Data Science: Techniques and Intelligent Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
1. Instigation and Development of Data Science
	1.1 Data Science
		1.1.1 Existence of Data Science
		1.1.2 Data Science Process
			1.1.2.1 Setting the Research Goal
			1.1.2.2 Retrieving Data
			1.1.2.3 Data Preparation
			1.1.2.4 Data Exploration
			1.1.2.5 Data Modeling
			1.1.2.6 Presentation and Automation
		1.1.3 Life Cycle – Data Science
	1.2 Relation between Data Science and Machine Learning
		1.2.1 Where Do We See Machine Learning in Data Science?
		1.2.2 Which Machine Algorithms are used in Data Science?
			1.2.2.1 Linear Regression Algorithm
			1.2.2.2 Decision Tree
			1.2.2.3 K-Means Clustering
		1.2.3 Application of Machine Learning in Data Science
	1.3 Tools for Data science
		1.3.1 R Programming
		1.3.2 Python
	1.4 Benefits and Applications
	1.5 Conclusion
	References
2. Role of Statistical Methods in Data Science
	2.1 Introduction
	2.2 Data Science and Statistics Terminologies
	2.3 Types of Statistics
		2.3.1 Descriptive
		2.3.2 Inferential
	2.4 How to Describe a Single Set of Data
	2.5 Statistical Analysis
		2.5.1 Quantitative Analysis
		2.5.2 Qualitative Analysis
		2.5.3 Measures of the Central Tendency
		2.5.4 Measures of Dispersion
	2.6 Tools to Measure Relationships
		2.6.1 Covariance
		2.6.2 Correlation
	2.7 Probability Distribution Function
		2.7.1 Cumulative Density Function
		2.7.2 Continuous Data Distributions
		2.7.3 Conditional Probability
		2.7.4 Bayes’ Theorem
	2.8 Hypothesis Testing
	2.9 Conclusion
	References
3. Real-World Applications of Data Science
	3.1 Banking and Finance
		3.1.1 Customer Data Management
		3.1.2 Real-Time Analytics
		3.1.3 Algorithmic Trading
		3.1.4 Providing Personalized Services
		3.1.5 Fraud Detection
	3.2 E-commerce and Retail Industry
		3.2.1 Potential Customer Analysis
		3.2.2 Customer Sentiment Analysis
		3.2.3 Optimizing Prices
		3.2.4 Inventory Management
		3.2.5 Lifetime Value Prediction
	3.3 Digital Marketing
		3.3.1 Smarter Planning for Online Marketing
		3.3.2 Business Intelligence with Smarter Decision-Making
		3.3.3 Managing Business Efficiently
		3.3.4 Automating Recruitment Process
	3.4 Healthcare and Medical Diagnosis
		3.4.1 Managing and Monitoring Patient Health and Data
		3.4.2 Medical Image Analysis
		3.4.3 Drug Research and Creation
		3.4.4 Patient Diagnosis and Preventing Diseases
		3.4.5 Providing Medical Virtual Assistance
	3.5 Manufacturing Industry
		3.5.1 Automating Product Design and Development
		3.5.2 Inventory Management and Demand Forecasting
		3.5.3 Monitoring of Manufacturing Units
		3.5.4 Real-Time Data of Performance and Quality
	3.6 Education System
		3.6.1 Monitoring Students’ and Teachers’ Requirements
		3.6.2 Measuring Students’ and Teachers’ Performance
		3.6.3 Innovating the Curriculum
		3.6.4 Automating Outcome-Based Teaching and Learning Process
	3.7 Entertainment Industry
		3.7.1 Predictive Analytics in the Film Industry
		3.7.2 Tracking Progress of Movies
		3.7.3 Generate Movie Revenue
		3.7.4 Improve Post-production of Movies
	3.8 Logistic Delivery and Transportation Units
		3.8.1 Reducing Shipping Costs through Delivery Path Optimization
		3.8.2 Monitoring Traffic and Weather Data from Sensors
	3.9 Shipping Sensitive Goods with Higher Quality
		3.9.1 Automation of Warehouses and the Supply Chain
	3.10 Digital Advertising Systems
		3.10.1 Price Comparison Websites
		3.10.2 Website Recommendation
	3.11 Internet Search Engines
		3.11.1 Proper Filtering
		3.11.2 Autocomplete
		3.11.3 Recommendation Engines
