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Emotional Intelligence-Driven Engineering

Edited by Abhishek Kumar, Suman Lata Tripathi, Inung Wijayanto, and Sugondo Hadiyoso
Copyright: 2026   |   Expected Pub Date: 2026
ISBN: 9781394389865  |  Hardcover  |  
576 pages
Price: $225 USD
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One Line Description
In a digital age where technical expertise is no longer enough, this book provides the essential tools to integrate empathy, psychology, and neuroscience into your design process for a deeper human-technology connection.

Audience
Academics, computer science engineers, and AI industry professionals exploring robotics and automation.


Description
Engineering has traditionally prioritized functional and technical factors, enhancing equipment for reliability, effectiveness, and efficiency. However, a dramatic perspective shift is changing the way technologies are created, developed, and implemented, as well as how we interact with them. This book provides an innovative perspective on the design and development of systems, services, and products by investigating the significant confluence of engineering, technology, and emotional intelligence. In the growing digital age of today, technological expertise is no longer sufficient to satisfy the wide range of consumer demands. It is equally important to comprehend, predict, and account for human emotion. This book incorporates the interdisciplinary method known as emotion-driven engineering, which includes emotional intelligence in the design, development, and implementation of engineering solutions. Based on disciplines including human-computer interface, design thinking, psychology, and neuroscience, Engineering with Emotion provides an efficient framework for developing products that connect with consumers. With both theoretical ideas and practical applications, the book is organized to help engineers, designers, and technologists integrate emotional awareness and empathy into their work.

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Author / Editor Details
Abhishek Kumar, PhD is an Associate Professor in the School of Electronics and Electrical Engineering at Lovely Professional University with more than 11 years of experience. He has published more than 55 research papers in respected journals, five books, and 20 patents, contributed to numerous conference proceedings and e-books, and registered five copyrights. His research interests include hardware security, cryptanalysis, and machine learning.

Suman Lata Tripathi, PhD is a Professor at Lovely Professional University with more than 22 years of experience in academics and research. She has published more than 125 research papers in refereed journals, conference proceedings, and e-books, 27 books, 14 Indian patents, and four copyrights. Her area of expertise includes microelectronics device modeling and characterization, low-power VLSI circuit design, VLSI design testing, and advanced FET design for IoT. 

Inung Wijayanto, PhD is an Associate Professor at Telkom University with more than 14 years of experience in academics and research. He has published more than 64 research papers in refereed journals and conference proceedings. His current research interests include biomedical signal and image processing and analysis, EEG and ECG signals processing, audio and image processing, computer vision, and medical instrumentation.

Sugondo Hadiyoso, PhD is working as Lecturer with more than 12 years of experience in School of Applied Science, Telkom University, Bandung, Indonesia. His area of expertise includes biomedical engineering, AI and applied sciences. He has published more than 100 research papers in refereed Springer, Elsevier, IEEE etc journals, conference proceedings. He contributes to various scientific meetings as a conference committee member, and session chairs, regular reviewer in Elsevier journals and MDPI.

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Table of Contents
Preface
1. Exploring Emotional Intelligence in Design Thinking
Sancheti Dipak D., Chaudhari Rajendra S., Deore Harshal S. and Bora Pradyumna M.
1.1 Introduction
1.2 Understanding Emotional Intelligence
1.3 Understanding Design Thinking
1.4 The Importance of Emotional Intelligence in Design Thinking for Engineering Solutions
1.5 Fundamentals of Emotional Intelligence in Design
1.5.1 The Five Pillars of EI in Design Thinking
1.5.2 How EI Enhances Human-Centered Design
1.5.3 The Design Thinking Process and EI
1.6 Applications and Case Studies
1.6.1 AI and Emotionally Aware UX Design
1.6.2 Smart IoT Devices with Emotional Sensitivity
1.6.3 Healthcare and Assistive Technologies
1.7 Challenges and Future Perspectives
1.7.1 Measuring Emotional Impact
1.7.2 Ethical Concerns in Emotion-Driven Design
1.7.3 Role of AI and Deep Learning in Future EI-Based Design
1.8 Conclusion
References
2. Exploring the Integration of Emotion and Engineering: An In-Depth Analysis of Emotional Intelligence in Design and Its Impact on Human-Centered Engineering Practices
Sancheti Santosh D., Sanghavi Mahesh R., Sanghavi Kainjan M. and Sancheti Dipak D.
