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Cognitive Internet of Things in Biomedical Devices

The Next Evolution in Smart Healthcare
Edited by Prateek Singhal and Pramod Kumar Mishra
Copyright: 2026   |   Expected Pub Date:2026/09/30
ISBN: 9781394477593  |  Hardcover  |  
374 pages

One Line Description
Bridging the gap between theory and real-world deployment, this essential guide reveals how cognitive IoT is moving healthcare past passive data tracking to usher in a new era of autonomous, real-time, and life-saving patient care.

Audience
Researchers, academics, clinical technologists, hospital IT managers, data scientists, and policymakers in biomedical engineering, computer science, and healthcare informatics.

Description
The convergence of artificial intelligence, machine learning, and the Internet of Things is fundamentally reshaping healthcare into an intelligent, adaptive, and patient-centric ecosystem. Traditional IoT systems in healthcare have largely functioned as passive data collectors, transmitting vital signs and patient information to healthcare providers. Cognitive IoT, however, introduces self-learning and autonomous decision-making capabilities that enable biomedical devices to analyze complex physiological patterns, predict potential health anomalies, and adapt interventions in real time. From smart prosthetics that respond to neural signals to AI-driven implants that regulate bodily functions, cognitive IoT is redefining the boundaries of medical possibilities. The book explores how this powerful synergy transcends incremental progress to fundamentally transform the way we monitor, diagnose, and manage patient health. Bridging the gap between theoretical foundations and practical applications, this volume covers core cognitive IoT concepts and cutting-edge biomedical innovations demonstrating autonomous diagnostics, intelligent drug delivery, and AI-powered wearables. It examines critical challenges, including federated learning for privacy preservation, cybersecurity vulnerabilities, and ethical considerations in AI-driven medical interventions. This book combines foundational knowledge, applied techniques, and case studies to serve as a comprehensive resource on how cognitive IoT is redefining the healthcare landscape and paving the way for a new era of responsive and intelligent patient care.
Readers will find the volume:
• Provides a comprehensive framework for integrating Cognitive IoT, specifically for biomedical devices;
• Unites advanced AI and machine learning theory with practical, clinically relevant applications in smart healthcare;
• Covers emerging trends like explainable AI for clinical trust and adaptive, patient-centric systems;
• Serves as a foundational text for both educational courses and industry research and development, featuring case studies and practical insights.

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Author / Editor Details
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.

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Table of Contents
Preface
Part 1: Foundations of AI in Medical Imaging and Healthcare Systems
1. Deep Learning for Medical Imaging Analysis: Techniques, Challenges, and Applications
Amrita, Khalil Haruna Aminu, Mustapha Mukhtar Muhammad and Ibrahim Nayaya Isah
1.1 Introduction
1.2 Literature Review
1.3 Deep Learning
Feedforward Neural Network
Convolutional Neural Network
Recurrent Neural Network
Autoencoder
Generative Adversarial Network
Transformer
