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E-raamat: Advanced Healthcare Systems: Empowering Physicians with IoT-Enabled Technologies

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ADVANCED HEALTHCARE SYSTEMS This book offers a complete package involving the incubation of machine learning, AI, and IoT in healthcare that is beneficial for researchers, healthcare professionals, scientists, and technologists.

The applications and challenges of machine learning and artificial intelligence in the Internet of Things (IoT) for healthcare applications are comprehensively covered in this book.

IoT generates big data of varying data quality; intelligent processing and analysis of this big data are the keys to developing smart IoT applications, thereby making space for machine learning (ML) applications. Due to its computational tools that can substitute for human intelligence in the performance of certain tasks, artificial intelligence (AI) makes it possible for machines to learn from experience, adjust to new inputs and perform human-like tasks. Since IoT platforms provide an interface to gather data from various devices, they can easily be deployed into AI/ML systems. The value of AI in this context is its ability to quickly mesh insights from data and automatically identify patterns and detect anomalies in the data that smart sensors and devices generateinformation such as temperature, pressure, humidity, air quality, vibration, and soundthat can be really helpful to rapid diagnosis.

Audience

This book will be of interest to researchers in artificial intelligence, the Internet of Things, machine learning as well as information technologists working in the healthcare sector.
Preface xvii
1 Internet of Medical Things--State-of-the-Art
1(20)
Kishor Joshi
Ruchi Mehrotra
1.1 Introduction
2(1)
1.2 Historical Evolution of IoT to IoMT
2(2)
1.2.1 IoT and IoMT--Market Size
4(1)
1.3 Smart Wearable Technology
4(3)
1.3.1 Consumer Fitness Smart Wearables
4(1)
1.3.2 Clinical-Grade Wearables
5(2)
1.4 Smart Pills
7(1)
1.5 Reduction of Hospital-Acquired Infections
8(1)
1.5.1 Navigation Apps for Hospitals
8(1)
1.6 In-Home Segment
8(1)
1.7 Community Segment
9(1)
1.8 Telehealth and Remote Patient Monitoring
9(3)
1.9 IoMT in Healthcare Logistics and Asset Management
12(1)
1.10 IoMT Use in Monitoring During COVID-19
13(1)
1.11 Conclusion
14(7)
References
15(6)
2 Issues and Challenges Related to Privacy and Security in Healthcare Using IoT, Fog, and Cloud Computing
21(12)
Hritu Raj
Mohit Kumar
Prashant Kumar
Amritpal Singh
Om Prakash Verma
2.1 Introduction
22(1)
2.2 Related Works
23(2)
2.3 Architecture
25(1)
2.3.1 Device Layer
25(1)
2.3.2 Fog Layer
26(1)
2.3.3 Cloud Layer
26(1)
2.4 Issues and Challenges
26(3)
2.5 Conclusion
29(4)
References
30(3)
3 Study of Thyroid Disease Using Machine Learning
33(10)
Shanu Verma
Rashmi Popli
Harish Kumar
3.1 Introduction
34(1)
3.2 Related Works
34(1)
3.3 Thyroid Functioning
35(1)
3.4 Category of Thyroid Cancer
36(1)
3.5 Machine Learning Approach Toward the Detection of Thyroid Cancer
37(4)
3.5.1 Decision Tree Algorithm
38(1)
3.5.2 Support Vector Machines
39(1)
3.5.3 Random Forest
39(1)
3.5.4 Logistic Regression
39(1)
3.5.5 Naive Bayes
40(1)
3.6 Conclusion
41(2)
References
41(2)
4 A Review of Various Security and Privacy Innovations for IoT Applications in Healthcare
43(16)
Abhishek Raghuvanshi
Umesh Kumar Singh
Chirag Joshi
4.1 Introduction
44(2)
4.1.1 Introduction to IoT
44(1)
4.1.2 Introduction to Vulnerability, Attack, and Threat
45(1)
4.2 IoT in Healthcare
46(2)
4.2.1 Confidentiality
46(1)
4.2.2 Integrity
46(1)
4.2.3 Authorization
46(1)
4.2.4 Availability
47(1)
4.3 Review of Security and Privacy Innovations for IoT Applications in Healthcare, Smart Cities, and Smart Homes
48(6)
4.4 Conclusion
54(5)
References
54(5)
5 Methods of Lung Segmentation Based on CT Images
