Artificial Intelligence in Data Mining
Theories and Applications
- 1st Edition - February 22, 2021
- Latest edition
- Editors: D. Binu, B.R. Rajakumar
- Language: English
- Paperback ISBN:9 7 8 - 0 - 1 2 - 8 2 0 6 0 1 - 0
- eBook ISBN:9 7 8 - 0 - 1 2 - 8 2 0 6 1 6 - 4
Artificial Intelligence in Data Mining: Theories and Applications offers a comprehensive introduction to data mining theories, relevant AI techniques, and their many real-w… Read more
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.
- Provides coverage of the fundamentals of Artificial Intelligence as applied to data mining, including computational intelligence and unsupervised learning methods for data clustering
- Presents coverage of key topics such as heuristic methods for data clustering, deep learning methods for data classification, and neural networks
- Includes case studies and real-world applications of AI techniques in data mining, for improved outcomes in clinical diagnosis, satellite data extraction, agriculture, security and defense
Engineers, biomedical engineers, and researchers in neural networks, as well as computer scientists with an interest in the area
1. Introduction
D. Binu and B.R. Rajakumar
2. Intelligence Methods for Data Mining Tasks
N. Jayalakshmi and Sridevi Mantha
3. Unsupervised Learning Methods for Data Clustering
Satish Chander and P. Vijaya
4. Heuristic Methods for Data Clustering
Rajasekhar Butta, M. Kamaraju, and V. Sumalatha
5. Deep Learning Methods for Data Classification
V.H. Arul
6. Neural Networks for Data Classification
Balachandra Kumaraswamy
7. Application of Artificial Intelligence in the Perspective of Data Mining
D. Menaga and S. Saravanan
8. Biomedical Data Mining for Improved Clinical Diagnosis
G. Nalinipriya, M. Geetha, R.Cristin, and Balajee Maram
9. Satellite Data: Big Data Extraction and Analysis
Rahul Kotawadekar
10. Advancement of Data Mining Methods for Improvement of Agricultural Methods and Productivity
Anush Prabhakaran, K.S. Chithra Lekshmi, and Ganesh Janarthanan
11. Advanced Data Mining for Defense and Security Applications
Pramod Pandurang Jadhav
- Edition: 1
- Latest edition
- Published: February 22, 2021
- Language: English
DB
D. Binu
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.
Affiliations and expertise
Managing Director and Co-Founder, Resbee Info Technologies Pvt. Ltd, IndiaBR
B.R. Rajakumar
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.
Affiliations and expertise
Director and Co-Founder, Resbee Info Technologies Pvt. Ltd, IndiaRead Artificial Intelligence in Data Mining on ScienceDirect