Graph Neural Network
Self-Organizing Map
Capsule Network
Deep Reinforcement Learning
1.4 Medical Imaging
Medical Imaging Techniques
1.5 Role of Deep Learning in Medical Imaging
1.6 Key Contributions of DL in Medical Imaging
1.7 Conclusion
References
2. Secure and Intelligent Healthcare: Integrating Blockchain, Federated Learning, and Cognitive IoT
Adarsh Tiwari, Renu Mishra, Sneha Sinha, Mamta Narwaria and Anmol Kr. Sah
2.1 Introduction
2.2 Cognitive IoT in Healthcare: Foundation and Relevance
2.3 Blockchain for Secure Health Data Exchange
2.4 Proposed Framework for Blockchain-Enabled CIoT Healthcare
2.5 Security, Privacy, and Compliance Considerations
2.6 Challenges and Future Directions
2.7 Some Proposed Framework and Its Validation
2.8 Conclusion
References
3. Artificial Intelligence in Nonalcoholic Fatty Liver Disease: Enhancing Prediction, Diagnosis, and Treatment Outcomes
Tanisha Salhotra, Amit Kumar Singh, Vivek Kumar Garg and Aditya Kamboj
3.1 Introduction
3.2 Identifying the Patients at Risk of NAFLD
3.3 Diagnosis of NAFLD Severity
3.4 Utilizing AI for Early Identification and Risk Categorization
3.5 Digital Pathology: AI-Powered Analysis of Biopsy Samples
3.6 Predicting Disease Progression
3.7 AI-Driven Imaging Techniques for Diagnosing NAFLD
3.8 Using AI to Advance NAFLD Drug Discovery and Clinical Trials NAFLD
3.9 Conclusion
List of Abbreviations
References
4. AI-Driven Early Disease Detection
K. Deepa, N. Sharmila Banu and G. Dhanraj
4.1 Introduction
4.2 Cognitive Impairment: Definitions, Prevalence, and Clinical Impact
4.3 Concept of Cognitive IoT
4.3.1 Core Features
4.3.2 IoT Devices in Cognitive Healthcare
4.3.3 Transformative Shift
4.4 Data Flow in Cognitive IoT Systems for Cognitive Health
Monitoring
4.4.1 Architecture of Cognitive IoT Systems
4.4.2 Data Gathering
4.4.3 Edge Computing
4.4.4 Cloud Analytics
4.4.5 Integration
4.5 AI Techniques in Cognitive IoT
4.5.1 Supervised Learning
4.5.2 Deep Learning
4.5.3 Natural Language Processing (NLP)
4.5.4 Reinforcement Learning (RL)
4.6 Enabling Technologies (Compressed)
4.6.1 Internet of Medical Things (IoMT)
4.6.2 Edge Computing
4.6.3 Cloud Computing
4.6.4 Big Data Analytics
4.6.5 Communication Protocols
4.7 Digital Biomarkers and Data Analytics
4.7.1 Key Digital Biomarkers
4.8 Wearable Devices in Clinical Trials
4.8.1 Patient Engagement
4.8.2 Speech Analytics
4.9 Challenges and Limitations
4.10 Future Directions
4.11 Conclusion
References
Part 2: Advanced Predictive Modeling and Diagnostic Applications
5. Modeling of Smart Health Systems with Delay Differential
Equations for Predictive Analytics
Pankaj Kumar, Pankaj Rai and Bimal Kumar Mishra
5.1 Introduction
5.2 Literature Review
5.3 Mathematical Model
5.3.1 Basic TDDE Formulation
5.3.2 Existence of the Solution for the Proposed DDE Model
5.3.3 Equilibrium and Linear Stability Analysis
5.3.4 Lyapunov-Based Global Stability
5.3.5 Sensitivity Analysis
5.3.6 AI-Augmented Delay Estimation
5.3.7 Application to ICU Patient Flow
5.4 Numerical Simulation and Validation
5.4.1 Simulation Methodology
5.4.2 Computational Implementation
5.4.3 Results
5.4.3.1 Impact of Delay (τ)
5.4.3.2 Effect of Treatment Efficacy (α)
5.4.3.3 Effect of Health Degradation Rate (β) on Health Dynamics
5.4.3.4 Comparison with ODE Model (No Delay)
5.4.4 Interpretation and Validation
5.5 Discussion
5.6 Conclusion
References
6. Neurodegenerative Disorders: An Overview of Parkinson
Disease, Alzheimer’s Disease, Dementia and Other Conditions
