Intro
Preface
Acknowledgements
About This Book
Contents
About the Editors
NLP for Sentiment Computation
1 Introduction
2 Natural Language and Sentiments
3 Lexical Based
4 Corpora Based
5 Aspect Based
6 Trends
6.1 Social Semantic
6.2 Multi Domain
7 Conclusion
References
Productizing an Artificial Intelligence Solution for Intelligent Detail Extraction-Synergy of Symbolic and Sub-Symbolic Artificial Intelligence Techniques
1 Introduction
2 Problem Description of Intelligent Detail Extraction
3 Components of an IDE
4 Survey of Work on Extraction of Characters
5 Case Study: Invoice Processing
5.1 Details
5.2 Architecture
5.3 Challenges
5.4 Insight
5.5 Discovery and Productizing
6 Results and Conclusion
References
Digital Consumption Pattern and Impacts of Social Media: Descriptive Statistical Analysis
1 Introduction
2 Review of Literature
3 Access of Internet Across Generations
4 Impact of Internet on Business-Management
5 Impact of Internet on Kids, Adolescents and Adults
6 Internet Service Providers (ISP) in India During This COVID-19 Lockdown
7 Objective and Methodology of Primary Data Collection
8 Data Analysis
9 Bi-variate Analysis
10 Conclusion
References
Applicational Statistics in Data Science and Machine Learning
1 Introduction
1.1 Statistics and Exploratory Data Analysis
1.2 Statistical Tools and Techniques
2 Sampling Techniques
2.1 Population Versus Sample
2.2 Sampling Methods
3 Types of Variables
3.1 Random Variable
3.2 Categorical Data
3.3 Numerical Data
3.4 Qualitative Data
3.5 Quantitative Data
4 Visualizing Data
4.1 Categorical Data
4.2 Numerical Data
5 Measures of Central Tendency
5.1 Mean
5.2 Median
5.3 Mode
5.4 Variance
5.5 Standard Deviation.

6 Distributions in Statistics
6.1 Probability Distributions
6.2 PMF Versus PDF
6.3 Common Probability Distributions
6.4 Kurtosis
6.5 Skewness in Distributions
6.6 Scaling and Transformations
7 Outlier Treatment
7.1 Understanding Outliers
7.2 Detecting Outliers
8 Correlation Analysis
8.1 Steps for Correlation Analysis
8.2 Autocorrelation Versus Partial Correlation
9 Variance and Covariance Analysis
9.1 Analysis of Variance (ANOVA)
9.2 Analysis of Covariance (ANCOVA)
9.3 Multiple Analysis of Variance (MANOVA)
9.4 Multiple Analysis of Covariance (MANCOVA)
10 Chi-Square Analysis
11 Z-Score
12 Bias Versus Variance
12.1 Bias-Variance Trade-Off
12.2 Overfitting and Underfitting
13 Hypothesis Testing
13.1 Errors in Hypothesis Testing
14 Conclusion
References
Evolutionary Algorithms-Based Machine Learning Models
1 Introduction
2 Application Domains
2.1 Engineering Applications
2.2 Applied Sciences
2.3 Disaster Management
2.4 Finance and Economy
2.5 Health
3 Analysis and Discussion
3.1 Issues
3.2 Gap Analysis
4 Conclusion
References
Application to Predict the Impact of COVID-19 in India Using Deep Learning
1 Introduction
2 Proposed Work
3 Proposed Modules
4 Deep Learning
4.1 CNN Model
5 System Implementation
5.1 Decomposition of the COVID-19 Data
6 Results and Analysis
7 Conclusion and Future Direction
References
Role of Data Analytics in Bio Cyber Physical Systems
1 Introduction
2 Cyber Physical Systems
2.1 CPS and IoT
2.2 Concept Map of Cyber Physical Systems
2.3 Bio Cyber Physical Systems
3 Health Wearables
3.1 Fitness Trackers/Smart Watches
3.2 Types of Sensors
3.3 Activity Log
3.4 Advanced Sensors
3.5 Data Gathering
4 Diabetes
4.1 Complications of Diabetes.

