WIREs Data Mining and Knowledge Discovery最新文献

{"title":"Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing","authors":"S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty","doi":"10.1002/widm.70008","DOIUrl":"https://doi.org/10.1002/widm.70008","url":null,"abstract":"As the Internet of Things (IoT) continues expanding its footprint across various sectors, robust security systems to mitigate associated risks are more critical than ever. Intrusion Detection Systems (IDS) are fundamental in safeguarding IoT infrastructures against malicious activities. This systematic review aims to guide future research by addressing six pivotal research questions that underscore the development of advanced IDS tailored for IoT environments. Specifically, the review concentrates on applying machine learning (ML) and deep learning (DL) technologies to enhance IDS capabilities. It explores various feature selection methodologies aimed at developing lightweight IDS solutions that are both effective and efficient for IoT scenarios. Additionally, the review assesses different datasets and balancing techniques, which are crucial for training IDS models to perform accurately and reliably. Through a comprehensive analysis of existing literature, this review highlights significant trends, identifies current research gaps, and suggests future studies to optimize IDS frameworks for the ever‐evolving IoT landscape.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"22 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Application of Wavelet Transformation and Artificial Intelligence Techniques in Healthcare: A Systemic Review","authors":"Samiul Based Shuvo, Syed Samiul Alam, Syeda Umme Ayman, Arbil Chakma, Massimo Salvi, Silvia Seoni, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya","doi":"10.1002/widm.70007","DOIUrl":"https://doi.org/10.1002/widm.70007","url":null,"abstract":"The integration of wavelet transformation and artificial intelligence techniques has demonstrated significant potential in healthcare applications. Wavelet analysis enables multi‐scale signal decomposition and feature extraction that, when combined with machine and deep learning approaches, enhance the accuracy and efficiency of medical data analysis. This systematic review synthesizes 112 relevant studies from 2013 to 2023 exploring wavelet‐based artificial intelligence in healthcare. Our analysis reveals that the discrete wavelet transform dominates (43% of studies), primarily used for feature extraction from biosignals (82%) and medical images. Major applications include cardiac abnormality detection (29%), neurological disorder diagnosis (27%), and mental health assessment (16%), with classification accuracies frequently exceeding 95%. Key findings indicate a shift from traditional machine learning to deep learning approaches after 2020, with emerging trends in hybrid architectures. The review identifies critical challenges in computational efficiency, optimal wavelet selection, and clinical validation. Future developments should focus on real‐time processing optimization, interpretable deep learning models, multi‐modal data fusion, and validation on larger clinical datasets, advancing the translation of these systems into practical clinical tools.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"1 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143736635","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review","authors":"Hakan Uyanik, Abdulkadir Sengur, Massimo Salvi, Ru‐San Tan, Jen Hong Tan, U. Rajendra Acharya","doi":"10.1002/widm.70002","DOIUrl":"https://doi.org/10.1002/widm.70002","url":null,"abstract":"Mental and neurological disorders significantly impact global health. This systematic review examines the use of artificial intelligence (AI) techniques to automatically detect these conditions using electroencephalography (EEG) signals. Guided by Preferred Reporting Items for Systematic Reviews and Meta‐Analysis (PRISMA), we reviewed 74 carefully selected studies published between 2013 and August 2024 that used machine learning (ML), deep learning (DL), or both of these two methods to detect neurological and mental health disorders automatically using EEG signals. The most common and most prevalent neurological and mental health disorder types were sourced from major databases, including Scopus, Web of Science, Science Direct, PubMed, and IEEE Xplore. Epilepsy, depression, and Alzheimer's disease are the most studied conditions that meet our evaluation criteria, 32, 12, and 10 studies were identified on these topics, respectively. Conversely, the number of studies meeting our criteria regarding stress, schizophrenia, Parkinson's disease, and autism spectrum disorders was relatively more average: 6, 4, 3, and 3, respectively. The diseases that least met our evaluation conditions were one study each of seizure, stroke, anxiety diseases, and one study examining Alzheimer's disease and epilepsy together. Support Vector Machines (SVM) were most widely used in ML methods, while Convolutional Neural Networks (CNNs) dominated DL approaches. DL methods generally outperformed traditional ML, as they yielded higher performance using huge EEG data. We observed that the complex decision process during feature extraction from EEG signals in ML‐based models significantly impacted results, while DL‐based models handled this more efficiently. AI‐based EEG analysis shows promise for automated detection of neurological and mental health conditions. Future research should focus on multi‐disease studies, standardizing datasets, improving model interpretability, and developing clinical decision support systems to assist in the diagnosis and treatment of these disorders.