2.1 Introduction
2.1.1 Background and Rationale
2.1.2 Research Objectives
2.1.3 Scope and Limitations
2.2 Understanding Emotions in Engineering
2.2.1 Historical Perspectives
2.2.2 Theoretical Frameworks
2.3 Emotional Intelligence in Design
2.3.1 Definition and Components
2.3.2 Measurement and Assessment
2.4 Human-Centered Engineering Practices
2.4.1 Principles and Frameworks
2.4.2 Case Studies
2.5 The Interplay of Emotion and Engineering
2.5.1 Emotional Design Theory
2.5.2 User Experience and Emotion
2.6 Implications for Engineering Education and Practice
2.6.1 Curriculum Integration
2.6.2 Professional Development
2.7 Conclusion and Future Directions
2.7.1 Key Findings and Contributions
2.7.2 Research Gaps and Opportunities
References
3. The Integration of Emotion and Engineering
Abinaya Swathiswaramurthi
3.1 Introduction
3.2 Understanding Emotions
3.3 Models and Frameworks for Emotion-Integrated Engineering
3.3.1 Affective Computing
3.3.2 Biometric and Psychological Measurement Techniques
3.3.2.1 EEG and Brainwave Analysis
3.3.2.2 Facial Expression Recognition
3.3.2.3 Galvanic Skin Response (GSR)
3.3.2.4 Heart Rate Variability (HRV)
3.4 Case Studies and Applications
3.4.1 Human-Centered AI
3.4.2 Robotics and Emotional Intelligence
3.4.3 Automotive Industry
3.5 Proposed Model
3.5.1 Data Acquisition Layer
3.5.1.1 Key Components
3.5.1.2 Application
3.5.2 Processing Layer (AI and Deep Learning)
3.5.2.1 Primary Activities
3.5.2.2 Practical Use Case
3.5.3 Adaptive System Behavior: Emotions Detected and Driven Response Mechanism
3.5.3.1 Primary Aspects
3.5.3.2 Emotional Cues Applied Case Study
3.6 Experimental Analysis
3.7 Results and Discussion
3.7.1 Advantages of Emotion-Integrated Systems
3.7.2 Applications in Robotics and Autonomous Systems
3.7.3 Challenges and Limitations
3.7.4 Future Directions
3.8 Expanding Applications and Future Trends
3.8.1 Healthcare: Emotion-Driven Diagnostic Tools
3.8.2 Education: Adaptive Learning Environments
3.8.3 Entertainment: Interactive Storytelling and Gaming
3.8.4 Smart Cities: Emotion-Aware Public Infrastructure
3.8.5 Future Trends: Neuro-Symbolic AI and Brain-Computer Interfaces (BCIs)
3.9 Ethical and Societal Implications
References
4. Emotional Intelligence in AI: Bridging the Gap between Humans and Machines
Bindu S., Smitha Gayathri D., Prashant M. K. and Mohan Kishore D.
4.1 Introduction
4.2 The Significance of Integrating Emotions into AI Systems
4.2.1 Emotional Intelligence in Decision-Making Systems
4.2.2 Challenges in Developing Emotionally Intelligent Systems
4.3 Understanding the Science of Human Emotions
4.3.1 Biological Basis of Emotions
4.3.2 Psychological Perspective on Emotions
4.3.3 Social and Cultural Dimensions of Emotions
4.3.4 The Role of Emotions in Decision-Making and Behavior
4.3.5 Harnessing Emotional Science for Well-Being and Innovation
4.4 Computational Models of Emotions and Emotion Recognition Techniques
4.4.1 Computational Models of Emotions
4.4.2 Emotion Recognition Techniques
4.5 Sentiment Analysis and Natural Language Processing (NLP)
4.5.1 Understanding Sentiment Analysis
4.5.1.1 Types of Sentiment Analysis
4.5.1.2 Role of Natural Language Processing (NLP) in Sentiment Analysis
4.5.2 Challenges in Sentiment Analysis
4.6 Case Study
4.7 Risks of Emotionally Manipulative AI
4.8 Conclusions
References
5. Emotionally Intelligent AI Assistants: Machine Learning for Enhanced Human–AI Interaction
Rahul Kumar Ghosh, Gourab Dutta, Sandip Chakraborty and Subhadip Nandi
5.1 Introduction
5.1.1 Importance of Empathetic AI in Human–Machine Interactions
5.1.2 Industry 5.0 and Human-Centered AI Design
5.2 Foundations of Emotionally Intelligent AI
5.2.1 Role of NLP, Sentiment Analysis, and Multimodal AI
5.3 Conversational AI and Emotion Recognition
5.3.1 Personalized User Experience with Emotion AI
5.3.2 Cross-Industry Applications of Emotion AI Assistants
5.4 Ethical Considerations and Challenges in Emotion AI
5.4.1 Privacy, Data Security, and Emotional Manipulation Risks
5.4.2 AI Governance and Regulatory Frameworks
5.5 Case Studies: Real-World Implementations of Emotion AI
5.5.1 AI-Driven Emotional Assistants in Emerging Markets
5.5.2 Corporate AI Strategies: Tech Industry Leaders
