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E-grāmata: Emerging Technologies for Healthcare: Internet of Things and Deep Learning Models

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“Emerging Technologies for Healthcare” begins with an IoT-based solution for the automated healthcare sector which is enhanced to provide solutions with advanced deep learning techniques.

The book provides feasible solutions through various machine learning approaches and applies them to disease analysis and prediction. An example of this is employing a three-dimensional matrix approach for treating chronic kidney disease, the diagnosis and prognostication of acquired demyelinating syndrome (ADS) and autism spectrum disorder, and the detection of pneumonia. In addition, it provides healthcare solutions for post COVID-19 outbreaks through various suitable approaches, Moreover, a detailed detection mechanism is discussed which is used to devise solutions for predicting personality through handwriting recognition; and novel approaches for sentiment analysis are also discussed with sufficient data and its dimensions.

This book not only covers theoretical approaches and algorithms, but also contains the sequence of steps used to analyze problems with data, processes, reports, and optimization techniques. It will serve as a single source for solving various problems via machine learning algorithms.

Preface xvii
Part I Basics of Smart Healthcare
1(128)
1 An Overview of IoT in Health Sectors
3(22)
Sheeba P. S.
1.1 Introduction
3(3)
1.2 Influence of IoT in Healthcare Systems
6(3)
1.2.1 Health Monitoring
6(1)
1.2.2 Smart Hospitals
7(1)
1.2.3 Tracking Patients
7(1)
1.2.4 Transparent Insurance Claims
8(1)
1.2.5 Healthier Cities
8(1)
1.2.6 Research in Health Sector
8(1)
1.3 Popular IoT Healthcare Devices
9(1)
1.3.1 Hearables
9(1)
1.3.2 Moodables
9(1)
1.3.3 Ingestible Sensors
9(1)
1.3.4 Computer Vision
10(1)
1.3.5 Charting in Healthcare
10(1)
1.4 Benefits of IoT
10(2)
1.4.1 Reduction in Cost
10(1)
1.4.2 Quick Diagnosis and Improved Treatment
10(1)
1.4.3 Management of Equipment and Medicines
11(1)
1.4.4 Error Reduction
11(1)
1.4.5 Data Assortment and Analysis
11(1)
1.4.6 Tracking and Alerts
11(1)
1.4.7 Remote Medical Assistance
11(1)
1.5 Challenges of IoT
12(1)
1.5.1 Privacy and Data Security
12(1)
1.5.2 Multiple Devices and Protocols Integration
12(1)
1.5.3 Huge Data and Accuracy
12(1)
1.5.4 Underdeveloped
12(1)
1.5.5 Updating the Software Regularly
12(1)
1.5.6 Global Healthcare Regulations
13(1)
1.5.7 Cost
13(1)
1.6 Disadvantages of IoT
13(1)
1.6.1 Privacy
13(1)
1.6.2 Access by Unauthorized Persons
13(1)
1.7 Applications of IoT
13(8)
1.7.1 Monitoring of Patients Remotely
13(1)
1.7.2 Management of Hospital Operations
14(1)
1.7.3 Monitoring of Glucose
14(1)
1.7.4 Sensor Connected Inhaler
15(1)
1.7.5 Interoperability
15(1)
1.7.6 Connected Contact Lens
15(1)
1.7.7 Hearing Aid
16(1)
1.7.8 Coagulation of Blood
16(1)
1.7.9 Depression Detection
16(1)
1.7.10 Detection of Cancer
17(1)
1.7.11 Monitoring Parkinson Patient
17(1)
1.7.12 Ingestible Sensors
18(1)
1.7.13 Surgery by Robotic Devices
18(1)
