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Handbook of Intelligent Automation Systems Using Computer Vision and Artificial Intelligence

Edited by Rupali Gill, Susheela Hooda, Durgesh Srivastava and Shilpi Harnal
Copyright: 2025   |   Expected Pub Date:2025/06/30
ISBN: 9781394302673  |  Hardcover  |  
538 pages

One Line Description
The book is essential for anyone seeking to understand and leverage the transformative power of intelligent automation technologies, providing crucial insights into current trends, challenges, and effective solutions that can significantly enhance operational efficiency and decision-making within organizations.

Audience
The book is designed for AI and data scientists, software developers and engineers in industry and academia, as well as business leaders and entrepreneurs who are interested in the applications of intelligent automation systems.

Description
Intelligent automation systems, also called cognitive automation, use automation technologies such as artificial intelligence, business process management, and robotic process automation, to streamline and scale decisionmaking across organizations. Intelligent automation simplifies processes, frees up resources, improves operational efficiencies, and has a variety of applications. Intelligent automation systems aim to reduce costs by augmenting the workforce and improving productivity and accuracy through consistent processes and approaches, which enhance quality, improve customer experience, and address compliance and regulations with confidence. Handbook of Intelligent Automation Using Computer Vision and Artificial Intelligence explores the significant
role, current trends, challenges, and potential solutions to existing challenges in the field of intelligent automation systems, making it an invaluable guide for researchers, industry professionals, and students looking to apply these innovative technologies.
Readers will find the volume:
• Offers comprehensive coverage on intelligent automation systems using computer vision and AI, covering everything from foundational concepts to real-world applications and ethical considerations;
• Provides actionable knowledge with case studies and best practices for intelligent automation systems, computer vision, and AI;
• Explores the integration of various techniques, including facial recognition, natural language processing, neuroscience and neuromarketing.

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Author / Editor Details
Rupali Gill, PhD is an associate professor and Dean at the Chitkara University Institute of Engineering and Technology. She has published over 40 technical research papers in leading journals, as well as a patent granted. Her interests include image processing, cloud computing, artificial intelligence, and machine learning.

Susheela Hooda, PhD is an associate professor and Head of Academic Delivery at Chitkara University Institute of Engineering and Technology with over 15 years of teaching and research experience. She has published over thirty technical research papers in leading international journals and conferences and more than ten international patents. Her research interests include software engineering, aspect-oriented software development, software testing, cloud computing, artificial intelligence, and machine learning.

Durgesh Srivastava, PhD is an associate professor and Head of Academic Operations at Chitkara University Institute of Engineering and Technology with over 15 years of research and academic experience. He has published over 30 papers in reputed national and international journals and conferences, as well as several patents and copyrights in the field of computer software. His research interests include machine learning, soft computing, pattern recognition, and software engineering, modeling, and design.

Shilpi Harnal, PhD is an assistant professor at Chitkara University, Punjab. She specializes in cloud computing with over 13 years of teaching experience. She has published over 30 research papers in various national and international peer-reviewed journals, books, and conferences. Her research interests include fog computing, underwater wireless sensor networks (UWSN), and artificial intelligence.

