Cognitive Internet of Things in Biomedical Devices

Prateek Singhal is an Assistant Professor in the School of Computer Science and Engineering, Chandigarh University, Uttar Pradesh, India, with five years of research and teaching experience. He has published journal articles, book chapters, and novels, displaying a dedication to sharing research findings with both academic and general audiences. His research and scholarly activities center on the interface of machine learning, artificial intelligence, and healthcare applications. Pramod Kumar Mishra, PhD is a Professor and Head of the Department of Computer Science and Engineering, Banaras Hindu University, Varanasi, India, with more than thirty years of experience in research and teaching. He has published several research articles in international journals and conferences, authored one book, and has several granted patents. His areas of interest include AI and machine learning algorithms, data analytics, parallel computing, high-performance clusters, and algorithm engineering.

• Preface xviiPart 1: Foundations of AI in Medical Imaging and Healthcare Systems 11 Deep Learning for Medical Imaging Analysis: Techniques, Challenges, and Applications 3Amrita, Khalil Haruna Aminu, Mustapha Mukhtar Muhammad and Ibrahim Nayaya Isah1.1 Introduction 41.2 Literature Review 51.3 Deep Learning 121.4 Medical Imaging 141.5 Role of Deep Learning in Medical Imaging 171.6 Key Contributions of DL in Medical Imaging 181.7 Conclusion 192 Secure and Intelligent Healthcare: Integrating Blockchain, Federated Learning, and Cognitive IoT 25Adarsh Tiwari, Renu Mishra, Sneha Sinha, Mamta Narwaria and Anmol Kr. Sah2.1 Introduction 262.2 Cognitive IoT in Healthcare: Foundation and Relevance 272.3 Blockchain for Secure Health Data Exchange 282.4 Proposed Framework for Blockchain-Enabled CIoT Healthcare 292.5 Security, Privacy, and Compliance Considerations 312.6 Challenges and Future Directions 342.7 Some Proposed Framework and Its Validation 362.8 Conclusion 393 Artificial Intelligence in Nonalcoholic Fatty Liver Disease: Enhancing Prediction, Diagnosis, and Treatment Outcomes 43Tanisha Salhotra, Amit Kumar Singh, Vivek Kumar Garg and Aditya Kamboj3.1 Introduction 443.2 Identifying the Patients at Risk of NAFLD 453.3 Diagnosis of NAFLD Severity 463.4 Utilizing AI for Early Identification and Risk Categorization 473.5 Digital Pathology: AI-Powered Analysis of Biopsy Samples 483.6 Predicting Disease Progression 493.7 AI-Driven Imaging Techniques for Diagnosing NAFLD 503.8 Using AI to Advance NAFLD Drug Discovery and Clinical Trials NAFLD 513.9 Conclusion 534 AI-Driven Early Disease Detection 57K. Deepa, N. Sharmila Banu and G. Dhanraj4.1 Introduction 574.2 Cognitive Impairment: Definitions, Prevalence, and Clinical Impact 594.3 Concept of Cognitive IoT 614.4 Data Flow in Cognitive IoT Systems for Cognitive Health Monitoring 624.5 AI Techniques in Cognitive IoT 644.6 Enabling Technologies (Compressed) 674.7 Digital Biomarkers and Data Analytics 684.8 Wearable Devices in Clinical Trials 694.9 Challenges and Limitations 714.10 Future Directions 724.11 Conclusion 72Part 2: Advanced Predictive Modeling and Diagnostic Applications 755 Modeling of Smart Health Systems with Delay Differential Equations for Predictive Analytics 77Pankaj Kumar, Pankaj Rai and Bimal Kumar Mishra5.1 Introduction 785.2 Literature Review 785.3 Mathematical Model 805.4 Numerical Simulation and Validation 845.5 Discussion 885.6 Conclusion 896 Neurodegenerative Disorders: An Overview of Parkinson Disease, Alzheimer's Disease, Dementia and Other Conditions 93Shreeya Arora, Namrata Dash and Kalpana Katiyar6.1 Introduction 946.2 Pathobiology of Neurodegeneration 966.3 Parkinson's Disease (PD) 1006.4 Alzheimer's Disease (AD) 1046.5 