	3.12 Airline Routing Planning
		3.12.1 Predicting Flight Delays
		3.12.2 Decide Route of Flight In Case of Emergency
		3.12.3 Running Customer Loyalty Programs Effectively
	3.13 Image and Speech Recognition Systems
		3.13.1 Image Recognition Systems
		3.13.2 Speech Recognition Systems
	3.14 Gaming/Sports
		3.14.1 Use Previous Gaming Experience to the Next Level
		3.14.2 Improve Player Moves Up to Higher Level
	3.15 Social Life and Social Networking
		3.15.1 Building and Maintaining Social Relationship
		3.15.2 Maintaining Friend Circles through Social Media
		3.15.3 Building Human Network for Social Causes
	3.16 Augmented Reality
		3.16.1 Operation Room Augmented with Remote Presence
		3.16.2 Social Media with Augmented Reality
	3.17 Self-Driving Cars and Robots
		3.17.1 Intelligent Systems for Self-Driving Cars
		3.17.2 Robotics and Automation
	3.18 Email Filtering and Character Recognitions
		3.18.1 Email Spam Filtering
		3.18.2 Optical Character Recognitions
	3.19 Genetics and Genomics Research
		3.19.1 Analyzing Impact of the DNA on the Health
		3.19.2 Analyzing Reaction of Genes to Various Medications
		3.19.3 Analyzing Set of Chromosomes in Humans, Animals
	References
4. HDWR_SmartNet: A Smart Handwritten Devanagari Word Recognition System Using Deep ResNet-Based on Scan Profile Method
	4.1 Introduction and Related Work
	4.2 Features of Devanagari Script
	4.3 Dataset Creation
	4.4 Proposed System Architecture
		4.4.1 Data Preprocessing and Data Augmentation
		4.4.2 Proposed Handwritten Devanagari Word Recognition System with Novel No-Segmentation Approach
			4.4.2.1 Cropper Method
			4.4.2.2 First Approach: Sliding Window Method without Segmentation
			4.4.2.3 Second Approach: Scan Profile Method
		4.4.3 ResNet114 Model: Devanagari Character Recognition Model
	4.5 Experiments, Results, and Discussion
		4.5.1 Network Training Parameters
		4.5.2 Experiment Results
	4.6 Conclusion and Future Work
	Dataset Accessibility Link
	References
5. Safe Social Distance Monitoring and Face Mask Detection for Controlling COVID-19 Spread
	5.1 Introduction
	5.2 Literature Survey
	5.3 Proposed Methodology
		5.3.1 Social Distance Monitoring Model
		5.3.2 Face Mask Detection Model
	5.4 Results
		5.4.1 For Social Distancing Monitoring Model
		5.4.2 For Face Mask Detection Model
	5.5 Conclusion
	References
6. Real-Time Virtual Fitness Tracker and Exercise Posture Correction
	6.1 Introduction
	6.2 Literature Review
		6.2.1 Motivation for the Research
	6.3 Methodology
		6.3.1 Brief Overview of Need for the System
		6.3.2 Enhancing 2D Body Tracking Performance
			6.3.2.1 Initial Body Pose Detection Using PoseNet
			6.3.2.2 Feature Tracking Using the Lucas– Kanade Algorithm
		6.3.3 Statistical Model of Proposed Model
	6.4 Results and Discussion
		6.4.1 Real-Time 2D Pose Estimation
		6.4.2 Repetition Counter Mechanism
		6.4.3 User Feedback and Posture Correction Mechanism
	6.5 Conclusion
	References
7. Role of Data Science in Revolutionizing Healthcare
	7.1 Introduction
	7.2 Applications of Data Science
	7.3 Data Science Technique Used for Diabetes Detection
	7.4 Methodology and Proposed Framework for Diabetes Detection
	7.5 Results
	7.6 Conclusion
	7.7 Future Scope
	References
8. Application of Artificial Intelligence Techniques in the Early-Stage Detection of Chronic Kidney Disease
	8.1 Introduction
	8.2 Literature Review
		8.2.1 Based on Supervised Machine Learning Algorithms
		8.2.2 Based on Deep Learning Techniques
	8.3 Methodology Used
		8.3.1 Machine Learning (ML) Methods
			8.3.1.1 Support Vector Machine (SVM)
			8.3.1.2 K-Nearest Neighbors (KNN)
