > Artificial Intelligence</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":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144900517","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 rapid proliferation of the Internet of Things (IoT) has integrated billions of smart devices into our daily lives, generating and exchanging vast amounts of critical data. While this connectivity offers significant benefits, it also introduces numerous security vulnerabilities. Addressing these vulnerabilities requires a comprehensive approach to hardware security, one that evaluates the interplay of various attacks and countermeasures to protect these systems. This article provides an extensive overview of hardware security strategies and explores contemporary attacks threatening connected systems. We begin by presenting state‐of‐the‐art side‐channel and fault attacks targeting embedded systems, emphasizing the wide range of IoT targets such as smart home devices, medical implants, industrial control systems, and automotive components. Next, we examine hardware‐based security primitives such as physically unclonable functions (PUFs) and physically related functions (PReFs), which have emerged as promising solutions for establishing a hardware root‐of‐trust in lightweight, resource‐constrained devices. These primitives provide robust alternatives to secure storage of cryptographic keys, essential for protecting the diverse array of IoT devices. Further, we discuss trusted architectures, hardware Trojans, and physical assurance mechanisms, highlighting their roles in enhancing security across different IoT environments. We conclude by exploring the expanse of machine learning‐assisted attacks, which present new and intriguing challenges across all the aforementioned security domains. This article aims to offer valuable insights into the current challenges and future directions of research in hardware security, particularly pertaining to the varied and expanding landscape of IoT devices.This article is categorized under: Technologies > Internet of ThingsTechnologies > Machine LearningCommercial, Legal, and Ethical Issues > Security and Privacy
{"title":"Hardware Security in the Connected World","authors":"Durba Chatterjee, Shuvodip Maitra, Nimish Mishra, Shubhi Shukla, Debdeep Mukhopadhyay","doi":"10.1002/widm.70034","DOIUrl":"https://doi.org/10.1002/widm.70034","url":null,"abstract":"The rapid proliferation of the Internet of Things (IoT) has integrated billions of smart devices into our daily lives, generating and exchanging vast amounts of critical data. While this connectivity offers significant benefits, it also introduces numerous security vulnerabilities. Addressing these vulnerabilities requires a comprehensive approach to hardware security, one that evaluates the interplay of various attacks and countermeasures to protect these systems. This article provides an extensive overview of hardware security strategies and explores contemporary attacks threatening connected systems. We begin by presenting state‐of‐the‐art side‐channel and fault attacks targeting embedded systems, emphasizing the wide range of IoT targets such as smart home devices, medical implants, industrial control systems, and automotive components. Next, we examine hardware‐based security primitives such as physically unclonable functions (PUFs) and physically related functions (PReFs), which have emerged as promising solutions for establishing a hardware root‐of‐trust in lightweight, resource‐constrained devices. These primitives provide robust alternatives to secure storage of cryptographic keys, essential for protecting the diverse array of IoT devices. Further, we discuss trusted architectures, hardware Trojans, and physical assurance mechanisms, highlighting their roles in enhancing security across different IoT environments. We conclude by exploring the expanse of machine learning‐assisted attacks, which present new and intriguing challenges across all the aforementioned security domains. This article aims to offer valuable insights into the current challenges and future directions of research in hardware security, particularly pertaining to the varied and expanding landscape of IoT devices.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Technologies > Internet of Things</jats:list-item> <jats:list-item>Technologies > Machine Learning</jats:list-item> <jats:list-item>Commercial, Legal, and Ethical Issues > Security and Privacy</jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"179 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850846","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}
Brain–computer interfaces (BCIs) have emerged as transformative tools, enabling direct communication between the brain and external devices, particularly for individuals with neuromuscular disabilities. This paper provides a comprehensive analysis of feature extraction (FE) methods across all major signal processing domains and various types of BCIs, addressing a significant gap in existing reviews and surveys that often focus exclusively on EEG‐based systems. Also, a detailed comparative analysis of FE techniques, highlighting their formulas, advantages, limitations, and practical applications, is provided. The study not only reviews state‐of‐the‐art methods but also evaluates recent research, identifying trends and gaps in the field. Key insights reveal a growing foundation for invasive BCI research, which, while currently limited, shows promise for future advancements. Based on this analysis, we identify and discuss open challenges such as inter‐subject variability, real‐time processing demands, integration of multiple modalities, and user training and adaptation. Additionally, we examine pressing concerns related to security, privacy, and the transferability of models. By addressing these challenges, this paper aims to guide the development of robust, efficient, and inclusive BCI systems, paving the way for cutting‐edge innovations and real‐world applications.This article is categorized under: Technologies > Machine LearningFundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