5.6 Future Trends and Research Directions
5.6.1 Human-AI Collaboration in Industry 5.0
5.6.2 Long-Term Vision: Emotion AI and Sustainable Development
5.7 Conclusion
References
6. Emotionally Intelligent Assistants with Machine Learning
Piyal Roy, Shivnath Ghosh, Amitava Podder and Saptarshi Kumar Sarkar
6.1 Introduction
6.1.1 Overview of Emotionally Intelligent Assistants
6.1.2 Role of Machine Learning in Emotional Intelligence
6.1.3 Importance and Applications in Modern Technology
6.2 Foundations of Emotional Intelligence
6.2.1 Defining Emotional Intelligence
6.2.2 Key Components
6.2.3 Emotional Intelligence in Human-Computer Interaction
6.3 Machine Learning for Emotional Intelligence
6.3.1 Overview of ML Techniques
6.3.2 Natural Language Processing for Emotion Detection
6.3.3 Sentiment Analysis and Emotion Classification
6.4 Data Collection and Preprocessing
6.4.1 Sources of Emotional Data: Text, Speech, and Visual Cues
6.4.2 Ethical Considerations in Data Collection
6.4.3 Preprocessing Techniques for Emotionally Relevant Data
6.5 Building Emotionally Intelligent Assistants
6.5.1 Architectures for Emotionally Intelligent Systems
6.5.2 Integrating Multimodal Data for Enhanced Understanding
6.5.3 Real-Time Emotion Recognition and Response Generation
6.6 Evaluation and Metrics
6.6.1 Measuring Emotional Intelligence in AI Systems
6.6.2 User Experience and Satisfaction Metrics
6.6.3 Benchmarking Emotionally Intelligent Assistants
6.7 Ethical and Societal Implications
6.7.1 Privacy Concerns in Emotionally Intelligent Systems
6.7.2 Bias and Fairness in Emotion Detection Algorithms
6.7.3 Future of Emotional AI and Human Relationships
6.8 Conclusion and Future Scope
References
7. Emotion AI: Advancing Emotional Recognition with Machine Learning
A. Prabhu Chakkaravarthy, J. Dhanalakshmi and D. Praveena Anjelin
7.1 Introduction
7.1.1 Importance of Emotion Recognition
7.1.2 Traditional vs. ML-Based Approaches
7.2 Related Work
7.2.1 Facial Expression Recognition Using CNNs
7.2.2 Speech-Based Emotion Recognition Using RNNs, Transformers, and NLP
7.2.3 Physiological Signal-Based Recognition (EEG, ECG, GSR)
7.2.4 Text-Based Sentiment Analysis Using SVM, LSTM, and BERT
7.3 Methodology
7.3.1 Sentiment140
7.3.2 DEAP
7.3.3 RAVDESS
7.3.4 FER
7.4 Preprocessing and Feature Engineering
7.4.1 Noise Reduction Techniques
7.4.2 Feature Extraction Methods
7.4.3 Data Augmentation
7.4.4 Feature Engineering (e.g., MFCCs, Frequency-Domain Transformations)
7.5 Results and Discussion
7.6 Challenges in Emotion Recognition
7.6.1 Cultural and Contextual Variability
7.6.2 Privacy and Ethical Concerns
7.6.3 Dataset Bias and Generalization
7.7 Applications of Emotion Detection
7.7.1 Mental Health and Healthcare
7.7.2 Marketing and Consumer Analytics
7.7.3 Security and Surveillance
7.7.4 Human-Computer Interaction and Gaming
7.8 Future Directions
7.8.1 Multi-Modal Emotion Recognition
7.8.2 Advances in Deep Learning
7.8.3 Ethical AI and Regulatory Considerations
7.9 Conclusion
References
8. Emotion-Sensitive Deep Learning Models
Reeaa Rana, Diveyam Mishra and Sandeep Kumar Jain
8.1 Understanding Emotion Sensitivity
8.1.1 Why Recognizing Emotions Matters
8.1.2 Challenges in Integrating Emotions with AI
8.1.2.1 Emotional Granularity
8.1.2.2 Cultural Differences in Emotional Expression
8.1.2.3 Real-Time Processing Challenges
8.1.2.4 Ethical Considerations
8.2 Understanding Emotion Data
8.2.1 Types of Emotional Data
8.2.1.1 Text
8.2.1.2 Speech
8.2.1.3 Facial Expression
8.2.1.4 Physiological Signals
8.2.2 Datasets Commonly Used for Emotion Recognition
8.2.3 Data Preprocessing Techniques for Different Modalities
8.3 Deep Learning Approach for Emotional Stability
8.3.1 CNNs for Visual Emotion Recognition
8.3.2 RNNs and LSTMs for Sequential Data Analysis
8.3.3 Multimodal Fusion: Integrating Diverse Data Sources
8.3.4 Attention Mechanisms: Focusing on Salient Features
8.4 Model Architectures and Framework
8.4.1 Pre-Trained Models and Transfer Learning
8.4.1.1 BERT (Bidirectional Encoder Representation from Transformers)
8.4.1.2 VGGFace
8.4.2 Generative Models for Synthetic Emotional Data Generation
8.5 Evaluation Metrics for Emotion-Sensitive Models
8.5.1 Standard Evaluation Metrics
