1st Edition

Artificial Intelligence
Fundamentals and Applications



  • Available for pre-order. Item will ship after July 5, 2021
ISBN 9780367559700
July 5, 2021 Forthcoming by CRC Press
272 Pages 80 B/W Illustrations

USD $140.00

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Book Description

This comprehensive reference text discusses fundamental concepts of artificial intelligence and its applications in a single volume.

The text presents detailed discussion of basic aspects and ethics in the field of artificial intelligence and its applications in areas including electronic devices and systems, consumer electronics, automobile engineering, manufacturing, robotics and automation, agriculture, banking and predictive analysis.

Aimed at senior undergraduate and graduate students in the field of electrical engineering, electronics engineering, manufacturing engineering, pharmacy and healthcare, this text:

  • Discusses advances in artificial intelligence and its applications.
  • Presents predictive analysis and data analysis using artificial intelligence.
  • Covers algorithms and pseudo-codes for different domains.
  • Discusses the latest development of AI in the field of practical speech recognition, machine translation, autonomous vehicles, and household robotics.
  • Covers applications of AI in fields including pharmacy and healthcare, electronic devices and systems, manufacturing, consumer electronics and robotics.

Table of Contents

Chapter 1: ARTIFICIAL INTELLIGENCE AND NANOTECHNOLOGY: A SUPER CONVERGENCE
1.1. Introduction
1.2. Utility of artificial intelligence
1.2.1. AI in scanning probe microscopy
1.2.2. Nano system design
1.2.3. Nanoscale simulation
1.2.4. Nanocomputing
1.3. Food Science
1.4. Nanobots in medicine
1.5. Summary
Chapter 2: Artificial intelligence in e-commerce: a business process analysis
2.1. Introduction 1
2.2. Artificial intelligence 2
2.3. E-commerce business processes and artificial intelligence 3
2.3.1. Marketing 4
2.3.1.1. Market research 4
2.3.1.2. Market stimulation 5
2.3.2. Transaction Processing 5
2.3.2.1. Terms negotiation 5
2.3.2.2. Order selection and priority 6
2.3.2.3. Order receipt 7
2.3.2.4. Order billing/payment management 7
2.3.3. Service and Support 7
2.3.3.1. Order scheduling/fulfillment delivery 8
2.3.3.2. Customer service and support 8
2.4. Concluding remarks 9
References 9
 Chapter 3: ABC of Digital Era with special reference to Banking Sector
3.1. Introduction
3.2. Artificial Intelligence in Banking Sector
3.3. ABC of Digital Era in Banking Sector
3.3.1. A as Artificial Intelligence
3.3.2. B as Big Tech
3.3.3. C as Core Banking and Cloud
3.4. Opportunities and Challenges in Banking Sector due to Digitalization
3.4.1. Opportunities
3.4.2. Challenges
3.5. Artificial Intelligence Used by Big Four Banks of India
3.5.1. State Bank of India
3.5.2. HDFC Bank
3.5.3 ICICI Bank
3.5.4. Axis Bank
3.6. Conclusion
Chapter 4:  Artificial Intelligence in Predictive Analysis of Insurance and Banking
4.1. Introduction
4.2. Predictive Analysis and Its Applications
 4.2.1. Predictive Analysis of Stock Prices Using DCC GARCH Model In R.
4.3. Genetic Algorithms
4.3.1. Genetic Algorithms in Portfolio Optimisation
4.3.2. Genetic Algorithms in Bank Profit Maximization
4.4. Anomaly Detection
4.4.1. Anomaly Detection to Identify Credit Card Frauds using Python
4.4.1.1. Python libraries
4.4.1.2. Anomaly detection in credit card dataset
4.4.2. A Demonstration of Anomaly Detection in Ethereum Prices using R.
4.4.2.1.Ethereum
4.4.2.2. Tidyverse
4.4.2.3 Anomaly detection
4.5. Conclusion
Chapter 5: Artificial Intelligence in Robotics and Automation
5.1. Introduction
5.2. History
5.3. Automation and Application Bots
5.4. Robots VS Chatbots VS Bots
5.4.1. Types of Bots
5.5. Natural Language Processing (NLP)
5.5.1. Natural Language Understanding (NLU)
5.5.2. Natural Language Generation
5.6. Robotics Process Automation (RPA)
5.6.1. Challenges in implementation of RPA
5.7. Financial Impact of AI and Automation
 5.8. Features of Automated Bots
5.9. Effect of AI and Automation
5.10. Challenges in implementing Automation
5.11. Myths of Automated Bots
5.12. Platform used for implementation
5.13. Conclusion

