Trends of Data Science and Applications: Theory and Practices (Studies in Computational Intelligence, 954)

✍ Scribed by Siddharth Swarup Rautaray (editor), Phani Pemmaraju (editor), Hrushikesha Mohanty (editor)

• Publisher: Springer
• Year: 2021
• Tongue: English
• Leaves: 345
• Category: Library

✦ Synopsis

This book includes an extended version of selected papers presented at the 11th Industry Symposium 2021 held during January 7–10, 2021. The book covers contributions ranging from theoretical and foundation research, platforms, methods, applications, and tools in all areas. It provides theory and practices in the area of data science, which add a social, geographical, and temporal dimension to data science research. It also includes application-oriented papers that prepare and use data in discovery research. This book contains chapters from academia as well as practitioners on big data technologies, artificial intelligence, machine learning, deep learning, data representation and visualization, business analytics, healthcare analytics, bioinformatics, etc. This book is helpful for the students, practitioners, researchers as well as industry professional.

✦ Table of Contents

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