8.5.1.1 Accuracy
8.5.1.2 Precision, Recall, and F1 Score
8.5.2 Confusion Matrix and ROC-AUC
8.5.2.1 Confusion Matrix
8.5.2.2 ROC-AUC
8.5.3 Emotion-Specific Evaluation Metrics
8.5.3.1 Valence-Arousal Models
8.5.3.2 Categorical vs. Dimensional Evaluation
8.5.4 Challenges in Evaluating Emotion-Sensitive Models
8.6 Challenges and Future Directions
8.6.1 Enhancing Robustness and Generalization of Models
8.6.2 Future Trends: Explainability, Adaptability, and Cross-Cultural Emotion Recognition
8.6.3 Real-World Applications and Emerging Techniques
8.7 Successful Implementations: Context and Value
8.7.1 Case Study 1: Improving Driver Safety Systems
8.7.2 Case Study 2: Bias and Inaccuracy in AI Recruitment Tools
8.7.3 Synthesized Lessons and Conclusion
8.7.3.1 Context is Paramount
8.7.3.2 Validity and Reliability are Non-Negotiable
8.7.3.3 Ethical Design is Foundational
8.7.3.4 Augmentation over Automation
8.8 Conclusion
References
9. Deep Learning for Emotion Detection: Making Machines Feel
Manjushree Nayak and Amisha Sukla
9.1 Introduction
9.2 The Heart of Emotion Detection: Key Algorithms
9.2.1 Convolutional Neural Networks (CNNs): Reading Emotion Off Faces
9.2.2 Recurrent Neural Networks (RNNs) with LSTM: Tuning into Speech Emotions
9.2.3 Transformers: Recognizing the Emotional Undercurrent of Text
9.3 Multimodal Emotion Recognition: Unifying Seeing, Hearing, and Reading Emotions
9.4 Methodology
9.4.1 Description of the Dataset
9.4.2 Feature Extraction
9.4.3 Deep Learning Model Architecture
9.4.4 Training Process
9.4.5 Model Evaluation
9.5 Dataset Overview
9.6 Result Analysis and Discussion
9.6.1 Comparison with Existing Models
9.6.1.1 Facial Expression Recognition (CNN-Based Model)
9.6.1.2 Speech Emotion Recognition (RNN + LSTM)
9.6.1.3 Text Sentiment Analysis (BERT-Based Model)
9.6.2 Performance Measures
9.6.3 Cross-Validation Methods
9.7 Conclusion
9.7.1 Interpretation of Results
9.7.2 Challenges and Limitations
9.7.3 Future Work
9.7.4 Ethical and Social Considerations
9.7.5 Final Remarks and Vision Beyond
References
10. Emotion-Aware AI for Facial Expression Analysis to Enhance Workforce Well-Being in Industry 4.0
U. Sinthuja, K. Kabilan and R. Meenakshisundaram
10.1 Introduction
10.1.1 Role of Machine Learning for Emotional Identification
10.1.1.1 Facial Expression Identification
10.1.1.2 Speech Emotion Identification (SEI)
10.1.1.3 Physiological Signal Identification
10.1.1.4 Multimodal Emotion Identification
10.1.1.5 EEG Emotion Identification
10.2 Survey
10.3 Analyzing the Algorithms of AI for FEI
10.3.1 AI Algorithms for Emotional Intelligence Analysis
10.3.2 AI-Powered Facial Analysis for Industry 4.0 Workspaces
10.3.3 Facial Emotion Identification (FEI) Process
10.3.4 Training Process of the CNN Model for Facial Emotion Identification
10.4 Enhancing the Industry 4.0 Work Environment with Facial Emotion Identification
10.4.1 Introduction
10.4.2 Problem Statement
10.4.3 Methodology a Facial Emotion Identification System (FEIS)
10.4.4 Implementation and Results
10.5 Conclusion
References
11. Emotion-Based Music Recommendation System
Abhishek Kumar
11.1 Introduction
11.2 Related Work
11.3 System Architecture
11.4 Emotion Detection Module
11.5 Emotion Classification
11.6 Music Metadata Tagging
11.7 Recommendation Engine
11.8 Implementation
11.9 Conclusion
References
12. Emotional Sensors: Emotion-Driven IoT
Subhadip Nandi, Gaurab Dutta and Rahul Kumar Ghosh
12.1 Introduction
12.2 Applications of Emotion-Driven IoT
12.2.1 Health and Well-Being
12.2.2 Smart Homes
12.2.3 Education
12.2.4 Retail and Customer Experience
12.2.5 Transport
12.2.6 Challenges and Ethical Considerations
12.3 Introduction to Emotion-Driven IoT (EIoT)
12.3.1 Definition and Importance of EIoT
12.3.2 Historical Evolution and Integration of Emotions in Computing
12.3.2.1 Early Psychological Foundations (1950s–1980s)
12.3.2.2 Emergence of Emotions Detection (C.1990s–2000s)
12.3.2.3 Incorporating AI and the Internet of Things in Emotion Recognition
(2010s and Beyond)
12.3.2.4 Practical Applications of Emotion-Driven IoT and Ethics Aspects
12.3.3 Future of Emotion-Driven IoT
12.4 Technological Foundations
12.4.1 Physiological Sensors