			8.3.1.3 Decision Tree Classifier
			8.3.1.4 Random Forest (RF)
			8.3.1.5 XGBoost
		8.3.2 Deep Learning (DL) Methods
			8.3.2.1 Artificial Neural Networks (ANN)
			8.3.2.2 Multilayer Perceptron (MLP)
			8.3.2.3 Recurrent Neural Network (RNN)
	8.4 Results and Discussion
	8.5 Conclusion and Future Work
	References
9. Multi-Optimal Deep Learning Technique for Detection and Classification of Breast Cancer
	9.1 Introduction
	9.2 Literature Review
	9.3 Material and Methodology
		9.3.1 Convolution Neural Network
		9.3.2 Image Acquisition
		9.3.3 Image Pre-Processing
		9.3.4 Image Segmentation
		9.3.5 Feature Extraction
		9.3.6 Classification
		9.3.7 Detection
		9.3.8 Performance Evaluation
	9.4 Results and Discussion
	9.5 Conclusion
	References
10. Realizing Mother’s Features Influential on Childbirth Experience, towards Creation of a Dataset
	10.1 Introduction
		10.1.1 Significance of Woman’s Reproductive Health
			10.1.1.1 Maternal Health as a Global Issue
			10.1.1.2 Significance of Maternal Health in India
		10.1.2 Lifestyle
		10.1.3 Data in Research
	10.2 Study of Features Influencing Pregnancy and Childbirth Experience
		10.2.1 Phases of a Woman’s Reproductive Age
		10.2.2 Features Selected for Study
		10.2.3 Designing Survey Form
	10.3 Data Collection
		10.3.1 Selection of Subjects
		10.3.2 Reaching Out to Subjects
		10.3.3 Challenges while Collecting Data
		10.3.4 Collection of Data
		10.3.5 Limitations
	10.4 MSF Dataset
		10.4.1 Dataset Description
		10.4.2 MSF Dataset Analysis
	10.5 Conclusion
	References
11. BERT- and FastText-Based Research Paper Recommender System
	11.1 Introduction
	11.2 Literature Review
	11.3 Dataset Description
	11.4 Proposed Methodology
		11.4.1 Keyword Extraction
			11.4.1.1 Add Norm
			11.4.1.2 Feedforward Neural Network
			11.4.1.3 Residual Connections
			11.4.1.4 Masked Language Model
		11.4.2 FastText
			11.4.2.1 Word Embeddings
			11.4.2.2 CBOW
			11.4.2.3 Skip-Gram
			11.4.2.4 Hierarchical Softmax
			11.4.2.5 Word n-Grams
		11.4.3 FastText Representation
		11.4.4 Limitations
		11.4.5 Future Scope
		11.4.6 Conclusion
		11.4.7 Applications
	References
12. Analysis and Prediction of Crime Rate against Women Using Classification and Regression Trees
	12.1 Introduction
		12.1.1 Machine Learning Approach
	12.2 Literature Survey
	12.3 Proposed Methodologies
		12.3.1 Data Preprocessing
		12.3.2 Splitting Train and Test Data
		12.3.3 Classification and Regression Trees (CART)
		12.3.4 Model Evaluation
		12.3.5 Data Visualization
	12.4 Result and Discussions
	12.5 Conclusion
	References
13. Data Analysis for Technical Business Incubation Performance Improvement
	13.1 Introduction
	13.2 Evolution of Business Incubators and Their Current State
	13.3 Success Factors
		13.3.1 Affiliation to Education Hubs
		13.3.2 Feasibility Study
		13.3.3 Availability of Funding
		13.3.4 Caliber of Entrepreneur
	13.4 Successful Incubates and Graduates
		13.4.1 Supportive Government Policies
		13.4.2 Stakeholder Consensus
		13.4.3 Competent and Properly Encouraged Management Team
		13.4.4 An Able Advisory Board
			13.4.4.1 Financial Sustainability
		13.4.5 Entry and Exit Criteria
		13.4.6 Networking
	13.5 Services Provided by Incubator
		13.5.1 Community Support
		13.5.2 Modus Operandi of Successful Business Incubations
			13.5.2.1 Principles
			13.5.2.2 Best Practices
	13.6 Result and Factor Analysis
		13.6.1 Table KMO and Bartlett’s Test
		13.6.2 Scree Plot of Individual Variances of Dimensions
		13.6.3 Scree Plot of Eigenvalues of Dimensions
	13.6.4 Correlation Plot
	13.7 Conclusion
	References
