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For Authors
Submit a ProposalAdvances in Fuzzy-Based Internet of Medical Things (IoMT)
 | Edited by Satya Prakash Yadav, Sudesh Yadav, Pethuru Raj and Victor Hugo C. de Albuquerque
Copyright: 2024 | Status: Published ISBN: 9781394242221 | Hardcover | 306 pages Price: $195 USD  |
One Line Description
This book explores the latest trends, transitions, and advancements of the Internet
of Medical Things whose integration through cloud-hosted software applications
adds required intelligence from tools such as medical instruments, scanners, and
appliances, enabling fuzzy logic to help medical professionals establish linguistic
concepts in deciding diagnosis and prognosis.
Audience
The book will be read and used by researchers in artificial intelligence, fuzzy logic, medical professionals, caregivers, health administrators, and policymakers.
The book will be read and used by researchers in artificial intelligence, fuzzy logic, medical professionals, caregivers, health administrators, and policymakers.
Description
The main goal of the book is to strengthen medical professionals, and caregivers by providing methods for achieving fuzzy logic-based health diagnosis and medication. The health condition and various physical parameters of humans, such as heartbeat rate, sugar level, blood pressure, temperature, and oxygen quality, are captured through a host of multifaceted sensors. Additionally, remote health monitoring, medication, and management are being facilitated through a host of
ingestible sensors, 5G communication, networked embedded systems, AI models running on cloud servers and edge devices, etc. Furthermore, chronic disease management is another vital domain getting increased attention. The distinct advancements in the fuzzy logic field are useful in various advanced medical care functionalities and facilities.
The readers will discover:
• New and innovative features of health care by using fuzzy logic that raises economic efficiency at macro and micro levels;
• Expounds on fuzzy logic techniques used in medical science;
• Describes the evolution of the fuzzy logic paradigm and how it helps physicians decide on diagnosis and prognosis;
• Uncovers how trust management is dealt with between patients and medical officials to help advance the fuzzy logic field;
• Provides case studies, various technology advancements, and practical aspects on the impacts and challenges of fuzzy-based Internet of Medical Things.
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The main goal of the book is to strengthen medical professionals, and caregivers by providing methods for achieving fuzzy logic-based health diagnosis and medication. The health condition and various physical parameters of humans, such as heartbeat rate, sugar level, blood pressure, temperature, and oxygen quality, are captured through a host of multifaceted sensors. Additionally, remote health monitoring, medication, and management are being facilitated through a host of
ingestible sensors, 5G communication, networked embedded systems, AI models running on cloud servers and edge devices, etc. Furthermore, chronic disease management is another vital domain getting increased attention. The distinct advancements in the fuzzy logic field are useful in various advanced medical care functionalities and facilities.
The readers will discover:
• New and innovative features of health care by using fuzzy logic that raises economic efficiency at macro and micro levels;
• Expounds on fuzzy logic techniques used in medical science;
• Describes the evolution of the fuzzy logic paradigm and how it helps physicians decide on diagnosis and prognosis;
• Uncovers how trust management is dealt with between patients and medical officials to help advance the fuzzy logic field;
• Provides case studies, various technology advancements, and practical aspects on the impacts and challenges of fuzzy-based Internet of Medical Things.
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Author / Editor Details
Satya Prakash Yadav, PhD is an associate professor at GL Bajaj Institute of Technology & Management in Uttar Pradesh, India. He completed a PhD in science and technology from the Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India. He has 17 years of education experience, 4 published books, and has two patents. Yadav is the chief editor of the Journal of Cyber Security in Computer Systems Science and Engineering.
Satya Prakash Yadav, PhD is an associate professor at GL Bajaj Institute of Technology & Management in Uttar Pradesh, India. He completed a PhD in science and technology from the Dr. A.P.J. Abdul Kalam Technical University, Lucknow, India. He has 17 years of education experience, 4 published books, and has two patents. Yadav is the chief editor of the Journal of Cyber Security in Computer Systems Science and Engineering.