Chapter 6: Artificial Intelligence: An Emerging Approach in Healthcare
6.1 Introduction
6.2 Scope & Relevance of Various Types of AI in Healthcare
6.3 AI’s Timeline in Healthcare
6.4 Implementation of AI Concepts in the Medical World
6.5 Current Researches that Contribute to the Advancement of AI
6.6 Key Issues & Challenges Ahead in AI
6.7. Conclusion

Chapter 7: Artificial intelligence and Personalized medicines:  A joint narrative on advancement in medical healthcare 1
7.1 Introduction 1
7.2 Need for personalized medicines 2
7.2.1 Contributors to personalized medicines 2
7.3 Application of AI in healthcare for development of precision medicines 3
7.4 In intensive care unit (ICU) 4
7.4.1 In intensive care unit (ICU) – to predict fluid requirement 4
7.4.2 To solve issues of personalized medicines 5
7.4.3. Revolutionizing cloud of AI and Healthcare 6
7.5 Conclusion 6
7.6 References 7

Chapter 8: Nanotechnology and Artificial Intelligence for Precision Medicine in Oncology
8.1 Introduction
8.2 Role of nanotechnology in medicine and healthcare
8.2.1 Nano drug design by AI
8.2.2 Artificial Intelligence
8.2.2.1 AI in Medicine
8.2.3 Precision Medicine
8.2.3.1 Applications of precision medicine
8.2.4 Deep Learning
8.2.4.1 Application
8.2.4.2 Implementation of deep learning in Medicine
8.2.4.3 Convolutional Neural Networks
8.2.4.4 CNN in precision medicine.
8.5 Conclusion
Chapter 9: Applications of Artificial Intelligence in Pharmaceutical and Drug Formulation
9.1 Introduction
9.2 Genetic Algorithm
9.3 Fuzzy Logic
9.4 Integrated software
9.5 Applications of Artificial Intelligence in Pharmaceuticals
9.6 Recognition of Pattern and Modelling the Data of Analysis
9.7 Modelling the Response Surface
9.8 In Assessment of Controlled release and Immediate release formulations
9.9 In Product Development
9.10 In predictive toxicology
9.11 Proteins function and structure prediction
9.12 Pharmacokinetics
9.13 Conclusion
Chapter 10: Role of Artificial Intelligence for Diagnosing Tuberculosis
10.1 Introduction
10.1.1 History of TB
10.1.2 Global Impact of TB
10.1.3 TB: India’s Silent Epidemic
10.1.4 Classification of TB
10.2 Technological Interventions for Diagnosis of TB
10.2.1 Artificial Intelligence (AI):
10.2.2 AI Techniques
10.2.3 Role of AI in Diagnosis of TB- Comparative Analysis
10.2.4 Limitations of Retrieved Literature
10.3 Conclusion
Chapter 11: APPLICATIONS OF ARTIFICIAL INTELLIGENCE IN DETECTION AND TREATMENT OF COVID-19

11.1. Introduction
11.2. Inception of artificial intelligence in healthcare
11.2.1. Applications of AI in healthcare-
11.3. Artificial Intelligence in the management of COVID-19
11.3.1. AI in Early Detection and Alert Systems
11.4. Role of AI in tracking and prediction of COVID-19
11.5. AI in COVID-19 Diagnosis
11.5.1. AI in the Treatment of COVID-19
11.5.2. AI in Maintenance of the Affected Areas and Dashboard
11.5.3. AI in Social Safety/ Surveillance/ Prevention of COVID-19
11.6. Conclusion
Chapter 12: Internet of Things powered Artificial Intelligence using Microsoft Azure Platform

12.1. Introduction
12.2. Computing requirements
12.3. Real time data analysis
12.4. AIoT: Integration of IoT&AI on Microsoft Azure platform
12.5. Steps to write a program in Rpi computer
12.5.1. Working with Microsoft Azure
12.6. Application areas of AIoT
12.7. Conclusion
Chapter 13: Load Balancing in Wireless Heterogeneous Network With Artificial Intelligence