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Table of Contents
Preface
1. Toward a Smarter Future: The Role of AI in Transforming Automation Systems
Manish Kumar Singla, Rupali Gill, Ramesh Kumar, Jyoti Gupta and Gaurav Sharma
1.1 Introduction
1.2 The Power of AI in IAS
1.3 Transforming Automation: A Multifaceted Impact
1.4 Benefits and Impact of IAS
1.5 The Spectrum of Applications: From Manufacturing to Beyond
1.5.1 Predictive Maintenance
1.5.2 Finance
1.5.3 Retail
1.5.4 Healthcare
1.5.5 Logistics and Supply Chain
1.5.6 Customer Service
1.6 Challenges and Considerations
1.6.1 Technical Hurdles
1.6.2 Cybersecurity Threats
1.6.3 Skill Gap
1.6.4 Ethical Concerns
1.6.5 Transparency and Explainability
1.6.6 Social and Regulatory Considerations
1.6.7 Regulation and Governance
1.6.8 Job Displacement
1.6.9 Workforce Impact
1.6.10 Income Inequality
1.7 Strategies for Mitigating Negative Impacts
1.8 Ethical Considerations of IAS
1.8.1 Bias and Fairness
1.8.2 Transparency and Explainability
1.8.3 Privacy Concerns
1.8.4 Mitigating Bias and Ensuring Fairness
1.8.5 Building Trust Thru Transparency
1.8.6 Safeguarding Privacy in the Age of Big Data
1.8.7 Addressing Job Displacement and Reskilling Needs
1.8.8 Shaping the Future with Ethical Considerations
1.9 Discussion
1.10 Conclusion
References
2. Industry 5.0: Mapping the Lens from Know How to Realization
Upinder Kumar, Mahender Singh Kaswan and Rakesh Kumar
2.1 Introduction
2.2 Basic Principles of Industry 5.0
2.2.1 Human-Machine Collaboration
2.2.2 Customization and Flexibility
2.2.3 Sustainability and Responsible Manufacturing
2.2.4 Evolving Technologies
2.2.5 Data-Driven Insights
2.3 Technologies and Their Roles in Industry 5.0
2.3.1 Artificial Intelligence
2.3.2 Internet of Things (IoT)
2.3.3 Blockchain
2.3.4 Virtual Reality and Augmented Reality
2.3.5 Robotics and Cobots
2.3.6 Cloud Computing
2.3.7 Edge Computing
2.3.8 Additive Manufacturing (3D Printing)
2.3.9 Cybersecurity
2.4 Operator 5.0
2.5 Education 5.0
2.5.1 Historical Background of Education 5.0
2.5.1.1 Education 1.0
2.5.1.2 Education 2.0
2.5.1.3 Education 3.0
2.5.1.4 Education 4.0
2.5.1.5 Education 5.0
2.6 Industry 5.0 and Sustainability
2.7 Conclusion
References
3. Intelligent Automation System Integration in Mobile and Industrial Robotics
for Enhanced Performance and Efficiency
Abdullah Bin Queyam, Ramesh Kumar, Anupma Gupta and Vipin Kumar
3.1 Introduction
3.2 Industrial Robotics
3.3 Anthropomorphic Robot: Bridging the Gap Between Humans and Machines
3.3.1 Forward Kinematics
3.3.2 Inverse Kinematics
3.3.3 Forward Dynamics
3.3.4 Inverse Dynamics
3.3.5 Denavit-Hartenberg Method
3.4 Case Study
3.4.1 Kinematics of a 3-DOF Anthropomorphic Manipulator
3.4.2 Mobile Robotics
3.4.3 Obstacle Avoidance
3.4.4 Trajectory Tracking
3.4.5 Posture Regulation
3.5 Conclusion
References
4. Automation of Data Flow Management Based on Artificial Intelligence in Systems with an Internal Distribution Mechanism
A.E. Rashidov, A.R. Akhatov, F.M. Nazarov and I.N. Turakulov
4.1 Introduction
4.1.1 The Importance of the Research and the Setting of the Problem
4.1.2 Distributed Computing Systems
4.1.3 Internal Distribution Mechanism
4.2 Methodology
4.2.1 Method
4.2.2 Algorithm for Managing Structured Data Streams Based on the Internal Distribution Mechanism
4.2.3 Optimization of the Number of Internal Distributions Based on Artificial Intelligence
4.2.4 Algorithms for Distribution of Data Streams in the Internal Distribution Mechanism
4.3 Results
4.3.1 Algorithms for Distribution of Data Streams in the Internal Distribution Mechanism
4.3.2 Results of Automating the Data Distribution Process Based on Artificial Intelligence Algorithms in the Internal Distribution Mechanism
