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Tele-Healthcare: Applications of Artificial Intelligence and Soft Computing Techniques [Kõva köide]

Edited by (esearch and Development, Intelligent Research Consultancy Services (iRCS), India), Edited by (School of Computer Science & Engineering, VIT Chennai, India), Edited by (Jawaharlal Nehru Technical University, India)
  • Formaat: Hardback, 416 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 13-Jul-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119841763
  • ISBN-13: 9781119841760
Teised raamatud teemal:
Tele-Healthcare: Applications of Artificial Intelligence and Soft Computing TechniquesSuurem pilt
  • Formaat: Hardback, 416 pages, kõrgus x laius x paksus: 10x10x10 mm, kaal: 454 g
  • Ilmumisaeg: 13-Jul-2022
  • Kirjastus: Wiley-Scrivener
  • ISBN-10: 1119841763
  • ISBN-13: 9781119841760
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9781119841760.html
Märksõnad:
TELE-HEALTHCARE This book elucidates all aspects of tele-healthcare which is the application of AI, soft computing, digital information, and communication technologies, to provide services remotely and manage ones healthcare.

Throughout the world, there are huge developing crises with respect to healthcare workforce shortages, as well as a growing burden of chronic diseases. As a result, e-health has become one of the fastest-growing service areas in the medical sector. E-health supports and ensures the availability of proper healthcare, public health, and health education services at a distance and in remote places. For the sector to grow and meet the need of the marketplace, e-health applications have become one of the fastest growing areas of research. However, to grow at a larger scale requires the following:







The availability of user cases for the exact identification of problems that need to be visualized. A well-supported market that can promote and adopt the e-health care concept.

Development of cost-effectiveness applications and technologies for successful implementation of e-health at a larger scale.

This book mainly focuses on these three points for the development and implementation of e-health services globally.

In this book the reader will find:





Details of the challenges in promoting and implementing the telehealth industry. How to expand a globalized agenda of personalized telehealth in integrative medical treatment for disease diagnosis and its industrial transformation. How to design machine learning techniques for improving the tele-healthcare system.