5 Case Studies of Diabetic Complications
5.1 Heart-Attack
5.2 Seizures and Strokes
6 Role of Neural Networks in the Case Scenarios
6.1 Convolutional Neural Network
7 Multi-channel CNN
8 Complication Prediction Through LSTM
9 Conclusion
References
Evolution of Sentiment Analysis: Methodologies and Paradigms
1 Introduction
2 Foundational Methods
2.1 Supervised
2.2 Unsupervised and Semi-supervised
3 Applications
4 Comparative Study
4.1 Convolutional and Recurrent Neural Network (with LSTMs)
4.2 Word Embeddings/Representations
4.3 Deep Belief Networks
4.4 Rule-Based and Other Classifiers
5 Latest Developments and State-of-the-Art
5.1 Transfer Learning and Language Models
5.2 Attention and the Transformer
5.3 Transformers-Based Architectures
5.4 Limits of Transfer Learning
6 Conclusions
References
Healthcare Analytics: An Advent to Mitigate the Risks and Impacts of a Pandemic
1 Introduction
1.1 Healthcare Sector
1.2 Analytics Domain
1.3 Application of Analytics in Healthcare Domain
2 Background
3 Research on Pandemics and Their Impacts
4 Development of Healthcare Information System and Healthcare Analytics
5 Results
6 Illustration
7 Conclusion
References
Image Classification for Binary Classes Using Deep Convolutional Neural Network: An Experimental Study
1 Introduction
2 The Dataset
3 Literature Review
4 Architecture, Methodology, and Results
5 Conclusion
References
Leveraging Analytics for Supply Chain Optimization in Freight Industry
1 Introduction
2 Literature Survey
3 Data Storage and Big Data Ecosystem
4 Data Processing and Manipulation
5 Analytics and Insights
6 Machine Learning Implementation
6.1 Demand-Supply Matchmaking
6.2 Pricing and Incentives.

6.3 User Segmentations to Understand User Activities
7 Comparative Study of Different Techniques
8 Chapter Takeaways and Significance
9 Conclusion and Future Scope
References
Trends and Application of Data Science in Bioinformatics
1 Introduction
2 Data Science
3 Application of Data Science in Bioinformatics
3.1 Genomics
3.2 Transcriptomics
3.3 Proteomics
3.4 Metabolomics
3.5 Epigenetics
4 Techniques in Data Science that Can Be Used for Bioinformatics
4.1 Machine Learning and Deep Learning
4.2 Parallel Computing
4.3 Cloud Computing
5 Future Perspectives
6 Conclusion
References
Mathematical and Algorithmic Aspects of Scalable Machine Learning
1 Introduction
1.1 Challenges in Scalable Machine Learning
1.2 Reasons for Scaling up Machine Learning
2 The Infrastructure of Scalable Machine Learning
2.1 Distributed File System
2.2 Distributed Topology for Machine Learning
3 MapReduce
3.1 Benefits of MapReduce
4 Linear Regression
4.1 Parallel Version of Linear Regression
5 Clustering
5.1 K-Mean Clustering
5.2 Parallel K-mean for a Scalable Environment
5.3 DBSCAN
5.4 Parallel DBSCAN
6 Parallelization of Support Vector Machine
7 Decision Tree
8 Conclusion
References
An Implementation of Text Mining Decision Feedback Model Using Hadoop MapReduce
1 Introduction
1.1 Conventional Process Flow of Text Mining
1.2 Applications of Text Mining
2 Literature Survey
3 Proposed Decision Feedback-Based Text Mining Model
4 Big Data Technologies
4.1 Hadoop Distributed File System
4.2 MapReduce
4.3 Pig
4.4 Hive
4.5 Sqoop
4.6 Oozie
4.7 Flume
4.8 ZooKeeper
5 Word Stemming
5.1 Pre-requisites for Stemming
5.2 Classification of Stemming
6 Proposed Porter Stemmer with Partitioner Algorithm (PSP).

7 Hadoop Cluster Operation Modes
8 Environment Setup
9 Implementation
9.1 Data Collection
9.2 Text Parsing
9.3 Text Filtering
9.4 Text Transformation
9.5 Feature Selection
9.6 Evaluate
10 Result and Discussion
11 Conclusion and Future Work
References
Business Analytics: Process and Practical Applications
1 Introduction
1.1 Definition
1.2 Goal
2 Process
2.1 CRISP-DM (Cross-Industry Standard Process for Data Mining)
2.2 SEMMA (Sample, Explore, Modify, Model, Assess)
2.3 Comparative Study
2.4 Others Approaches
3 Types of Analytics
3.1 Descriptive Analytics
3.2 Diagnostic Analytics
3.3 Predictive Analytics
3.4 Prescriptive Analytics
3.5 Comparative Study
4 Domain and Applications
5 Recommendation System(s)-An approach
5.1 Types of Recommendation Systems
5.2 Benefits of Recommendation System
5.3 An Example
5.4 Challenges of Recommendation Systems
5.5 Comparative Study
6 Tools
7 Conclusion
References
Challenges and Issues of Recommender System for Big Data Applications
1 Introduction
1.1 Recommendation System Architecture
1.2 Big Data
2 The Cold Start Problem in Recommendation
2.1 New User Cold Start Problem
2.2 New Item Cold Start Problem
3 Scalability
3.1 Scalable Neighborhood Algorithm
4 Proactive Recommender System
4.1 Proactive Recommendation
4.2 Intelligent Proactive Recommender System
5 Conclusion
References.