{"title":"Exploring the Evolution of Feature Extraction Methods in Brain–Computer Interfaces (BCIs): A Systematic Review of Research Progress and Future Trends","authors":"Shweta Thakur, Samriti Thakur, Aryan Rana, Pankaj Kumar, Kranti Kumar, Chien‐Ming Chen","doi":"10.1002/widm.70040","DOIUrl":"https://doi.org/10.1002/widm.70040","url":null,"abstract":"Brain–computer interfaces (BCIs) have emerged as transformative tools, enabling direct communication between the brain and external devices, particularly for individuals with neuromuscular disabilities. This paper provides a comprehensive analysis of feature extraction (FE) methods across all major signal processing domains and various types of BCIs, addressing a significant gap in existing reviews and surveys that often focus exclusively on EEG‐based systems. Also, a detailed comparative analysis of FE techniques, highlighting their formulas, advantages, limitations, and practical applications, is provided. The study not only reviews state‐of‐the‐art methods but also evaluates recent research, identifying trends and gaps in the field. Key insights reveal a growing foundation for invasive BCI research, which, while currently limited, shows promise for future advancements. Based on this analysis, we identify and discuss open challenges such as inter‐subject variability, real‐time processing demands, integration of multiple modalities, and user training and adaptation. Additionally, we examine pressing concerns related to security, privacy, and the transferability of models. By addressing these challenges, this paper aims to guide the development of robust, efficient, and inclusive BCI systems, paving the way for cutting‐edge innovations and real‐world applications.This article is categorized under: <jats:list list-type=\"bullet\"> <jats:list-item>Technologies > Machine Learning</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":"746 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144850845","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}
Remote photoplethysmography (rPPG) has emerged as a vital technology for remote healthcare, offering non‐invasive and accessible health monitoring through off‐the‐shelf standard video cameras. rPPG facilitates the assessment of key health indicators like heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO2) from video data, providing advantages in early disease diagnosis and routine health assessments. Recognizing its potential, researchers from multiple fields have substantially progressed rPPG by establishing a strong theoretical basis for signal acquisition and developing signal processing and data‐driven algorithms for rPPG extraction. While most rPPG reviews primarily focus on HR signal extraction methods, our research provides an overview of the potential scope of rPPG. We systematically organize research on rPPG signal acquisition and extraction techniques and provide a critical review of recent rPPG advancements in diverse health parameter estimation. Besides providing a thorough HR estimation review, we incorporate the extraction of derivative signals such as RR and SpO2 from rPPG data, including their applications and limitations. We also highlight the adaptation of Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) techniques with rPPG technologies, and accumulate available critical rPPG resources like datasets, codes, and tutorials. Finally, we identify challenges and research gaps, such as motion artifacts, varying lighting conditions, and differences in skin tone. We aim to uplift advancements in rPPG systems by outlining future research directions. Our comprehensive review aims to support the development of robust and safe applications by advancing the field of contactless health parameter sensing.This article is categorized under: Application Areas > Health CareTechnologies > Machine LearningFundamental Concepts of Data and Knowledge > Human Centricity and User Interaction
{"title":"A State‐Of‐The‐Art Survey of Remote Photoplethysmography for Contactless Health Parameters Sensing","authors":"Shadman Sakib, Zahid Hasan, Nirmalya Roy","doi":"10.1002/widm.70039","DOIUrl":"https://doi.org/10.1002/widm.70039","url":null,"abstract":"Remote photoplethysmography (rPPG) has emerged as a vital technology for remote healthcare, offering non‐invasive and accessible health monitoring through off‐the‐shelf standard video cameras. rPPG facilitates the assessment of key health indicators like heart rate (HR), respiratory rate (RR), and blood oxygen saturation (SpO<jats:sub>2</jats:sub>) from video data, providing advantages in early disease diagnosis and routine health assessments. Recognizing its potential, researchers from multiple fields have substantially progressed rPPG by establishing a strong theoretical basis for signal acquisition and developing signal processing and data‐driven algorithms for rPPG extraction. While most rPPG reviews primarily focus on HR signal extraction methods, our research provides an overview of the potential scope of rPPG. We systematically organize research on rPPG signal acquisition and extraction techniques and provide a critical review of recent rPPG advancements in diverse health parameter estimation. Besides providing a thorough HR estimation review, we incorporate the extraction of derivative signals such as RR and SpO<jats:sub>2</jats:sub> from rPPG data, including their applications and limitations. We also highlight the adaptation of Machine Learning (ML), Deep Learning (DL), and Computer Vision (CV) techniques with rPPG technologies, and accumulate available critical rPPG resources like datasets, codes, and tutorials. Finally, we identify challenges and research gaps, such as motion artifacts, varying lighting conditions, and differences in skin tone. We aim to uplift advancements in rPPG systems by outlining future research directions. Our comprehensive review aims to support the development of robust and safe applications by advancing the field of contactless health parameter sensing.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Application Areas > Health Care</jats:list-item> <jats:list-item>Technologies > Machine Learning</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":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144792362","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}