14. Satellite Imagery-Based Wildfire Detection Using Deep Learning
	14.1 Introduction to the Proposed Chapter
	14.2 Literature Review
	14.3 Gaps in the Present Study
	14.4 Proposed System and Algorithm
		14.4.1 Algorithm
			14.4.1.1 Adam Optimizer In-Depth
			14.4.1.2 Adam Configuration/Hyper Parameters
			14.4.1.3 Window/Block-Based Analysis
			14.4.1.4 Binary Cross-Entropy Loss
	14.5 Detailed Design
		14.5.1 System Architecture
		14.5.2 Design Diagrams
	14.6 Conclusion
	References
15. Low-Resource Language Document Summarization: A Challenge
	15.1 Introduction
	15.2 Literature Survey
	15.3 Approaches for Automatic Summarization
		15.3.1 Lexical Chaining Approach
	15.4 BERT Approach
	15.5 Conclusion
	References
16. Eclectic Analysis of Classifiers for Fake News Detection
	16.1 Introduction
	16.2 Related Work
	16.3 Dataset Description
		16.3.1 Preprocessing
	16.4 Modeling and Evaluation
		16.4.1 Performance Metrics
			16.4.1.1 Accuracy
			16.4.1.2 F1-Score
			16.4.1.3 Recall
			16.4.1.4 Precision Score
			16.4.1.5 Confusion Matrix
		16.4.2 Hyperparameter Tuning
			16.4.2.1 RandomizedSearchCV
			16.4.2.2 GridSearchCV
		16.4.3 Evaluation and Analysis
			16.4.3.1 Model Implementation Using Logistic Regression
			16.4.3.2 Model Implementation Using Naïve Bayes
			16.4.3.3 Model Implementation Using KNN
			16.4.3.4 Model Implementation Using Decision Trees
			16.4.3.5 Model Implementation Using Random Forest
			16.4.3.6 Model Implementation Using Boosting Ensemble Classifiers
			16.4.3.7 Model Implementation Using LSTM
	16.5 Conclusion, Limitations and Future Scope
	References
17. Data Science and Machine Learning Applications for Mental Health
	17.1 Introduction
	17.2 Review of Literature
	17.3 Detection of Mental Health Disorders through Social Media
	17.4 Study of Data Mining and Machine Learning Techniques for Diagnosing Depression
		17.4.1 Data Mining Approach to Discover Association Rules to Diagnose Depression
		17.4.2 Machine Learning Approach to Detect Depression
	17.5 Conclusion and Future Scope
	References
18. Analysis of Ancient and Modern Meditation Techniques on Human Mind and Body and Their Effectiveness in COVID-19 Pandemic
	18.1 Introduction
	18.2 Meditation and Mindfulness
	18.3 Literature Survey
	18.4 Foundation of Study
	18.5 Data Collection
		18.5.1 SOS-S
		18.5.2 BITe
		18.5.3 SPANE
	18.6 Data Analysis
		18.6.1 Data Description
		18.6.2 Chi-Square Test for Independence of Groups
			18.6.2.1 Chi-Square Test for Determining a Relation between the Groups and Their Gender
			18.6.2.2 Chi-Square Test for Determining a Relation between the Groups and Their Age
		18.6.3 Data Visualization for the Ancient and Modern Meditation
		18.6.4 Statistical Analysis
			18.6.4.1 Jarque–Bera Test for Goodness of Fit
			18.6.4.2 Comparison of SOS-S Scores of Ancient and Modern Meditation Groups
			18.6.4.3 Comparison of BITe Scores of Ancient and Modern Meditation Groups
			18.6.4.4 Comparison of SPANE-P Scores of Ancient and Modern Meditation Groups
			18.6.4.5 Comparison of SPANE-N Scores of Ancient and Modern Meditation Groups
			18.6.4.6 Comparison of SPANE-B Scores of Ancient and Modern Meditation Groups
	18.7 Time Series Modeling
		18.7.1 Modeling SOS-S Scores for Ancient and Modern Meditation Groups
		18.7.2 Modeling BITe Scores for Ancient and Modern Meditation Groups
		18.7.3 Modeling SPANE-B Scores for Ancient and Modern Meditation Groups
	18.8 MEDit Architecture
		18.8.1 Meditation Component
		18.8.2 Evaluation Component
		18.8.3 Discovery Component
	18.9 Conclusion and Future Work
	References
Index