Sudesh Yadav, PhD is an assistant professor at Government College in Haryana, India. She has a PhD in computer science from Guru Gobind Singh Indraprastha University, Delhi, India. Her main areas of interest are artificial intelligence, digital image processing, and soft computing. Along with
publishing research papers, Yadav has also reviewed research papers from international journals and conferences.
publishing research papers, Yadav has also reviewed research papers from international journals and conferences.
Pethura Raj, PhD is chief architect and vice president at Reliance Jio Platforms Ltd in Bangalore, India. He has a PhD in computer science and automation from the Indian Institute of Science in Bangalore, India. His areas of interest focus on artificial intelligence, model optimization, and reliability engineering. He has published thirty research papers and edited forty-two books.
Victor Hugo C. de Albuquerque, PhD works in the Department of Teleinformatics Engineering at the Federal University of Ceara, Brazil. He has a PhD in mechanical engineering from the Federal University of Paraiba. His research concentrates on applied computing and Intelligent systems.
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Table of Contents
Preface
1. IoMT—Applications, Benefits, and Future Challenges in the Healthcare Domain
E. M. N. Sharmila, K. Rama Krishna, G. N. R. Prasad, Byram Anand, Chetna Vaid Kwatra and Dhiraj Kapila
1.1 Introduction
1.2 Literature Review
1.3 Healthcare System and IoT Overview
1.4 IoT-Based Healthcare System
1.5 Smart Healthcare System—Benefits
1.6 Smart Healthcare System—Applications
1.7 IoT Applications
1.8 Different Layers in the IoMT
1.9 Data Collection Using the IoMT
1.10 IoT Future Benefits
1.11 IoT Healthcare for the Future
1.12 Conclusion
References
2. Fuzzy-Based IoMT System Design Challenges
Ramakrishna Kolikipogu, Shivaputra and Makarand Upadhyaya
2.1 Introduction
2.1.1 Multiple Criteria Analysis
2.1.2 AHP Methods
2.1.3 Topics
2.2 TOPSIS Method
2.2.1 Medical IoT Challenge
2.2.2 Fuzzy Analytic Hierarchy Process
2.2.3 Calculating of Local and Global Weights
2.2.4 Fuzzy TOPSIS Technique
2.3 Results and Discussion
2.4 Conclusion
References
3. Development and Implementation of a Fuzzy Logic-Based Framework for the Internet of Medical Things (IoMT)
Santosh Reddy P., Mamatha A., Akshatha Kamath, Sreelatha P. K., Santosh Y. N. and Pallavi C. V.
3.1 Introduction
3.2 Literature Review
3.3 The Integration of a Fuzzy Logic System
3.4 Fuzzy Latent Semantic Analysis
3.5 Fuzzy in Healthcare
3.6 Conclusion
References
4. Detecting Healthcare Issues Using a Neuro-Fuzzy Classifier
D. Saravanan, R. Parthiban, G. Arunkumar, D. Suganthi, Revathi R. and U. Palani
4.1 Introduction
4.1.1 Processing of the Medical Image
4.1.2 Proposed Systems
4.1.3 Histogram-Based Method
4.1.4 Stages of Image Enhancement
4.1.5 Soft Computer Techniques
4.2 Clustering
4.2.1 K-Means Clustering
4.2.2 C-Means Clustering
4.3 Fuzzy Clustering
4.3.1 C-Means Fuzzy Clustering
4.3.2 Neuro-Fuzzy Model
4.4 Fuzzy Based on Image Fusion
4.5 Neuro Techniques
4.6 Results of Fuzzy Logic