Audience

Researchers and post-graduate students in biomedical engineering, artificial intelligence, and information technology; medical doctors and practitioners and industry experts in the healthcare sector; healthcare sector network administrators.
Preface xv
1 Machine Learning-Assisted Remote Patient Monitoring with Data Analytics
1(26)
Vinutha D. C. Kavyashree
G. T. Raju
1.1 Introduction
2(3)
1.1.1 Traditional Patient Monitoring System
2(1)
1.1.2 Remote Monitoring System
3(1)
1.1.3 Challenges in RPM
4(1)
1.2 Literature Survey
5(3)
1.2.1 Machine Learning Approaches in Patient Monitoring
7(1)
1.3 Machine Learning in RPM
8(7)
1.3.1 Support Vector Machine
9(1)
1.3.2 Decision Tree
10(1)
1.3.3 Random Forest
11(1)
1.3.4 Logistic Regression
11(1)
1.3.5 Genetic Algorithm
12(1)
1.3.6 Simple Linear Regression
12(1)
1.3.7 KNN Algorithm
13(1)
1.3.8 Naive Bayes Algorithm
14(1)
1.4 System Architecture
15(6)
1.4.1 Data Collection
16(1)
1.4.2 Data Pre-Processing
17(1)
1.4.3 Apply Machine Learning Algorithm and Prediction
18(3)
1.5 Results
21(2)
1.6 Future Enhancement
23(1)
1.7 Conclusion 24 References
24(3)
2 A Survey on Recent Computer-Aided Diagnosis for Detecting Diabetic Retinopathy
27(32)
C. Priyadharsini
R. Jagadeesh Kantian
Farookh Khadeer Hussain
2.1 Introduction
28(1)
2.2 Diabetic Retinopathy
28(3)
2.2.1 Features of DR
28(1)
2.2.2 Stages of DR
29(2)
2.3 Overview of DL Models
31(2)
2.3.1 Convolution Neural Network
31(1)
2.3.2 Autoencoders
32(1)
2.3.3 Boltzmann Machine and Deep Belief Network
32(1)
2.4 Data Set
33(1)
2.5 Performance Metrics
34(2)
2.6 Literature Survey
36(16)
2.6.1 Segmentation of Blood Vessels
36(13)
2.6.2 Optic Disc Feature
49(1)
2.6.3 Lesion Detections
50(1)
2.6.3.1 Exudate Detection
50(1)
2.6.3.2 MA and HM
51(1)
2.6.4 DR Classification
51(1)
2.7 Discussion and Future Directions
52(1)
2.8 Conclusion
53(6)
References
53(6)
3 A New Improved Cryptography Method-Based e-Health Application in Cloud Computing Environment
59(26)
Dipesh Kumar
Nirupama Mandal
Yugal Kumar
3.1 Introduction
60(2)
3.1.1 Contribution
61(1)
3.2 Motivation
62(1)
3.3 Related Works
62(2)
3.4 Challenges
64(1)
3.5 Proposed Work
64(2)
3.6 Proposed Algorithm for Encryption
66(7)
3.6.1 Demonstration of Encryption Algorithm
66(1)
3.6.1.1 When the Number of Columns Selected in the Table is Even
66(3)
3.6.1.2 When the Number of Columns Selected in the Table is Odd
69(3)
3.6.2 Flowchart for Encryption
72(1)
3.7 Algorithm for Decryption
73(5)
3.7.1 Demonstration of Decryption Algorithm
73(1)
3.7.1.1 When the Number of Columns Selected in the Table is Even
73(2)
3.7.1.2 When the Number of Columns Selected in the Table is Odd
75(3)
3.7.2 Flowchart of Decryption Algorithm
78(1)
3.8 Experiment and Result
78(2)
3.9 Conclusion
80(5)
References
80(5)
4 Cutaneous Disease Optimization Using Teledermatology Underresourced Clinics
85(16)
M. Supriya
K. Murugan
T. Shanmugaraja
T. Venkatesh
4.1 Introduction
86(1)
4.2 Materials and Methods
87(1)
4.2.1 Clinical Setting and Teledermatology Workflow
87(1)
4.2.2 Study Design, Data Collection, and Analysis
87(1)
4.3 Proposed System
88(7)
4.3.1 Teledermatology in an Underresourced Clinic
88(1)
4.3.2 Teledermatology Consultations from Uninsured Patients
89(1)
4.3.3 Teledermatology for Patients Lacking Access to Dermatologists