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"8 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143599125","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Survey on Latest Advances in Natural Language Processing Applications of Generative Adversarial Networks","authors":"Canan Koç, Fatih Özyurt, Lazsla Barna Iantovics","doi":"10.1002/widm.70004","DOIUrl":"https://doi.org/10.1002/widm.70004","url":null,"abstract":"Data mining and natural language processing (NLP) are fundamental fields that interact in many ways. Text mining shares many topics, such as sentiment analysis and content understanding. Combining these two fields enables more efficient mining of text data and the extraction of valuable information. In particular, the GAN (Generative Adversarial Network) architecture has achieved success in image generation and has started to be used on text data. However, training GANs is fraught with difficulties due to the complexity of text data. Linguistic studies show important differences between languages. Language is characterized by fluidity, ambiguity, and context‐sensitive interpretations, and text‐generating GAN models can struggle to deal with these complexities. The interaction between data quality, language structure, and complex interpretation can lead to inconsistency and ambiguity in the text production of GAN models. These problems are particularly pronounced when complexities such as semantic subtleties, idiomatic expressions, and context‐dependent usages come into play. Text generation is an area of GAN models used in NLP to generate language and enrich text‐based applications. Work in this area can contribute to analyzing, classifying, and processing text data. Many methods and techniques have been proposed to improve the performance of text GANs. However, some problems may be encountered in the optimization of these methods. Therefore, it is essential to use optimized methods. In conclusion, GANs can be an important tool to improve text generation in NLP. Still, they require continuous research and innovation to deal with factors such as language complexity and data quality.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"33 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143518791","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Survey of Approaches to Early Rumor Detection on Microblogging Platforms: Computational and Socio‐Psychological Insights","authors":"Lazarus Kwao, Yang Yang, Jie Zou, Jing Ma","doi":"10.1002/widm.70001","DOIUrl":"https://doi.org/10.1002/widm.70001","url":null,"abstract":"Social media, particularly microblogging platforms, are essential for rapid information sharing and public discussion but often allow rumors, that is, unverified information, to spread rapidly during events or persist over time. These platforms also offer opportunities to study the dynamics of rumors and develop computational methods to assess their veracity. In this paper, we provide a comprehensive review of existing theoretical foundations, interdisciplinary challenges, and emerging advancements in rumor detection research, with a focus on integrating theoretical and computational approaches. Drawing on insights from computer science, cognitive psychology, and sociology, we explore methodologies, such as multimodal fusion, graph‐based models, and attention mechanisms, while highlighting gaps in real‐world scalability, ethical transparency, and cross‐platform adaptability. Using a systematic literature review and bibliometric analysis, we identify trends, methods, and gaps in current research. Our findings emphasize interdisciplinary collaboration to develop adaptable, efficient, and ethical rumor detection strategies. We also highlight the critical role of combining socio‐psychological insights with advanced computational techniques to address the human factors in rumor spread. Furthermore, we emphasize the importance of designing systems that remain effective across diverse cultural and linguistic contexts, enhancing their global applicability. We propose a conceptual framework integrating diverse theories and computational techniques, offering a roadmap for improving detection systems and addressing misinformation challenges on microblogging platforms.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"172 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143477754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"ICT‐Driven Data Mining Analysis in Civil Engineering: A Scientometric Review","authors":"Kashvi Sood","doi":"10.1002/widm.70000","DOIUrl":"https://doi.org/10.1002/widm.70000","url":null,"abstract":"In the contemporary landscape, the remarkable evolution of civil engineering is being driven by the pervasive integration of Information and Communication Technology (ICT). ICT‐driven innovations are playing a crucial role in advancing sustainable development goals by promoting energy efficiency, minimizing resource consumption, and fostering resilient infrastructure. Solutions such as smart grids, intelligent transportation systems, and sustainable urban planning are integral to this progress to address global challenges. The goal of the current study is to conduct a scientometric analysis of scholarly literature published in the recent decade within the domain of ICT‐assisted civil engineering. To achieve this, the study categorizes the civil engineering field into seven major subfields. It includes structural engineering, geotechnical engineering, transportation engineering, water resources engineering, environmental engineering, construction management, and urban planning and design. Employing CiteSpace as the analytical tool, the research offers insights into the intellectual foundations of the civil engineering. This is accomplished through reference co‐citation analysis, cluster analysis, and burst reference analysis. The results demonstrate the adoption of advanced technologies such as Internet of Things (IoT), Machine Learning (ML), Extreme Gradient Boosting (XGBoost), and artificial neural networks in resolving complex civil engineering challenges that reflect the dynamism and diversity of the field. Moreover, it addresses current research challenges within this knowledge domain and explores potential research prospects. The findings emphasize the importance of collaborative efforts among academia, industry stakeholders, and government entities.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"50 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143435149","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions","authors":"Hong‐Seng Gan, Muhammad Hanif Ramlee, Zimu Wang, Akinobu Shimizu","doi":"10.1002/widm.1574","DOIUrl":"https://doi.org/10.1002/widm.1574","url":null,"abstract":"Medical image segmentation is prerequisite in computer‐aided diagnosis. As the field experiences tremendous paradigm changes since the introduction of foundation models, technicality of deep medical segmentation model is no longer a privilege limited to computer science researchers. A comprehensive educational resource suitable for researchers of broad, different backgrounds such as biomedical and medicine, is needed. This review strategically covers the evolving trends that happens to different fundamental components of medical image segmentation such as the emerging of multimodal medical image datasets, updates on deep learning libraries, classical‐to‐contemporary development in deep segmentation models and latest challenges with focus on enhancing the interpretability and generalizability of model. Last, the conclusion section highlights on future trends in deep medical segmentation that worth further attention and investigations.