Relevant bibliographies by topics / Highlighting its application in various domains such as data mining

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Academic literature on the topic 'Highlighting its application in various domains such as data mining'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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Journal articles on the topic "Highlighting its application in various domains such as data mining"

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Yuan, Xiaoxi. "Higher dimensional sports statistics and real-time game prediction." Advances in Engineering Innovation 8, no. 1 (2024): 9–18. http://dx.doi.org/10.54254/2977-3903/8/2024071.

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The rapid expansion of comprehensive sports datasets and the successful application of data mining techniques in various domains have given rise to the emergence of sports data prediction techniques. These techniques enable the extraction of hidden knowledge that can significantly impact the sports industry, as more and more clubs are using Machine Learning (ML) and Deep Learning (DL) methods to manage athletes and training. In this research, the focusing and intriguing aspects is predicting the outcomes of a specific basketball athletes, which has garnered significant attention for research.
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Kumar, Nitish. "OSINT (OPEN SOURCE INTELLIGENCE) Exploring the Power of Open Source Intelligence in Modern Decision-Making." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 05 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem34025.

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Open Source Intelligence (OSINT) has emerged as a powerful tool in the information age, offering valuable insights to individuals, organizations, and governments. This paper explores the significance of OSINT in contemporary decision-making processes, highlighting its role in providing timely, relevant, and actionable information from publicly available sources. The first section elucidates the concept of OSINT, delineating its scope, sources, and methodologies. OSINT encompasses a wide array of publicly accessible information, including social media posts, news articles, government reports, a
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Silva, Anabela Costa, José Machado, and Paulo Sampaio. "Predictive quality model for customer defects." TQM Journal 36, no. 9 (2024): 155–74. http://dx.doi.org/10.1108/tqm-09-2023-0302.

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PurposeIn the context of the journey toward digital transformation and the realization of a fully connected factory, concepts such as data science, artificial intelligence (AI), machine learning (ML) and even predictive models emerge as indispensable pillars. Given the relevance of these topics, the present study focused on the analysis of customer complaint data, employing ML techniques to anticipate complaint accountability. The primary objective was to enhance data accessibility, harnessing the potential of ML models to optimize the complaint handling process and thereby positively contribu
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Asha, Asha, and Dr Balkishan. "Systematic Review on the Suspicious Profiles Detection on Online Social Media Data." Oriental journal of computer science and technology 10, no. 3 (2017): 644–52. http://dx.doi.org/10.13005/ojcst/10.03.13.

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Escalating crimes on digital facet alarms the law enforcement bodies to keep a gaze on online activities which involve massive amount of data. This will raise a need to detect suspicious activities on online available social media data by optimizing investigations using data mining tools. This paper intends to throw some light on the data mining techniques which are designed and developed for closely examining social media data for suspicious activities and profiles in different domains. Additionally, this study will categorize the techniques under various groups highlighting their important f
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H., Ramprasanth, and M. Shanmugapriya Dr. "SURVEY ON DATA MINING FOR WEB SEMANTIC ANALYSIS." IJRSET JUNE Volume 9 Issue 6 9, no. 6 (2022): 23–27. https://doi.org/10.5281/zenodo.6793309.

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Data mining plays an important role in various human activities because it extracts the unknown useful patterns (or knowledge). Due to its capabilities, data mining become an essential task in large number of application domains such as banking, retail, medical, insurance, bioinformatics, etc. To take a holistic view of the research trends in the area of data mining, a comprehensive survey is presented in this paper. This paper presents a systematic and comprehensive survey of various data mining tasks and techniques. Further, various real-life applications of data mining are presented in this
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Jain, Dr Prerna, Prof Kalukuri Princy Niveditha, Prof Neha Thakre, Ridhima Sapre, and Rishita Jaiswal. "A Review Paper on Data Mining Methods and their Applications." International Journal of Innovative Research in Computer and Communication Engineering 11, no. 11 (2023): 11767–73. http://dx.doi.org/10.15680/ijircce.2023.1111057.