1.7.14 Hand Sanitizing
18(1)
1.7.15 Efficient Drug Management
19(1)
1.7.16 Smart Sole
19(1)
1.7.17 Body Scanning
19(1)
1.7.18 Medical Waste Management
20(1)
1.7.19 Monitoring the Heart Rate
20(1)
1.7.20 Robot Nurse
20(1)
1.8 Global Smart Healthcare Market
21(1)
1.9 Recent Trends and Discussions
22(1)
1.10 Conclusion
23(2)
References
23(2)
2 IoT-Based Solutions for Smart Healthcare
25(44)
Pankaj Jain
Sonia F Panesar
Bableen Flora Talwar
Mahesh Kumar Sah
2.1 Introduction
26(3)
2.1.1 Process Flow of Smart Healthcare System
26(1)
2.1.1.1 Data Source
26(1)
2.1.1.2 Data Acquisition
27(1)
2.1.1.3 Data Pre-Processing
27(1)
2.1.1.4 Data Segmentation
28(1)
2.1.1.5 Feature Extraction
28(1)
2.1.1.6 Data Analytics
28(1)
2.2 IoT Smart Healthcare System
29(4)
2.2.1 System Architecture
30(1)
2.2.1.1 Stage 1: Perception Layer
30(2)
2.2.1.2 Stage 2: Network Layer
32(1)
2.2.1.3 Stage 3: Data Processing Layer
32(1)
2.2.1.4 Stage 4: Application Layer
33(1)
2.3 Locally and Cloud-Based IoT Architecture
33(2)
2.3.1 System Architecture
33(1)
2.3.1.1 Body Area Network (BAN)
34(1)
2.3.1.2 Smart Server
34(1)
2.3.1.3 Care Unit
35(1)
2.4 Cloud Computing
35(3)
2.4.1 Infrastructure as a Service (IaaS)
37(1)
2.4.2 Platform as a Service (PaaS)
37(1)
2.4.3 Software as a Service (SaaS)
37(1)
2.4.4 Types of Cloud Computing
37(1)
2.4.4.1 Public Cloud
37(1)
2.4.4.2 Private Cloud
38(1)
2.4.4.3 Hybrid Cloud
38(1)
2.4.4.4 Community Cloud
38(1)
2.5 Outbreak of Arduino Board
38(1)
2.6 Applications of Smart Healthcare System
39(4)
2.6.1 Disease Diagnosis and Treatment
41(1)
2.6.2 Health Risk Monitoring
42(1)
2.6.3 Voice Assistants
42(1)
2.6.4 Smart Hospital
42(1)
2.6.5 Assist in Research and Development
43(1)
2.7 Smart Wearables and Apps
43(1)
2.8 Deep Learning in Biomedical
44(11)
2.8.1 Deep Learning
46(1)
2.8.2 Deep Neural Network Architecture
47(2)
2.8.3 Deep Learning in Bioinformatic
49(1)
2.8.4 Deep Learning in Bioimaging
49(1)
2.8.5 Deep Learning in Medical Imaging
50(3)
2.8.6 Deep Learning in Human-Machine Interface
53(1)
2.8.7 Deep Learning in Health Service Management
53(2)
2.9 Conclusion
55(14)
References
55(14)
3 QLattice Environment and Feyn QGraph Models--A New Perspective Toward Deep Learning
69(24)
Vinayak Bharadi
3.1 Introduction
70(1)
3.1.1 Machine Learning Models
70(1)
3.2 Machine Learning Model Lifecycle
71(4)
3.2.1 Steps in Machine Learning Lifecycle
71(1)
3.2.1.1 Data Preparation
72(1)
3.2.1.2 Building the Machine Learning Model
72(1)
3.2.1.3 Model Training
72(1)
3.2.1.4 Parameter Selection
72(1)
3.2.1.5 Transfer Learning
73(1)
3.2.1.6 Model Verification
73(1)
3.2.1.7 Model Deployment
74(1)
3.2.1.8 Monitoring
74(1)
3.3 A Model Deployment in Keras
75(5)
3.3.1 Pima Indian Diabetes Dataset
75(1)
3.3.2 Multi-Layered Perceptron Implementation in Keras
76(1)
3.3.3 Multi-Layered Perceptron Implementation With Dropout and Added Noise
77(3)
3.4 QLattice Environment
80(7)
3.4.1 Feyn Models
80(2)
3.4.1.1 Semantic Types
82(1)
3.4.1.2 Interactions
83(1)
3.4.1.3 Generating QLattice
83(1)
3.4.2 QLattice Workflow
83(1)
3.4.2.1 Preparing the Data
84(1)
3.4.2.2 Connecting to QLattice
84(1)
3.4.2.3 Generating QGraphs