Shreeya Arora, Namrata Dash and Kalpana Katiyar
6.1 Introduction
6.1.1 Socioeconomic Effects and Impact on Healthcare
6.2 Pathobiology of Neurodegeneration
6.2.1 Protein Misfolding, Aggregation, and Toxic Conformations
6.2.2 Dysfunction of Mitochondria and Failure of Energy Metabolism
6.2.3 Imbalance of Oxidative Stress and Redox Status
6.2.4 Excitotoxic Effects and Dysregulation of Calcium
6.2.5 Neuroinflammatory Responses: Actions of Microglia and Astrocytes
6.2.6 Disrupted Proteostasis and Impaired Autophagy-Lysosomal Pathways
6.3 Parkinson’s Disease (PD)
6.3.1 Genetic Components (SNCA, LRRK2, PARK2, PINK1, and DJ-1)
6.3.2 Key Pathological Features: Lewy Bodies and Dopaminergic Neuron Loss
6.3.3 Molecular Mechanisms: α-Synuclein Aggregation and Mitochondrial Stress
6.3.4 Clinical Manifestations: Motor and Non-Motor Symptoms
6.3.5 Diagnostic Indicators and Neuroimaging Techniques
6.3.6 Treatment Methods: Dopamine Replacement, Neurostimulation, and Emerging Neuroprotective Strategies
6.3.7 Case Study: Clinical and Molecular Differences in Early-Onset vs. Late-Onset Parkinson’s
6.4 Alzheimer’s Disease (AD)
6.4.1 Genetic and Epigenetic Risk Factors: APOE ε4, PSEN1/2, APP
6.4.2 The Amyloid Cascade Hypothesis and Tau Hyperphosphorylation
6.4.3 Synaptic Dysfunction and Network Connectivity Failure
6.4.4 Clinical Symptoms and Progression of Disease (MCI to Advanced AD)
6.4.5 Biomarkers in Fluids and Imaging (CSF Aβ42, Tau, PET Tracers)
6.4.6 Existing Pharmacological Treatments and New Anti-Amyloid/Tau Approaches
6.4.7 Case Study: Diagnostic Challenges between Familial and Sporadic Alzheimer’s
6.5 Dementia and Related Conditions
6.5.1 Classification and Neuropathological Features (Vascular Dementia, DLB, FTD, Mixed)
6.5.1.1 Dementia with Lewy Bodies (DLB) and Lewy Body (LB) Diseases (Including Parkinson’s Disease Dementia, PDD)
6.5.1.2 Frontotemporal Dementia (FTD)
6.5.1.3 Mixed Dementia
6.5.2 Molecular Mechanisms That Differentiate Dementia Subtypes
6.5.2.1 Vascular Mechanisms and Neuroinflammation
6.5.2.2 Genetics and Risk Factors
6.5.3 Clinical Progression and Differentiation in Diagnosis
6.5.3.1 Some of the Clinical Progressions are Mentioned Below
6.5.3.2 Diagnostic Challenges and Tools
6.5.4 Overlapping Conditions and Comorbidities with PD and AD
6.5.4.1 Clinical and Mechanistic Overlaps
6.5.4.2 Implications for Management and Prognosis
6.5.5 Case Study: Distinguishing Lewy Body Dementia from Dementia Associated with Parkinson’s
6.5.5.1 Case Study
6.5.5.2 Diagnostic Reasoning
6.6 Other Neurodegenerative Disorders
6.6.1 Huntington’s Disease: Expansion of Polyglutamine and Degeneration of Striatum
6.6.2 Amyotrophic Lateral Sclerosis (ALS)
6.6.3 Prion Disorders: Misfolded PrP and Mechanisms of Infectious Proteins
6.6.4 Rare Neurodegenerative Syndromes with Notable Emerging Research
6.6.5 Case Study: Huntington’s Disease—Clinical Application of Genetic Counseling
6.7 Diagnostics and Biomarker Development
6.7.1 Neuroimaging Techniques (MRI, PET, fMRI, and DTI)
6.7.2 Biomarker Panels in Cerebrospinal Fluid and Blood
6.7.3 Profiling Genomic, Transcriptomic, and Proteomic Data
6.7.4 Digital Biomarkers and Diagnostic Assistance through Artificial Intelligence
6.7.5 Case Study: Early Diagnosis Using Biomarkers in Alzheimer’s and Parkinson’s
6.8 Therapeutics and Translational Advances
6.8.1 Differentiating between Disease-Modifying Therapies and Symptomatic Treatment
6.8.2 Small-Molecule Inhibitors and Immunotherapy Options (Monoclonal Antibodies, Vaccines)