4.4 Discussion
4.5 Conclusion
References
5. Robotic Process Automation (RPA) and Virtual Reality Implementation in Engineering Education
Jabar H. Yousif, Ahmad Kayed and Maryam G. Aljabri
5.1 Introduction
5.2 Ethical Factors
5.3 Research Design
5.4 Research Questions
5.5 Experimental Design
5.6 Results and Discussion
5.7 Conclusion
References
6. Ethical Issues of Intelligent Automation Systems
V. Punitha, R. Sivanesan, P. Sharmila and G. Nithyakala
6.1 Introduction
6.1.1 Artificial Intelligence
6.1.2 The Rise of Artificial Intelligence
6.1.3 The Internet of Things (IoT) and Smart Systems
6.1.4 The Future of Intelligent Design
6.1.5 Robotic Process Automation (RPA)
6.1.5.1 Benefits of Robotic Process Automation (RPA)
6.1.5.2 Challenges of Robotic Process Automation (RPA)
6.1.6 Business Process Management
6.1.6.1 Benefits of Business Process Management (BPM)
6.1.6.2 Challenges of Business Process Management (BPM)
6.2 Intelligent Automation Systems
6.2.1 Benefits of Intelligent Automation Systems
6.2.2 Healthcare Systems
6.2.3 Business Process Management
6.2.4 Prompt Engineering
6.3 The Ethical Implications of Intelligent Automation Systems
6.3.1 Data Security and Privacy
6.3.1.1 Security Problems
6.3.2 Lack of IT Readiness & Skill Gap
6.3.3 Decision-Making by IAS
6.3.4 Data Sharing Policies and Regulations
6.3.5 Bias and Fairness in IAS
6.3.5.1 Case Studies for Bias in AI Systems
6.4 Case Studies of Ethical Issues in IAS Decision-Making
6.4.1 Ethical Issues in Healthcare
6.4.2 Ethical Considerations in NLP-Based Tools
6.4.3 Ethical Considerations in Prompt Engineering
6.4.4 Job Displacement
6.5 Environmental Impacts of IAS
6.6 Existing Ethical Frameworks of IAS
6.7 Conclusion
References
7. IAS and Facial Recognition System
Ritu, Yogesh Shahare, Dinesh Singh Dhakar and Ritu Jain
7.1 Introduction
7.2 Literature Review
7.3 Understanding Intelligent Automation Systems (IASs)
7.4 Advancements in Facial Recognition Technology
7.5 Integration with Intelligent Automation Systems
7.6 Challenges and Limitations
7.7 Future Prospects and Emerging Trends
7.8 Security and Surveillance Applications
7.9 Ethical and Societal Implications
7.10 Conclusion
References
8. An Image Synthesis Using Progressive Generative Adversarial Networks (PGANs)
Ajay Pal Singh, Parvez Rahi and Vinod Kumar
8.1 Introduction
8.2 How Does GAN Work?
8.3 The Birth of GANs: Recognizing the Need for Adversarial Frameworks
8.4 Proposed Solutions
8.4.1 Objectives and Goals
8.4.2 Evaluation and Selection of Specifications/Features
8.5 Deep Learning Structures
8.6 Analysis and Feature Finalization Subject to Constraints
8.7 Design Flow
8.7.1 Introduction
8.7.2 Data Preprocessing
8.7.3 Data Visualization
8.7.4 Discriminator Architecture
8.7.5 Generator Architecture
8.7.6 Model Compilation
8.7.7 Training Loop
8.7.8 Training Visualization
8.7.9 Image Generation
8.7.10 Conditional Image Generation
8.8 The Comprehensive Design Flows
8.8.1 Design Constraints
8.8.2 Hardware Constraints
8.8.3 Software Dependencies
8.8.4 Data Directory Structure
8.8.5 Image Size and Batch Size
8.8.6 Training Parameters
8.8.7 Model Architecture
8.8.8 Loss Function and Optimizers
8.9 Principal Results
8.9.1 Discriminator and Generator Performance
8.9.2 Diversity and Quality
8.9.3 Evaluation of the Testing Set
8.9.4 Fine-Tuning Opportunities
8.9.5 Future Work
8.9.6 Extended Training
8.9.7 Application-Specific Considerations
8.10 Training Stability
8.10.1 Architectural Enhancements
8.10.2 Hyperparameter Tuning
8.10.3 Loss Function Exploration
8.10.4 Transfer Learning
8.10.5 Conditional GANs
8.10.6 Progressive Growing Techniques