84(1)
3.4.2.4 Fitting, Sorting, and Updating QGraphs
85(1)
3.4.2.5 Model Evaluation
86(1)
3.5 Using QLattice Environment and QGraph Models for COVID-19 Impact Prediction
87(6)
References
91(2)
4 Sensitive Healthcare Data: Privacy and Security Issues and Proposed Solutions
93(36)
Abhishek Vyas
Satheesh Abimannan
Ren-Hung Hwang
4.1 Introduction
94(3)
4.1.1 Types of Technologies Used in Healthcare Industry
94(1)
4.1.2 Technical Differences Between Security and Privacy
95(1)
4.1.3 HIPAA Compliance
95(2)
4.2 Medical Sensor Networks/Medical Internet of Things/Body Area Networks/WBANs
97(15)
4.2.1 Security and Privacy Issues in WBANs/WMSNs/WMIOTs
101(11)
4.3 Cloud Storage and Computing on Sensitive Healthcare Data
112(7)
4.3.1 Security and Privacy in Cloud Computing and Storage for Sensitive Healthcare Data
114(5)
4.4 Blockchain for Security and Privacy Enhancement in Sensitive Healthcare Data
119(3)
4.5 Artificial Intelligence, Machine Learning, and Big Data in Healthcare and Its Efficacy in Security and Privacy of Sensitive Healthcare Data
122(2)
4.5.1 Differential Privacy for Preserving Privacy of Big Medical Healthcare Data and for Its Analytics
124(1)
4.6 Conclusion
124(5)
References
125(4)
Part II Employment of Machine Learning in Disease Detection
129(150)
5 Diabetes Prediction Model Based on Machine Learning
131(26)
Ayush Kumar Gupta
Sourabh Yadav
Priyanka Bhartiya
Divesh Gupta
5.1 Introduction
131(2)
5.2 Literature Review
133(2)
5.3 Proposed Methodology
135(12)
5.3.1 Data Accommodation
135(1)
5.3.1.1 Data Collection
135(1)
5.3.1.2 Data Preparation
136(2)
5.3.2 Model Training
138(1)
5.3.2.1 K Nearest Neighbor Classification Technique
139(1)
5.3.2.2 Support Vector Machine
140(2)
5.3.2.3 Random Forest Algorithm
142(2)
5.3.2.4 Logistic Regression
144(1)
5.3.3 Model Evaluation
145(1)
5.3.4 User Interaction
145(1)
5.3.4.1 User Inputs
146(1)
5.3.4.2 Validation Using Classifier Model
146(1)
5.3.4.3 Truth Probability
146(1)
5.4 System Implementation
147(6)
5.5 Conclusion
153(4)
References
153(4)
6 Lung Cancer Detection Using 3D CNN Based on Deep Learning
157(24)
Siddhant Panda
Vasudha Chhetri
Vikas Kumar Jaiswal
Sourabh Yadav
6.1 Introduction
157(2)
6.2 Literature Review
159(2)
6.3 Proposed Methodology
161(7)
6.3.1 Data Handling
161(1)
6.3.1.1 Data Gathering
161(1)
6.3.1.2 Data Pre-Processing
162(1)
6.3.2 Data Visualization and Data Split
162(1)
6.3.2.1 Data Visualization
162(1)
6.3.2.2 Data Split
162(1)
6.3.3 Model Training
163(1)
6.3.3.1 Training Neural Network
163(3)
6.3.3.2 Model Optimization
166(2)
6.4 Results and Discussion
168(10)
6.4.1 Gathering and Pre-Processing of Data
169(1)
6.4.1.1 Gathering and Handling Data
169(1)
6.4.1.2 Pre-Processing of Data
170(1)
6.4.2 Data Visualization
171(2)
6.4.2.1 Resampling
173(1)
6.4.2.2 3D Plotting Scan
173(1)
6.4.2.3 Lung Segmentation
173(2)
6.4.3 Training and Testing of Data in 3D Architecture
175(3)
6.5 Conclusion
178(3)
References
178(3)
7 Pneumonia Detection Using CNN and ANN Based on Deep Learning Approach
181(22)
Priyanka Bhartiya
Sourabh Yadav
Ayush Gupta
Divesh Gupta
7.1 Introduction
182(1)
7.2 Literature Review
183(2)