90(2)
4.3.4 Teledermatologist Management from Nonspecialists
92(1)
4.3.5 Segment Factors of Referring PCPs and Their Patients
93(1)
4.3.6 Teledermatology Operational Considerations
94(1)
4.3.7 Instruction of PCPs
94(1)
4.4 Challenges
95(1)
4.5 Results and Discussion
95(6)
4.5.1 Challenges of Referring to Teledermatology Services 96 References
98(3)
5 Cognitive Assessment Based on Eye Tracking Using Device-Embedded Cameras via Tele-Neuropsychology
101(16)
T. Shanmugaraja
T. Venkatesh
M. Supriya
K. Murugan
5.1 Introduction
102(1)
5.2 Materials and Methods
102(1)
5.3 Framework Elements
102(4)
5.3.1 Eye Tracker Camera
102(1)
5.3.2 Test Construction
103(3)
5.3.3 Web Camera
106(1)
5.3.4 Camera for Eye Tracking
106(1)
5.4 Proposed System
106(3)
5.4.1 Camera for Tracking Eye
106(2)
5.4.2 Web Camera
108(1)
5.4.3 Scoring
108(1)
5.4.4 Eye Tracking Camera
108(1)
5.4.5 Web Camera Human-Coded Scoring
108(1)
5.5 Subjects
109(1)
5.5.1 Characteristics of Subject
109(1)
5.6 Methodology
110(1)
5.6.1 Analysis of Data
110(1)
5.7 Results
110(2)
5.8 Discussion
112(2)
5.9 Conclusion
114(3)
References
115(2)
6 Fuzzy-Based Patient Health Monitoring System
117(42)
T. Venkatesh
K. Murugan
M. Supriya
T. Shantnugaraja
Rekha Chakravarthi
6.1 Introduction
118(4)
6.1.1 General Problem
119(1)
6.1.2 Existing Patient Monitoring and Diagnosis Systems
119(1)
6.1.3 Fuzzy Logic Systems
120(2)
6.2 System Design
122(3)
6.2.1 Hardware Requirements
122(1)
6.2.1.1 Functional Requirements
123(2)
6.2.1.2 Nonfunctional Specifications
125(1)
6.3 Software Architecture
125(15)
6.3.1 The Data Acquisition Unit (DAQ) Application Programmable Interface (API)
126(2)
6.3.2 Flowchart---API
128(1)
6.3.3 Foreign Tag IDs
129(1)
6.3.4 Database Manager
130(1)
6.3.5 Database Designing
130(1)
6.3.6 The Fuzzy Logic System
131(1)
6.3.6.1 Introduction to Fuzzy Logic
131(1)
6.3.6.2 The Modified Prior Alerting Score (MPAS)
132(2)
6.3.6.3 Structure of the Fuzzy Logic System
134(1)
6.3.7 Designing a System in Fuzzy
135(1)
6.3.7.1 Input Variables
135(3)
6.3.7.2 The Output Variable
138(2)
6.4 Results and Discussion
140(15)
6.4.1 Hardware Sensors Validation
140(1)
6.4.2 Implementations, Testing, and Evaluation of the Fuzzy Logic Engine
141(5)
6.4.3 Normal Group (NRM)
146(1)
6.4.4 Low Risk Group
146(7)
6.4.5 High Risk Group (HRG)
153(2)
6.5 Conclusions and Future Work
155(4)
6.5.1 Summary and Concluding Remarks
155(1)
6.5.2 Future Directions
155(1)
References
155(4)
7 Artificial Intelligence: A Key for Detecting COVID-19 Using Chest Radiography
159(20)
C. Vinothini
P. Anitha
J. Priya
A. Abirami
S. Akash
7.1 Introduction
160(2)
7.2 Related Work
162(1)
7.2.1 Traditional Approach
162(1)
7.2.2 Deep Learning-Based Approach
163(1)
7.3 Materials and Methods
163(8)
7.3.1 Data Set and Data Pre-Processing
163(2)
7.3.2 Proposed Model
165(6)
7.4 Experiment and Result
171(3)
7.4.1 Experiment Setup
171(2)
7.4.2 Comparison with Other Models
173(1)
7.5 Results
174(1)
7.6 Conclusion
175(4)
References
176(3)
8 An Efficient IoT Framework for Patient Monitoring and Predicting Heart Disease Based on Machine Learning Algorithms
179(22)
S. Shanthi
R. Nidhya
Uma Perumal
Manish Kumar
8.1 Introduction
180(2)
8.2 Literature Survey
182(1)
8.3 Machine Learning Algorithms
183(1)
