Intelligent Data Analysis From Data Gathering to Data Comprehension 1st edition by Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna 1119544467 9781119544463

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ISBN-10 :  1119544467 

ISBN-13 : 9781119544463

Author : Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna 

This book focuses on methods and tools for intelligent data analysis, aimed at narrowing the increasing gap between data gathering and data comprehension, and emphasis will also be given to solving of problems which result from automated data collection, such as analysis of computer-based patient records, data warehousing tools, intelligent alarming, effective and efficient monitoring, and so on. This book aims to describe the different approaches of Intelligent Data Analysis from a practical point of view: solving common life problems with data analysis tools.

Intelligent Data Analysis: From Data Gathering to Data Comprehension 1st Table of contents:

1 Intelligent Data Analysis: Black Box Versus White Box Modeling
1.1 Introduction
1.2 Interpretation of White Box Models
1.3 Interpretation of Black Box Models
1.4 Issues and Further Challenges
1.5 Summary
References
2 Data: Its Nature and Modern Data Analytical Tools
2.1 Introduction
2.2 Data Types and Various File Formats
2.3 Overview of Big Data
2.4 Data Analytics Phases
2.5 Data Analytical Tools
2.6 Database Management System for Big Data Analytics
2.7 Challenges in Big Data Analytics
2.8 Conclusion
References
3 Statistical Methods for Intelligent Data Analysis: Introduction and Various Concepts
3.1 Introduction
3.2 Probability
3.3 Descriptive Statistics
3.4 Inferential Statistics
3.5 Statistical Methods
3.6 Errors
3.7 Conclusion
References
4 Intelligent Data Analysis with Data Mining: Theory and Applications
4.1 Introduction to Data Mining
4.2 Data and Knowledge
4.3 Discovering Knowledge in Data Mining
4.4 Data Analysis and Data Mining
4.5 Data Mining: Issues
4.6 Data Mining: Systems and Query Language
4.7 Data Mining Methods
4.8 Data Exploration
4.9 Data Visualization
4.10 Probability Concepts for Intelligent Data Analysis (IDA)
Reference
5 Intelligent Data Analysis: Deep Learning and Visualization
5.1 Introduction
5.2 Deep Learning and Visualization
5.3 Data Processing and Visualization
5.4 Experiments and Results
5.5 Conclusion
References
6 A Systematic Review on the Evolution of Dental Caries Detection Methods and Its Significance in Data Analysis Perspective
6.1 Introduction
6.2 Different Caries Lesion Detection Methods and Data Characterization
6.3 Technical Challenges with the Existing Methods
6.4 Result Analysis
6.5 Conclusion
Acknowledgment
References
7 Intelligent Data Analysis Using Hadoop Cluster – Inspired MapReduce Framework and Association Rule Mining on Educational Domain
7.1 Introduction
7.2 Learning Analytics in Education
7.3 Motivation
7.4 Literature Review
7.5 Intelligent Data Analytical Tools
7.6 Intelligent Data Analytics Using MapReduce Framework in an Educational Domain
7.7 Results
7.8 Conclusion and Future Scope
References
8 Influence of Green Space on Global Air Quality Monitoring: Data Analysis Using K-Means Clustering Algorithm
8.1 Introduction
8.2 Material and Methods
8.3 Results
8.4 Quantitative Analysis
8.5 Discussion
8.6 Conclusion
References
9 IDA with Space Technology and Geographic Information System
9.1 Introduction
9.2 Geospatial Techniques
9.3 Comparative Analysis
9.4 Conclusion
References
10 Application of Intelligent Data Analysis in Intelligent Transportation System Using IoT
10.1 Introduction to Intelligent Transportation System (ITS)
10.2 Issues and Challenges of Intelligent Transportation System (ITS)
10.3 Intelligent Data Analysis Makes an IoT-Based Transportation System Intelligent
10.4 Intelligent Data Analysis for Security in Intelligent Transportation System
10.5 Tools to Support IDA in an Intelligent Transportation System
References
11 Applying Big Data Analytics on Motor Vehicle Collision Predictions in New York City
11.1 Introduction
11.2 Materials and Methods
11.3 Classification Algorithms and K-Fold Validation Using Data Set Obtained from NYPD (2012–2017)
11.4 Results
11.5 Discussion
11.6 Conclusion
References
12 A Smart and Promising Neurological Disorder Diagnostic System: An Amalgamation of Big Data, IoT, and Emerging Computing Techniques
12.1 Introduction
12.2 Statistics of Neurological Disorders
12.3 Emerging Computing Techniques
12.4 Related Works and Publication Trends of Articles
12.5 The Need for Neurological Disorders Diagnostic System
12.6 Conclusion
References
13 Comments-Based Analysis of a Bug Report Collection System and Its Applications
13.1 Introduction
13.2 Background
13.3 Related Work
13.4 Data Collection Process
13.5 Analysis of Bug Reports
13.6 Threats to Validity
13.7 Conclusion
References
Notes
14 Sarcasm Detection Algorithms Based on Sentiment Strength
14.1 Introduction
14.2 Literature Survey
14.3 Experiment
14.4 Results and Evaluation
14.5 Conclusion
References
Notes
15 SNAP: Social Network Analysis Using Predictive Modeling
15.1 Introduction
15.2 Literature Survey
15.3 Comparative Study
15.4 Simulation and Analysis
15.5 Conclusion and Future Work
References
16 Intelligent Data Analysis for Medical Applications
16.1 Introduction
16.2 IDA Needs in Medical Applications
16.3 IDA Methods Classifications
16.4 Intelligent Decision Support System in Medical Applications
16.5 Conclusion
References
17 Bruxism Detection Using Single-Channel C4-A1 on Human Sleep S2 Stage Recording
17.1 Introduction
17.2 History of Sleep Disorder
17.3 Electroencephalogram Signal
17.4 EEG Data Measurement Technique
17.5 Literature Review
17.6 Subjects and Methodology
17.7 Data Analysis of the Bruxism and Normal Data Using EEG Signal
17.8 Result
17.9 Conclusions
Acknowledgments
References
18 Handwriting Analysis for Early Detection of Alzheimer’s Disease
18.1 Introduction and Background
18.2 Proposed Work and Methodology
18.3 Results and Discussions
18.4 Conclusion

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Tags: Intelligent, Data Analysis, Data Gathering, Data Comprehension, Deepak Gupta, Siddhartha Bhattacharyya, Ashish Khanna