4.7 Conclusion
Bibliography
5. Development of the Fuzzy Logic System for Monitoring of Patient Health
Norma Ramírez-Asís, Ursula Lezameta-Blas, Anil Kumar Bisht, G. Arunkumar, Jose Rodriguez-Kong and D. Saravanan
5.1 Introduction
5.2 Literature Review
5.3 Fuzzy Logic System in Healthcare
5.4 Proposed Design
5.5 Overall System Architecture 86
5.6 Modified Early Warning Score
5.7 Conclusion
References
6. Management of Trust Between Patient and IoT Using Fuzzy Logic Theory
L. Rajeshkumar, J. Rachel Priya, Konatham Sumalatha, G. Arunkumar, D. Suganthi and D. Saravanan
6.1 Introduction
6.2 Scalable Trust Management
6.2.1 Experience €
6.2.2 Recommendation (R)
6.2.3 Device Classification (D)
6.3 IoT Integration
6.3.1 IoT Device Authentication
6.3.2 Confidentiality and Non-Repudiation
6.3.3 Trust Management and Data Leakage
6.4 Approaches to Blockchain Solutions for IoT Applications
6.5 Implementation and Result
6.5.1 Experimental Setup
6.5.2 Implementing Smart Contracts for IoT Solutions
6.5.3 Experimental Purposes for SCHIS (Smart Contract-Based Health Insurance System)
6.6 Conclusion
References
7. Improving the Efficiency of IoMT Using Fuzzy Logic Methods
K. Kiran Kumar, S. Sivakumar, Pramoda Patro and RenuVij
7.1 Introduction
7.2 Related to Work
7.2.1 Fuzzy Interface System for DSM
7.3 Problem Formulation
7.4 System Model Implementation
7.5 Performance Evaluation
7.6 Results
7.6.1 Result of FIS with Proposed FLC in Cold Cities
7.6.2 FLC Proposed Cost in Cold Cities
7.6.3 Proposed FIS Using PAR
7.6.4 Maintaining the Proposed FIS
7.7 Discussion
7.8 Conclusion
Bibliography
8. An Intelligent IoT-Based Healthcare System Using Fuzzy Neural Networks
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi and D. Suganthi
8.1 Introduction
8.1.1 Work with HEMS
8.1.2 Based for Algorithms in DSM
8.1.3 Fuzzy Inference System for DSM
8.2 Problem Formulation
8.2.1 Controller for FIS Logic
8.2.2 The Proposed Model
8.2.3 Model of Residential Heating System
8.3 House Heating System
8.4 Simulation Results of FIS
8.5 Conclusions
References
9. An Enhanced Fuzzy Deep Learning (IFDL) Model for Pap-Smear Cell Image Classification
Rakesh S., Smrita Barua, D. Anitha Kumari and Naresh E.
9.1 Introduction
9.2 Work Related
9.3 Methods and Materials
9.3.1 Deep Neural Network Model
9.3.2 Dataset Preprocessing
9.3.3 Parameters of the Model
9.4 Results
9.5 Conclusion
References
10. Classification and Diagnosis of Heart Diseases Using Fuzzy Logic Based on IoT
Srinivas Kolli, Pramoda Patro, Rupak Sharma and Amit Sharma
10.1 Introduction
10.2 Related Works of IoMT
10.3 Hybrid Model for Hortonworks Data Platform (HDP)
10.4 Ant Lion Optimization and Hybrid Linear Discriminant
10.4.1 ALO
10.4.2 Linear Discriminant Analysis
10.5 Result
10.5.1 Dataset Description
10.5.2 Performance Metrics
10.6 Conclusions
References
11. Implementation of a Neuro-Fuzzy-Based Classifier for the Detection of Types 1 and 2 Diabetes
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi and D. Suganthi
11.1 Introduction
11.2 Methodology
11.3 Proposed Approach
11.3.1 Feature Selection
11.3.2 Neuro-Fuzzy Temporal Classification
