Preface xxix
1 Exploring Emotional Intelligence in Design Thinking 1
Sancheti Dipak D., Chaudhari Rajendra S., Deore Harshal S. and Bora Pradyumna M.
1.1 Introduction 2
1.2 Understanding Emotional Intelligence 3
1.3 Understanding Design Thinking 5
1.4 The Importance of Emotional Intelligence in Design Thinking for Engineering Solutions 7
1.5 Fundamentals of Emotional Intelligence in Design 8
1.6 Applications and Case Studies 12
1.7 Challenges and Future Perspectives 15
1.8 Conclusion 17
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 23
Sancheti Santosh D., Sanghavi Mahesh R., Sanghavi Kainjan M. and Sancheti Dipak D.
2.1 Introduction 24
2.2 Understanding Emotions in Engineering 28
2.3 Emotional Intelligence in Design 31
2.4 Human-Centered Engineering Practices 33
2.5 The Interplay of Emotion and Engineering 36
2.6 Implications for Engineering Education and Practice 40
2.7 Conclusion and Future Directions 42
3 The Integration of Emotion and Engineering 49
Abinaya Swathiswaramurthi
3.1 Introduction 50
3.2 Understanding Emotions 51
3.3 Models and Frameworks for Emotion-Integrated Engineering 53
3.4 Case Studies and Applications 61Contents vii
3.5 Proposed Model 62
3.6 Experimental Analysis 67
3.7 Results and Discussion 68
3.8 Expanding Applications and Future Trends 71
3.9 Ethical and Societal Implications 78
4 Emotional Intelligence in AI: Bridging the Gap between Humans and Machines 81
Bindu S., Smitha Gayathri D., Prashant M. K. and Mohan Kishore D.
4.1 Introduction 82
4.2 The Significance of Integrating Emotions into AI Systems 85
4.3 Understanding the Science of Human Emotions 89
4.4 Computational Models of Emotions and Emotion Recognition Techniques 92
4.5 Sentiment Analysis and Natural Language Processing (NLP) 95
4.6 Case Study 100
4.7 Risks of Emotionally Manipulative AI 100
4.8 Conclusions 102
5 Emotionally Intelligent AI Assistants: Machine Learning for Enhanced Human-AI Interaction 107
Rahul Kumar Ghosh, Gourab Dutta, Sandip Chakraborty and Subhadip Nandi
5.1 Introduction 108
5.2 Foundations of Emotionally Intelligent AI 113
5.3 Conversational AI and Emotion Recognition 117
5.4 Ethical Considerations and Challenges in Emotion AI 123
5.5 Case Studies: Real-World Implementations of Emotion AI 128
5.6 Future Trends and Research Directions 131
5.7 Conclusion 136
6 Emotionally Intelligent Assistants with Machine Learning 143
Piyal Roy, Shivnath Ghosh, Amitava Podder and Saptarshi Kumar Sarkar
6.1 Introduction 144
6.2 Foundations of Emotional Intelligence 148
6.3 Machine Learning for Emotional Intelligence 152
6.4 Data Collection and Preprocessing 157
6.5 Building Emotionally Intelligent Assistants 162
6.6 Evaluation and Metrics 165
6.7 Ethical and Societal Implications 170x Contents
6.8 Conclusion and Future Scope 175
7 Emotion AI: Advancing Emotional Recognition with Machine Learning 179
A. Prabhu Chakkaravarthy, J. Dhanalakshmi and D. Praveena Anjelin
7.1 Introduction 180
7.2 Related Work 182
7.3 Methodology 186
7.4 Preprocessing and Feature Engineering 187
7.5 Results and Discussion 189
7.6 Challenges in Emotion Recognition 192
7.7 Applications of Emotion Detection 192
7.8 Future Directions 193
7.9 Conclusion 194
8 Emotion-Sensitive Deep Learning Models 197
Reeaa Rana, Diveyam Mishra and Sandeep Kumar Jain
8.1 Understanding Emotion Sensitivity 198
8.2 Understanding Emotion Data 200
8.3 Deep Learning Approach for Emotional Stability 203
8.4 Model Architectures and Framework 205
8.5 Evaluation Metrics for Emotion-Sensitive Models 208
8.6 Challenges and Future Directions 210
8.7 Successful Implementations: Context and Value 212
8.8 Conclusion 214
9 Deep Learning for Emotion Detection: Making Machines Feel 219
Manjushree Nayak and Amisha Sukla
9.1 Introduction 220
9.2 The Heart of Emotion Detection: Key Algorithms 221
9.3 Multimodal Emotion Recognition: Unifying Seeing, Hearing, and Reading Emotions 222
9.4 Methodology 224
9.5 Dataset Overview 230
9.6 Result Analysis and Discussion 231
9.7 Conclusion 234
10 Emotion-Aware AI for Facial Expression Analysis to Enhance Workforce Well-Being in Industry 4.0 241
U. Sinthuja, K. Kabilan and R. Meenakshisundaram
10.1 Introduction 242
10.2 Survey 246
10.3 Analyzing the Algorithms of AI for FEI 248
10.4 Enhancing the Industry 4.0 Work Environment with Facial Emotion Identification 252
10.5 Conclusion 254
11 Emotion-Based Music Recommendation System 257
Abhishek Kumar
11.1 Introduction 257
11.2 Related Work 259
11.3 System Architecture 260
11.4 Emotion Detection Module 260
11.5 Emotion Classification 261
11.6 Music Metadata Tagging 261
11.7 Recommendation Engine 262
11.8 Implementation 262
11.9 Conclusion 267
12 Emotional Sensors: Emotion-Driven IoT 271
Subhadip Nandi, Gaurab Dutta and Rahul Kumar Ghosh
12.1 Introduction 272
12.2 Applications of Emotion-Driven IoT 273
12.3 Introduction to Emotion-Driven IoT (EIoT) 276
12.4 Technological Foundations 279
12.5 AI and ML Techniques in Emotion Classification 281
12.6 Proposed Solutions and Advancements 290
12.7 Future Research Directions 290
13 Neuro-IoT: Merging Brain Signals with Smart Electronics 297
Amandeep Kaur, Ramandeep Sandhu, Indu Rani, Gaganpreet Kaur and Deepika Ghai
13.1 Introduction 298
13.2 Understanding Neuro-IoT 301
13.3 Applications of Neuro-IoT 306
13.4 Related Work 309
13.5 Challenges and Ethical Considerations 314
13.6 Technological Advancements 316
13.7 Conclusion 320
14 Personalized Voice Assistant with Emotional Intelligence Using NLP and GCP 325
Bavithra K., Nivetha G., D. Yashwanth Daran and Manasha K. G.