12.4.2 Behavioral and Facial Recognition Sensors
12.4.3 Wearable Devices for Emotion Recognition
12.4.4 Data Collection
12.4.5 Data Analysis and Results
12.5 AI and ML Techniques in Emotion Classification
12.5.1 Communication Protocols and Data Processing Techniques
12.5.2 Data Processing Techniques in EIoT
12.5.3 Cloud and EDGE Computing in EIoT
12.5.4 Applications of EIoT
12.5.4.1 Smart Homes and Adaptive Environments
12.5.4.2 Workplace Productivity and Employee Well-Being
12.5.4.3 Personalized Education and Learning Systems
12.5.5 Challenges in Emotion-Driven IoT
12.5.5.1 Data Privacy, Security, and Ethical Issues
12.5.5.2 Bias in Emotion Recognition Algorithms
12.5.5.3 Real-Time Processing and Computational Limitations
12.5.5.4 Scalability and Integration Challenges
12.6 Proposed Solutions and Advancements
12.7 Future Research Directions
12.7.1 Towards Improvement of Accuracy for Real-Time Emotion Recognition
12.7.2 Development of Light-Weight AI for Wearables
12.7.3 Legal and Ethical Consideration in EIoT
12.7.4 Multi-Modal Emotion Recognition Techniques
12.7.5 Analysis of the Long-Term Impact of Emotion-Driven Interactions
References
13. Neuro-IoT: Merging Brain Signals with Smart Electronics
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
13.1 Introduction
13.1.1 Background
13.1.2 Objectives
13.1.3 Structure of the Chapter
13.1.4 Significance of Neuro-IoT
13.1.5 Research Methodology
13.2 Understanding Neuro-IoT
13.2.1 Definition and Components
13.2.2 Brain-Computer Interfaces (BCIs)
13.2.3 Smart Electronics and IoT
13.2.4 The Role of Artificial Intelligence
13.2.5 Applications of Neuro-IoT: Transforming Industries through Brain-Inspired Intelligent Systems
13.2.6 Challenges and Ethical Considerations: Transforming Industries through Brain-Inspired Intelligent Systems
13.3 Applications of Neuro-IoT
13.3.1 Healthcare
13.3.1.1 Brain-Powered Prosthetics
13.3.1.2 Mental Health Recognition
13.3.2 People Analytics
13.3.2.1 Cognitive Analysis in Workforce Optimization
13.3.3 Neuromarketing
13.3.3.1 Consumer Behavior Analysis
13.3.4 Infrastructure Finance
13.3.4.1 Neural-Based Data Approaches
13.3.5 Education
13.3.5.1 Adaptive Learning Environments
13.4 Related Work
13.4.1 Overview of Existing Research
13.4.2 Key Findings
13.4.3 Gaps in Literature
13.4.4 Future Directions
13.4.5 Key Studies in Neuro-IoT
13.5 Challenges and Ethical Considerations
13.5.1 Technical Challenges
13.5.1.1 Data Quality and Reliability
13.5.1.2 Integration and Interoperability
13.5.2 Ethical Considerations
13.5.2.1 Data Privacy and Security
13.5.2.2 Informed Consent
13.5.2.3 Cognitive Manipulation
13.5.3 Regulatory Frameworks
13.5.3.1 Collaborative Efforts
13.5.3.2 Continuous Monitoring and Adaptation for Neuro-IoT Technologies
13.5.4 Conclusion
13.6 Technological Advancements
13.6.1 Enhanced Brain-Computer Interfaces (BCIs)
13.6.2 Integration of AI and Machine Learning
13.6.3 Interdisciplinary Collaboration
13.6.3.1 Collaborative Research Initiatives
13.6.3.2 Engaging Stakeholders in Ethical Discussions
13.6.4 New Applications
13.6.4.1 Education and Learning
13.6.4.2 Public Safety and Security
13.6.4.3 Environmental Monitoring
13.6.5 Ethical Considerations in Future Development
13.6.5.1 Establishing Ethical Guidelines
13.6.5.2 Continuous Monitoring and Adaptation
13.7 Conclusion
13.7.1 Summary of Key Insights
13.7.2 Implications for Future Research
13.7.3 Final Thoughts
References
14. Personalized Voice Assistant with Emotional Intelligence Using NLP and GCP
Bavithra K., Nivetha G., D. Yashwanth Daran and Manasha K. G.
14.1 Introduction
14.2 Literature Survey
14.3 Objective
14.4 Existing Methodology
14.4.1 Advancements with Machine Learning and NLP
14.4.1.1 Intent Recognition and Understanding Context
14.4.1.2 Entity Recognition and Dialogue Management
14.4.1.3 Natural Language Generation (NLG)
14.4.1.4 Speech Emotion Recognition (SER)
14.4.2 Limitations of Existing Methodologies
14.4.3 Gap Analysis: What’s Missing?
14.5 Proposed Methodology
14.6 Research Methodology
14.7 Packages Used
14.8 Code Snippets
14.9 Natural Language Processing (NLP)
14.10 Result
14.11 Future Scope
References
15. Emotional Algorithms – Machines to Understand Human Feelings
Madhankumar C.
