Artificial Intelligence and Global Society: Impact and Practices

Cover
Half Title
Title Page
Copyright Page
Contents
Preface
Editors
Contributors
1. Artificial Intelligence: Revolution, Definitions, Ethics, and Foundation
	1.1. Revolution
	1.2. Applications
		1.2.1. Gaming
		1.2.2. Technology
		1.2.3. Computer Vision
		1.2.4. Music Industry
		1.2.5. Retail Industry
		1.2.6. Banking Industry
		1.2.7. Agricultural Industry
		1.2.8. Healthcare Industry
		1.2.9. Sports Industry
		1.2.10. Definition Types
			1.2.10.1. Thinking Like Humans
			1.2.10.2. Acting Like Humans
			1.2.10.3. Thinking Rationally
			1.2.10.4. Acting Rationally
		1.2.11. Definition Comparison
		1.2.12. Foundation Fields
			1.2.12.1. Philosophy
			1.2.12.2. Mathematics
			1.2.12.3. Statistics
			1.2.12.4. Economics
			1.2.12.5. Neuroscience
			1.2.12.6. Psychology
			1.2.12.7. Computer Engineering
			1.2.12.8. Control Theory
			1.2.12.9. Linguistics
	1.3 . Ethics of Artificial Intelligence
		1.3.1. Unemployment
		1.3.2. Distribution of Wealth
		1.3.3. Influence of AI on Human Evolution
			1.3.3.1. Argument
			1.3.3.2. Racism
			1.3.3.3. Evil AI
			1.3.3.4. Singularity
			1.3.3.5. Rights and Identity
			1.3.3.6. Sentient AI
	References
2. Impact of Digitization of Governance on Society
	2.1. Introduction
	2.2. Proposed Model for Digital Literacy Training
		2.2.1. VLE Model
			2.2.1.1. Case Study of Akoli Village
		2.2.2. Educational Institution Model
			2.2.2.1. Case Study of Narsingapur Village
		2.2.3. Challenges Faced During Training
	2.3. Impact Stories of Digitization of Governance on Society
	2.4. Growing Trends in Telangana
	References
3. The Impact of AI on World Economy
	3.1. The Evolved World Economy
	3.2. The Ongoing Evolution
	3.3. The Substitutes and Complements
	3.4. Moving Lock Stock and Barrel
	3.5. The Impact: In a Nutshell
	References
4. Human Behavior Prediction and Artificial Intelligence
	4.1. Introduction
		4.1.1. Enhanced Computing Power
		4.1.2. Huge Data
		4.1.3. Better Algorithms
		4.1.4. Broad Investment
	4.2. Why Human Behavior Prediction?
		4.2.1. Medical Diagnostics and Health
		4.2.2. Education and Training
		4.2.3. Workplace and Product Testing
		4.2.4. Advertisement and Media
		4.2.5. User Interface (UI) and User Experience (UX) Testing
		4.2.6. Gaming and Virtual reality (VR)
		4.2.7. Architecture and Simulation
		4.2.8. Politics and Leadership
	4.3. Online vs. Offline Behavior
	4.4. Challenges in Human Behavior Prediction
		4.4.1. Data Privacy
		4.4.2. Data Transparency
	4.5. How Is Personality Prediction Related to Human Behavior Prediction?
		4.5.1. Big 5 Personality Traits
			4.5.1.1. Openness
			4.5.1.2. Conscientiousness
			4.5.1.3. Neuroticism
			4.5.1.4. Agreeableness
			4.5.1.5. Extraversion
	4.6. Negative Side of the Coin
	4.7. Conclusion and Future Research Directions
	References
5. Emotion Recognition for Human Machine Interaction
	5.1. Introduction
	5.2. Emotion Representation Models
		5.2.1. Discrete Model
		5.2.2. Dimensional Model
		5.2.3. Presence Arousal Dominance Model
	5.3. Emotion Recognition Approaches
		5.3.1. Knowledge-Based Approaches
		5.3.2. Statistical Approach
		5.3.3. Hybrid Approach
	5.4. Related Work in Emotion Recognition
		5.4.1. Facial Expressions
		5.4.2. Speech Signals
		5.4.3. Physiological Signals
	5.5. EEG-Based Emotion Recognition
		5.5.1. EEG-Based Emotion Recognition Using Linear Hjorth Features