6.8.3 Cell Replacement Strategies: Neurons and Glia Derived from iPSCs
6.8.4 Approaches to Gene Therapy (AAV, CRISPR-Based)
6.8.5 Neuroprotective Agents and Modulators of Metabolism
6.8.6 Non-Pharmacological Strategies: Cognitive Rehabilitation and Lifestyle Changes
6.8.6.1 Cognitive Rehabilitation
6.8.7 Case Study: Patient Outcomes and Limitations in Deep Brain Stimulation for Parkinson’s
6.9 Future Directions in Neurodegenerative Research
6.9.1 Approaches to Precision and Personalized Medicine
6.9.2 Integration of Multiomics Data and Systems Biology Approaches
6.9.3 Predictive Modeling and Utilization of Digital Twins in Neurodegenerative Disease Progression
6.9.4 Challenges in Ethics, Regulation, and Clinical Trials
6.9.5 Case Study: Integration of Clinical Data Across AD, PD, and ALS for Personalized Medicine
6.10 Conclusion
6.10.1 A Unified Perspective on Common and Distinct Mechanisms
6.10.2 Clinical and Research Gaps
6.10.3 Pathways Forward for Advancing Translational Neuroscience
Bibliography
7. Deep Learning-Based Diagnostic System for Histopathological Detection of Uterine Cancer Subtypes
Subbulakshmi T. and Atharva Bandekar
7.1 Introduction
Diagnostic Challenges in Endometrial Pathology
Background and Clinical Motivation
7.2 AI Advances in Histopathology: State of the Art and Gaps
7.2.1 Deep Learning for Tissue Classification
7.2.2 Digital Pathology and Telemedicine
7.2.3 Privacy and Federated Learning
7.2.4 Interpretability and Grad-CAM
7.2.5 Digital Twin and Clinical Decision Support
7.3 Proposed AI Pathology Pipeline Building Method
7.3.1 Preprocessing and Augmentation
7.3.2 Model Architecture
7.3.3 Federated Learning Setup
7.3.4 Explainability and Digital Twin Integration
7.3.5 Real-World Case Studies
7.4 Discussion: Implications and Future Directions
7.4.1 Limitations
7.4.2 Conclusion and Future Directions Real-World Impact
7.4.3 Future Research
Bibliography
8. AI-Powered Smart Prosthetics and Neural Interfaces
Janu Chandak and Pery Patel
8.1 Introduction
8.1.1 AI Evolution in Prosthetics
8.1.2 Neural Interface Technologies Overview
8.2 Hardware Components
8.2.1 Electrode Technology
8.2.2 Wireless Data Transmission
8.3 AI Algorithms for Neural Decoding
8.3.1 Deep Learning for Movement Prediction
8.3.2 Real-Time Adaptability
8.4 Sensory Restoration Via Haptic Feedback
8.4.1 Texture and Pressure Simulation
8.4.2 Closed‐Loop Control
8.5 Ethical and Privacy Considerations
8.6 Applications in Rehabilitation and Performance
8.7 Challenges and Future Directions
References
Part 3: Intelligent Systems and Human-Machine Integration
9. Neural and Muscular Feedback-Driven Adaptive Prosthetics
Janu Chandak and Pery Patel
9.1 Introduction
9.1.1 Evolution of Prosthetics from Mechanical to Neuro-Integrated Systems
9.1.2 Role of Cloud of Things (CloT) in Adaptive Healthcare
9.2 Physiological Signal Acquisition
9.2.1 Neural Signal Mapping (EEG, Invasive BCI)
9.2.2 Muscular Signal Acquisition (sEMG, IMU Sensors)
9.3 Signal Processing and AI Integration
9.3.1 Preprocessing Neural/Muscular Data
9.3.2 Machine Learning for Movement Prediction
9.4 Actuator and Feedback Systems
9.4.1 Motor Control Algorithms
9.4.2 Sensory Feedback Mechanisms
9.5 Case Studies
9.5.1 Lower Limb Gait Adaptation
9.5.2 Upper Limb Dexterity Restoration
9.6 Challenges and Limitations
9.7 Future Prospects in Adaptive Prosthetics
Bibliography
10. Decoding Brain Signals Using a Hybrid LSTM-CNN Deep Learning Model
Sankalp Chakre, Sarvesh Chaudhari, Nirdosh Chavhan, Adwait Gondhalekar and Riddhi Mirajkar