11.4 Result with Discussion
11.5 Conclusion and Future Scope
Bibliography
12. IoMT Type-2 Fuzzy Logic Implementation
Sasanko Sekhar Gantayat, K. M. Pimple and Pokkuluri Kiran Sree
12.1 Introduction
12.1.1 Motivational
12.1.2 Contribution and Organization
12.2 Related Work
12.2.1 Sensor Monitoring System
12.2.2 Environmental Monitoring System
12.2.3 FIS Monitoring System
12.2.4 Data Fusion and Detection of Outliers
12.2.5 Prediction Techniques
12.2.6 Consensus Methods and Metrics
12.3 Rationale
12.3.1 Description Scenario
12.3.2 Fuzzy Set of Type 2
12.3.3 Driven Uncertainly Mechanism
12.3.4 Fusion Data of Multi-Sensor
12.3.5 Detection of an Outlier
12.3.6 Sensor Confidence
12.3.7 FIS Process of Type 2
12.4 Performance Evolution
12.4.1 Comparison with a Type 1 in FIS
12.4.2 Comparison with Other Models
12.4.3 Discussion on Sensor Coverage Impact
12.5 Conclusions
References
13. Feature Extraction and Diagnosis of Heart Diseases Using Fuzzy-Based IoMT
Tribhangin Dichpally, Yatish Wutla, Vallabhaneni Uday and Rohith Sai Midigudla
13.1 Introduction
13.2 Literature Survey
13.3 Prediction of Heart Disease by IoMT
13.3.1 Configuration of the System
13.3.2 IoT
13.3.3 Deep Framework
13.4 Feature Extraction from Signals
13.4.1 Overall Current Distortion of Signal
13.4.2 The Entropy Function
13.4.3 Mean of Minimum and Maximum
13.5 Optimized Cascaded CNN
13.5.1 GSO
13.6 Results and Discussion
13.6.1 Analysis Based on Heuristic Techniques
13.6.2 Comparative Analysis
13.6.3 K-Fold Validation
13.7 Conclusion
References
14. An Intelligent Heartbeat Management System Utilizing Fuzzy Logic
K. Suresh Kumar, R. Sudha, T. Suguna and M. K. Dharani
14.1 Introduction
14.2 Literature Survey
14.3 Implementation of the System Design
14.3.1 Architecture System
14.3.2 Design System
14.3.3 ThingSpeak
14.4 Analysis and Result
14.4.1 Measurements of Heartbeat Rate
14.4.2 Temperature Measurements
14.4.3 Air Measurements
14.4.4 Cloud from Data
14.5 Conclusion
References
15. Functional Fuzzy Logic and Algorithm for Medical Data Management Mechanism Monitoring
U. Moulali, Bhargavi Peddi Reddy, Srikanth Bhyrapuneni, Shruthi S.K., Shaik Khaleel Ahamed and Harikrishna Bommala
15.1 Introduction
15.2 Fuzzy Logic System Integration
15.3 FLSA
15.4 FIS in Data Healthcare
15.5 Conclusion
Bibliography
16. Using IoT to Evaluate the Effectiveness of Online Interactive Tools in Healthcare
K. Suresh Kumar, Chinmaya Kumar Nayak, Chamandeep Kaur and Ahmed Hesham Sedky
16.1 Introduction
16.2 Healthcare and Applications of the IoT
16.2.1 Healthcare Monitoring Systems
16.2.2 Benefits of Using IoMT Healthcare
16.3 Review of Related Studies
16.4 IoWT
16.4.1 IoT Healthcare System
16.4.2 Classification of Health Sensors
16.5 IoT Challenges of Healthcare
16.5.1 Security and Privacy
16.5.2 Based on Energy
16.5.3 Based on Integration
16.6 Results and Recommendations
16.7 Conclusion
Bibliography
17. Integration of Edge Computing and Fuzzy Logic to Monitor Novel Coronavirus
K. Rama Krishna, R. Sudha, G. N. R. Prasad and Jithender Reddy Machana
17.1 Introduction
17.2 Literature Review
17.3 COVID Detection
17.3.1 Function and Workflow of FLCD