59(10)
Amit Verma
Thipendra P. Singh
5.1 Introduction
59(1)
5.2 Semi-Automated Algorithm for Lung Segmentation
60(3)
5.2.1 Algorithm for Tracking to Lung Edge
60(2)
5.2.2 Outlining the Region of Interest in CT Images
62(1)
5.2.2.1 Locating the Region of Interest
62(1)
5.2.2.2 Seed Pixels and Searching Outline
62(1)
5.3 Automated Method for Lung Segmentation
63(1)
5.3.1 Knowledge-Based Automatic Model for Segmentation
63(1)
5.3.2 Automatic Method for Segmenting the Lung CT Image
64(1)
5.4 Advantages of Automatic Lung Segmentation Over Manual and Semi-Automatic Methods
64(1)
5.5 Conclusion
65(4)
References
65(4)
6 Handling Unbalanced Data in Clinical Images
69(12)
Amit Verma
6.1 Introduction
70(1)
6.2 Handling Imbalance Data
71(5)
6.2.1 Cluster-Based Under-Sampling Technique
72(3)
6.2.2 Bootstrap Aggregation (Bagging)
75(1)
6.3 Conclusion
76(5)
References
76(5)
7 IoT-Based Health Monitoring System for Speech-Impaired People Using Assistive Wearable Accelerometer
81(20)
Ishita Banerjee
Madhumathy P.
7.1 Introduction
82(2)
7.2 Literature Survey
84(2)
7.3 Procedure
86(7)
7.4 Results
93(4)
7.5 Conclusion
97(4)
References
97(4)
8 Smart IoT Devices for the Elderly and People with Disabilities
101(14)
K. N. D. Saile
Kolisetti Navatha
8.1 Introduction
101(1)
8.2 Need for IoT Devices
102(1)
8.3 Where Are the IoT Devices Used?
103(1)
8.3.1 Home Automation
103(1)
8.3.2 Smart Appliances
104(1)
8.3.3 Healthcare
104(1)
8.4 Devices in Home Automation
104(1)
8.4.1 Automatic Lights Control
104(1)
8.4.2 Automated Home Safety and Security
104(1)
8.5 Smart Appliances
105(1)
8.5.1 Smart Oven
105(1)
8.5.2 Smart Assistant
105(1)
8.5.3 Smart Washers and Dryers
106(1)
8.5.4 Smart Coffee Machines
106(1)
8.5.5 Smart Refrigerator
106(1)
8.6 Healthcare
106(6)
8.6.1 Smart Watches
107(1)
8.6.2 Smart Thermometer
107(1)
8.6.3 Smart Blood Pressure Monitor
107(1)
8.6.4 Smart Glucose Monitors
107(1)
8.6.5 Smart Insulin Pump
108(1)
8.6.6 Smart Wearable Asthma Monitor
108(1)
8.6.7 Assisted Vision Smart Glasses
109(1)
8.6.8 Finger Reader
109(1)
8.6.9 Braille Smart Watch
109(1)
8.6.10 Smart Wand
109(1)
8.6.11 Taptilo Braille Device
110(1)
8.6.12 Smart Hearing Aid
110(1)
8.6.13 E-Alarm
110(1)
8.6.14 Spoon Feeding Robot
110(1)
8.6.15 Automated Wheel Chair
110(2)
8.7 Conclusion
112(3)
References
112(3)
9 IoT-Based Health Monitoring and Tracking System for Soldiers
115(22)
Kavitha N.
Madhumathy P.
9.1 Introduction
116(1)
9.2 Literature Survey
117(1)
9.3 System Requirements
118(1)
9.3.1 Software Requirement Specification
119(1)
9.3.2 Functional Requirements
119(1)
9.4 System Design
119(10)
9.4.1 Features
121(1)
9.4.1.1 On-Chip Flash Memory
122(1)
9.4.1.2 On-Chip Static RAM
122(1)
9.4.2 Pin Control Block
122(1)
9.4.3 UARTs
123(1)
9.4.3.1 Features
123(1)
9.4.4 System Control
123(1)
9.4.4.1 Crystal Oscillator
123(1)
9.4.4.2 Phase-Locked Loop
124(1)
9.4.4.3 Reset and Wake-Up Timer
124(1)
9.4.4.4 Brown Out Detector
125(1)
9.4.4.5 Code Security
125(1)
9.4.4.6 External Interrupt Inputs
125(1)
9.4.4.7 Memory Mapping Control
125(1)
9.4.4.8 Power Control
126(1)
9.4.5 Real Monitor
126(1)
9.4.5.1 GPS Module
126(1)
9.4.6 Temperature Sensor
127(1)
9.4.7 Power Supply
128(1)
9.4.8 Regulator
128(1)
9.4.9 LCD
128(1)
9.4.10 Heart Rate Sensor
129(1)
9.5 Implementation
129(4)
9.5.1 Algorithm
130(1)
9.5.2 Hardware Implementation
130(1)
9.5.3 Software Implementation
131(2)
9.6 Results and Discussions
133(3)
9.6.1 Heart Rate
133(2)
9.6.2 Temperature Sensor
135(1)
9.6.3 Panic Button
135(1)
9.6.4 GPS Receiver
135(1)
9.7 Conclusion
136(1)
References
136(1)
10 Cloud-IoT Secured Prediction System for Processing and Analysis of Healthcare Data Using Machine Learning Techniques
137(36)
G. K. Kamalam
S. Anitha
10.1 Introduction
138(1)
10.2 Literature Survey
139(2)
10.3 Medical Data Classification
141(1)
10.3.1 Structured Data
142(1)
10.3.2 Semi-Structured Data
