Industry 4.0, AI, and data science : research trends and challenges / edited by Vikram Bali, Kakoli Banerjee, Narendra Kumar, Sanjay Gour, and Sunil Kumar Chawla.

Format:

Book

Contributor:

Bali, Vikram, editor.

Series:

Demystifying Technologies for Computational Excellence

Language:

English

Subjects (All):

Industry 4.0.

Physical Description:

1 online resource (283 pages)

Edition:

First edition.

Place of Publication:

Boca Raton, FL : CRC Press, 2021.

Summary:

"The aim of this book is to provide insight into Data Science and Artificial Learning Techniques based on Industry 4.0, conveys how Machine Learning & Data Science are becoming an essential part of industrial and academic research. Varying from healthcare to social networking and everywhere hybrid models for Data Science, Al, and Machine Learning are being used. The book describes different theoretical and practical aspects and highlights how new systems are being developed. Along with focusing on the research trends, challenges and future of AI in Data Science, the book explores the potential for integration of advanced AI algorithms, addresses the challenges of Data Science for Industry 4.0, covers different security issues, includes qualitative and quantitative research, and offers case studies with working models. This book also provides an overview of AI and Data Science algorithms for readers who do not have a strong mathematical background. Undergraduates, postgraduates, academicians, researchers, and industry professionals will benefit from this book and use it as a guide"-- Provided by publisher.

Contents:

Cover

Half Title

Series Information

Title Page

Copyright Page

Table of Contents

Contributors

Preface

Editors

1 Predicting Fraudulent Motor Vehicle Insurance Claims Using Data Mining Model

1.1 Introduction

1.2 Proposed Model

1.2.1 Statement of the Problem

1.3 Literature Review

1.3.1 The Concept of Insurance

1.3.2 Insurance Fraud

1.3.3 Fraud Predictive Variables

1.3.4 Machine Learning Algorithms

1.3.4.1 Naive Bayes

1.3.4.2 Decision Trees

1.3.4.3 Logistic Regression

1.3.4.4 Support Vector Machines (SVM)

1.4 Methodology

1.4.1 Data Acquisition and Description

1.4.2 Data Pre-Processing

1.4.3 Encoding

1.5 Application of Classification Algorithms

1.6 Results

1.6.1 Attributes of Predictor Variables

1.6.2 Variables with the Most Predictive Influence

1.6.3 The Classification Algorithms Used

1.7 Conclusions and Future Scope

1.7.1 Recommendation of Future Studies

References

2 Novel 8: 1 Multiplexer for Low Power and Area Efficient Design in Industry 4.0:

2.1 Introduction

2.1.1 Importance of Industry 4.0 in the Field of Electronics

2.1.2 Applications of Multiplexer

2.2 Introduction to Basic Multiplexer

2.2.1 Transmission Gate Logic Based 2:1 MUX

2.2.2 2:1 MUX Using Pass Transistor Logic

2.2.3 2:1 MUX Using Cmos Logic

2.2.4 Comparative Analysis

2.3 8:1 Multiplexer

2.3.1 8:1 Multiplexer Using Lower Order Multiplexers

2.4 conventional 8:1 MUX Using Transmission gate Logic

2.5 Proposed 20t Design for 8:1 MUX

2.5.1 Simulation Results and Comparative Analysis

2.6 Conclusion

3 Data Science and AI for E-Governance: A Step Towards Society 5.0

3.1 Introduction

3.2 Data Science for E-Government and Society

3.3 Artificial Intelligence for E-Government and Society

3.4 Tools and Techniques.

3.5 Future Prospects and Trends

3.6 Conclusions

4 Application Areas of Data Science and AI for Improved Society 5.0 Era

4.1 Introduction

4.2 Data Science and Society

4.2.1 Data Science and Society Interrelation

4.2.2 Process of Data Science

4.2.3 Application Areas of Data Science

4.3 E-Government

4.3.1 Role of E-Governance in Society

4.3.2 E-Government Services With Artificial Intelligence

4.3.3 Applications of E-Governance

4.4 Emerging Technologies

4.4.1 IoT (Internet of Things)

4.4.2 Artificial Intelligence (AI)

4.4.3 Blockchain

4.5 AI in Society

4.5.1 Healthcare

4.5.2 Education

4.5.2 Communication

4.5.3 Banking

4.6 Conclusion

5 Applying Machine-Learning and Internet of Things in Healthcare

5.1 Introduction

5.2 Related Work

5.2.1 Internet of Things in Healthcare

5.3 Sensor Data Fusion

5.3.1 Wearable Sensor in Healthcare

5.3.1.1 Heartbeat Sensors

5.3.1.2 Sensors for Respiration Rate

5.3.1.3 Body Temperature Sensors

5.3.1.4 Circulatory Strain

5.4 Prototype for a System in Healthcare Using IoT

5.4.1 Central Nodes and Wearable Senor

5.4.2 Less-Range Communications

5.4.3 Wide-Range Communications

5.4.4 Safe Cloud Storage Architecture and Machine Learning

5.5 Application Area of Iot in Healthcare

5.5.1 Home Healthcare

5.5.2 Mobile Health and Electronic Health

5.5.3 Hospital Management

5.6 Conclusion and Future Scope

6 Artificial Intelligence: The New Expert in Medical Treatment

6.1 Introduction

6.1.1 Benefits of AI in Medical Field

6.2 Computer AIded Diagnosis

6.2.1 Detection of Interval Changes in Whole Body Bone Scan

6.3 Ann in Computer AIded Diagnosis

6.3.1 Fundamental Steps in Diagnosis

6.3.2 Cardiovascular Diseases

6.3.3 Diabetes

6.4 NLP and Cdss.

6.5 Advancements of AI in Medical Field

6.5.1 Radiology

6.5.2 Cardiology

6.5.3 Surgery

6.5.4 Hospital Administration

6.5.5 Other Recent Advancements in Healthcare

6.6 Conclusion

7 Machine Learning Approach for Breast Cancer Early Diagnosis

7.1 Introduction

7.2 About the Dataset

7.3 Methodology

7.3.1 Experimental Environment

7.3.2 Data Visualization

7.3.3 Machine Learning Classification

7.3.4 Parameters Used to Generate Classification Report

7.3.5 Results of the Wisconsin Dataset

7.4 Discussion On Results

7.4.1 Model Performance

7.5 Summary of Results

7.6 Conclusion

8 Intelligent Surveillance System Using Machine Learning

8.1 Introduction

8.2 Proposed Method

8.2.1 Research Design

8.2.2 Related Works

8.3 Implementation

8.3.1 Live Face Detection

8.3.2 Face Recognition

8.3.3 Security Measures

8.4 Results

8.5 Conclusion

8.5.1 Future Enhancements