17.3.2 Data Collection Layer
17.3.3 Cloud Layer
17.4 Setup of FLCD
17.4.1 Data Refinement Phase
17.4.2 Fuzzification
17.4.3 Defuzzification
17.5 Result
17.5.1 Confusion Matrix
17.6 Conclusion
References
18. Implementation of IoT in Healthcare Barriers and Future Challenges
Aravindan Srinivasan, Veeresh Rampur, Munagala Madhu Sudhan Rao and Ravinjit Singh
18.1 Introduction
18.2 The IoT in Healthcare
18.3 IoT Smart Features
18.4 The IoT Process in the Health Process
18.5 Applications
18.6 IoT Barriers and Future Challenges
18.7 Discussion
18.8 Conclusion
References
Index
Back to Top
Preface
1. IoMT—Applications, Benefits, and Future Challenges in the Healthcare Domain
E. M. N. Sharmila, K. Rama Krishna, G. N. R. Prasad, Byram Anand, Chetna Vaid Kwatra and Dhiraj Kapila
1.1 Introduction
1.2 Literature Review
1.3 Healthcare System and IoT Overview
1.4 IoT-Based Healthcare System
1.5 Smart Healthcare System—Benefits
1.6 Smart Healthcare System—Applications
1.7 IoT Applications
1.8 Different Layers in the IoMT
1.9 Data Collection Using the IoMT
1.10 IoT Future Benefits
1.11 IoT Healthcare for the Future
1.12 Conclusion
References
2. Fuzzy-Based IoMT System Design Challenges
Ramakrishna Kolikipogu, Shivaputra and Makarand Upadhyaya
2.1 Introduction
2.1.1 Multiple Criteria Analysis
2.1.2 AHP Methods
2.1.3 Topics
2.2 TOPSIS Method
2.2.1 Medical IoT Challenge
2.2.2 Fuzzy Analytic Hierarchy Process
2.2.3 Calculating of Local and Global Weights
2.2.4 Fuzzy TOPSIS Technique
2.3 Results and Discussion
2.4 Conclusion
References
3. Development and Implementation of a Fuzzy Logic-Based Framework for the Internet of Medical Things (IoMT)
Santosh Reddy P., Mamatha A., Akshatha Kamath, Sreelatha P. K., Santosh Y. N. and Pallavi C. V.
3.1 Introduction
3.2 Literature Review
3.3 The Integration of a Fuzzy Logic System
3.4 Fuzzy Latent Semantic Analysis
3.5 Fuzzy in Healthcare
3.6 Conclusion
References
4. Detecting Healthcare Issues Using a Neuro-Fuzzy Classifier
D. Saravanan, R. Parthiban, G. Arunkumar, D. Suganthi, Revathi R. and U. Palani
4.1 Introduction
4.1.1 Processing of the Medical Image
4.1.2 Proposed Systems
4.1.3 Histogram-Based Method
4.1.4 Stages of Image Enhancement
4.1.5 Soft Computer Techniques
4.2 Clustering
4.2.1 K-Means Clustering
4.2.2 C-Means Clustering
4.3 Fuzzy Clustering
4.3.1 C-Means Fuzzy Clustering
4.3.2 Neuro-Fuzzy Model
4.4 Fuzzy Based on Image Fusion
4.5 Neuro Techniques
4.6 Results of Fuzzy Logic
4.7 Conclusion
Bibliography
5. Development of the Fuzzy Logic System for Monitoring of Patient Health
Norma Ramírez-Asís, Ursula Lezameta-Blas, Anil Kumar Bisht, G. Arunkumar, Jose Rodriguez-Kong and D. Saravanan
5.1 Introduction
5.2 Literature Review
5.3 Fuzzy Logic System in Healthcare
5.4 Proposed Design
5.5 Overall System Architecture 86
5.6 Modified Early Warning Score
5.7 Conclusion
References
6. Management of Trust Between Patient and IoT Using Fuzzy Logic Theory
L. Rajeshkumar, J. Rachel Priya, Konatham Sumalatha, G. Arunkumar, D. Suganthi and D. Saravanan
6.1 Introduction
6.2 Scalable Trust Management
6.2.1 Experience €
6.2.2 Recommendation (R)