8.4 Problem Statement
184(1)
8.5 Proposed Work
185(7)
8.5.1 Data Set Description
185(1)
8.5.2 Collection of Values Through Sensor Nodes
186(1)
8.5.3 Storage of Data in Cloud
187(1)
8.5.4 Prediction with Machine Learning Algorithms
188(1)
8.5.4.1 Data Cleaning and Preparation
188(1)
8.5.4.2 Data Splitting
189(1)
8.5.4.3 Training and Testing
189(1)
8.5.5 Machine Learning Algorithms
189(1)
8.5.5.1 Naive Bayes Algorithm
189(1)
8.5.5.2 Decision Tree Algorithm
190(1)
8.5.5.3 K-Neighbors Classifier
191(1)
8.5.5.4 Logistic Regression
192(1)
8.6 Performance Analysis and Evaluation
192(5)
8.7 Conclusion
197(4)
References
197(4)
9 BABW: Biometric-Based Authentication Using DWT and FFNN
201(20)
R. Kingsy Grace
M. S. Geetha Devasena
R. Manimegalai
9.1 Introduction
202(1)
9.2 Literature Survey
203(5)
9.3 BABW: Biometric Authentication Using Brain Waves
208(3)
9.4 Results and Discussion
211(4)
9.5 Conclusion
215(6)
References
216(5)
10 Autism Screening Tools With Machine Learning and Deep Learning Methods: A Review
221(28)
D. Pavithra
A. N. Jayanthi
R. Nidhya
S. Balamurugan
10.1 Introduction
222(1)
10.2 Autism Screening Methods
223(5)
10.2.1 Autism Screening Instrument for Educational Planning---3rd Version
224(1)
10.2.2 Quantitative Checklist for Autism in Toddlers
224(1)
10.2.3 Autism Behavior Checklist
224(1)
10.2.4 Developmental Behavior Checklist-Early Screen
225(1)
10.2.5 Childhood Autism Rating Scale Version 2
225(1)
10.2.6 Autism Spectrum Screening Questionnaire (ASSQ)
226(1)
10.2.7 Early Screening for Autistic Traits
226(1)
10.2.8 Autism Spectrum Quotient
226(1)
10.2.9 Social Communication Questionnaire
227(1)
10.2.10 Child Behavior Check List
227(1)
10.2.11 Indian Scale for Assessment of Autism
227(1)
10.3 Machine Learning in ASD Screening and Diagnosis
228(10)
10.4 DL in ASD Diagnosis
238(4)
10.5 Conclusion
242(7)
References
242(7)
11 Drug Target Module Mining Using Biological Multifunctional Score-Based Coclustering
249(36)
R. Gowri
R. Rathipriya
11.1 Introduction
249(1)
11.2 Literature Study
250(3)
11.3 Materials and Methods
253(5)
11.3.1 Biological Terminologies
253(3)
11.3.2 Functional Coherence
256(1)
11.3.3 Biological Significances
257(1)
11.3.4 Existing Approach: MR-CoC
257(1)
11.4 Proposed Approach: MR-CoCmulti
258(6)
11.4.1 Biological Score Measures for DTM
259(1)
11.4.2 Multifunctional Score-Based Co-Clustering Approach
259(5)
11.5 Experimental Analysis
264(16)
11.5.1 Experimental Results
265(15)
11.6 Discussion
280(1)
11.7 Conclusion
280(5)
Acknowledgment
281(1)
References
281(4)
12 The Ascendant Role of Machine Learning Algorithms in the Prediction of Breast Cancer and Treatment Using Telehealth
285(32)
K. R. Jothi
S. Oswalt Manoj
Ananya Singhal
Suruchi Parashar
12.1 Introduction
286(3)
12.1.1 Objective
287(1)
12.1.2 Description and Goals
287(1)
12.1.2.1 Data Exploration
288(1)
12.1.2.2 Data Pre-Processing
288(1)
12.1.2.3 Feature Scaling
288(1)
12.1.2.4 Model Selection and Evaluation
288(1)
12.2 Literature Review
289(15)
12.3 Architecture Design and Implementation
304(6)
12.4 Results and Discussion
310(2)
12.5 Conclusion
312(1)
12.6 Future Work
313(4)
References
314(3)
13 Remote Patient Monitoring: Data Sharing and Prediction Using Machine Learning
317(22)
Mohammed Hameed Alhameed
S. Shanthi