Dementia and Related Conditions 1086.6 Other Neurodegenerative Disorders 1136.7 Diagnostics and Biomarker Development 1166.8 Therapeutics and Translational Advances 1196.9 Future Directions in Neurodegenerative Research 1246.10 Conclusion 1277 Deep Learning-Based Diagnostic System for Histopathological Detection of Uterine Cancer Subtypes 133Subbulakshmi T. and Atharva Bandekar7.1 Introduction 1347.2 AI Advances in Histopathology: State of the Art and Gaps 1357.3 Proposed AI Pathology Pipeline Building Method 1387.4 Discussion: Implications and Future Directions 1498 AI-Powered Smart Prosthetics and Neural Interfaces 155Janu Chandak and Pery Patel8.1 Introduction 1568.2 Hardware Components 1588.3 AI Algorithms for Neural Decoding 1608.4 Sensory Restoration Via Haptic Feedback 1618.5 Ethical and Privacy Considerations 1638.6 Applications in Rehabilitation and Performance 1658.7 Challenges and Future Directions 165Part 3: Intelligent Systems and Human-Machine Integration 1739 Neural and Muscular Feedback-Driven Adaptive Prosthetics 175Janu Chandak and Pery Patel9.1 Introduction 1769.2 Physiological Signal Acquisition 1839.3 Signal Processing and AI Integration 1919.4 Actuator and Feedback Systems 1949.5 Case Studies 1979.6 Challenges and Limitations 1999.7 Future Prospects in Adaptive Prosthetics 20010 Decoding Brain Signals Using a Hybrid LSTM-CNN Deep Learning Model 209Sankalp Chakre, Sarvesh Chaudhari, Nirdosh Chavhan, Adwait Gondhalekar and Riddhi Mirajkar10.1 Introduction 21010.2 Literature Survey 21110.3 Proposed Methodology 21510.4 Result 22310.5 Conclusion 22710.6 Future Scope 22711 Multimodal GNN-Based Recommendation System for Children's Movies with Genre-Aware Evaluation and Visualization 231Lucky Harichandan and Satyabrata Lenka11.1 Introduction 23211.2 Related Work 23311.3 Dataset and Preprocessing 23411.4 Results and Discussion 24011.5 Conclusion 24212 Market Adoption Strategies for Cognitive IoT in Smart Healthcare 245Shrutika Mishra and Priyanshu Mishra12.1 Introduction 24612.2 Literature Review 24912.3 Research Methodology 25112.4 Limitations 25512.5 Future Research Directions 258Part 4: Data-Driven Healthcare Management and Cross-Domain Applications 26113 Data-Driven Decision-Making in Healthcare Management Using Cognitive IoT: A Business and Management Perspective 263Priyanshu Mishra and Shrutika Mishra13.1 Introduction 26413.2 Literature Review 26613.3 Research Methodology 27413.4 Future Directions 27813.5 Conclusion 27914 Predictive Analytics in Healthcare 283Amrita, Kabiru Uba Kiru and Saeed Aliyu Usman14.1 Introduction 28414.2 Literature Review 28514.3 Predictive Analytics 29014.4 Healthcare 29414.5 Roles of Predictive Analytics in Healthcare 29414.6 Technologies Supporting Predictive Analytics in Healthcare 29614.7 Challenges in Implementing Predictive Analytics in Healthcare 29714.8 Conclusion 29815 Effect of Packaging Material in the Supply Chain: A Case Study of Dairy Packaging 303Saureng Kumar and Ankita Panwar15.1 Introduction 30415.2 Literature Review 30615.3 Material and Method 30715.4 Result 30815.5 Conclusion 31116 The Role of Machine Learning in Advancing Intelligent Conversational Systems 315Sonu Rana, Ayantika Das, Shankar Prasad Mitra and Riya Sil16.1 Introduction 31616.2 Literature Review 31816.3 Role of Machine Learning in Conversational AI Development 31816.4 Training and Evaluation 32316.5 Advanced Topics and Emerging Trends 32716.6 Ethical Considerations 32916.7 Conclusion 32917 A Data-Driven Approach to Predicting Dengue Fever: Integrating Climate Data and Machine Learning 333Eswar Reddy and Tannistha Pal17.1 Introduction 33417.2 Literature Review 33517.3 Dataset and Features 33717.4 Proposed Method 33917.5 Results 34317.6 Discussion 34617.7 Conclusion 346References 347Index 349