Artificial Intelligence: Applications and Innovations (Chapman & Hall/Distributed Computing and Intelligent Data Analytics Series)

301 Pages·2022·22.061 MB·English

by  Rashmi Priyadarshini (editor), R M Mehra (editor), Amit Sehgal (editor), Prabhu Jyot Singh (editor)

Artificial Intelligence
Artificial Intelligence: Applications and Innovations is a book about the
science of artificial intelligence (AI). AI is the study of the design of intel-
ligent computational agents. This book provides a valuable resource for
researchers, scientists, professionals, academicians and students dealing
with the new challenges and advances in the areas of AI and innovations.
This book also covers a wide range of applications of machine learning such
as fire detection, structural health and pollution monitoring and control.
Key Features
• Provides insight into prospective research and application areas
related to industry and technology
• Discusses industry- based inputs on success stories of technology
adoption
• Discusses technology applications from a research perspective in the
field of AI
• Provides a hands- on approach and case studies for readers of the
book to practice and assimilate learning
This book is primarily aimed at graduates and post- graduates in computer
science, information technology, civil engineering, electronics and elec-
trical engineering and management.
Chapman & Hall/ Distributed Computing and Intelligent Data
Analytics Series
Series Editors: Niranjanamurthy M. and Sudeshna Chakraborty
Machine learning and Optimization Models for Optimization in Cloud
Punit Gupta, Mayank Kumar Goyal, Sudeshna Chakraborty,
Ahmed A. Elngar
Computer Applications in Engineering and Management
Parveen Berwal, Jagjit Singh Dhatterwal, Kuldeep Singh Kaswan,
Shashi Kant
Physics and Astrophysics: Glimpses of the Progress
Subal Kar
Artificial Intelligence: Applications and Innovations
Rashmi Priyadarshini, R. M. Mehra, Amit Sehgal, Prabhu Jyot Singh
For more information about this series please visit: www.routle dge.com/
Chap man–Hall Dist ribu ted- Comput ing- and- Inte llig ent- Data- Analyt ics-
Ser ies/ book- ser ies/ DCID
Artificial Intelligence
Applications and Innovations
Edited by
Rashmi Priyadarshini, R M Mehra,
Amit Sehgal and Prabhu Jyot Singh
Cover image: Shutterstock © vs148
First edition published 2023
by CRC Press
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and by CRC Press
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CRC Press is an imprint of Taylor & Francis Group, LLC
© 2023 selection and editorial matter, Rashmi Priyadarshini, R M Mehra, Amit Sehgal and Prabhu Jyot
Singh; individual chapters, the contributors
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Library of Congress Cataloging‑in‑Publication Data
Names: Priyadarshini, Rashmi, editor.
Title: Artificial intelligence : applications and innovations / edited by
Rashmi Priyadarshini, R M Mehra, Amit Sehgal, Prabhu Jyot Singh.
Description: First edition. | Boca Raton, FL : Chapman & Hall/CRC Press, 2023. |
Series: Chapman &Hall/CRC distributed computing and intelligent data analytics series |
Includes bibliographical references and index.
Identifiers: LCCN 2022002785 (print) | LCCN 2022002786 (ebook)
Subjects: LCSH: Artificial intelligence. | Artificial intelligence–Industrial applications.
Classification: LCC TA347.A78 A795 2023 (print) | LCC TA347.A78 (ebook) |
DDC 006.3–dc23/eng/20220407
LC record available at https://lccn.loc.gov/2022002785
LC ebook record available at https://lccn.loc.gov/2022002786
ISBN: 9781032108230 (hbk)
ISBN: 9781032305554 (pbk)
ISBN: 9781003217237 (ebk)
DOI: 10.1201/ 9781003217237
Typeset in Minion
by Newgen Publishing UK
Contents
Preface, xvii
Editor Biographies, xix
List of Contributors, xxiii
Chapter 1 n Introduction to Artificial Intelligence 1
GaGandeep Kaur, SatiSh Saini and amit SehGal
1.1 HUMAN AND ARTIFICIAL INTELLIGENCE 1
1.1.1 The Turing Test 2
1.1.2 Cognitive Modelling – Thinking Humanly 3
1.1.3 The Laws of Thought Approach 6
1.1.4 The Rational Agent Approach 6
1.2 AI – AN OVERVIEW 6
1.2.1 Goals of AI 7
1.2.2 Advantages of AI Systems 7
1.2.3 Challenges of AI Systems 8
1.2.3.1 Computing Power 8
1.2.3.2 Trust Deficit 9
1.2.3.3 Limited Knowledge 9
1.2.3.4 Human‑ level 9
1.2.3.5 Data Privacy and Security 10
1.2.3.6 The Bias Problem 10
1.2.3.7 Data Scarcity 10
v
vi n Contents
1.3 AI’S HISTORY 11
1.4 AI WORKING 13
1.5 TYPES OF AI 14
1.5.1 Artificial Narrow Intelligence (ANI) 14
1.5.2 Artificial General Intelligence (AGI) 14
1.5.3 Artificial Super Intelligence (ASI) 14
1.6 APPLICATIONS OF AI 15
1.6.1 AI in Astronomy 15
1.6.2 AI in Healthcare 15
1.6.3 AI in Gaming 15
1.6.4 AI in Finance 15
1.6.5 AI in Data Security 15
1.6.6 AI in Social Media 16
1.6.7 AI in Travel and Transport 16
1.6.8 AI in the Automotive Industry 16
1.6.9 AI in Robotics 16
1.6.10 AI in Entertainment 16
1.6.11 AI in Agriculture 16
1.6.12 AI in E- commerce 17
1.6.13 AI in Education 17
1.7 FUTURE OF AI 17
REFERENCES 17
Chapter 2 n Machine Learning – Principles and Algorithms 21
GaGandeep Kaur, SatiSh Saini and amit SehGal
2.1 INTRODUCTION 21
2.2 ML APPLICATIONS 23
2.3 ML KEY ELEMENTS 24
2.4 TYPES OF LEARNING 24
2.4.1 Supervised Learning 25
2.4.1.1 Decision Tree Algorithm 26
2.4.1.2 Naive Bayes Algorithm 27
Contents n vii