Uma Perumal
Fathe Jeribi
13.1 Introduction
318(3)
13.1.1 Patient Monitoring in Healthcare System
318(3)
13.2 Literature Survey
321(1)
13.3 Problem Statement
322(1)
13.4 Machine Learning
322(4)
13.4.1 Introduction
322(2)
13.4.2 Cloud Computing
324(1)
13.4.3 Design and Architecture
325(1)
13.5 Proposed System
326(5)
13.6 Results and Discussions
331(2)
13.7 Privacy and Security Challenges
333(1)
13.8 Conclusions and Future Enhancement
334(5)
References
335(4)
14 Investigations on Machine Learning Models to Envisage Coronavirus in Patients
339(20)
R. Sabitha
J. Shanthini
R. M. Bhavadharini
S. Karthik
14.1 Introduction
340(1)
14.2 Categories of ML Algorithms in Healthcare
341(2)
14.3 Why ML to Fight COVID-19? Tools and Techniques
343(1)
14.4 Highlights of ML Algorithms Under Consideration
344(5)
14.5 Experimentation and Investigation
349(4)
14.6 Comparative Analysis of the Algorithms
353(1)
14.7 Scope of Enhancement for Better Investigation
354(5)
References
356(3)
15 Healthcare Informatics: Emerging Trends, Challenges, and Analysis of Medical Imaging
359(24)
G. Karthick
N.S. Nithya
15.1 Emerging Trends and Challenges in Healthcare Informatics
360(4)
15.1.1 Advanced Technologies in Healthcare Informatics
360(1)
15.1.2 Intelligent Smart Healthcare Devices Using IoT With DL
361(1)
15.1.3 Cyber Security in Healthcare Informatics
362(1)
15.1.4 Trends, Challenges, and Issues in Healthcare IT Analytics
363(1)
15.2 Performance Analysis of Medical Image Compression Using Wavelet Functions
364(7)
15.2.1 Introduction
364(2)
15.2.2 Materials and Methods
366(1)
15.2.3 Wavelet Basis Functions
367(1)
15.2.3.1 Haar Wavelet
367(1)
15.2.3.2 DB Wavelet
368(1)
15.2.3.3 Bior Wavelet
368(1)
15.2.3.4 Rbio Wavelet
368(1)
15.2.3.5 Symlets Wavelet
369(1)
15.2.3.6 Coif Wavelet
369(1)
15.2.3.7 Dmey Wavelet
369(1)
15.2.3.8 FK Wavelet
369(1)
15.2.4 Compression Methods
370(1)
15.2.4.1 Embedded Zero-Trees of Wavelet Transform
370(1)
15.2.4.2 Set Partitioning in Hierarchical Trees
370(1)
15.2.4.3 Adaptively Scanned Wavelet Difference Reduction
370(1)
15.2.4.4 Coefficient Thresholding
371(1)
15.3 Results and Discussion
371(9)
15.3.1 Mean Square Error
371(1)
15.3.2 Peak Signal to Noise Ratio
371(9)
15.4 Conclusion
380(3)
15.4.1 Summary
380(1)
References
380(3)
Index 383
R. Nidhya, PhD, is an assistant professor in the Department of Computer Science & Engineering, Madanapalle Institute of Technology & Science, affiliated to Jawaharlal Nehru Technical University, Anantapuram, India. She has published many research articles in SCI journals and her research interests include wireless body area networks, network security, and data mining.

Manish Kumar, PhD, is an assistant professor in the School of Computer Science & Engineering, VIT Chennai. His research interests include soft computing applications for bioinformatics problems and computational intelligence.

S. Balamurugan, PhD, is the Director of Research and Development, Intelligent Research Consultancy Services (iRCS), Coimbatore, Tamilnadu, India. He is also Director of the Albert Einstein Engineering and Research Labs (AEER Labs), as well as Vice-Chairman, Renewable Energy Society of India (RESI), India. He has published 45 books, 200+ international journals/ conferences, and 35 patents.