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This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges
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Jain, Dr Prerna, Prof Kalukuri Princy Niveditha, Prof Neha Thakre, Ridhima Sapre, and Rishita Jaiswal. "A Review Paper on Data Mining Methods and Their Applications." International Journal of Innovative Research in Computer and Communication Engineering 12, no. 04 (2023): 4730–36. http://dx.doi.org/10.15680/ijircce.2024.1204367.

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This research paper provides a comprehensive review of data mining methods and their applications. Over the past decade, data mining has emerged as a crucial tool in extracting useful patterns and insights from large datasets across various domains. The paper begins by outlining the foundational principles and techniques of data mining, including classification, clustering, association rule mining, and anomaly detection. It then proceeds to explore the evolution and advancements of these techniques throughout the specified timeframe, highlighting key developments, methodologies, and challenges
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Pabreja, Kavita. "Comparison of Different Classification Techniques for Educational Data." International Journal of Information Systems in the Service Sector 9, no. 1 (2017): 54–67. http://dx.doi.org/10.4018/ijisss.2017010104.

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Data mining has been used extensively in various domains of application for prediction or classification. Data mining improves the productivity of its analysts tremendously by transforming their voluminous, unmanageable and prone to ignorable information into usable pieces of knowledge and has witnessed a great acceptance in scientific, bioinformatics and business domains. However, for education field there is still a lot to be done, especially there is plentiful research to be done as far as Indian Universities are concerned. Educational Data Mining is a promising discipline, concerned with d
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Ortega-Guzmán, Víctor H., Luis Gutiérrez-Preciado, Francisco Cervantes, and Mildreth Alcaraz-Mejia. "A Methodology for Knowledge Discovery in Labeled and Heterogeneous Graphs." Applied Sciences 14, no. 2 (2024): 838. http://dx.doi.org/10.3390/app14020838.

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Graph mining has emerged as a significant field of research with applications spanning multiple domains, including marketing, corruption analysis, business, and politics. The exploration of knowledge within graphs has garnered considerable attention due to the exponential growth of graph-modeled data and its potential in applications where data relationships are a crucial component, and potentially being even more important than the data themselves. However, the increasing use of graphs for data storing and modeling presents unique challenges that have prompted advancements in graph mining alg
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Chen, Taiying, Jieting Lian, and Baiwei Sun. "An Exploration of the Development of Computerized Data Mining Techniques and Their Application." International Journal of Computer Science and Information Technology 3, no. 1 (2024): 206–12. http://dx.doi.org/10.62051/ijcsit.v3n1.26.

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The process of data mining involves extracting valuable information and knowledge from vast amounts of data, encompassing various fields such as statistics, machine learning, and database theory. It transcends mere data processing tools, employing intelligent methods for data analysis and stands as a pivotal technology in the era of big data. Its applications span across numerous domains including consumer behavior analysis, market marketing strategies, risk management, medical diagnosis, and fraud detection, with notable prominence in the realm of commerce. In e-commerce platforms, data minin
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Book chapters on the topic "Highlighting its application in various domains such as data mining"

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León, Fabian, Dawood Al Chanti, Anne Champagnac, et al. "An Introduction to Artificial Intelligence in Medicine and Its Role in Oral Potentially Malignant Disorders (OPMD)." In Critical Issues in Head and Neck Oncology. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-84539-0_2.

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Abstract This book chapter explores the potential of Artificial Intelligence (AI) to improve clinical workflows for the diagnosis of Oral Potential Malignant Disorders (OPMDs) and the prevention of Oral Squamous Cell Carcinoma (OSCC). It provides a background on AI, including its history and evolution, with a focus on Machine Learning (ML) and Deep Learning (DL) techniques. The chapter also presents the use of AI in OPMD diagnosis and its application in various domains, including mobile technologies, medical images, clinical records, and others. It also acknowledges the limitations and challen
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Bhimavarapu, Krishna, Sruthi Yenninti, and Yarraguntla Jayalakshmi. "ASSOCIATION RULE MINING ALGORITHMS AND ITS APPLICATIONS." In Futuristic Trends in Computing Technologies and Data Sciences Volume 3 Book 5. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bact5p3ch7.