8.10.7 Dynamic Data Augmentation
8.11 GAN Applications
8.12 Conclusion
References
9. Future Direction in Sign Language Recognition: A Review
Nidhi Goel, Lekha Rani and Pradeepta Kumar Sarangi
9.1 Introduction
9.2 Sign Languages Around the World
9.2.1 American Sign Language (ASL)
9.2.2 British Sign Language (BSL)
9.2.3 Chinese Sign Language (CSL)
9.2.4 Australian Sign Language (Auslan)
9.2.5 Indian Sign Language (ISL)
9.3 Sign Language Linguistics
9.4 Motivation
9.5 Objective
9.6 Related Work
9.7 Approaches
9.7.1 Glove-Based Technique
9.7.2 Vision-Based Technique
9.8 Proposed Methodology
9.8.1 Training Dataset/Data Acquisition
9.8.2 Preprocessing
9.8.3 Feature Extraction
9.8.4 Classification
9.9 Conclusion and Future Scope
References
10. Understanding Computer Vision for Intelligent Autonomous Systems
Summiya Parveen and Aruna Tomar
10.1 Introduction
10.2 Fundamentals of Computer Vision
10.2.1 Image Acquisition
10.2.2 Pre-Processing
10.2.3 Feature Extraction
10.2.4 Classification
10.3 Applications of Computer Vision in IAS
10.3.1 Quality Control in Manufacturing
10.3.2 Object Recognition in Logistics
10.3.3 Facial Recognition in Security Systems
10.3.4 Autonomous Vehicles
10.3.5 Medical Imaging
10.4 Challenges and Emerging Techniques
10.4.1 Robustness to Variability
10.4.2 Real-Time Processing
10.4.3 Privacy and Ethical Concerns
10.4.4 Deep Learning and Convolutional Neural Networks
10.4.5 Multi-Modal Integration
10.5 Future Directions and Conclusion
References
11. Computer Vision and Artificial Intelligence for Intelligence Automation Systems (IAS)
Dharmendra Dangi, Vaibhav Suman, Amit Bhagat and Dheeraj Kumar Dixit
11.1 Introduction
11.2 Artificial Intelligence
11.2.1 Some Important Techniques
11.2.2 Types of Artificial Intelligence
11.2.3 Role and Benefits of AI
11.3 Computer Vision
11.3.1 Application of Computer Vision
11.3.2 Working of Computer Vision
11.3.3 Importance of Computer Vision for IAS
11.4 Conclusion
11.5 Future Scope
References
12. Neural Network Approaches for Intelligent Decision-Making in Automation
S.Z. Rufai, Inam Ul Haq, H.A. Shah and Mir Abrar Fayaz
12.1 Introduction
12.2 Role of Neural Networks in Modern Automation
12.3 Fundamental Principles of Neural Networks
12.3.1 Basics of Neural Network Architecture
12.3.2 Learning Complex Relationships Through Iterative Processes
12.3.3 Overview of Neural Network Training Mechanisms
12.4 Neural Network Architectures in Automation Systems
12.5 Comparative Analysis of Different Architectures
12.5.1 Centralized Automation Systems
12.5.2 Distributed Automation Systems
12.5.3 Hierarchical Automation Systems
12.5.4 Networked Automation Systems
12.5.5 Cloud-Based Automation Systems
12.6 Neural Network Applications in Automation
12.7 Training Strategies for Neural Networks
12.7.1 Supervised Learning Methodologies
12.7.2 Reinforcement Learning Paradigms
12.7.3 Model-Based Methods
12.7.4 Transfer Learning Techniques
12.8 Practical Considerations for Deployment
12.9 Conclusion
References
13. A Novel Approach for Object Detection Technique Using Deep Learning
Kumud Sachdeva and Rajan Sachdeva
13.1 Introduction
13.1.1 Object Detection Techniques
13.1.2 Deep Learning-Based Object Detection
13.2 Literature Survey
13.3 Deep Learning Methods
13.3.1 CAFFE Object Detection System
13.3.2 Keras Object Detection Model
13.4 Deep Learning Models
13.4.1 Visual Geometry Group-16 (VGG-16)
13.4.2 Visual Geometry Group-19 (VGG-19)
13.4.3 Inception
13.4.4 Number of Epochs
13.4.5 Activation Functions
13.5 Experimental Results
13.6 Conclusion and Future Scope
Bibliography
14. Role of AI in Mental Health Care
Kala K.U., Prabhakaran Mathialagan, Solomon Jebaraj N.R. and Sambath Kumar S.
