Intelligent Data Analysis: From Data Gathering to Data Comprehension (The Wiley Series in Intelligent Signal and Data Processing) - Tapa dura

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Deepak Gupta completed his PhD (CSE) at Dr. APJ Abdul Kalam Technical University, Lucknow, India; his M.E. (CTA) at Delhi College of Engineering, New Delhi, India; and his B.Tech at Guru Gobind Singh Indraprastha University, Delhi, India. He completed his postdoctoral research on the Internet of Things (IoT) at the National Institute of Telecommunications, Ghaziabad, India. He is a guest editor for SCI and SCOPUS and has co-authored 38 books and published 95 research papers.

Siddhartha Bhattacharyya, PhD, is a Professor of Computer Science at CHRIST (Deemed to be University), Bengaluru, India.

Ashish Khanna received his PhD from the National Institute of Technology, Kurukshetra, India. He completed his M.Tech and B.Tech at Guru Gobind Singh Indraprastha University, Delhi, India in 2004. He has published 100 research papers and co-authored 22 textbooks on engineering. His research includes distributed computing, distributed systems, cloud computing, and opportunistic networks. He completed his postdoctoral research on the Internet of Things (IoT) at the National Institute of Telecommunications, Ghaziabad, India.

Kalpna Sagar received her B.Tech from Indira Gandhi Institute of Technology, Guru Gobind Singh Indraprastha University, Delhi, India and her M.Tech from University School of Information, Communication and Technology, Guru Gobind Singh Indraprastha University, Delhi, India. She is currently pursuing her PhD. Her research includes software engineering, human-computer interaction, and data-mining. She has published numerous research papers and is currently an Assistant Professor and Assistant Dean of Academics at KIET Group of Institutions, Dr. APJ Abdul Kalam Technical University, Lucknow, India.

De la contraportada

A practical guide to using Intelligent Data Analysis to help solve common life problems presented by data analysis tools

In Intelligent Data Analysis, the authors have collected insightful knowledge on the complex topics of data abstraction, machine learning, pattern recognition, and specialized statistical analysis. In-depth information has been crafted to be understood by students and professionals alike, providing an overall look at the tools needed to solve problems resulting from automated data collection.

Intelligent Data Analysis discusses common issues found in computer-based patient record-keeping, data warehousing tools, and intelligent alarm systems and presents methods of effective and efficient monitoring. The book provides readers with practical problem-solving insights to address typical issues faced in the engineering and medical fields specifically.

The text covers a variety of topics within Intelligent Data Analysis (IDA), including:

• Linear Regression and Decision Trees
• Interpretation of Black Box Models including Individual Conditional Expectations and Local Interpretable Model-agnostic Explanations
• An examination of IDA
• Applications of IDA and Machine Learning
• Model Interpretability

This exhaustive guide provides in-depth information on how IDA can be improved and implemented in engineering and biomedical applications for students, researchers, and professionals developing systems that include IDA technology.