14.1 Introduction 326
14.2 Literature Survey 327xviii Contents
14.3 Objective 328
14.4 Existing Methodology 328
14.5 Proposed Methodology 331
14.6 Research Methodology 333
14.7 Packages Used 335
14.8 Code Snippets 337
14.9 Natural Language Processing (NLP) 338
14.10 Result 338
14.11 Future Scope 339
15 Emotional Algorithms - Machines to Understand Human Feelings 341
Madhankumar C.
15.1 Defining Emotional AI and Affective Computing 342
15.2 Importance of Emotion Recognition in AI-Driven Decision-Making 342
15.3 Traditional Rule-Based Sentiment Analysis vs. Deep Learning-Based Affect Recognition 343
15.4 Key Challenges in Emotional AI 344
15.5 Emerging Trends in Emotional AI 344
15.6 Deep Learning and Affective Neural Networks 346
15.7 Empathetic AI and Human-Centric Chatbots 349Contents xix
15.8 Ethics, Bias, and Privacy in Emotional AI 350
15.9 Future Innovations and Applications in Emotional AI 351
15.10 AI in Customer Engagement and Personalization 353
15.11 Challenges and Research Directions in Emotional AI 360
15.12 Final Thoughts 369
16 Emotional Indicators in Cybersecurity: Developing a Framework for Early Insider Threat Detection 373
Soumya Roy, Kaushik Chanda, Subhadip Nandi and Anudeepa Gon
16.1 Introduction 374
16.2 Methodology and Implementation 377
16.3 Results and Evaluation 379
16.4 Comparison with Existing Frameworks 382
16.5 Conclusion 383
17 The Role of Cobots in Shifting from Automation to Collaboration 387
Rajesh Singh, Aashna Sinha, Vivek Kumar Singh and Praveen Kumar Malik
17.1 Introduction to Cobots 388
17.2 Features of the Cobots 389
17.3 The Function of Cobots in Industries 390
17.4 Conclusion 395
18 Enhancing Quality Control and Predictive Maintenance with Data Insights 399
Rajesh Singh, Anita Gehlot, Fraiz Parveen and Praveen Kumar Malik
18.1 Introduction 400
18.2 Quality Control and Predictive Maintenance 402
18.3 Predictive Maintenance Using Machine Learning 405
18.4 Case Study 406
18.5 Discussion 407
18.6 Conclusion 408
19 Emotion Detection Using Pre-Trained CNN Models: A Deep Learning Approach with Real-Time Implementation 411
Pratyush Rai, Naman Gupta, Aryan Singh, Nagendra Prabhu S. and Arun Kumar
19.1 Introduction 412
19.2 Literature Assessment 417
19.3 Deep Getting to Know and CNN for Emotion Recognition 426
19.4 Proposed System Architecture 430
19.5 Data Preprocessing and Dataset 434
19.6 Applications on the Actual International Usage for Emotion-Based Recognition 440
19.7 Data Availability Statement 442
19.8 Conclusion 442
20 Emotionally Intelligent AI Assistant Powered by Machine Learning and NLP 445
Kushagra Purohit, Gaurav Gupta, S. Nagendra Prabhu and Arun Kumar
20.1 Introduction 446
20.2 Literature Investigation 447
20.3 System Analysis 451
20.4 Result Analysis 455
20.5 Convolutional Neural Network (CNN) 460
20.6 Conclusion 462
21 Neuro-IoT and Emotion Recognition: Merging Brain Signals with Smart Electronics for Emotionally Intelligent Systems 465
Vishal Jain, Archan Mitra and Sanchita Paul
21.1 Introduction 466
21.2 Literature Review 469
21.3 Methodology 474
21.4 Findings 477
21.5 Discussion 479
21.6 Conclusion and Future Work 481
22 EmoHeart: Human-Centered First-Emotion Smart IoT Devices for Cardiology 485
Abdul Razak Mohamed Sikkander, Suman Lata Tripathi, Joel J. P. C. Rodrigues and Radhakrishnan
22.1 Introduction 486
22.2 Research Objectives 488
22.3 Methodologies 488
22.4 Challenges and Obstacles 493
22.5 Future Perspectives 496
22.6 Conclusions 498
23 Natural Bioactive Compounds as Cardioprotective Agents: A Promising Avenue for Heart Health 503
Abdul Razak Mohamed Sikkander, Suman Lata Tripathi, Joel J. P. C. Rodrigues, Nitin Wahi, G. Theivanathan and Fatma Bassyouni
23.1 Introduction 504
23.2 Research and Methodologies 507
23.3 Results 525
23.4 Conversations 525
23.5 Challenges and Obstacles 529
23.6 Future Perspectives 530
23.7 Conclusions 531
References 532
Index 539