		5.5.2. Nonlinear Features-Based Emotion Recognition (NFER) Using EEG
		5.5.3. Range and Relationship Estimation of EEG Frequency Bands for Emotion Recognition
	5.6. Conclusion
	References
6. Text, Visual and Multimedia Sentiment-Analysis, and Sentiment-Prediction
	6.1. Introduction
	6.2. Sentiments-Analysis Categories, Inputs, and Outputs
	6.3. Sentiment-Analysis Techniques
		6.3.1. Text-Sentiments Analysis (TSA) Using Sentiment-Lexicon
		6.3.2. Sentiment CNN Technique for TSA and Sentiment-Prediction
	6.4. Research Studies on Sentiment-Analysis
		6.4.1. Colloborative Filtering for Sentiment-Prediction
		6.4.2. CNN/Fine-Tuned CNNs for Visual Sentiment-Analysis (VSA) and Multimedia Sentiment-Analysis (MMSA)
	6.5. Challenges in Sentiment-Analysis and Prediction
	6.6. Application Areas of Sentiment-Analysis
		6.6.1. Social Media Monitoring
		6.6.2. Brand Monitoring
		6.6.3. Consumer Feedback
		6.6.4. Real-Time Sentiment-Analysis Using Tweets
		6.6.5. Real-Time Sentiment-Analysis and Stock-Market Predictions
		6.6.6. Sentiment-Analysis in Transport
	6.7. Conclusions
	References
7. Transfer Learning with Convolution Neural Networks Models: An Evolutional Comparison
	7.1. Artificial Intelligence
		7.1.1. Machine Learning
		7.1.2. Deep Learning
	7.2. Convolutional Neural Networks
		7.2.1. Feedforward Neural Network and Convolution Neural Network
		7.2.2. Characteristics of CNN
	7.3. Transfer Learning
		7.3.1. Basic Steps of Transfer Learning
	7.4. ImageNet Dataset and ILVRC
		7.4.1. AlexNet (2012)
		7.4.2. ZF Net (2013)
		7.4.3. VGG Net (2014)
		7.4.4. GoogLeNet (2014)
		7.4.5. Residual Network (ResNet2015)
		7.4.6. Summary of ILSVRC Winner Models
		7.4.7. Recently Used Pre-Trained Models Summary
	7.5. Results
	7.6. Conclusion
	References
8. Multicriteria Decision-Making Using Interval Valued Neutrosophic Soft Set
	8.1. Introduction
	8.2. Neutrosophic Set
		8.2.1. Neutrosophic Soft Set
	8.3. Interval Neutrosophic Soft Set
		8.3.1. A Numerical Illustration
	8.4. Research Methodology
	8.5. Empirical Study on Customer Choice towards Supermarket
		8.5.1. Results and Discussions
		8.5.2. Experimental Comparative Analysis
		8.5.3. Managerial Implications
	8.6. Conclusion
	References
9. Artificial Intelligence in Healthcare
	9.1. Introduction
	9.2. Technological Changes that Impact Human Lifestyle Changes
	9.3. Data Generation Trends
	9.4. Data Generation by AI in Healthcare
	9.5. Conclusion
	Suggested Reading
		Online Documents
10. Computer-Aided Cataract Detection Using MLP and SVM
	10.1. Introduction
	10.2. Literature Review
	10.3. Background
	10.4. Requirement Analysis
	10.5. Solutions and Recommendations
	10.6. Methodologies
	10.7. Results and Discussion
	10.8. Conclusion
	References
11. Artificial Intelligence Wave: Reshaping Indian Healthcare Sector
	11.1. Introduction
	11.2. Artificial Intelligence
	11.3. Application of AI in the Service Sector
		11.3.1. Agriculture
		11.3.2. Aviation
		11.3.3. Computer Science
		11.3.4. Education
		11.3.5. Data Analytics
		11.3.6. Heavy Industry
		11.3.7. Recruitment
		11.3.8. Media
		11.3.9. Music Industry
		11.3.10. News Publishing
		11.3.11. Defense
		11.3.12. Power Electronics
		11.3.13. Transportation
		11.3.14. Medical
	11.4. Healthcare Sector in India
	11.5. Data Flow in Healthcare System
	11.6. Transformation of Global and Indian Healthcare by Implementing Artificial Intelligence
	11.7. Challenges
	11.8. Future
	11.9. Conclusion
