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Weak Supervision: A Survey on Predictive Maintenance WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-12 Antonio M. Martínez‐Heredia, Sebastián Ventura
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Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-12 Abdul Aziz Noor, Awais Manzoor, Muhammad Deedahwar Mazhar Qureshi, M. Atif Qureshi, Wael Rashwan
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Review on Information Fusion‐Based Data Mining for Improving Complex Anomaly Detection WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-09 Sorin‐Claudiu Moldovan, Laszlo Barna Iantovics
Anomaly predicated upon multiple distributed hybrid sensors frequently uses hybrid approaches, integrating techniques derived from statistical analysis, probability, data mining, machine learning, deep learning, and signal denoising. Many of these methods are based on the analysis of irregularities, data continuity, correlation, and data consistency, aiming to discern anomalous patterns from normal
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Long Document Classification in the Transformer Era: A Survey on Challenges, Advances, and Open Issues WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-09 Renzo Alva Principe, Nicola Chiarini, Marco Viviani
Automatic Document Classification (ADC) refers to the process of automatically categorizing or labeling documents into predefined classes or categories. Its effectiveness may depend on various factors, including the models used for the formal representation of documents, the classification techniques applied, or a combination of both. Recently, Transformer models have gained popularity due to their
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The Role of Causality in Explainable Artificial Intelligence WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-07 Gianluca Carloni, Andrea Berti, Sara Colantonio
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AI-Assisted Literature Review: Integrating Visualization and Geometric Features for Insightful Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-05-01 Grigorios Papageorgiou, Ekaterini Skamnia, Polychronis Economou
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Neuromorphic Computing and Applications: A Topical Review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-28 Pavan Kumar Enuganti, Basabdatta Sen Bhattacharya, Teresa Serrano Gotarredona, Oliver Rhodes
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A Systematic Review on Process Mining for Curricular Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-23 Daniel Calegari, Andrea Delgado
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How AI Contributes to Poverty Alleviation: A Systematic Literature Review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-21 Sepehr Ghazinoory, Mercedeh Pahlavanian, Mehdi Fatemi, Fatemeh Parvin, Sayna Ahad Bhat
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Transforming Disaster Risk Reduction With AI and Big Data: Legal and Interdisciplinary Perspectives WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-14 Kwok P. Chun, Thanti Octavianti, Nilay Dogulu, Hristos Tyralis, Georgia Papacharalampous, Ryan Rowberry, Pingyu Fan, Mark Everard, Maria Francesch-Huidobro, Wellington Migliari, David M. Hannah, John Travis Marshall, Rafael Tolosana Calasanz, Chad Staddon, Ida Ansharyani, Bastien Dieppois, Todd R. Lewis, Juli Ponce, Silvia Ibrean, Tiago Miguel Ferreira, Chinkie Peliño-Golle, Ye Mu, Manuel Davila Delgado
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Algorithmic Profiling and Facial Recognition in EU Border Control: Examining ETIAS Decision-Making, Privacy and Law WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-11 Abhishek Thommandru, Varda Mone, Fayzulloyev Shokhijakhon, Giyosbek Mirzayev
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Mapping the Landscape of Personalization: A Comprehensive Review of Prediction and Trends in Recommendation Systems WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-11 Tamanna Sachdeva, Lalit Mohan Goyal, Mamta Mittal
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A Brief Review on Benchmarking for Large Language Models Evaluation in Healthcare WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-09 Leona Cilar Budler, Hongyu Chen, Aokun Chen, Maxim Topaz, Wilson Tam, Jiang Bian, Gregor Stiglic
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A Comprehensive Review on Data-Driven Methods of Lithium-Ion Batteries State-of-Health Forecasting WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-08 Thien Pham, Hung Bui, Mao Nguyen, Quang Pham, Vinh Vu, Triet Le, Tho Quan
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A Systematic Survey of Graph Convolutional Networks for Artificial Intelligence Applications WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-04-08 Amutha Sadasivan, Kavipriya Gananathan, Joe Dhanith Pal Nesamony Rose Mary, Surendiran Balasubramanian
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Optimizing Intrusion Detection for IoT: A Systematic Review of Machine Learning and Deep Learning Approaches With Feature Selection and Data Balancing WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-03-28 S. Kumar Reddy Mallidi, Rajeswara Rao Ramisetty
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Application of Wavelet Transformation and Artificial Intelligence Techniques in Healthcare: A Systemic Review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-03-28 Samiul Based Shuvo, Syed Samiul Alam, Syeda Umme Ayman, Arbil Chakma, Massimo Salvi, Silvia Seoni, Prabal Datta Barua, Filippo Molinari, U. Rajendra Acharya
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Issue Information WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-03-28
Click on the article title to read more.