7.3 Proposed Methodology
185(9)
7.3.1 Data Gathering
185(1)
7.3.1.1 Data Collection
185(1)
7.3.1.2 Data Pre-Processing
186(1)
7.3.1.3 Data Split
186(1)
7.3.2 Model Training
187(2)
7.3.2.1 Training of Convolutional Neural Network
189(2)
7.3.2.2 Training of Artificial Neural Network
191(2)
7.3.3 Model Fitting
193(1)
7.3.3.1 Fit Generator
193(1)
7.3.3.2 Validation of Accuracy and Loss Plot
193(1)
7.3.3.3 Testing and Prediction
193(1)
7.4 System Implementation
194(5)
7.4.1 Data Gathering, Pre-Processing, and Split
194(1)
7.4.1.1 Data Gathering
194(1)
7.4.1.2 Data Pre-Processing
195(1)
7.4.1.3 Data Split
196(1)
7.4.2 Model Building
196(1)
7.4.3 Model Fitting
197(1)
7.4.3.1 Fit Generator
197(1)
7.4.3.2 Validation of Accuracy and Loss Plot
197(1)
7.4.3.3 Testing and Prediction
198(1)
7.5 Conclusion
199(4)
References
199(4)
8 Personality Prediction and Handwriting Recognition Using Machine Learning
203(34)
Vishal Patil
Harsh Mathur
8.1 Introduction to the System
204(4)
8.1.1 Assumptions and Limitations
206(1)
8.1.1.1 Assumptions
206(1)
8.1.1.2 Limitations
206(1)
8.1.2 Practical Needs
206(1)
8.1.3 Non-Functional Needs
206(1)
8.1.4 Specifications for Hardware
207(1)
8.1.5 Specifications for Applications
207(1)
8.1.6 Targets
207(1)
8.1.7 Outcomes
207(1)
8.2 Literature Survey
208(4)
8.2.1 Computerized Human Behavior Identification Through Handwriting Samples
208(1)
8.2.2 Behavior Prediction Through Handwriting Analysis
209(1)
8.2.3 Handwriting Sample Analysis for a Finding of Personality Using Machine Learning Algorithms
209(1)
8.2.4 Personality Detection Using Handwriting Analysis
210(1)
8.2.5 Automatic Predict Personality Based on Structure of Handwriting
210(1)
8.2.6 Personality Identification Through Handwriting Analysis: A Review
210(1)
8.2.7 Text Independent Writer Identification Using Convolutional Neural Network
210(1)
8.2.8 Writer Identification Using Machine Learning Approaches
211(1)
8.2.9 Writer Identification from Handwritten Text Lines
211(1)
8.3 Theory
212(8)
8.3.1 Pre-Processing
212(3)
8.3.2 Personality Analysis
215(1)
8.3.3 Personality Characteristics
216(1)
8.3.4 Writer Identification
217(2)
8.3.5 Features Used
219(1)
8.4 Algorithm To Be Used
220(4)
8.5 Proposed Methodology
224(2)
8.5.1 System Flow
225(1)
8.6 Algorithms vs. Accuracy
226(5)
8.6.1 Implementation
228(3)
8.7 Experimental Results
231(1)
8.8 Conclusion
232(1)
8.9 Conclusion and Future Scope
232(5)
Acknowledgment
232(1)
References
233(4)
9 Risk Mitigation in Children With Autism Spectrum Disorder Using Brain Source Localization
237(14)
Joy Karan Singh
Deepti Kakkar
Tanu Wadhera
9.1 Introduction
238(1)
9.2 Risk Factors Related to Autism
239(4)
9.2.1 Assistive Technologies for Autism
240(1)
9.2.2 Functional Connectivity as a Biomarker for Autism
241(1)
9.2.3 Early Intervention and Diagnosis
242(1)
9.3 Materials and Methodology
243(2)
9.3.1 Subjects
243(1)
9.3.2 Methods
243(1)
9.3.3 Data Acquisition and Processing
243(1)
9.3.4 sLORETA as a Diagnostic Tool
244(1)
9.4 Results and Discussion
245(2)
9.5 Conclusion and Future Scope
247(4)
References
247(4)
10 Predicting Chronic Kidney Disease Using Machine Learning
251(28)