15.1 Defining Emotional AI and Affective Computing
15.2 Importance of Emotion Recognition in AI-Driven Decision-Making
15.3 Traditional Rule-Based Sentiment Analysis vs. Deep Learning-Based Affect Recognition
15.4 Key Challenges in Emotional AI
15.5 Emerging Trends in Emotional AI
15.5.1 AI-Powered Emotion Recognition
15.5.1.1 Facial Expression Analysis
15.5.1.2 Voice Sentiment Analysis
15.5.1.3 Text-Based Emotion Detection
15.6 Deep Learning and Affective Neural Networks
15.6.1 Multimodal Emotion Recognition
15.6.2 Self-Learning AI Models for Adaptive Emotional Responses
15.6.3 Reinforcement Learning for Human-AI Emotional Interaction
15.7 Empathetic AI and Human-Centric Chatbots
15.7.1 Emotionally Intelligent AI Assistants
15.7.2 AI-Powered Mental Health Support Systems
15.7.3 AI-Powered Empathetic Robots
15.8 Ethics, Bias, and Privacy in Emotional AI
15.8.1 Bias in AI Emotion Detection
15.8.2 Privacy Risks in Emotion Data Collection
15.8.3 Ethical AI Frameworks for Responsible Emotional AI
15.9 Future Innovations and Applications in Emotional AI
15.9.1 Healthcare and Mental Health AI
15.9.2 AI-Powered Stress and Depression Monitoring
15.9.3 AI-Assisted Early Diagnosis of Neurological Disorders
15.10 AI in Customer Engagement and Personalization
15.10.1 Emotion-Aware Chatbots for Real-Time Customer Support
15.10.2 AI-Driven Personalized Advertisements and Content Recommendations
15.10.3 AI-Powered Real-Time Feedback in Virtual Classrooms
15.10.4 Social Robotics and Emotional Machines
15.10.5 Emotionally Aware Companion Robots for Elderly and Disabled Individuals
15.10.6 AI-Driven Social Interaction Robots for Children with Autism
15.10.7 AI-Powered Humanoid Robots in Smart Homes and Workplaces
15.11 Challenges and Research Directions in Emotional AI
15.11.1 Data Quality and Diversity
15.11.2 Multi-Modal Emotion Recognition
15.11.3 Real-Time Processing and Scalability
15.11.4 Ethical and Privacy Concerns
15.11.5 Transparency and Explainability
15.11.6 Human-AI Interaction and Trust
15.11.7 Addressing Emotional Bias and Fairness
15.11.8 Emotional AI for Well-Being and Mental Health
15.11.9 Challenges in Emotion Recognition Accuracy
15.11.10 Balancing Real-Time Emotion Detection with Computational Efficiency
15.11.11 Bias and Fairness in Emotion-Aware AI Models
15.11.12 Privacy and Ethical Concerns in Emotion Recognition Data Collection
15.11.13 Conclusion and Future Research
15.11.14 The Future of AI-Driven Emotional Intelligence
15.11.15 The Need for Responsible Emotional AI Research
15.11.16 Potential Advancements in Cognitive AI and Brain-Inspired Emotion Modeling
15.12 Final Thoughts
Bibliography
16. Emotional Indicators in Cybersecurity: Developing a Framework for Early Insider Threat Detection
Soumya Roy, Kaushik Chanda, Subhadip Nandi and Anudeepa Gon
16.1 Introduction
16.2 Methodology and Implementation
16.3 Results and Evaluation
16.4 Comparison with Existing Frameworks
16.5 Conclusion
References
17. The Role of Cobots in Shifting from Automation to Collaboration
Rajesh Singh, Aashna Sinha, Vivek Kumar Singh and Praveen Kumar Malik
17.1 Introduction to Cobots
17.2 Features of the Cobots
17.3 The Function of Cobots in Industries
17.4 Conclusion
References
18. Enhancing Quality Control and Predictive Maintenance with Data Insights
Rajesh Singh, Anita Gehlot, Fraiz Parveen and Praveen Kumar Malik
18.1 Introduction
18.2 Quality Control and Predictive Maintenance
18.3 Predictive Maintenance Using Machine Learning
18.4 Case Study
18.5 Discussion
18.6 Conclusion
References
19. Emotion Detection Using Pre-Trained CNN Models: A Deep Learning Approach with Real-Time Implementation
Pratyush Rai, Naman Gupta, Aryan Singh, Nagendra Prabhu S. and Arun Kumar
19.1 Introduction
19.1.1 Emotion Recognition Comprehension
19.1.2 The Role of Emotion Detection in Brand New International Packages
19.1.3 History of Emotion Recognition