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Automated Detection of Neurological and Mental Health Disorders Using EEG Signals and Artificial Intelligence: A Systematic Review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-03-12 Hakan Uyanik, Abdulkadir Sengur, Massimo Salvi, Ru‐San Tan, Jen Hong Tan, U. Rajendra Acharya
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
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Survey on Latest Advances in Natural Language Processing Applications of Generative Adversarial Networks WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-02-27 Canan Koç, Fatih Özyurt, Laszlo Barna Iantovics
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
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A Survey of Approaches to Early Rumor Detection on Microblogging Platforms: Computational and Socio‐Psychological Insights WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-02-24 Lazarus Kwao, Yang Yang, Jie Zou, Jing Ma
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
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ICT‐Driven Data Mining Analysis in Civil Engineering: A Scientometric Review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-02-17 Kashvi Sood
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
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A Review on Medical Image Segmentation: Datasets, Technical Models, Challenges and Solutions WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2025-01-21 Hong‐Seng Gan, Muhammad Hanif Ramlee, Zimu Wang, Akinobu Shimizu
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
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Trace Encoding Techniques for Multi‐Perspective Process Mining: A Comparative Study WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-12-10 Antonino Rullo, Farhana Alam, Edoardo Serra
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
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Hyper‐Parameter Optimization of Kernel Functions on Multi‐Class Text Categorization: A Comparative Evaluation WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-28 Michael Loki, Agnes Mindila, Wilson Cheruiyot
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
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Dimensionality Reduction for Data Analysis With Quantum Feature Learning WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-21 Shyam R. Sihare
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)
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Business Analytics in Customer Lifetime Value: An Overview Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-06 Onur Dogan, Abdulkadir Hiziroglu, Ali Pisirgen, Omer Faruk Seymen
In customer‐oriented systems, customer lifetime value (CLV) has been of significant importance for academia and marketing practitioners, especially within the scope of analytical modeling. CLV is a critical approach to managing and organizing a company's profitability. With the vast availability of consumer data, business analytics (BA) tools and approaches, alongside CLV models, have been applied
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Knowledge Graph for Solubility Big Data: Construction and Applications WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-11-01 Xiao Haiyang, Yan Ruomei, Wu Yan, Guan Lixin, Li Mengshan
Dissolution refers to the process in which solvent molecules and solute molecules attract and combine with each other. The extensive solubility data generated from the dissolution of various compounds under different conditions, is distributed across structured or semi‐structured formats in various media, such as text, web pages, tables, images, and databases. These data exhibit multi‐source and unstructured
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Application‐Based Review of Soft Computational Methods to Enhance Industrial Practices Abetted by the Patent Landscape Analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-31 S. Tamilselvan, G. Dhanalakshmi, D. Balaji, L. Rajeshkumar
Soft computing is a collective methodology that touches all engineering and technology fields owing to its easiness in solving various problems while comparing the conventional methods. Many analytical methods are taken over by this soft computing technique and resolve it accurately and the soft computing has given a paradigm shift. The flexibility in soft computing results in swift knowledge acquisition
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Using Machine Learning for Systematic Literature Review Case in Point: Agile Software Development WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-29 Itzik David, Roy Gelbard
Systematic literature reviews (SLRs) are essential for researchers to keep up with past and recent research in their domains. However, the rapid growth in knowledge creation and the rising number of publications have made this task increasingly complex and challenging. Moreover, most systematic literature reviews are performed manually, which requires significant effort and creates potential bias.