	References
12. Adoption of Artificial Intelligence in Industrial Sectors and Its Impact
	12.1. Introduction
	12.2. Application of Artificial Intelligence in Various Domains
		12.2.1. Public Healthcare
		12.2.2. Transportation
		12.2.3. Finance and Economy
		12.2.4. Environment
	12.3. Challenges and Advantages of AI
		12.3.1. Challenges
		12.3.2. Benefits of Artificial Intelligence
	12.4. Conclusion
	References
13. Proposed Model of Agriculture Big Data for Crop Disease Classification and Recommendation
	13.1. Introduction
	13.2. Big Data in Soil
		13.2.1. Volume
		13.2.2. Variety
		13.2.3. Velocity
		13.2.4. Variability
	13.3. Material and Methods
		13.3.1. Naïve Bayes Classifier
		13.3.2. SVM Classifiers
		13.3.3. Mean Average Precision (mAP)
	13.4. Crop Disease Classification System Using Machine Learning
		13.4.1. Implementation Process of Naïve Bayes
		13.4.2. Implementation Process of Support Vector Machine (SVM)
			13.4.2.1. Text Categorization
			13.4.2.2. Feature Selection
			13.4.2.3. Text Representation
	13.5. Experiment Analysis of Machine Learning Algorithms
		13.5.1. Performance Comparison of Machine Learning Algorithms
	13.6. Conclusion
	References
14. Machine Intelligence versus Terrorism
	14.1. Introduction
		14.1.1. The Role of Collaboration in Counterterrorism
	14.2. Machine Intelligence
		14.2.1. Types of Machine Intelligence
		14.2.2. Cognitive Computing
		14.2.3. Artificial Intelligence
		14.2.4. Machine Learning
		14.2.5. Deep Learning
		14.2.6. Intelligence
		14.2.7. Types of Intelligence
	14.3. Relationships between AI, MI, BI, ML, and Big Data
	14.4. Big Data
		14.4.1. Velocity
		14.4.2. Veracity
		14.4.3. Variety
		14.4.4. Volume
	14.5. Terrorism
		14.5.1. Terrorism in India
	14.6. Machine Intelligence versus Terrorism
		14.6.1. The Role of Machine Intelligence in Preventing Terrorism
		14.6.2. A Conceptual One Level Data Flowchart
	References
15. IoT Crypt - An Intelligent System for Securing IoT Devices Using Artificial Intelligence and Machine Learning
	15.1. Introduction
		15.1.1. Building a Foundation for the Internet of Things
	15.2. IoT Architecture
		15.2.1. IoT Components
	15.3. IoT Security
	15.4. Artificial Intelligence, Machine Learning, and Deep Learning
		15.4.1. Artificial Intelligence
		15.4.2. Machine Learning
		15.4.3. Deep Learning
	15.5. Building an Artificial Intelligent System
		15.5.1. Intelligent Systems
		15.5.2. Expert Systems
		15.5.3. Machine Learning for Security of IoT Applications
		15.5.4. Some Machine Learning Methodsx
		15.5.5. Intelligent Systems and the Internet of Things
	15.6. Proposed Work
		15.6.1. Formulate a Concept
		15.6.2. Make a Research
		15.6.3. Split the Problem
		15.6.4. Control for Consistency
		15.6.5. Map Out Key Components of Our Expert System for Refinement
		15.6.6. Re-evaluate the Expert System and Prioritize Issues for Enhancement and Refinement Quarterly
	15.7. Experimental Analysis
	15.8. Conclusion and Future Work
	References
16. Intelligent Systems: Enhanced Security Using Deep Learning Technology
	16.1. Introduction
	16.2. How Deep Learning Techniques Differ from Machine LearningTechniques
	16.3. Deep Learning and Neural Networks
	16.4. General Outline of a Face Recognition System
	16.5. Block Diagrams
	16.6. Input Images
	16.7. Read Images
	16.8. Face Detection
	16.9. Pre-Processing
		16.9.1. What Is FaceNet and Why Is It Used?
		16.9.2. Embeddings
	16.10. Image Filtering