14.1 Introduction
14.2 Significance of Addressing Mental Health Challenges
14.3 Prevalent Mental Health Disorders
14.4 Impact of Mental Health on Physical Well-Being
14.5 The Societal Implications of Mental Health Disorders
14.6 Significance of Early Recognition of Mental Health Matters
14.7 Strategies for the Early Recognition of Mental Health Challenges
14.8 Role of Technology in Mental Health Care
14.9 AI in Mental Health Care
14.10 AI in Screening and Assessment of Mental Health Issues
14.11 AI in Personalized Treatment Planning
14.12 AI in Digital Therapeutic Interventions
14.13 AI Chatbot’s and Virtual Assistants in Mental Health Care
14.14 Data Analysis and Predictive Modeling in Mental Health Care
14.15 AI in Mental Health Monitoring
14.16 Conclusion and Future Work
References
15. Application Areas of Computer Vision and AI in Intelligent Automation Systems
Vinod Kumar, Chander Prabha, Ajay Pal Singh and Raj Kumar
15.1 Introduction
15.2 Advanced Techniques in CV and AI for IAS
15.2.1 The Power of Deep Learning (DL)
15.2.2 Exploring IA Methods and Solutions for Future Evolution
15.3 Why We Use AI in Research and Services Today
15.4 The Association Across AI, ML, and DL
15.5 Exploring Deep Learning and Neural Systems
15.5.1 Semi-Supervised Learning
15.5.2 Reinforcement Learning (RL)
15.6 Delving into Deep Neural Networks’ Learning Approaches
15.6.1 Unveiling Deep Neural Networks Learning and Analytical Methods
15.7 Rule-Based Modeling: A Cornerstone of AI Development
15.8 The Role of Fuzzy Logic and Distributed Logic in AI
15.9 AI and CV Technologies for Advancing Manufacturing Industries
15.10 AI and CV Revolutionizing Healthcare Innovations
15.11 Innovative Solutions for Agriculture and Environment
15.12 Innovative Solutions for Retail and Consumer Goods
15.13 Revolutionizing Transportation and Logistics with AI and CV
15.14 Advancing AI through Case-Based Reasoning (CBR)
15.15 Text Mining and NLP in IAS
15.16 Exploring Artificial Intelligence Applications and Challenges
15.17 Exploring Artificial Intelligence in Computer Vision Tasks
15.18 Conclusion
References
16. A Real-Time Speech-Text Conversion System Using Deep Learning Technique
K. Saranya and P. Jeevananthan
16.1 Introduction
16.2 Related Works
16.3 Problem Definition
16.4 System Specification
16.5 Methodology and Flowchart
16.6 Audio Conversion
16.7 Results and Discussion
16.8 Conclusion
References
17. Transforming the Evaluation: The Crucial Role of Natural Language Processing in Intelligent Automation System
Pratibha, Bhavna Sharma, Sana Bharti, Susheela Hooda and Shilpi Harnal
17.1 Introduction
17.1.1 The Background: An Overview of NLP and Intelligent Automation History
17.1.2 Early Endeavors in Intelligent Automation Systems
17.1.3 Understanding the Essence: NLP’s Contribution to Intelligent Automation
17.1.4 Reviewed Studies
17.2 Natural Language Processing (NLP) as the Foundation of Intelligent Automation
17.2.1 Improving Efficiency: How NLP Can Help Streamline Automation Processes
17.2.2 The Role of Natural Language Processing (NLP) in Enabling Seamless Communication Between Humans and Machines
17.3 Exploring Current Applications
17.3.1 Revolution in Customer Service: Chatbots and Virtual Assistants
17.3.2 Text Analytics and Sentiment Analysis
17.3.3 Information Management by Extracting and Summarizing Documents
17.3.4 Role of Adaptive Learning in the Training System
17.4 Future Directions for NLP in an Automated Environment