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Adversarial Attacks in Explainable Machine Learning: A Survey of Threats Against Models and Humans WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-28 Jon Vadillo, Roberto Santana, Jose A. Lozano
Reliable deployment of machine learning models such as neural networks continues to be challenging due to several limitations. Some of the main shortcomings are the lack of interpretability and the lack of robustness against adversarial examples or out‐of‐distribution inputs. In this paper, we comprehensively review the possibilities and limits of adversarial attacks for explainable machine learning
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Reflecting on a Decade of Evolution: MapReduce‐Based Advances in Partitioning‐Based, Hierarchical‐Based, and Density‐Based Clustering (2013–2023) WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-21 Tanvir Habib Sardar
The traditional clustering algorithms are not appropriate for large real‐world datasets or big data, which is attributable to computational expensiveness and scalability issues. As a solution, the last decade's research headed towards distributed clustering using the MapReduce framework. This study conducts a bibliometric review to assess, establish, and measure the patterns and trends of the MapReduce‐based
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A Conceptual Framework for Human‐Centric and Semantics‐Based Explainable Event Detection WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-18 Taiwo Kolajo, Olawande Daramola
Explainability in the field of event detection is a new emerging research area. For practitioners and users alike, explainability is essential to ensuring that models are widely adopted and trusted. Several research efforts have focused on the efficacy and efficiency of event detection. However, a human‐centric explanation approach to existing event detection solutions is still lacking. This paper
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An overview of current developments and methods for identifying diabetic foot ulcers: A survey WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-09 L. Jani Anbarasi, Malathy Jawahar, R. Beulah Jayakumari, Modigari Narendra, Vinayakumar Ravi, R. Neeraja
Diabetic foot ulcers (DFUs) present a substantial health risk across diverse age groups, creating challenges for healthcare professionals in the accurate classification and grading. DFU plays a crucial role in automated health monitoring and diagnosis systems, where the integration of medical imaging, computer vision, statistical analysis, and gait information is essential for comprehensive understanding
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Multimodal emotion recognition: A comprehensive review, trends, and challenges WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-09 Manju Priya Arthanarisamy Ramaswamy, Suja Palaniswamy
Automatic emotion recognition is a burgeoning field of research and has its roots in psychology and cognitive science. This article comprehensively reviews multimodal emotion recognition, covering various aspects such as emotion theories, discrete and dimensional models, emotional response systems, datasets, and current trends. This article reviewed 179 multimodal emotion recognition literature papers
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Artificial intelligence in assessing cardiovascular diseases and risk factors via retinal fundus images: A review of the last decade WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-10-09 Mirsaeed Abdollahi, Ali Jafarizadeh, Amirhosein Ghafouri‐Asbagh, Navid Sobhi, Keysan Pourmoghtader, Siamak Pedrammehr, Houshyar Asadi, Ru‐San Tan, Roohallah Alizadehsani, U. Rajendra Acharya
Cardiovascular diseases (CVDs) are the leading cause of death globally. The use of artificial intelligence (AI) methods—in particular, deep learning (DL)—has been on the rise lately for the analysis of different CVD‐related topics. The use of fundus images and optical coherence tomography angiography (OCTA) in the diagnosis of retinal diseases has also been extensively studied. To better understand
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Continual learning and its industrial applications: A selective review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-09-24 J. Lian, K. Choi, B. Veeramani, A. Hu, S. Murli, L. Freeman, E. Bowen, X. Deng
In many industrial applications, datasets are often obtained in a sequence associated with a series of similar but different tasks. To model these datasets, a machine‐learning algorithm, which performed well on the previous task, may not have as strong a performance on the current task. When the architecture of the algorithm is trained to adapt to new tasks, often the whole architecture needs to be
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Lead–lag effect of research between conference papers and journal papers in data mining WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-09-24 Yue Huang, Runyu Tian