Chronic diseases (CDs) present a global health challenge due to their complex, overlapping symptoms and the limitations of traditional diagnostic methods. Artificial intelligence (AI)‐based techniques, particularly Machine Learning (ML) and Meta‐Heuristic Optimization (MHO) algorithms, have emerged as powerful tools for addressing these challenges. This review examines ML and MHO‐based approaches for the multi‐classification of CDs, highlighting how MHO enhances ML frameworks by addressing key limitations such as class imbalance and suboptimal feature selection. Despite these advancements, MHO‐based methods face challenges, including computational complexity and algorithmic biases, which require further research. By critically analyzing existing studies and identifying gaps, this paper provides a foundation for developing more robust and efficient diagnostic models for CDs.This article is categorized under: Application Areas > Health CareTechnologies > Machine LearningTechnologies > Prediction
{"title":"Meta‐Heuristic Optimization for the Multi‐Classification of Chronic Disease: A Review With Machine Learning Perspectives","authors":"Akansha Singh, Nupur Prakash, Anurag Jain","doi":"10.1002/widm.70030","DOIUrl":"https://doi.org/10.1002/widm.70030","url":null,"abstract":"Chronic diseases (CDs) present a global health challenge due to their complex, overlapping symptoms and the limitations of traditional diagnostic methods. Artificial intelligence (AI)‐based techniques, particularly Machine Learning (ML) and Meta‐Heuristic Optimization (MHO) algorithms, have emerged as powerful tools for addressing these challenges. This review examines ML and MHO‐based approaches for the multi‐classification of CDs, highlighting how MHO enhances ML frameworks by addressing key limitations such as class imbalance and suboptimal feature selection. Despite these advancements, MHO‐based methods face challenges, including computational complexity and algorithmic biases, which require further research. By critically analyzing existing studies and identifying gaps, this paper provides a foundation for developing more robust and efficient diagnostic models for CDs.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Application Areas > Health Care</jats:list-item> <jats:list-item>Technologies > Machine Learning</jats:list-item> <jats:list-item>Technologies > Prediction</jats:list-item> </jats:list>","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"148 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144747364","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}
Machine learning (ML) has rapidly scaled in capacity and complexity, yet blind spots persist beneath its high performance façade. In order to shed more light on this argument, this paper presents a curated catalogue of 175 unconventional concepts, each capturing a paradox, tension, or overlooked risk in modern ML practice. Through nine themes spanning data quality, model architecture and training, interpretability and explainability, fairness and bias, model behavior and limitations, evaluation and metrics, multimodal and system integration, practical and societal implications, and causal reasoning, we provide conceptual definitions, illustrative examples, and actionable mitigation strategies. This review equips practitioners and researchers with a structured taxonomy for diagnosing and preempting the brittle edges of modern ML systems and offers a paradox detection and remediation framework (PDRF) to anticipate limitations, design more thoughtful evaluation protocols, and develop ML systems that balance predictive power with epistemic transparency.This article is categorized under: Fundamental Concepts of Data and Knowledge > Data ConceptsFundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Computational Intelligence
{"title":"A Guide to Machine Learning Epistemic Ignorance, Hidden Paradoxes, and Other Tensions","authors":"M. Z. Naser","doi":"10.1002/widm.70038","DOIUrl":"https://doi.org/10.1002/widm.70038","url":null,"abstract":"Machine learning (ML) has rapidly scaled in capacity and complexity, yet blind spots persist beneath its high performance façade. In order to shed more light on this argument, this paper presents a curated catalogue of 175 unconventional concepts, each capturing a paradox, tension, or overlooked risk in modern ML practice. Through nine themes spanning data quality, model architecture and training, interpretability and explainability, fairness and bias, model behavior and limitations, evaluation and metrics, multimodal and system integration, practical and societal implications, and causal reasoning, we provide conceptual definitions, illustrative examples, and actionable mitigation strategies. This review equips practitioners and researchers with a structured taxonomy for diagnosing and preempting the brittle edges of modern ML systems and offers a paradox detection and remediation framework (PDRF) to anticipate limitations, design more thoughtful evaluation protocols, and develop ML systems that balance predictive power with epistemic transparency.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Fundamental Concepts of Data and Knowledge > Data Concepts</jats:list-item> <jats:list-item>Fundamental Concepts of Data and Knowledge > Big Data Mining</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":"6 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144693602","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}