Monika Gupta
Parul Gupta
10.1 Introduction
252(1)
10.2 Machine Learning Techniques for Prediction of Kidney Failure
253(16)
10.2.1 Analysis and Empirical Learning
254(1)
10.2.2 Supervised Learning
255(1)
10.2.3 Unsupervised Learning
256(1)
10.2.3.1 Understanding and Visualization
257(1)
10.2.3.2 Odd Detection
257(1)
10.2.3.3 Object Completion
258(1)
10.2.3.4 Information Acquisition
258(1)
10.2.3.5 Data Compression
258(1)
10.2.3.6 Capital Market
258(1)
10.2.4 Classification
259(1)
10.2.4.1 Training Process
260(1)
10.2.4.2 Testing Process
260(1)
10.2.5 Decision Tree
261(2)
10.2.6 Regression Analysis
263(1)
10.2.6.1 Logistic Regression
263(2)
10.2.6.2 Ordinal Logistic Regression
265(1)
10.2.6.3 Estimating Parameters
266(2)
10.2.6.4 Multivariate Regression
268(1)
10.3 Data Sources
269(3)
10.4 Data Analysis
272(2)
10.5 Conclusion
274(1)
10.6 Future Scope
274(5)
References
274(5)
Part III Advanced Applications of Machine Learning in Healthcare
279(102)
11 Behavioral Modeling Using Deep Neural Network Framework for ASD Diagnosis and Prognosis
281(18)
Tanu Wadhera
Deepti Kakkar
Rajneesh Rani
11.1 Introduction
282(2)
11.2 Automated Diagnosis of ASD
284(8)
11.2.1 Deep Learning
289(1)
11.2.2 Deep Learning in ASD
290(1)
11.2.3 Transfer Learning Approach
290(2)
11.3 Purpose of the
Chapter
292(1)
11.4 Proposed Diagnosis System
293(1)
11.5 Conclusion
294(5)
References
295(4)
12 Random Forest Application of Twitter Data Sentiment Analysis in Online Social Network Prediction
299(16)
Arnav Munshi
M. Arvindhan
Thirunavukkarasu K.
12.1 Introduction
300(2)
12.1.1 Motivation
300(1)
12.1.2 Domain Introduction
300(2)
12.2 Literature Survey
302(2)
12.3 Proposed Methodology
304(7)
12.4 Implementation
311(1)
12.5 Conclusion
311(4)
References
311(4)
13 Remedy to COVID-19: Social Distancing Analyzer
315(22)
Sourabh Yadav
13.1 Introduction
315(3)
13.2 Literature Review
318(3)
13.3 Proposed Methodology
321(7)
13.3.1 Person Detection
321(3)
13.3.1.1 Frame Creation
324(1)
13.3.1.2 Contour Detection
325(1)
13.3.1.3 Matching with COCO Model
326(1)
13.3.2 Distance Calculation
326(1)
13.3.2.1 Calculation of Centroid
326(1)
13.3.2.2 Distance Among Adjacent Centroids
327(1)
13.4 System Implementation
328(5)
13.5 Conclusion
333(4)
References
334(3)
14 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability
337(22)
Shubham Joshi
Radha Krishna Rambola
14.1 Introduction
338(2)
14.2 Related Work
340(4)
14.2.1 Adoption of IoT in Vehicle to Ensure Driver Safety
341(1)
14.2.2 IoT in Healthcare System
341(2)
14.2.3 The Technology Used in Assistance Systems
343(1)
14.2.3.1 Adaptive Cruise Control (ACC)
343(1)
14.2.3.2 Lane Departure Warning
343(1)
14.2.3.3 Parking Assistance
343(1)
14.2.3.4 Collision Avoidance System
343(1)
14.2.3.5 Driver Drowsiness Detection
344(1)
14.2.3.6 Automotive Night Vision
344(1)
14.3 Objectives, Context, and Ethical Approval
344(1)
14.4 Technical Background
345(1)
14.4.1 IoT With Health
345(1)
14.4.2 Machine-to-Machine (M2M) Communication
345(1)
14.4.3 Device-to-Device (D2D) Communication
345(1)
14.4.4 Wireless Sensor Network
346(1)
14.4.5 Crowdsensing
346(1)