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Association rule mining is a powerful data mining technique that aims to uncover valuable relationships and patterns within extensive datasets. This technique involves the discovery of associations, correlations, and dependencies between items or attributes in various types of databases. Association rule mining has found wide applications in diverse fields such as retail, healthcare, recommendation systems, and more. This paper provides an overview of association rule mining techniques, discussing algorithms, performance metrics, and their significance in understanding complex interactions. Th
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Desarkar, Anindita, and Ajanta Das. "Exploration of Healthcare Using Data Mining Techniques." In Big Data Management and the Internet of Things for Improved Health Systems. IGI Global, 2018. http://dx.doi.org/10.4018/978-1-5225-5222-2.ch014.

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Huge amount of data is generated from Healthcare transactions where data are complex, voluminous and heterogeneous in nature. This large dataset can be used as an ideal store which can be analyzed for knowledge discovery as well as various future predictions. So, Data mining is becoming increasingly popular as it offers set of innovative tools and techniques to handle this kind of data set whereas traditional methods have limitations for that. In summary, providing the better patient care and reduction in healthcare cost are two major goals of application of data mining in healthcare. Initiall
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Gupta, Preeti, and Vishal Bhatnagar. "Data Preprocessing for Dynamic Social Network Analysis." In Data Mining in Dynamic Social Networks and Fuzzy Systems. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-4213-3.ch002.

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The social network analysis is of significant interest in various application domains due to its inherent richness. Social network analysis like any other data analysis is limited by the quality and quantity of data and for which data preprocessing plays the key role. Before the discovery of useful information or pattern from the social network data set, the original data set must be converted to a suitable format. In this chapter we present various phases of social network data preprocessing. In this context, the authors discuss various challenges in each phase. The goal of this chapter is to
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Zare, Danial, Luis Fernandez-Sanz, Vera Pospelova, and Ines López-Baldominos. "A Comprehensive Survey on Text Mining From Theory to Practice." In Advances in Computational Intelligence and Robotics. IGI Global, 2025. https://doi.org/10.4018/979-8-3693-9606-3.ch009.

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Text mining refers to the process of extracting useful information from large volumes of unstructured text data. This paper presents a comprehensive survey of text mining, covering foundational theories and practical applications across various domains within the field of Natural Language Processing (NLP). The study begins by examining the core challenges and historical development of text mining, providing context through an exploration of major areas where text mining techniques have significantly evolved. We offer an in-depth, step-by-step analysis of key algorithms in the text mining pipel
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Malakhov, Kyrylo. "Exploring Research-Related Design: A Comprehensive Information System for Knowledge Production—OntoChatGPT." In Modern Information Technologies in Scientific Research and Educational Activities. Iowa State University Digital Press, 2024. http://dx.doi.org/10.31274/isudp.151.01.

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This article presents a comprehensive methodology for enhancing the capabilities of conversational AI systems, focusing on ChatGPT, through the integration of ontology-driven structured prompts and meta-learning techniques. The proposed approach, exemplified by the RDWE OntoChatGPT system, demonstrates significant potential for improving productivity and expanding knowledge bases in specific subject domains and languages. By formalizing methods for creating and configuring structured prompts and context ontologies, the system enables ChatGPT to provide specific information using selected conte
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Singh, Bhupinder, Christian Kaunert, and Ritu Gautam. "Artificial Intelligence in Detecting Herding and Market Overreaction." In Advances in Marketing, Customer Relationship Management, and E-Services. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7827-4.ch001.

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Herding behavior, where individuals in a group act collectively without centralized direction, is a phenomenon observed in various domains, including financial markets, consumer behavior and social media trends. Detecting herding behavior is crucial for understanding market dynamics, predicting economic crises, and managing risks. Artificial Intelligence (AI) has emerged as a powerful tool in identifying and analyzing herding patterns due to its ability to process vast amounts of data and uncover complex patterns that are not easily visible through traditional methods. The future of AI in this
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Bugingo, Emmanuel, Emmanuelie Leone Ndimubenshi, Célestin Tshimanga Kamanga, Fancois Xavier Rugema, Olivier Habimana, and Jennifer Batamuliza. "Application of AHP in Decision-Making: Case Studies and Practical Implementation." In The Art of Decision Making - Applying AHP in Practice [Working Title]. IntechOpen, 2024. https://doi.org/10.5772/intechopen.1006966.