9 Cyber Security: An Approach to Secure Iot From Cyber Attacks Using Deep Learning

9.1 Introduction

9.2 Related Work

9.3 Iot Key Components

9.4 Security of Iot Devices

9.5 Possible Available Attack Vectors On Iot

9.6 Encryption Algorithms to Protect Data of IoT Devices From Cyber Attacks

9.7 Challenges in Cyber Security of Iot Devices

9.8 Proposed Solution for Ensuring Security of Iot Devices

9.9 Conclusion

10 Learning the Dynamic Change of User Interests From Noise Web Data

10.1 Introduction

10.2 Existing Research Works

10.2.1 The Influence of User Interest On the Noise Web Data Reduction Process

10.2.2 Addressing Dynamic Change in User Interests

10.3 Proposed Research Work

10.3.1 Recency Adjustment Measure

10.3.2 Dynamic Threshold Values

10.3.3 User Interests Classification.

10.3.4 Noise Web Data Learning: Its Significance to Web Usage Mining

10.4 Experimental Design

10.4.1 Discussion of Results

10.5 Conclusion

11 Artificial Intelligence Techniques Based Routing Protocols in Vanets: A Review

11.1 Introduction

11.2 Routing in Vanets

11.2.1 Wireless Standards for Vanet Routing

11.2.2 Issues in Routing

11.2.3 Artificial Intelligent Techniques

11.2.4 Benefits of Artificial Intelligence Techniques Based Vanet Routing

11.3 Artificial Intelligence Techniques Based Vanet Routing Protocols

11.3.1 Game Theory

11.3.2 Fuzzy System

11.3.3 Bio Inspired Algorithms

11.3.4 Learning Systems (LS)

11.3.5 Software Agent Systems

11.4 Overview of Artificial Intelligence Based Vanet Routing Protocols

11.5 Future Research Scope

12 A Comparison of different Consensus Protocols: The Backbone of the Blockchain Technology

12.1 Introduction

12.1.1 Block Diagram of Blockchain

12.1.2 Consensus Algorithms

12.1.3 The Problem With Byzantine Fault Tolerance

12.1.4 Need Consensus Algorithms

12.2 Blockchain Technology

12.2.1 Consensus Mechanism: The Backbone of the Network

12.2.2 Different Types of Consensus Protocols

12.3 Working of Different Types of Consensus Protocols

12.3.1 PoW (Proof-of-Work)

12.3.2 PoS (Proof-of-Stake)

12.3.3 DPoS (Delegated Proof-of-Stake)

12.3.4 LPoS (Leased Proof-of-Stake)

12.3.5 PoET (Proof of Elapsed Time)

12.3.6 PBFT (Practical Byzantine Fault Tolerance)

12.3.7 SBFT (Simplified Byzantine Fault Tolerance)

12.3.8 DBFT (Delegated Byzantine Fault Tolerance)

12.3.9 DAG (Directed Acyclic Graphs)

12.3.10 PoA (Proof-of-Activity)

12.3.11 PoI (Proof-of-Importance)

12.3.12 PoC (Proof-of-Capacity)

12.3.13 PoB (Proof-of-Burn)

12.3.14 PoWeight (Proof-of-Weight).

12.4 Comparison Between Consensus Mechanisms

12.5 Conclusion

13 Blockchain in AI: Review of Decentralized Smart System

13.1 Introduction

13.1.1 Artificial Intelligence

13.1.2 Blockchain

13.1.3 Blockchain and AI

13.1.4 How Can Blockchain Transform AI? Redefined Intelligence

13.2 Blockchain-Enabled AI Applications

13.2.1 Blockchain-AI Architecture for Coronavirus Fighting

13.2.1.1 Blockchain-Based Solutions for Coronavirus Fighting

13.2.1.2 AI-Based Solutions for Coronavirus Fighting

13.2.2 Blockchain and AI Technology for Novel Coronavirus Disease 2019 Self-Testing

13.2.3 Securing Data With Blockchain and AI

13.2.4 Blockchain Integration With Robotics and AI

13.2.5 Blockchain and Deep Reinforcement Learning Empowered Intelligent 5G Beyond

13.2.6 A Blockchain-Enabled Intelligent Iot Architecture With AI

13.2.7 Application of Blockchain With AI

13.2.7.1 Global Payments

13.2.7.2 Blockchain Music

13.2.7.3 Government

13.2.7.4 Blockchain Identity

13.2.7.5 Optimization

13.2.7.6 Model Development

13.3 Conclusion and Future Scope

14 Financial Portfolio Optimization: An AI Based Decision-Making Approach

14.1 Introduction

14.2 Literature Review

14.3 Methodology

14.4 Analysis

14.5 Conclusion and Future Scope

15 Intelligent Framework and Metrics for Assessment of Smart Cities

15.1 Introduction

15.2 Review of Existing Literature

15.3 Definition of Proposed Assessment Framework

15.3.1 Municipal Corporation

15.3.2 Safety and Security

15.3.3 Ict Infrastructure

15.3.4 Interconnectivity

15.3.5 Sustainability

15.4 Critical Event Management

15.5 Assessment Framework

15.6 Rating Criteria

15.7 Conclusions

Index.

Notes:

Description based on print version record.

ISBN:

1-00-309718-9

1-000-41345-4

1-003-09718-9

1-000-41348-9

9781003097181

OCLC:

1255228552