		16.10.1. Spatial Filtering
		16.10.2. Median Filtering
	16.11. Feature Learning
		16.11.1. Feature Selection
		16.11.2. Feature Extraction
	16.12. One-Shot Learning
	16.13. Triplet Loss
	16.14. How Can This Mechanism Be Made into a Product?
	16.15. Face Recognition Based Online Attendance System
	16.16. Intent Prediction
	16.17. Face Recognition-Based Gate Access
	16.18. Face Recognition-Based Payment Services
	Conclusion
	References
17. Methods for Generating Text by Eye Blink and Eye-Gaze Pattern for Locked-In Syndrome Patients
	17.1. Introduction
	17.2. Locked-In Syndrome
	17.3. Brain-Computer Interface (BCI)
	17.4. Challenges Faced by BCI
	17.5. Face Detection
	17.6. Eye Detection
	17.7. Detection of Eye Gaze
	17.8. Convolutional Neural Network
	17.9. Haar-Cascade
	17.10 Product Functions
	17.11 Proposed Model
	17.12 Detection of Eye Blink with Facial Landmarks
	17.13 Eye Blink Detection
	17.14 Eye-Gaze Detection
	17.15 Conclusion
	17.16 Future Work
	References
18. Kinship Verification Using Convolutional Neural Network
	18.1. Introduction
	18.2. Methods of Kinship Verification from Images
	18.3. Kinship Verification from Videos
	18.4. Datasets
	18.5. Conclusion
	References
19. Machine Intelligence-Based Approach for Effective Terrorism Monitoring
	19.1. Introduction
	19.2. Proposed Solution
		19.2.1. Prediction
		19.2.2. Audio Processing
	19.3. Proposed Work
		19.3.1. Description
		19.3.2. Algorithm for Rival Check Analysis
		19.3.3. Algorithm of Rival Check Cross Correlator
	19.4. Rival Check Correlator Eliminates the Intersecting Combinations
	19.5. Application Specific Illustrations
		19.5.1. Driver Variables
	19.6. Conclusion
	References
20. Utilizing Artificial Intelligence to Design Delay and Energy-Aware Wireless Sensor Networks
	20.1. Introduction: Wireless Sensor Networks
		20.1.1. Artificial Intelligence-Based WSNs
		20.1.2. Basic Elements of Wireless Sensor Networks
			20.1.2.1. Sensors
			20.1.2.2. Observers
			20.1.2.3. Sensing Objects
		20.1.3. Features of Wireless Sensor Networks
			20.1.3.1. Application-Related
			20.1.3.2. Data-Centered
			20.1.3.3. Large-Scale Distribution
			20.1.3.4. Dynamic Topology
			20.1.3.5. High Reliability
			20.1.3.6. Self-Organization
	20.2. Applications of Wireless Sensor Networks
		20.2.1. Military Applications
		20.2.2. Environmental Monitoring
		20.2.3. Health Applications
		20.2.4. Home-Automation
		20.2.5. Industrial Applications
	20.3. QoS Parameters
		20.3.1. Network Lifetime (NL)
		20.3.2. End-to-End Delay
		20.3.3. Throughput
	20.4. Literature Review
		20.4.1. Random Deployment
		20.4.2. Deterministic Deployment
	20.5. Random and Deterministic Deployment Approaches
		20.5.1. Network Model 1: Optimization of ML-MAC Protocol
			20.5.1.1. Design Procedure
		20.5.2. Network Model 2: 2D AND 3D Wireless Sensor Networks
			20.5.2.1. Simulation Setup
		20.5.3. Network Model 3: Random and Deterministic Deployments
			20.5.3.1. Relay Node Problem
			20.5.3.2. Random Deployment
			20.5.3.3. Effective Deployment (Grid)
			20.5.3.4. Effective Deployment (Circular)
	20.6. Simulations and Result Analysis
		20.6.1. Network Model 1: Optimization of Ml-Mac Protocol
		20.6.2. Network Model 2: 2D and 3D Wireless Sensor Networks
		20.6.3. Network Model 3: Random and Deterministic Deployments
			20.6.3.1. Effect on End-to-End Delay
			20.6.3.2. Effect on Network Lifetime
	20.7. Future Road Maps
	References
Index