With renewable energy being aggressively integrated into the grid, energy supplies are becoming vulnerable to weather and the environment, and are often incapable of meeting population demands at a large scale if not accurately predicted for energy planning. Understanding consumers' power demands ahead of time and the influences of weather on consumption and generation can help producers generate effective power management plans to support the target demand. In addition to the high correlation with the environment, consumers' behaviors also cause non‐stationary characteristics of energy data, which is the main challenge for energy prediction. In this survey, we perform a review of the literature on prediction methods in the energy field. So far, most of the available research encompasses one type of generation or consumption. There is no research approaching prediction in the energy sector as a whole and its correlated features. We propose to address the energy prediction challenges from both consumption and generation sides, encompassing techniques from statistical to machine learning techniques. We also summarize the work related to energy prediction, electricity measurements, challenges related to energy consumption and generation, energy forecasting methods, and real‐world energy forecasting resources, such as datasets and software solutions for energy prediction.This article is categorized under: Application Areas > Industry Specific ApplicationsTechnologies > PredictionTechnologies > Machine Learning
{"title":"Statistical and Machine Learning Approaches for Electrical Energy Forecasting","authors":"Solange Machado, Xingquan Zhu","doi":"10.1002/widm.70033","DOIUrl":"https://doi.org/10.1002/widm.70033","url":null,"abstract":"With renewable energy being aggressively integrated into the grid, energy supplies are becoming vulnerable to weather and the environment, and are often incapable of meeting population demands at a large scale if not accurately predicted for energy planning. Understanding consumers' power demands ahead of time and the influences of weather on consumption and generation can help producers generate effective power management plans to support the target demand. In addition to the high correlation with the environment, consumers' behaviors also cause non‐stationary characteristics of energy data, which is the main challenge for energy prediction. In this survey, we perform a review of the literature on prediction methods in the energy field. So far, most of the available research encompasses one type of generation or consumption. There is no research approaching prediction in the energy sector as a whole and its correlated features. We propose to address the energy prediction challenges from both consumption and generation sides, encompassing techniques from statistical to machine learning techniques. We also summarize the work related to energy prediction, electricity measurements, challenges related to energy consumption and generation, energy forecasting methods, and real‐world energy forecasting resources, such as datasets and software solutions for energy prediction.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Application Areas > Industry Specific Applications</jats:list-item> <jats:list-item>Technologies > Prediction</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":"671 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629782","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}
Crowdsourcing has recently evolved as a distributed human problem‐solving method and has received considerable interest from academics and practitioners in various domains. The proliferation of crowdsourcing has made it much simpler to utilize the intelligence and adaptability of many people to learn new knowledge to solve the problem of acquiring new knowledge. In the past, numerous crowdsourcing works have highlighted multiple aspects; however, no surveys have been conducted that focus on the entire crowdsourcing process. This concentrated survey provides a comprehensive review of the technical advances from a systematic perspective. This survey systematically reviews technical advances for a crowdsourcing process that contains four dimensions: task modeling, crowdsourcing data acquisition, the learning process, and predictive model learning, and proposes a comprehensive and scalable framework from CROWD4AI (Crowdsourcing Framework with 4 Dimensions for Artificial Intelligence). In addition, this paper focuses on each dimension's potential challenges and future direction, encouraging researchers to participate in crowdsourcing. To bridge theory with practice, we also include a detailed case study that demonstrates the real‐world application of our proposed framework in the context of annotating cultural heritage damages using crowdsourced input. The case study illustrates how the framework supports effective task design, label collection, robust learning strategies, and accurate predictive modeling in a practical setting.This article is categorized under: Technologies > CrowdsourcingTechnologies > Machine Learning
{"title":"A Systematic Literature Survey of Crowdsourcing: Current Status and Future Perspectives","authors":"Himanshu Suyal, Avtar Singh","doi":"10.1002/widm.70037","DOIUrl":"https://doi.org/10.1002/widm.70037","url":null,"abstract":"Crowdsourcing has recently evolved as a distributed human problem‐solving method and has received considerable interest from academics and practitioners in various domains. The proliferation of crowdsourcing has made it much simpler to utilize the intelligence and adaptability of many people to learn new knowledge to solve the problem of acquiring new knowledge. In the past, numerous crowdsourcing works have highlighted multiple aspects; however, no surveys have been conducted that focus on the entire crowdsourcing process. This concentrated survey provides a comprehensive review of the technical advances from a systematic perspective. This survey systematically reviews technical advances for a crowdsourcing process that contains four dimensions: task modeling, crowdsourcing data acquisition, the learning process, and predictive model learning, and proposes a comprehensive and scalable framework from CROWD4AI (Crowdsourcing Framework with 4 Dimensions for Artificial Intelligence). In addition, this paper focuses on each dimension's potential challenges and future direction, encouraging researchers to participate in crowdsourcing. To bridge theory with practice, we also include a detailed case study that demonstrates the real‐world application of our proposed framework in the context of annotating cultural heritage damages using crowdsourced input. The case study illustrates how the framework supports effective task design, label collection, robust learning strategies, and accurate predictive modeling in a practical setting.