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.