10.1 Introduction
10.2 Literature Survey
10.3 Proposed Methodology
10.3.1 Data Acquisition
10.3.2 Data Preprocessing
10.3.2.1 Band-Pass Filtering
10.3.2.2 Channel Normalization
10.3.2.3 Artifact Removal
10.3.2.4 Segmentization into Fixed-Length Windows
10.3.2.5 Label Encoding and Annotation Mapping
10.3.3 Feature Extraction Using CNN
10.3.3.1 1D Convolution Layers for Spatial Feature Extraction
10.3.3.2 Batch Normalization and Dropout
10.3.3.3 Max-Pooling for Dimensionality Reduction
10.3.4 Temporal Feature Learning Using BiLSTM
10.3.4.1 Why Use BiLSTM for EEG Data?
10.3.4.2 BiLSTM Architecture for EEG Signals
10.3.4.3 Dropout for Overfitting Prevention
10.3.5 Classification Layer
10.3.5.1 Fully Connected Dense Layer
10.3.5.2 Softmax Activation Function for Multiclass Classification
10.3.5.3 Loss Function: Cross-Entropy Loss
10.3.5.4 Optimizer: Adam (Adaptive Moment Estimation)
10.3.6 Model Training and Evaluation
10.4 Result
10.4.1 LSTM Model
10.4.2 CNN Model
10.4.3 Performance of the Hybrid CNN-LSTM Model
10.4.4 Comparative Analysis of EEG Classification Models
10.4.5 Hybrid CNN-LSTM Model
10.5 Conclusion
10.6 Future Scope
References
11. Multimodal GNN-Based Recommendation System for Children’s Movies with Genre-Aware Evaluation and Visualization
Lucky Harichandan and Satyabrata Lenka
11.1 Introduction
11.2 Related Work
11.3 Dataset and Preprocessing
11.3.1 Genre Cleaning and Encoding
11.3.2 Model Architecture
11.3.3 Mathematical Formulation
11.3.4 Embedding Layer
11.3.5 Graph Convolution Layer
11.3.6 Rating Prediction
11.3.7 Loss Function
11.4 Results and Discussion
11.5 Conclusion
References
12. Market Adoption Strategies for Cognitive IoT in Smart
Healthcare
Shrutika Mishra and Priyanshu Mishra
12.1 Introduction
12.2 Literature Review
12.3 Research Methodology
12.4 Limitations
12.5 Future Research Directions
References
Part 4: Data-Driven Healthcare Management and Cross-Domain Applications
13. Data-Driven Decision-Making in Healthcare Management Using Cognitive IoT: A Business and Management Perspective
Priyanshu Mishra and Shrutika Mishra
13.1 Introduction
13.2 Literature Review
13.3 Research Methodology
13.4 Future Directions
13.5 Conclusion
References
14. Predictive Analytics in Healthcare
Amrita, Kabiru Uba Kiru and Saeed Aliyu Usman
14.1 Introduction
14.2 Literature Review
14.3 Predictive Analytics
14.3.1 How Predictive Analytics Works
14.3.2 Techniques Used in Predictive Analytics
14.4 Healthcare
14.5 Roles of Predictive Analytics in Healthcare
14.6 Technologies Supporting Predictive Analytics in Healthcare
14.7 Challenges in Implementing Predictive Analytics in Healthcare
14.8 Conclusion
References
15. Effect of Packaging Material in the Supply Chain: A Case Study of Dairy Packaging
Saureng Kumar and Ankita Panwar
15.1 Introduction
15.2 Literature Review
15.3 Material and Method
15.4 Result
15.5 Conclusion
References
16. The Role of Machine Learning in Advancing Intelligent
Conversational Systems
Sonu Rana, Ayantika Das, Shankar Prasad Mitra and Riya Sil
16.1 Introduction
16.2 Literature Review
16.3 Role of Machine Learning in Conversational AI Development
16.3.1 Data Collection and Preprocessing
16.3.2 Natural Language Understanding (NLU)
16.3.3 Dialogue Management (DM)
16.3.4 Natural Language Generation (NLG)
16.4 Training and Evaluation
16.4.1 Supervised and Unsupervised Learning
16.4.2 Fine-Tuning Pre-Trained Models
16.4.3 Evaluation of Machine Learning (ML)
16.4.4 Feedback and Refinement
16.4.5 Deployment and Maintenance
16.5 Advanced Topics and Emerging Trends
16.5.1 Multimodal AI
16.5.2 Zero-Shot and Few-Shot Learning
16.6 Ethical Considerations
16.7 Conclusion
References
17. A Data-Driven Approach to Predicting Dengue Fever: Integrating Climate Data and Machine Learning
Eswar Reddy and Tannistha Pal
17.1 Introduction
17.2 Literature Review
17.3 Dataset and Features
17.4 Proposed Method
17.5 Results
17.6 Discussion
17.7 Conclusion
References
Index

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