6.2.3 Device Classification (D)
6.3 IoT Integration
6.3.1 IoT Device Authentication
6.3.2 Confidentiality and Non-Repudiation
6.3.3 Trust Management and Data Leakage
6.4 Approaches to Blockchain Solutions for IoT Applications
6.5 Implementation and Result
6.5.1 Experimental Setup
6.5.2 Implementing Smart Contracts for IoT Solutions
6.5.3 Experimental Purposes for SCHIS (Smart Contract-Based Health Insurance System)
6.6 Conclusion
References
7. Improving the Efficiency of IoMT Using Fuzzy Logic Methods
K. Kiran Kumar, S. Sivakumar, Pramoda Patro and RenuVij
7.1 Introduction
7.2 Related to Work
7.2.1 Fuzzy Interface System for DSM
7.3 Problem Formulation
7.4 System Model Implementation
7.5 Performance Evaluation
7.6 Results
7.6.1 Result of FIS with Proposed FLC in Cold Cities
7.6.2 FLC Proposed Cost in Cold Cities
7.6.3 Proposed FIS Using PAR
7.6.4 Maintaining the Proposed FIS
7.7 Discussion
7.8 Conclusion
Bibliography
8. An Intelligent IoT-Based Healthcare System Using Fuzzy Neural Networks
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi and D. Suganthi
8.1 Introduction
8.1.1 Work with HEMS
8.1.2 Based for Algorithms in DSM
8.1.3 Fuzzy Inference System for DSM
8.2 Problem Formulation
8.2.1 Controller for FIS Logic
8.2.2 The Proposed Model
8.2.3 Model of Residential Heating System
8.3 House Heating System
8.4 Simulation Results of FIS
8.5 Conclusions
References
9. An Enhanced Fuzzy Deep Learning (IFDL) Model for Pap-Smear Cell Image Classification
Rakesh S., Smrita Barua, D. Anitha Kumari and Naresh E.
9.1 Introduction
9.2 Work Related
9.3 Methods and Materials
9.3.1 Deep Neural Network Model
9.3.2 Dataset Preprocessing
9.3.3 Parameters of the Model
9.4 Results
9.5 Conclusion
References
10. Classification and Diagnosis of Heart Diseases Using Fuzzy Logic Based on IoT
Srinivas Kolli, Pramoda Patro, Rupak Sharma and Amit Sharma
10.1 Introduction
10.2 Related Works of IoMT
10.3 Hybrid Model for Hortonworks Data Platform (HDP)
10.4 Ant Lion Optimization and Hybrid Linear Discriminant
10.4.1 ALO
10.4.2 Linear Discriminant Analysis
10.5 Result
10.5.1 Dataset Description
10.5.2 Performance Metrics
10.6 Conclusions
References
11. Implementation of a Neuro-Fuzzy-Based Classifier for the Detection of Types 1 and 2 Diabetes
Chamandeep Kaur, Mohammed Saleh Al Ansari, Vijay Kumar Dwivedi and D. Suganthi
11.1 Introduction
11.2 Methodology
11.3 Proposed Approach
11.3.1 Feature Selection
11.3.2 Neuro-Fuzzy Temporal Classification
11.4 Result with Discussion
11.5 Conclusion and Future Scope
Bibliography
12. IoMT Type-2 Fuzzy Logic Implementation
Sasanko Sekhar Gantayat, K. M. Pimple and Pokkuluri Kiran Sree
12.1 Introduction
12.1.1 Motivational
12.1.2 Contribution and Organization
12.2 Related Work
12.2.1 Sensor Monitoring System
12.2.2 Environmental Monitoring System
12.2.3 FIS Monitoring System
12.2.4 Data Fusion and Detection of Outliers
12.2.5 Prediction Techniques
12.2.6 Consensus Methods and Metrics
12.3 Rationale
12.3.1 Description Scenario
12.3.2 Fuzzy Set of Type 2
12.3.3 Driven Uncertainly Mechanism
12.3.4 Fusion Data of Multi-Sensor
12.3.5 Detection of an Outlier