19.1.4 How Deep Learning Drives Emotion Popularity
19.1.5 Demanding Situations in Emotion Popularity
19.1.6 Motives behind This Work
19.2 Literature Assessment
19.2.1 Traditional Approaches to Emotion Recognition
19.2.2 Deep Learning for Emotion Popularity
19.2.3 Datasets Used for Education and Normalization
19.2.4 Evaluation of Version Performance
19.3 Deep Getting to Know and CNN for Emotion Recognition
19.3.1 Understanding of Convolutional Neural Networks (CNNs)
19.3.1.1 Performance
19.3.1.2 Principal Components of CNNs
19.3.1.3 CNN Model Architecture Used in This Study
19.3.1.4 Education and Optimization Procedures
19.3.1.5 Advantages of the Pre-Skilled Version
19.4 Proposed System Architecture
19.4.1 Gadget Workflow
19.4.2 Face Detection
19.4.3 Image Pre-Processing
19.4.4 CNN Model for Emotion Popularity
19.4.5 Emotion Recognition
19.4.6 Real-Time Emotion Evaluation
19.5 Data Preprocessing and Dataset
19.5.1 The Variables Considered
19.5.2 Assessment of the FER2013 Dataset
19.5.3 Significance of Preprocessing in Emotion Recognition
19.5.4 Preprocessing Operations
19.5.5 The Importance of Preprocessing in Model Performance
19.5.6 Challenges in Statistics Preprocessing
19.6 Applications on the Actual International Usage for Emotion-Based Recognition
19.6.1 Analyses of Customer Sentiments
19.6.2 Monitoring Mental Health
19.6.3 Human–Laptop Interaction
19.6.4 Safety and Surveillance
19.7 Data Availability Statement
19.8 Conclusion
Bibliography
20. Emotionally Intelligent AI Assistant Powered by Machine Learning and NLP
Kushagra Purohit, Gaurav Gupta, S. Nagendra Prabhu and Arun Kumar
20.1 Introduction
20.1.1 Multimodal Visual Recognition System
20.1.2 Voice Interaction and Language Adaptation
20.1.3 Emotional Intelligence and Learning Capabilities
20.2 Literature Investigation
20.2.1 Current System
20.3 System Analysis
20.3.1 Contemporary System Limitations
20.3.2 Proposed AI NLP Multimodal
20.3.3 Objective
20.3.4 Proposed Architecture
20.3.5 Dataset Pre-Processing
20.3.6 Dataset Classification and Analysis
20.4 Result Analysis
20.4.1 Accuracy
20.4.2 Precision
20.4.3 Recall
20.4.4 F1 Score
20.5 Convolutional Neural Network (CNN)
20.6 Conclusion
Bibliography
21. Neuro-IoT and Emotion Recognition: Merging Brain Signals with Smart Electronics for Emotionally Intelligent Systems
Vishal Jain, Archan Mitra and Sanchita Paul
21.1 Introduction
21.1.1 Rise of Neuro-IoT: Convergence of Neuroscience, IoT, and AI
21.1.2 Importance of EEG in Detecting Internal Affective States
21.1.3 Statement of the Problem
21.1.4 Research Objective
21.2 Literature Review
21.2.1 Introduction to Neuro-IoT and Emotion Recognition
21.2.2 EEG Signal Acquisition and Processing
21.2.3 AI and Deep Learning Models for Emotion Classification
21.2.4 Applications in Healthcare
21.2.5 Ethical and Privacy Concerns
21.2.6 Real-World Implementations and Case Studies
21.2.7 Current Technical Challenges
21.2.8 Research Gap
21.3 Methodology
21.3.1 System Architecture
21.3.2 Data Collection
21.3.3 Signal Preprocessing
21.3.4 Model Development
21.3.5 System Deployment
21.4 Findings
21.5 Discussion
21.6 Conclusion and Future Work
References
22. EmoHeart: Human-Centered First-Emotion Smart IoT Devices for Cardiology
Abdul Razak Mohamed Sikkander, Suman Lata Tripathi, Joel J. P. C. Rodrigues and Radhakrishnan
22.1 Introduction
22.2 Research Objectives
22.3 Methodologies
22.3.1 Literature Review
22.3.2 Physiological Basis
22.3.3 Emotion Recognition Algorithms
22.3.4 Current State of Emotion-Sensing Technologies
22.3.4.1 Sensor Types
22.3.4.2 ECG
22.3.4.3 Resting ECG
22.3.4.4 Ambulatory ECG
22.3.4.5 Exercise Stress Test/Stress Test
22.3.5 PPG
22.3.6 GSR
22.3.7 IoT Device Development
22.3.8 Clinical Validation
22.3.9 Data Analytics
22.4 Challenges and Obstacles
22.4.1 Technical Challenges
22.4.2 Clinical and Regulatory Challenges
22.4.3 User-Centered Challenges
