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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
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Addressing privacy concerns with wearable health monitoring technology WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-23 C. L. V. Sivakumar, Varda Mone, Rakhmanov Abdumukhtor
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Correction to “Expression of Concern: Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREsData Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278” WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-20
Wang, C., Zhang, Q., Liu, W., Liu, Y. & Miao, L. Facial feature discovery for ethnicity recognition. WIREs Data Mining Knowl. Discov. 9, e1278 (2019). https://doi.org/10.1002/widm.1278. WIREs Data Mining Knowl. Discov., 10, e1386 (2020). https://doi.org/10.1002/widm.1386 The originally published version of this Expression of Concern has been updated to include new information raised to us by a third
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A systematic review on detection and adaptation of concept drift in streaming data using machine learning techniques WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-19 Shruti Arora, Rinkle Rani, Nitin Saxena
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Causality and causal inference for engineers: Beyond correlation, regression, prediction and artificial intelligence WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-03-09 M. Z. Naser
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Knowledge graph-driven data processing for business intelligence WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-02-11 Lipika Dey
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Artificial intelligence for atrial fibrillation detection, prediction, and treatment: A systematic review of the last decade (2013–2023) WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-02-04 Massimo Salvi, Madhav R. Acharya, Silvia Seoni, Oliver Faust, Ru-San Tan, Prabal Datta Barua, Salvador García, Filippo Molinari, U. Rajendra Acharya
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Comparing programming languages for data analytics: Accuracy of estimation in Python and R WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-02-02 Chelsey Hill, Lanqing Du, Marina Johnson, B. D. McCullough
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A literature review on satellite image time series forecasting: Methods and applications for remote sensing WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-01-29 Carlos Lara-Alvarez, Juan J. Flores, Hector Rodriguez-Rangel, Rodrigo Lopez-Farias
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A survey of autonomous monitoring systems in mental health WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-01-24 Abinaya Gopalakrishnan, Raj Gururajan, Xujuan Zhou, Revathi Venkataraman, Ka Ching Chan, Niall Higgins
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The role of lifelong machine learning in bridging the gap between human and machine learning: A scientometric analysis WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2024-01-10 Muhammad Abulaish, Nesar Ahmad Wasi, Shachi Sharma
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A survey of episode mining WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-28 Oualid Ouarem, Farid Nouioua, Philippe Fournier-Viger
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A survey of episode mining WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-28 Oualid Ouarem, Farid Nouioua, Philippe Fournier-Viger
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Multispectral data mining: A focus on remote sensing satellite images WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-22 Sin Liang Lim, Jaya Sreevalsan-Nair, B. S. Daya Sagar
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Multispectral data mining: A focus on remote sensing satellite images WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-22 Sin Liang Lim, Jaya Sreevalsan-Nair, B. S. Daya Sagar
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Deepfake detection using deep learning methods: A systematic and comprehensive review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-20 Arash Heidari, Nima Jafari Navimipour, Hasan Dag, Mehmet Unal
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Deepfake detection using deep learning methods: A systematic and comprehensive review WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-20 Arash Heidari, Nima Jafari Navimipour, Hasan Dag, Mehmet Unal
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The use of gene expression datasets in feature selection research: 20 years of inherent bias? WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-16 Bruno I. Grisci, Bruno César Feltes, Joice de Faria Poloni, Pedro H. Narloch, Márcio Dorn
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The use of gene expression datasets in feature selection research: 20 years of inherent bias? WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-16 Bruno I. Grisci, Bruno César Feltes, Joice de Faria Poloni, Pedro H. Narloch, Márcio Dorn
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Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-07 Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Abdelghani Dahou, Saeed Hamood Alsamhi, Laith Abualigah, Rehab Ali Ibrahim, Ahmed A. Ewees
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Evolution toward intelligent communications: Impact of deep learning applications on the future of 6G technology WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-11-07 Mohamed Abd Elaziz, Mohammed A. A. Al-qaness, Abdelghani Dahou, Saeed Hamood Alsamhi, Laith Abualigah, Rehab Ali Ibrahim, Ahmed A. Ewees
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The state-of-art review of ultra-precision machining using text mining: Identification of main themes and recommendations for the future direction WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-10-15 Wai Sze YIP, Hengzhou Edward Yan, Baolong Zhang, Suet To
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The state-of-art review of ultra-precision machining using text mining: Identification of main themes and recommendations for the future direction WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-10-15 Wai Sze YIP, Hengzhou Edward Yan, Baolong Zhang, Suet To
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Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022 WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-09-28 Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim
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Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020–2022 WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-09-28 Cheng Zhang, Nilam Nur Amir Sjarif, Roslina Ibrahim
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Pre-trained language models: What do they know? WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-09-21 Nuno Guimarães, Ricardo Campos, Alípio Jorge
Large language models (LLMs) have substantially pushed artificial intelligence (AI) research and applications in the last few years. They are currently able to achieve high effectiveness in different natural language processing (NLP) tasks, such as machine translation, named entity recognition, text classification, question answering, or text summarization. Recently, significant attention has been
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Pre-trained language models: What do they know? WIREs Data Mining Knowl. Discov. (IF 6.4) Pub Date : 2023-09-21 Nuno Guimarães, Ricardo Campos, Alípio Jorge