2.4.1.3 Support Vector Machines 29
2.4.1.4 Random Forest Algorithm 30
2.4.1.5 Linear Regression 33
2.4.1.6 Ordinary Least Squares Regression
Algorithm 35
2.4.1.7 Logistic Regression 35
2.4.1.8 Ensemble Methods 36
2.4.2 Unsupervised Learning 37
2.4.2.1 K‑ means for Clustering Algorithm 37
2.4.2.2 Apriori Algorithm 40
2.4.2.3 Principal Component Analysis (PCA) 42
2.4.2.4 Singular Value Decomposition 43
2.4.2.5 Independent Component Analysis 44
2.4.3 Reinforcement Learning 44
2.4.3.1 Learn the Model 44
2.4.3.1.1 World Model 46
2.4.3.1.2 Imagination Augmented
Agent (I2A) 46
2.4.3.1.3 Model- Based Priors for
Model- Free Reinforcement
Learning 47
2.4.3.1.4 Model- Based Value Expansion 47
2.4.3.2 Given the Model– Alpha Zero
Approach 47
2.4.3.3 Model‑ Free Reinforcement Learning 47
2.4.3.4 Policy Optimization Approach 48
2.4.3.4.1 Policy Gradient Method 48
2.4.3.4.2 Asynchronous Advantage
Actor- Critic (A3C) 50
2.4.3.4.3 Trust Region Policy
Optimization (TRPO) 51
2.4.3.4.4 Proximal Policy
Optimization (PPO) 51
viii n Contents
2.5 SUMMARY 51
REFERENCES 51
Chapter 3 n Applications of Machine Learning and
Deep Learning 55
GaGandeep Kaur, SatiSh Saini and amit SehGal
3.1 MACHINE LEARNING APPLICATIONS 55
3.1.1 Image Recognition 55
3.1.2 Speech Recognition 56
3.1.3 Traffic Prediction 56
3.1.4 Product Endorsement 57
3.1.5 Self- driving Cars 57
3.1.6 Email Spam and Malware Filtering 57
3.1.7 Virtual Personal Assistant 57
3.1.8 Online Fraud Detection 57
3.1.9 Stock Market Trading 58
3.1.10 Medical Diagnosis 58
3.1.11 Automatic Language Translation 58
3.2 DEEP LEARNING 58
3.3 MACHINE LEARNING VS. DEEP LEARNING 59
3.4 HOW DEEP LEARNING WORKS 60
3.5 APPLICATIONS OF DEEP LEARNING 61
3.5.1 Law Enforcement 61
3.5.2 Financial Services 61
3.5.3 Customer Service 61
3.5.4 Healthcare 61
3.6 DEEP LEARNING ALGORITHMS 62
3.6.1 Convolutional Neural Networks (CNNs) 62
3.6.2 Long Short- Term Memory Networks
(LSTMs) 63
3.6.3 Recurrent Neural Networks
(RNNs) 63
Contents n ix

3.6.4 Generative Adversarial Networks (GANs) 64
3.6.5 Radial Basis Function Networks (RBFNs) 64
3.6.6 Multilayer Perceptrons (MLPs) 65
3.6.7 Self- organizing Maps (SOMs) 66
3.6.8 Deep Belief Networks (DBNs) 66
3.6.9 Restricted Boltzmann Machines (RBMs) 67
3.6.10 Autoencoders 68
3.7 SUMMARY 68
REFERENCES 69
Chapter 4 n Environmental Monitoring in Wireless
Sensor Networks using AI 71
arpana miShra and raShmi priyadarShini
4.1 INTRODUCTION OF ENVIRONMENTAL
MONITORING 71
4.2 APPLICATIONS OF WIRELESS SENSOR
NETWORK (WSN) 72
4.2.1 Air Monitoring 72
4.2.2 Water Monitoring 72
4.2.3 Biodiversity 73
4.2.4 Waste Monitoring 73
4.2.5 Distant Sensing 73
4.2.6 Enterprise Monitoring 74
4.3 WSN FOR ENVIRONMENTAL MONITORING 74
4.3.1 Autonomy 75
4.3.2 Reliability 75
4.3.3 Robustness 76
4.4 CLIMATE MONITORING SYSTEM APPLICATIONS 76
4.5 AGRICULTURAL MONITORING 76
4.6 HABITAT MONITORING 78
4.7 ARTIFICIAL INTELLIGENCE AND WSN 78
4.8 REMOTE SENSOR NETWORKS 80