Artificial Intelligence in Data Mining: Theories and Applications

by D. Binu (Editor), B.R. Rajakumar (Editor)

Overview

Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications. This book is written by experienced engineers for engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area.

Product Details

ISBN-13:	9780128206010
Publisher:	Elsevier Science
Publication date:	02/22/2021
Pages:	270
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

D. Binu is a Managing Director and Co-founder of Resbee Info Technologies Pvt. Ltd, India. He is a data scientist who specializes in integrating Artificial Intelligence (AI) technologies into e-learning platforms, utilising his eagerness, experience and passion to invent. He has contributed to developing the AI-based learning management system, named e-khool, which established Resbee Info Technologies Pvt. Ltd. He has contributed in the areas of AI, machine learning and data mining, with sound publications in reputed journals (including, IEEE transaction articles). He has published over 25 publications and filed more than 20 Indian patents.

B.R. Rajakumar is a Director and Co-Founder of Resbee Info Technologies Pvt. Ltd, India. He is the Lead Researcher of the in-house research unit in Resbee Info Technologies, focusing on deploying Artificial Intelligence (AI) on e-learning platforms. He is also the technical advisor of the AI-based research products, and played a lead role in developing E-Khool, a next generation AI-enabled LMS. He has multidisciplinary expertise, specifically in AI and Soft Computing applications. He is the founder of the Lion Algorithm and published his research outcomes in refereed International Journals. He has filed 20+ Indian patents and holds researcher awards for his contributions.

Table of Contents

1. Introduction
2. Intelligence Methods for Data Mining Tasks
3. Unsupervised Learning Methods for Data Clustering
4. Heuristic Methods for Data Clustering
5. Deep Learning Methods for Data Classification
6. Neural Networks for Data Classification
7. Application of Artificial Intelligence in the Perspective of Data Mining
8. Biomedical Data Mining for Improved Clinical Diagnosis
9. Satellite Data: Big Data Extraction and Analysis
10. Advancement of Data Mining Methods for Improvement of Agricultural Methods and Productivity
11. Advanced Data Mining for Defense and Security Applications

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From the Publisher

Offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-world applications