13.1.  INTRODUCTION
13.2 Different types of Artificial Intelligence
13.2.1 Reactive Machines AI
13.2.2. Limited Memory AI
13.2.3. Theory of Mind AI
13.2.4. Self-knowledge AI
13.2.5 Artificial Narrow Intelligence (ANI)
13.2.6 Artificial General Intelligence (AGI)
13.2.7. Artificial Strong Intelligence (ASI)
13.3. Advantages of Artificial Intelligence
13.4 Disadvantages of Artificial Intelligence  
13.5   Artificial Intelligence: Methods and Applications
13.6. AI In Wireless Heterogeneous Networks (WHN)
13.7. Importance of Load Balancing In AI
13.7.1  Machine learning in a wireless heterogeneous network 
13.7.2  Neural network in a wireless heterogeneous network 
13.7.3  Fuzzy logic for a wireless network
13.7.4  Genetic algorithm
13.7.5  Particle Swarm Optimization (PSO)
13.7.6.  Artificial Bee Colony (ABC)
13.7.7  Markov Models And Bayesian-Based Games
13.8. Conclusion
Chapter 14:  Applications of Artificial Intelligence Techniques in the Power System

14.1 Introduction
14.1.1. Need of artificial intelligence in power system
14.2. Types and classification of Artificial Intelligent Techniques
14.2.1. Artificial Neural Network 
14.2.1.1. Classification of Artificial Neural Network
14.2.1.2  Advantages and Disadvantages of Artificial Neural Network

14.2.1.3 Applications of ANN in power system

14.2.2. Fuzzy Logic
14.2.2.1 Advantages and Disadvantages of Fuzzy Logic
14.2.2.2 Applications of Fuzzy logic in power system
14.2.3. Expert System
 
14.2.3.1 Advantages and Disadvantages of expert system
14.2.3.2. Applications of Expert System in power system
14.2.4. Genetic Algorithm (GA)
14.2.4.1 Advantages and disadvantages of Genetic Algorithm
14.2.4.2. Applications of Genetic Algorithm in power system
14.3. Comparison of AI techniques in power system
14.4. Applications of Artificial Intelligence in power system
14.5. Conclusion


Chapter 15: Impact of Artificial Intelligence In Aviation and Space Sector
15.1. Introduction
15.2. Artificial Intelligence in Airline Passenger Identification
15.2.1 Facial Recognition
15.3. Artificial intelligence in Airline Baggage Identification
15.4. Artificial Intelligence in Airline Customer Satisfaction
15.5. Artificial Intelligence in Aircraft Safety and Maintenance
15.6. Artificial Intelligence influence in Remote Sensing
15.6.1. Classification
15.6.2. Change Detection
15.6.3 Feature Extraction
15.6.4. In-Orbit Image Processing
15.7. Artificial Intelligence in Spacecraft Dynamics
15.8. Future Prospects
15.9. Conclusion
Chapter 16: Artificial Intelligence for Weather Forecasting


16.1. Introduction
16.2. Related Work:
16.2.1 Multiple Linear Regression Model (MLR)
16.2.2 Artificial Neural Network (ANN)
16.2.3 Deep Learning Models
16.2.3.1 Recurrent Neural Networks
16.2.3.2 LSTM network Long Short-Term Memory (LSTM)
16.3. Summary

Chapter 17: Molecular Mining: Applications in Pharmaceutical Sciences
17.1 Introduction
17.2 Why molecular mining?
17.3 Tools involved in data mining
17.4 Data Science
17.5 Machine Learning
17.6 ML Techniques
17.7 Machine Learning Approaches for Mining of Molecules
17.8 Procedure
17.9 Conclusion

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Editor(s)

Biography

Cherry Bhargava is currently working as an associate professor and head, school of electrical and electronics engineering at Lovely Professional University, Punjab, India. She has more than fourteen years of teaching and research experience. She has published research papers in journals of national and international repute. She has seven books related to reliability, artificial intelligence and digital electronics to her credit. She has registered three copyrights and filed two patents. She is a recipient of various national and international awards for being outstanding faculty in engineering and excellent researcher. She is an active reviewer and editorial member of various prominent SCI and Scopus indexed journals. She is a lifetime member of IET, IAENG, NSPE, IAOP, WASET, and reliability research group. Her area of expertise includes the reliability of electronic systems, digital electronics, VLSI design, artificial intelligence, and related technologies. Pradeep Kumar Sharma is presently working as an associate professor, school of pharmaceutical sciences, Lovely Professional University, Punjab, India. He has more than thirteen years of teaching and research experience. He has published technical research papers in SCI, Scopus indexed quality journals and national/international conferences. His areas of research include computer-aided drug design using artificial intelligence, pharmaceutical validation using artificial intelligence, brain tumor detection using artificial intelligence and intelligent medicine development for the treatment of renal dysfunction. He has taught courses including organic synthesis, medicinal chemistry and pharmaceutical analysis at undergraduate and graduate levels.