17.4.1 NLP with Intelligent Automation
17.4.2 Integrated Multimodal
17.4.3 Ethical Imperatives: Ensuring Responsible AI in NLP-Driven Automation
17.4.4 Zero-Shot Learning: Leveraging Little Data to Adapt to New Tasks
17.4.5 Adaptation Based on Domain Area
17.4.6 Explainable AI: Demystifying NLP’s Decision-Making Processes
17.5 Challenges and Opportunities
17.5.1 Addressing Privacy and Security in NLP-Driven Automation
17.5.2 Filling the Gap: Promoting Inclusivity and Accessibility
17.6 Developing Talent: Cultivating Natural Language Processing Masters of Tomorrow
17.7 Conclusion and Future Scope
References
18. IAS and Its Impact in Neuroscience
G. Vijaya and K. Ramesh
18.1 Introduction
18.1.1 Intelligent Automation (IA)
18.1.2 Intelligent Automation Systems (IAS)
18.2 Neuroscience
18.3 Integration of Neuroscience with Intelligent Automation Systems (IAS)
18.4 Challenges in Integrating IAS in Neuroscience Applications
18.5 Application Areas of Neuroscience in Intelligent Automation Systems
18.5.1 Major Difficulties in Applying AI to Precision Medicine
18.6 Conclusion
References
19. Intelligent Automation Systems (IAS) and Its Application in Neuroscience
Bikram Kar and Amit Kumar
19.1 Introduction
19.1.1 Defining Neuroscience and Intelligent Automation Systems
19.1.2 Importance of Bridging Neuroscience and Intelligent Automation
19.1.3 Overview of the Chapter Structure
19.2 Understanding Neurosciences
19.2.1 Fundamentals of Neuroscience
19.2.2 Cognitive Neuroscience
19.2.3 Neural Computation
19.3 How Neuroscience Can Help in Understanding Intelligent Automation Systems (IAS)
19.3.1 Neuroscientific Insights into Cognitive Processes
19.3.2 Brain-Inspired Algorithms and Models
19.4 Application Areas of Neuroscience in IAS
19.4.1 Healthcare and Medicine
19.4.2 Finance and Economics
19.4.3 Manufacturing and Industry
19.5 Connecting IAS and Neuroscience
19.5.1 Neurofeedback and Brain-Computer Interfaces (BCIs)
19.5.2 Cognitive Modeling and Human-Machine Interaction
19.5.3 Neuroergonomics
19.6 Challenges and Future Directions
19.6.1 Ethical and Social Implications
19.6.2 Technical Challenges
19.6.3 Future Trends
19.7 Conclusion
References
20. A Neuromarketing Framework for Data-Driven Intelligent Automation in Marketing
Jyoti Kesarwani, Himanshu Rai and Rahul Kesarwani
20.1 Introduction
20.2 Literature Review
20.2.1 Consumer Behavior and Neuroscience
20.2.2 Neuromarketing Theories and Techniques
20.2.2.1 Functional Magnetic Resonance Imaging (fMRI)
20.2.2.2 Electroencephalography (EEG)
20.2.2.3 Magnetoencephalography (MEG)
20.2.2.4 Steady-State Topography (SST)
20.2.2.5 Positron Emission Tomography (PET)
20.2.2.6 Eye-Tracking
20.2.2.7 Facial Coding
20.2.2.8 Implicit Response Testing (IRT)
20.2.2.9 Galvanic Skin Response (GSR)
20.2.2.10 Facial Electromyography
20.3 Proposed Neuromarketing Framework
20.3.1 Data Collection Module
20.3.2 Data Processing
20.3.3 Layer of Information Gathering
20.3.4 Decision-Making System and Automation
20.4 Benefits and Applications of Neuromarketing
20.4.1 Personalization Through Neurographic Segmentation
20.4.2 Understanding Neurographic Segmentation
20.4.3 Benefits of Neurographic Segmentation
20.4.3.1 Deeper Understanding of Consumer Behavior
20.4.3.2 Enhanced Personalization Efforts
20.4.3.3 Improved Campaign Performance
20.4.3.4 Competitive Advantage
20.5 Real-Time Campaign Optimization Using Biometric Feedback