22.4.4 Business and Economic Challenges
22.5 Future Perspectives
22.5.1 Advancements in Emotion Recognition
22.5.2 Integration with Emerging Technologies
22.5.3 Expansion into New Markets and Applications
22.5.4 Enhanced User Experience and Engagement
22.5.5 Addressing Emerging Challenges and Opportunities
22.6 Conclusions
References
23. Natural Bioactive Compounds as Cardioprotective Agents: A Promising Avenue for Heart Health
Abdul Razak Mohamed Sikkander, Suman Lata Tripathi, Joel J. P. C. Rodrigues, Nitin Wahi, G. Theivanathan and Fatma Bassyouni
23.1 Introduction
23.1.1 The Growing Problem of Heart Disease
23.1.2 Methods of Action
23.1.2.1 Antioxidant Activity
23.1.2.2 Anti-Inflammatory Activity
23.1.2.3 Improvement of Cardiovascular Function
23.1.3 Purpose and Objectives
23.1.3.1 Polyphenols
23.1.3.2 Terpenes
23.1.3.3 Alkaloids
23.2 Research and Methodologies
23.2.1 In Vitro Revisions
23.2.1.1 Cell Culture Lessons
23.2.1.2 Cell Lines Used
23.2.1.3 Experimental Design
23.2.1.4 Advantages
23.2.2 In Vivo Revisions
23.2.2.1 Animal Representations
23.2.2.2 Echocardiography and Cardiac Imaging
23.2.2.3 Bioanalytical Techniques
23.2.2.4 Computational Modeling
23.2.2.5 Systematic Assessments and Meta-Analyses
23.2.3 Clinical Trials
23.2.3.1 Types of Clinical Trials
23.2.3.2 Phases of Clinical Trials
23.2.3.3 Outcome Measures
23.2.4 Bioanalytical Methods
23.2.4.1 High-Performance Liquid Chromatography (HPLC)
23.2.4.2 Mass Spectrometry (MS)
23.2.4.3 Nuclear Magnetic Resonance (NMR) Spectroscopy
23.2.4.4 Gas Chromatography (GC)
23.2.4.5 Capillary Electrophoresis (CE)
23.2.4.6 Fourier Transform Infrared (FTIR) Spectroscopy
23.2.4.7 UPLC
23.2.4.8 Uses in the Study of Natural Bioactive Compounds
23.2.5 Computational Modeling
23.2.5.1 Molecular Docking
23.2.5.2 Pharmacophore Modeling
23.2.5.3 Quantum Mechanics (QM) Calculations
23.2.5.4 Molecular Dynamics (MD) Simulations
23.2.5.5 Virtual Screening
23.2.5.6 Applications in Natural Bioactive Compound Research
23.2.6 Systematic Assessments and Meta-Analyses
23.2.6.1 Systematic Reviews
23.2.6.2 Meta-Analyses
23.2.6.3 Software and Tools
23.2.6.4 Guidelines for Reporting
23.2.7 Polyphenols as Cardioprotective Agents from Natural Bioactive Compounds
23.2.7.1 Classification of Polyphenols
23.2.7.2 Polyphenols’ Cardioprotective Effects
23.2.8 Terpenes as Cardioprotective Agents from Natural Bioactive Compounds
23.2.8.1 Terpenes’ Cardioprotective Effects
23.2.8.2 Potential Uses in Medicine
23.2.9 Alkaloids as Cardioprotective Agents from Natural Bioactive Compounds
23.2.9.1 Classification of Alkaloids
23.2.9.2 Alkaloids’ Cardioprotective Effects
23.2.9.3 Potential Uses in Medicine Preventing Heart Disease
23.3 Results
23.3.1 Flavonoids
23.3.2 Polyphenols
23.3.3 Terpenes
23.3.4 Alkaloids
23.4 Conversations
23.4.1 Mechanisms of Action
23.4.1.1 Antioxidant Mechanisms
23.4.1.2 Cardioprotective Effects
23.4.1.3 Anti-Inflammatory Mechanisms
23.4.1.4 Cardioprotective Effects
23.4.1.5 Anti-Apoptotic Mechanisms
23.4.1.6 Cardioprotective Effects
23.4.2 Structure-Activity Relationship
23.4.2.1 Functional Groups
23.4.2.2 Frameworks for Molecules
23.4.2.3 Binding Affinity
23.4.2.4 Solubility and Bioavailability
23.4.2.5 Metabolic Stability
23.4.3 Synergistic Benefits
23.4.4 Clinical Implications
23.5 Challenges and Obstacles
23.5.1 Bioavailability and Pharmacokinetics
23.5.2 Safety and Effectiveness
23.5.3 Framework for Regulation
23.6 Future Perspectives
23.6.1 Customized Healthcare
23.6.2 Delivery Systems Based on Nanotechnology
23.6.3 Derivatives of Synthetic and Semi-Synthetic Materials
23.6.4 Therapies in Combinations
23.6.5 Prevention of Cardiovascular Disease
23.6.6 The Field of Regenerative Medicine
23.6.6.1 Stem Cell Therapy
23.6.6.2 Tissue Engineering
23.6.7 Artificial Intelligence Technique
23.7 Conclusions
References
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