The examination of the lead–lag effect between different publication types, incorporating a temporal dimension, is very significant for assessing research. In this article, we introduce a novel framework to quantify the lead–lag effect between the research topics of conference papers and journal papers. We first identify research topics via the text‐embedding‐based topic modeling technique BERTopic
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From 3D point‐cloud data to explainable geometric deep learning: State‐of‐the‐art and future challenges WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-09-17 Anna Saranti, Bastian Pfeifer, Christoph Gollob, Karl Stampfer, Andreas Holzinger
We present an exciting journey from 3D point‐cloud data (PCD) to the state of the art in graph neural networks (GNNs) and their evolution with explainable artificial intelligence (XAI), and 3D geometric priors with the human‐in‐the‐loop. We follow a simple definition of a “digital twin,” as a high‐precision, three‐dimensional digital representation of a physical object or environment, captured, for
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Digital twins in healthcare: Applications, technologies, simulations, and future trends WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-09-06 Mohamed Abd Elaziz, Mohammed A. A. Al‐qaness, Abdelghani Dahou, Mohammed Azmi Al‐Betar, Mona Mostafa Mohamed, Mohamed El‐Shinawi, Amjad Ali, Ahmed A. Ewees
The healthcare industry has witnessed significant interest in applying DTs (DTs), due to technological advancements. DTs are virtual replicas of physical entities that adapt to real‐time data, enabling predictions of their physical counterparts. DT technology enhances understanding of disease occurrence, enabling more accurate diagnoses and treatments. Integrating emerging technologies like big data
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A taxonomy of automatic differentiation pitfalls WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-09-03 Jan Hückelheim, Harshitha Menon, William Moses, Bruce Christianson, Paul Hovland, Laurent Hascoët
Automatic differentiation is a popular technique for computing derivatives of computer programs. While automatic differentiation has been successfully used in countless engineering, science, and machine learning applications, it can sometimes nevertheless produce surprising results. In this paper, we categorize problematic usages of automatic differentiation, and illustrate each category with examples
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Advancements in Q‐learning meta‐heuristic optimization algorithms: A survey WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-08-19 Yang Yang, Yuchao Gao, Zhe Ding, Jinran Wu, Shaotong Zhang, Feifei Han, Xuelan Qiu, Shangce Gao, You‐Gan Wang
This paper reviews the integration of Q‐learning with meta‐heuristic algorithms (QLMA) over the last 20 years, highlighting its success in solving complex optimization problems. We focus on key aspects of QLMA, including parameter adaptation, operator selection, and balancing global exploration with local exploitation. QLMA has become a leading solution in industries like energy, power systems, and
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Exploring the convergence of Metaverse, Blockchain, and AI: A comprehensive survey of enabling technologies, applications, challenges, and future directions WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-08-19 Mueen Uddin, Muath Obaidat, Selvakumar Manickam, Shams Ul Arfeen Laghari, Abdulhalim Dandoush, Hidayat Ullah, Syed Sajid Ullah
The Metaverse, distinguished by its capacity to integrate the physical and digital realms seamlessly, presents a dynamic virtual environment offering diverse opportunities for engagement across innovation, entertainment, socialization, and commercial endeavors. However, the Metaverse is poised for a transformative evolution through the convergence of contemporary technological advancements, including
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The evolution of frailty assessment using inertial measurement sensor‐based gait parameter measurements: A detailed analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-08-13 Arslan Amjad, Shahzad Qaiser, Monika Błaszczyszyn, Agnieszka Szczęsna
Frailty is a significant issue in geriatric health, may cause adverse effects such as falls, delirium, weight loss, or physical decline. Over time, various methods have been developed for measuring frailty, including clinical judgment, the frailty index, the clinical frailty scale, and the comprehensive geriatric assessment. These traditional frailty assessment approaches rely on healthcare professionals
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Medical intelligence for anxiety research: Insights from genetics, hormones, implant science, and smart devices with future strategies WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-08-04 Faijan Akhtar, Md Belal Bin Heyat, Arshiya Sultana, Saba Parveen, Hafiz Muhammad Zeeshan, Stalin Fathima Merlin, Bairong Shen, Dustin Pomary, Jian Ping Li, Mohamad Sawan
This comprehensive review article embarks on an extensive exploration of anxiety research, navigating a multifaceted landscape that incorporates various disciplines, such as molecular genetics, hormonal influences, implant science, regenerative engineering, and real‐time cardiac signal analysis, all while harnessing the transformative potential of medical intelligence [medical + artificial intelligence