12.3.6 Sensor Confidence
12.3.7 FIS Process of Type 2
12.4 Performance Evolution
12.4.1 Comparison with a Type 1 in FIS
12.4.2 Comparison with Other Models
12.4.3 Discussion on Sensor Coverage Impact
12.5 Conclusions
References
13. Feature Extraction and Diagnosis of Heart Diseases Using Fuzzy-Based IoMT
Tribhangin Dichpally, Yatish Wutla, Vallabhaneni Uday and Rohith Sai Midigudla
13.1 Introduction
13.2 Literature Survey
13.3 Prediction of Heart Disease by IoMT
13.3.1 Configuration of the System
13.3.2 IoT
13.3.3 Deep Framework
13.4 Feature Extraction from Signals
13.4.1 Overall Current Distortion of Signal
13.4.2 The Entropy Function
13.4.3 Mean of Minimum and Maximum
13.5 Optimized Cascaded CNN
13.5.1 GSO
13.6 Results and Discussion
13.6.1 Analysis Based on Heuristic Techniques
13.6.2 Comparative Analysis
13.6.3 K-Fold Validation
13.7 Conclusion
References
14. An Intelligent Heartbeat Management System Utilizing Fuzzy Logic
K. Suresh Kumar, R. Sudha, T. Suguna and M. K. Dharani
14.1 Introduction
14.2 Literature Survey
14.3 Implementation of the System Design
14.3.1 Architecture System
14.3.2 Design System
14.3.3 ThingSpeak
14.4 Analysis and Result
14.4.1 Measurements of Heartbeat Rate
14.4.2 Temperature Measurements
14.4.3 Air Measurements
14.4.4 Cloud from Data
14.5 Conclusion
References
15. Functional Fuzzy Logic and Algorithm for Medical Data Management Mechanism Monitoring
U. Moulali, Bhargavi Peddi Reddy, Srikanth Bhyrapuneni, Shruthi S.K., Shaik Khaleel Ahamed and Harikrishna Bommala
15.1 Introduction
15.2 Fuzzy Logic System Integration
15.3 FLSA
15.4 FIS in Data Healthcare
15.5 Conclusion
Bibliography
16. Using IoT to Evaluate the Effectiveness of Online Interactive Tools in Healthcare
K. Suresh Kumar, Chinmaya Kumar Nayak, Chamandeep Kaur and Ahmed Hesham Sedky
16.1 Introduction
16.2 Healthcare and Applications of the IoT
16.2.1 Healthcare Monitoring Systems
16.2.2 Benefits of Using IoMT Healthcare
16.3 Review of Related Studies
16.4 IoWT
16.4.1 IoT Healthcare System
16.4.2 Classification of Health Sensors
16.5 IoT Challenges of Healthcare
16.5.1 Security and Privacy
16.5.2 Based on Energy
16.5.3 Based on Integration
16.6 Results and Recommendations
16.7 Conclusion
Bibliography
17. Integration of Edge Computing and Fuzzy Logic to Monitor Novel Coronavirus
K. Rama Krishna, R. Sudha, G. N. R. Prasad and Jithender Reddy Machana
17.1 Introduction
17.2 Literature Review
17.3 COVID Detection
17.3.1 Function and Workflow of FLCD
17.3.2 Data Collection Layer
17.3.3 Cloud Layer
17.4 Setup of FLCD
17.4.1 Data Refinement Phase
17.4.2 Fuzzification
17.4.3 Defuzzification
17.5 Result
17.5.1 Confusion Matrix
17.6 Conclusion
References
18. Implementation of IoT in Healthcare Barriers and Future Challenges
Aravindan Srinivasan, Veeresh Rampur, Munagala Madhu Sudhan Rao and Ravinjit Singh
18.1 Introduction
18.2 The IoT in Healthcare
18.3 IoT Smart Features
18.4 The IoT Process in the Health Process
18.5 Applications
18.6 IoT Barriers and Future Challenges
18.7 Discussion
18.8 Conclusion
References
Index
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