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"9 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142991502","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Trace Encoding Techniques for Multi‐Perspective Process Mining: A Comparative Study","authors":"Antonino Rullo, Farhana Alam, Edoardo Serra","doi":"10.1002/widm.1573","DOIUrl":"https://doi.org/10.1002/widm.1573","url":null,"abstract":"Process mining (PM) comprises a variety of methods for discovering information about processes from their execution logs. Some of them, such as trace clustering, trace classification, and anomalous trace detection require a preliminary preprocessing step in which the raw data is encoded into a numerical feature space. To this end, encoding techniques are used to generate vectorial representations of process traces. Most of the PM literature provides trace encoding techniques that look at the control flow, that is, only encode the sequence of activities that characterize a process trace disregarding other process data that is fundamental for effectively describing the process behavior. To fill this gap, in this article we show 19 trace encoding methods that work in a multi‐perspective manner, that is, by embedding events and trace attributes in addition to activity names into the vectorial representations of process traces. We also provide an extensive experimental study where these techniques are applied to real‐life datasets and compared to each other.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"21 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-12-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142804570","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Hyper‐Parameter Optimization of Kernel Functions on Multi‐Class Text Categorization: A Comparative Evaluation","authors":"Michael Loki, Agnes Mindila, Wilson Cheruiyot","doi":"10.1002/widm.1572","DOIUrl":"https://doi.org/10.1002/widm.1572","url":null,"abstract":"In recent years, machine learning (ML) has witnessed a paradigm shift in kernel function selection, which is pivotal in optimizing various ML models. Despite multiple studies about its significance, a comprehensive understanding of kernel function selection, particularly about model performance, still needs to be explored. Challenges remain in selecting and optimizing kernel functions to improve model performance and efficiency. The study investigates how gamma parameter and cost parameter influence performance metrics in multi‐class classification tasks using various kernel‐based algorithms. Through sensitivity analysis, the impact of these parameters on classification performance and computational efficiency is assessed. The experimental setup involves deploying ML models using four kernel‐based algorithms: Support Vector Machine, Radial Basis Function, Polynomial Kernel, and Sigmoid Kernel. Data preparation includes text processing, categorization, and feature extraction using TfidfVectorizer, followed by model training and validation. Results indicate that Support Vector Machine with default settings and Radial Basis Function kernel consistently outperforms polynomial and sigmoid kernels. Adjusting gamma improves model accuracy and precision, highlighting its role in capturing complex relationships. Regularization cost parameters, however, show minimal impact on performance. The study also reveals that configurations with moderate gamma values achieve better balance between performance and computational time compared to higher gamma values or no gamma adjustment. The findings underscore the delicate balance between model performance and computational efficiency by highlighting the trade‐offs between model complexity and efficiency.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"84 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142753720","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Dimensionality Reduction for Data Analysis With Quantum Feature Learning","authors":"Shyam R. Sihare","doi":"10.1002/widm.1568","DOIUrl":"https://doi.org/10.1002/widm.1568","url":null,"abstract":"To improve data analysis and feature learning, this study compares the effectiveness of quantum dimensionality reduction (qDR) techniques to classical ones. In this study, we investigate several qDR techniques on a variety of datasets such as quantum Gaussian distribution adaptation (qGDA), quantum principal component analysis (qPCA), quantum linear discriminant analysis (qLDA), and quantum t‐SNE (qt‐SNE). The Olivetti Faces, Wine, Breast Cancer, Digits, and Iris are among the datasets used in this investigation. Through comparison evaluations against well‐established classical approaches, such as classical PCA (cPCA), classical LDA (cLDA), and classical GDA (cGDA), and using well‐established metrics like loss, fidelity, and processing time, the effectiveness of these techniques is assessed. The findings show that cPCA produced positive results with the lowest loss and highest fidelity when used on the Iris dataset. On the other hand, quantum uniform manifold approximation and projection (qUMAP) performs well and shows strong fidelity when tested against the Wine dataset, but ct‐SNE shows mediocre performance against the Digits dataset. Isomap and locally linear embedding (LLE) function differently depending on the dataset. Notably, LLE showed the largest loss and lowest fidelity on the Olivetti Faces dataset. The hypothesis testing findings showed that the qDR strategies did not significantly outperform the classical techniques in terms of maintaining pertinent information from quantum datasets. More specifically, the outcomes of paired <jats:italic>t</jats:italic>‐tests show that when it comes to the ability to capture complex patterns, there are no statistically significant differences between the cPCA and qPCA, the cLDA and qLDA, and the cGDA and qGDA. According to the findings of the assessments of mutual information (MI) and clustering accuracy, qPCA may be able to recognize patterns more clearly than standardized cPCA. Nevertheless, there is no discernible improvement between the qLDA and qGDA approaches and their classical counterparts.","PeriodicalId":501013,"journal":{"name":"WIREs Data Mining and Knowledge Discovery","volume":"71 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-11-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142678437","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}