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A brief review on quantum computing based drug design WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-17 Poulami Das, Avishek Ray, Siddhartha Bhattacharyya, Jan Platos, Vaclav Snasel, Leo Mrsic, Tingwen Huang, Ivan Zelinka
Design and development of new drug molecules are essential for the survival of human society. New drugs are designed for therapeutic purposes to combat new diseases. Besides treating new diseases, new drug development is also needed to treat pre‐existing diseases more effectively and reduce the existing drugs' side effects. The design of drugs involves several steps, from the discovery of the drug
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A systematic review and research recommendations on artificial intelligence for automated cervical cancer detection WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-16 Smith K. Khare, Victoria Blanes‐Vidal, Berit Bargum Booth, Lone Kjeld Petersen, Esmaeil S. Nadimi
Early diagnosis of abnormal cervical cells enhances the chance of prompt treatment for cervical cancer (CrC). Artificial intelligence (AI)‐assisted decision support systems for detecting abnormal cervical cells are developed because manual identification needs trained healthcare professionals, and can be difficult, time‐consuming, and error‐prone. The purpose of this study is to present a comprehensive
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Machine learning for pest detection and infestation prediction: A comprehensive review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-15 Mamta Mittal, Vedika Gupta, Mohammad Aamash, Tejas Upadhyay
Pests pose a major danger to a variety of industries, including agriculture, public health, and ecosystems. Fast and precise pest detection, as well as the ability to predict infestations, are required for effective pest management tactics. This paper provides a comprehensive literature review on this subject to provide an overview of the state of research on pest detection and infestation prediction
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Onset of a conceptual outline map to get a hold on the jungle of cluster analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-12 Iven Van Mechelen, Christian Hennig, Henk A. L. Kiers
The domain of cluster analysis is a meeting point for a very rich multidisciplinary encounter, with cluster‐analytic methods being studied and developed in discrete mathematics, numerical analysis, statistics, data analysis, data science, and computer science (including machine learning, data mining, and knowledge discovery), to name but a few. The other side of the coin, however, is that the domain
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Machine learning applied to tourism: A systematic review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-07-04 José Carlos Sancho Núñez, Juan A. Gómez‐Pulido, Rafael Robina Ramírez
The application of machine learning techniques in the field of tourism is experiencing a remarkable growth, as they allow to propose efficient solutions to problems present in this sector, by means of an intelligent analysis of data in their specific context. The increase of work in this field requires an exhaustive analysis through a quantitative approach of research activity, contributing to a deeper
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A systematic review of multidimensional relevance estimation in information retrieval WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-05-07 Georgios Peikos, Gabriella Pasi
In information retrieval, relevance is perceived as a multidimensional and dynamic concept influenced by user, task, and domain factors. Relying on this perspective, researchers have introduced multidimensional relevance models addressing diverse search tasks across numerous knowledge domains. Through our systematic review of 72 studies, we categorize research based on domain specificity and the distinct
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Predictive machine learning in optimizing the performance of electric vehicle batteries: Techniques, challenges, and solutions WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-04-04 Vankamamidi S. Naresh, Guduru V. N. S. R. Ratnakara Rao, D. V. N. Prabhakar
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Navigating the metaverse: A technical review of emerging virtual worlds WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-30 H. M. K. K. M. B. Herath, Mamta Mittal, Aman Kataria
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A review of reasoning characteristics of RDF‐based Semantic Web systems WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-28 Simona Colucci, Francesco M. Donini, Eugenio Di Sciascio
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Does a language model “understand” high school math? A survey of deep learning based word problem solvers WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-25 Sowmya S. Sundaram, Sairam Gurajada, Deepak Padmanabhan, Savitha Sam Abraham, Marco Fisichella