14.5 IoT Infrastructural Components for Vehicle Assistance System
346(3)
14.5.1 Communication Technology
346(1)
14.5.2 Sensor Network
347(1)
14.5.3 Infrastructural Component
348(1)
14.5.4 Human Health Detection by Sensors
348(1)
14.6 IoT-Enabled Vehicle Assistance System of Highway Resourcing for Smart Healthcare and Sustainability
349(4)
14.7 Challenges in Implementation
353(1)
14.8 Conclusion
353(6)
References
354(5)
15 Aids of Machine Learning for Additively Manufactured Bone Scaffold
359(22)
Nimisha Rahul Shirbhate
Sanjay Bokade
15.1 Introduction
360(4)
15.1.1 Bone Scaffold
360(2)
15.1.2 Bone Grafting
362(1)
15.1.3 Comparison Bone Grafting and Bone Scaffold
363(1)
15.2 Research Background
364(1)
15.3 Statement of Problem
364(1)
15.4 Research Gap
365(1)
15.5 Significance of Research
366(1)
15.6 Outline of Research Methodology
366(11)
15.6.1 Customized Design of Bone Scaffold
366(1)
15.6.2 Manufacturing Methods and Biocompatible Material
367(1)
15.6.2.1 Conventional Scaffold Fabrication
368(1)
15.6.2.2 Additive Manufacturing
369(1)
15.6.2.3 Application of Additive Manufacturing/3D Printing in Healthcare
370(6)
15.6.2.4 Automated Process Monitoring in 3D Printing Using Supervised Machine Learning
376(1)
15.7 Conclusion
377(4)
References
377(4)
Index 381
Monika Mangla, received her PhD from Thapar Institute of Engineering & Technology, Patiala, Punjab, in 2019. Currently, she is working as an assistant professor in the Department of Computer Engineering at Lokmanya Tilak College of Engineering (LTCoE), Navi Mumbai.

Nonita Sharma is working as assistant professor, National Institute of Technology, Jalandhar. She received the B. Tech degree in Computer Science Engineering in 2002, the M. Tech degree in Computer Science engineering in 2004, and her PhD degree in Wireless Sensor Network from the National Institute of Technology, Jalandhar, India in 2017.

Poonam Mittal received her PhD from J.C Bose University of Science and Technology YMCA, Faridabad, India, in 2019. Currently, she is working as an assistant professor in the Department of Computer Engineering at J.C Bose University of Science and Technology YMCA, Faridabad, India.

Vaishali Mehta Wadhwa obtained her PhD in Facility Location Problems from Thapar University. Her research interests include approximation algorithms, location modeling, IoT, cloud computing and machine learning. She has multiple articles and 2 patents to her name.

Thirunavakkarasu K. is a distinguished academician with over twenty-two years of experience in teaching and working in the software industry. Curently, he is heading the Department of BCA and Specialization at Galgotias University. He has done Bachelor in computer science from the University of Madras in 1994 and received 3 masters degrees in computer science.

Shahnawaz Khan is an assistant professor and serving as Secretary-General of Scientific Research Council at University College of Bahrain. He holds a PhD (Computer Science) from the Indian Institute of Technology (BHU), India.
Biežāk uzdotie jautājumi par e-grāmatām Biežāk uzdotie jautājumi par e-grāmatām
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