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Technologies > Crowdsourcing</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":"52 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629784","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}
This paper presents a comprehensive review on the various techniques and methodologies employed to detect stress among individuals. The review encompasses a broad spectrum of methods, including physiological measurements, wearable technology, machine learning and deep learning algorithms, and contactless image‐based techniques. The paper outlines the physiological markers commonly associated with stress, such as Electrocardiogram (ECG), Electroencephalography (EEG), Photoplethysmography (PPG), and Skin Galvanic response. It examines the various wearable and contactless techniques to acquire data. Furthermore, it explores the integration of machine learning and deep learning techniques for the development of predictive stress detection models, highlighting their accuracy. It also addresses the potential of multispectral and hyperspectral imaging in this area. Some of the publicly available datasets are also discussed in this paper.This article is categorized under: Application Areas > Health CareTechnologies > Machine Learning
{"title":"Machine Learning and Deep Learning Techniques to Detect Mental Stress Using Various Physiological Signals: A Critical Insight","authors":"Megha Khandelwal, Arun Sharma","doi":"10.1002/widm.70035","DOIUrl":"https://doi.org/10.1002/widm.70035","url":null,"abstract":"This paper presents a comprehensive review on the various techniques and methodologies employed to detect stress among individuals. The review encompasses a broad spectrum of methods, including physiological measurements, wearable technology, machine learning and deep learning algorithms, and contactless image‐based techniques. The paper outlines the physiological markers commonly associated with stress, such as Electrocardiogram (ECG), Electroencephalography (EEG), Photoplethysmography (PPG), and Skin Galvanic response. It examines the various wearable and contactless techniques to acquire data. Furthermore, it explores the integration of machine learning and deep learning techniques for the development of predictive stress detection models, highlighting their accuracy. It also addresses the potential of multispectral and hyperspectral imaging in this area. Some of the publicly available datasets are also discussed in this paper.This article is categorized under: <jats:list list-type=\"simple\"> <jats:list-item>Application Areas > Health Care</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":"7 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629783","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}
Vision‐language models (VLMs) integrate visual and textual information, enabling a wide range of applications such as image captioning and visual question answering, making them crucial for modern AI systems. However, their high computational demands pose challenges for real‐time applications. This has led to a growing focus on developing efficient vision‐language models. In this survey, we review key techniques for optimizing VLMs on edge and resource‐constrained devices. We also explore compact VLM architectures, frameworks, and provide detailed insights into the performance–memory trade‐offs of efficient VLMs. Furthermore, we establish a GitHub repository at MPSC‐GitHub to compile all surveyed papers, which we will actively update. Our objective is to foster deeper research in this area.This article is categorized under: Fundamental Concepts of Data and Knowledge > Big Data MiningTechnologies > Internet of ThingsTechnologies > Artificial Intelligence
{"title":"A Survey on Efficient Vision‐Language Models","authors":"Gaurav Shinde, Anuradha Ravi, Emon Dey, Shadman Sakib, Milind Rampure, Nirmalya Roy","doi":"10.1002/widm.70036","DOIUrl":"https://doi.org/10.1002/widm.70036","url":null,"abstract":"Vision‐language models (VLMs) integrate visual and textual information, enabling a wide range of applications such as image captioning and visual question answering, making them crucial for modern AI systems. However, their high computational demands pose challenges for real‐time applications. This has led to a growing focus on developing efficient vision‐language models. In this survey, we review key techniques for optimizing VLMs on edge and resource‐constrained devices. We also explore compact VLM architectures, frameworks, and provide detailed insights into the performance–memory trade‐offs of efficient VLMs. Furthermore, we establish a GitHub repository at MPSC‐GitHub to compile all surveyed papers, which we will actively update. Our objective is to foster deeper research in this area.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>Technologies > Internet of Things</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":"23 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144622234","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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