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The Analytic Hierarchy Process (AHP) is a powerful decision-making tool for handling complex, multi-criteria situations. Developed by Thomas L. Saaty in the 1970s, AHP structures problems into a hierarchical format, enabling the systematic evaluation of alternatives based on multiple criteria through pairwise comparisons and numerical scales. Consequently, this chapter explores AHP’s methodology, practical applications, and implementation strategies. It provides a detailed guide on the AHP process, from problem definition and hierarchy structuring to conducting pairwise comparisons, calculatin
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Indumathi, S. "Deep Learning Techniques for Enhancing Autonomous System Capabilities." In Python Programming Strategies for Deploying Artificial Intelligence in Autonomous Systems. RADemics Research Institute, 2024. https://doi.org/10.71443/9788197282140-06.

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The rapid advancement of autonomous systems has underscored the critical role of deep learning in enhancing their capabilities. This book chapter provides a comprehensive examination of how deep learning techniques are applied to improve various aspects of autonomous systems, focusing on transfer learning and its impact on performance. Key areas explored include the application of simulated data for training, domain-invariant representation learning, and the fine-tuning of robot policies to adapt to new environments. Emphasis was placed on the utilization of domain-adversarial neural networks,
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Ali, Ayman A., Ahmed Ashraf, and Kamel H. Rahouma. "Anomaly Detection in Healthcare Monitoring Survey." In Practice, Progress, and Proficiency in Sustainability. IGI Global, 2024. https://doi.org/10.4018/979-8-3693-7117-6.ch002.

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This chapter delves into the realm of anomaly detection in Wireless Sensor Networks (WSNs) and the Internet of Things (IoT), emphasizing their pivotal role in bolstering security. Focusing on diverse domains such as healthcare, environmental monitoring, and process industries, the chapter consolidates findings from various studies employing innovative anomaly detection techniques. One notable approach integrates supervised and unsupervised methods for continuous patient monitoring, showcasing successful anomaly detection in physiological variables using an autoencoder and XGBoost algorithm. Th
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Conference papers on the topic "Highlighting its application in various domains such as data mining"

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Spengler, Ana Caroline Fernandes, and Paulo Sérgio Lopes de Souza. "The Impact of Hyperledger Fabric Setup on Blockchain Performance when Using Large Volumes of Heterogeneous Medical Data." In Simpósio em Sistemas Computacionais de Alto Desempenho. Sociedade Brasileira de Computação, 2023. http://dx.doi.org/10.5753/wscad.2023.235910.

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Blockchain can be seen as a data distribution tool that guarantees immutability. As its use continues to expand across various sectors, it becomes increasingly important to investigate Blockchain’s performance concerning its different components and data originating from diverse application domains. This study explores the blockchain ecosystem, focusing on block creation, validation, network size, and partition processes. The chosen methodology involves utilizing Hyperledger Fabric for sharing medical information. To assess performance, Hyperledger Caliper was employed to collect throughput an
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Zhao, Yinan, Zhanxun Dong, Gang Liu, Xiaozhang Dong, and Suiping Hu. "Strategic Enhancement of C-UAS through Advanced Human-Computer Collaborative Command and Control Mechanism." In 2024 AHFE International Conference on Human Factors in Design, Engineering, and Computing (AHFE 2024 Hawaii Edition). AHFE International, 2024. http://dx.doi.org/10.54941/ahfe1005637.

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The command and control of unmanned aircraft systems (UASs) faces challenges such as multiple and heterogeneous sources of intelligence information, high operational agility requirements, complex and diverse threat patterns. The structures of traditional command information systems are difficult to effectively address these challenges, highlighting the urgency for innovative approaches. The efficacy of counter-UAS operations hinges upon the capability of combat systems to swiftly process intelligence, identify dynamic situational changes, and deploy operational resources in an accurate and eff
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