20.5.1 Understanding Biometric Feedback
20.5.2 Benefits of Real-Time Campaign Optimization
20.5.2.1 Immediate Understanding
20.5.2.2 Enhanced Targeting
20.5.2.3 Flexible Modifications
20.6 Mitigating Bias in AI Through Neuromarketing Data
20.6.1 Understanding Bias in AI
20.6.2 The Role of Neuromarketing Data
20.6.3 Benefits of Integrating Neuromarketing Data into AI
20.6.3.1 Enhanced Understanding of Human Behavior
20.6.3.2 Mitigation of Bias
20.6.3.3 Improved Accuracy and Fairness
20.6.3.4 Improved Customer Engagement
20.7 Other Potential Applications
20.8 Conclusion
References
21. Neuroscience and Intelligent Automation System
Harpreet Kaur and Pannem Shreya
21.1 Introduction
21.1.1 Evolution and Development of the Nervous System
21.2 Intelligent Automation
21.3 Technologies and Software Associated with IA Systems
21.3.1 Robotic Process Automation
21.3.2 Artificial Intelligence
21.3.3 Machine Learning
21.3.4 Reinforcement Learning
21.3.5 Neuron Networks
21.4 History of Developments in AI and Neuroscience
21.5 Essential Technologies for Developing IAS
21.6 Discoveries Related to Neuroscience
21.7 Applications of Artificial Intelligence in Neuroscience
21.7.1 Creation of Chip-Based Intelligence Similar to the Brain
21.7.2 Projects Based on the Development of Brain-Like Intelligence
21.8 Artificial Neural Network Versus Biological Neural Network
21.9 Developments of Intelligent Automation Systems Models
21.9.1 LIDA (Learning Intelligent Distribution Agent) Architecture
21.9.2 Human-Machine Interaction (HMI)
21.9.3 Cognitive Automation
21.9.4 Emotion Recognition
21.9.5 Continuous Learning
21.10 AI for Neuroscience Development
21.10.1 Brain-Computer Interfaces
21.10.2 Neuroimaging Analysis
21.11 Neuromarketing
21.12 AI Inspired by Brain Science
21.13 Current State
21.14 Conclusion
References
22. Unveiling the Visual World Through AI-Powered Computer Vision
Sonia Kumari Shishodia, Shuchi Sharma, Eram Khan and Logesh Babu
22.1 Introduction
22.2 The Human Eye Anatomy
22.2.1 The Cornea: From Protective Barrier to Vision Refiner
22.2.2 Scleral Role in Supporting and Protecting the Eye
22.2.3 Understanding the Dynamic Nature of Pupil Regulation
22.2.4 The Lens: Components, Adaptation, and Age-Related Changes Including Cataract Formation
22.2.5 Computer Vision
22.2.6 The Significance of Recreating Vision by Mimicking the Human Eye
22.2.7 Artificial Intelligence–Powered Computer Vision
22.2.8 AI-Powered Visual Understanding
22.2.9 Key Concepts and Techniques in AI-Powered Visual Understanding
22.2.9.1 Image Classification and Object Detection
22.2.9.2 Semantic Segmentation and Instance Segmentation
22.2.9.3 Object Tracking and Pose Estimation
22.2.9.4 AI-Powered Computer Vision
22.2.9.5 Object Detection and Recognition
22.2.9.6 Visual Understanding in Natural Language Processing (NLP)
22.2.9.7 Advanced Algorithms and Deep Learning Techniques
22.3 Key Techniques
22.3.1 Advancements in Technologies
22.4 Applications of AI-Powered Computer Vision Across Industries
22.4.1 Diagnosis and Medical Imaging in Health Care
22.4.2 Enhancing Safety and Navigation Autonomous Vehicles
22.4.3 Personalized Shopping Experience in Retail
22.4.4 Immersive Gaming and Virtual Reality in Entertainment
22.5 Threat Detection and Monitoring in Surveillance and Security
22.6 Trends and Future Directions in AI-Powered Computer Vision
22.7 Conclusion
References
Index

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