Artificial Intelligence. Fundamentals and Applications

Cover
Half Title
Title Page
Copyright Page
Table of Contents
Preface
Editors
Contributors
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 Nanosystem Design
		1.2.3 Nanoscale Simulation
		1.2.4 Nanocomputing
	1.3 Food Science
	1.4 Nanobots in Medicine
	1.5 Summary
	References
Chapter 2 Artificial Intelligence in E-Commerce: A Business Process Analysis
	2.1 Introduction
	2.2 Artificial Intelligence
		2.2.1 AI Mimicking Human Intelligence
		2.2.2 AI Exceeding Human Intelligence
	2.3 E-Commerce Business Processes and Artificial Intelligence
		2.3.1 Marketing
			2.3.1.1 Market Research
			2.3.1.2 Market Stimulation
		2.3.2 Transaction Processing
			2.3.2.1 Terms Negotiation
			2.3.2.2 Order Selection and Priority
			2.3.2.3 Order Receipt
			2.3.2.4 Order Billing/Payment Management
		2.3.3 Service and Support
			2.3.3.1 Order Scheduling/Fulfillment Delivery
			2.3.3.2 Customer Service and Support
	2.4 Concluding Remarks
	References
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 Four BIG 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
	References
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 Optimization
		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 Data set
		4.4.2 A Demonstration of Anomaly Detection in Ethereum Prices Using R
			4.4.2.1 Ethereum
			4.4.2.2 Tidy verse
			4.4.2.3 Anomaly Detection
	4.5 Conclusion
	References
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
		5.9.1 Human Resource
		5.9.2 Drones and Self-Driving Cars
		5.9.3 Education
		5.9.4 Cybersecurity
		5.9.5 Defense Forces
		5.9.6 Home
		5.9.7 Health Care
	5.10 Challenges in implementing Automation
		5.10.1 Business Case Issues
		5.10.2 Analysis of Process
		5.10.3 Post-Implementation Adoption
		5.10.4 Choosing Right Vendor
	5.11 Myths of Automated Bots
		5.11.1 Robots are Humanoid
		5.11.2 Automation Will Replace the Human Workforce
		5.11.3 Accuracy
		5.11.4 Expensive
		5.11.5 Internal Environment of Organization
		5.11.6 Robots Can Be Left Unattended
	5.12 Platform Used for Implementation
		5.12.1 Python
		5.12.2 Tensor Flow
		5.12.3 R
		5.12.4 Scikit-Learn
		5.12.5 Automation Anywhere
		5.12.6 UiPath
	5.13 Conclusion
	References
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
	References
Chapter 7 Artificial Intelligence and Personalized Medicines: A Joint Narrative on Advancement in Medical Healthcare
	7.1 Introduction
	7.2 Need for Personalized Medicines
		7.2.1 Contributors to Personalized Medicines
	7.3 Application of AI in Healthcare for Development of Precision Medicines
	7.4 In Intensive Care Unit (ICU)
		7.4.1 In Intensive Care Unit (ICU)—To Predict the Fluid Requirement
		7.4.2 To Solve Issues of Personalized Medicines
		7.4.3 Revolutionizing Cloud of AI and Healthcare
	7.5 Conclusion
	References
Chapter 8 Nanotechnology and Artificial Intelligence for Precision Medicine in Oncology
	8.1 Introduction
		8.1.1 Fundamentals of Nanotechnology
	8.2 Role of Nanotechnology in Medicine and Healthcare
		8.2.1 Nanodrug 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
	References
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 Modeling the Data of Analysis
	9.7 Modeling 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
	References
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 the Diagnosis of TB—Comparative Analysis
		10.2.4 Limitations of Retrieved Literature
	10.3 Conclusion
	References
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
		11.4.1 Machine Learning
		11.4.2 BlueDot Technology
		11.4.3 Spatial Analysis
		11.4.4 Enter Telco Analytics
		11.4.5 Social Media
	11.5 AI in COVID-19 Diagnosis
		11.5.1 Real-Time Reverse Transcriptase Polymerase Chain Reaction (rRT-PCR
		11.5.2 Antibody Detection Test
		11.5.3 Isothermal Nucleic Acid Amplification
		11.5.4 CT Imaging Analysis
		11.5.5 Detection Using the Sensors of Smartphones
	11.6 AI in the Treatment of COVID-19
	11.7 AI in Maintenance of the Affected Areas and Dashboard
		11.7.1 Johns Hopkins University Centre for Systems Science and Engineering Dashboard (JHU CSSE)
		11.7.2 The World Health Organization (WHO) Dashboard
	11.8 AI in Social Safety/Surveillance/Prevention of COVID-19
	11.9 Conclusion
	References
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
	References
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
		13.6.1 Machine Learning in a Wireless Heterogeneous Network
		13.6.2 Neural Network in a Wireless Heterogeneous Network
		13.6.3 Fuzzy Logic for a Wireless Network
		13.6.4 Genetic Algorithm
		13.6.5 Particle Swarm Optimization (PSO)
		13.6.6 Artificial Bee Colony (ABC)
		13.6.7 Markov Models and Bayesian-Based Games
	13.8 Conclusion
	References
Chapter 14 Applications of Artificial Intelligence Techniques in the Power Systems
	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
	References
Chapter 15 Impact of Artificial Intelligence in the 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
	References
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
	References
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
	References
Index