142(1)
10.4 Data Analysis
142(2)
10.4.1 Descriptive Analysis
142(1)
10.4.2 Diagnostic Analysis
143(1)
10.4.3 Predictive Analysis
143(1)
10.4.4 Prescriptive Analysis
143(1)
10.5 ML Methods Used in Healthcare
144(1)
10.5.1 Supervised Learning Technique
144(1)
10.5.2 Unsupervised Learning
145(1)
10.5.3 Semi-Supervised Learning
145(1)
10.5.4 Reinforcement Learning
145(1)
10.6 Probability Distributions
145(5)
10.6.1 Discrete Probability Distributions
146(1)
10.6.1.1 Bernoulli Distribution
146(1)
10.6.1.2 Uniform Distribution
147(1)
10.6.1.3 Binomial Distribution
147(1)
10.6.1.4 Normal Distribution
148(1)
10.6.1.5 Poisson Distribution
148(1)
10.6.1.6 Exponential Distribution
149(1)
10.7 Evaluation Metrics
150(6)
10.7.1 Classification Accuracy
150(1)
10.7.2 Confusion Matrix
150(1)
10.7.3 Logarithmic Loss
151(1)
10.7.4 Receiver Operating Characteristic Curve, or ROC Curve
152(1)
10.7.5 Area Under Curve (AUC)
152(1)
10.7.6 Precision
153(1)
10.7.7 Recall
153(1)
10.7.8 Fl Score
153(1)
10.7.9 Mean Absolute Error
154(1)
10.7.10 Mean Squared Error
154(1)
10.7.11 Root Mean Squared Error
155(1)
10.7.12 Root Mean Squared Logarithmic Error
155(1)
10.7.13 R-Squared/Adjusted R-Squared
156(1)
10.7.14 Adjusted R-Squared
156(1)
10.8 Proposed Methodology
156(10)
10.8.1 Neural Network
158(1)
10.8.2 Triangular Membership Function
158(1)
10.8.3 Data Collection
159(1)
10.8.4 Secured Data Storage
159(2)
10.8.5 Data Retrieval and Merging
161(1)
10.8.6 Data Aggregation
162(1)
10.8.7 Data Partition
162(1)
10.8.8 Fuzzy Rules for Prediction of Heart Disease
163(1)
10.8.9 Fuzzy Rules for Prediction of Diabetes
164(1)
10.8.10 Disease Prediction With Severity and Diagnosis
165(1)
10.9 Experimental Results
166(3)
10.10 Conclusion
169(4)
References
169(4)
11 CloudloT-Driven Healthcare: Review, Architecture, Security Implications, and Open Research Issues
173(82)
Juttaid LatiefShah
Heena Farooq Bhatand Asiflqbal Khan
11.1 Introduction
174(6)
11.2 Background Elements
180(7)
11.2.1 Security Comparison Between Traditional and IoT Networks
185(2)
11.3 Secure Protocols and Enabling Technologies for CloudloT Healthcare Applications
187(4)
11.3.1 Security Protocols
187(1)
11.3.2 Enabling Technologies
188(3)
11.4 CloudloT Health System Framework
191(8)
11.4.1 Data Perception/Acquisition
192(1)
11.4.2 Data Transmission/Communication
193(1)
11.4.3 Cloud Storage and Warehouse
194(1)
11.4.4 Data Flow in Healthcare Architecture- A Conceptual Framework
194(3)
11.4.5 Design Considerations
197(2)
11.5 Security Challenges and Vulnerabilities
199(15)
11.5.1 Security Characteristics and Objectives
200(2)
11.5.1.1 Confidentiality
202(1)
11.5.1.2 Integrity
202(1)
11.5.1.3 Availability
202(1)
11.5.1.4 Identification and Authentication
202(1)
11.5.1.5 Privacy
203(1)
11.5.1.6 Light Weight Solutions
203(1)
11.5.1.7 Heterogeneity
203(1)
11.5.1.8 Policies
203(1)
11.5.2 Security Vulnerabilities
203(2)
11.5.2.1 IoT Threats and Vulnerabilities
205(3)
11.5.2.2 Cloud-Based Threats
208(6)
11.6 Security Countermeasures and Considerations
214(23)
11.6.1 Security Countermeasures
214(1)
11.6.1.1 Security Awareness and Survey
214(1)
11.6.1.2 Security Architecture and Framework
215(1)
11.6.1.3 Key Management
216(1)
11.6.1.4 Authentication
217(1)
11.6.1.5 Trust
218(1)
11.6.1.6 Cryptography
219(1)
11.6.1.7 Device Security
219(1)
11.6.1.8 Identity Management
220(1)
11.6.1.9 Risk-Based Security/Risk Assessment
220(1)
11.6.1.10 Block Chain-Based Security
220(1)
11.6.1.11 Automata-Based Security
220(14)
11.6.2 Security Considerations
234(3)
11.7 Open Research Issues and Security Challenges
237(3)
11.7.1 Security Architecture
237(1)
11.7.2 Resource Constraints
238(1)
11.7.3 Heterogeneous Data and Devices
238(1)
11.7.4 Protocol Interoperability
238(1)
11.7.5 Trust Management and Governance
239(1)
11.7.6 Fault Tolerance
239(1)
11.7.7 Next-Generation 5G Protocol
240(1)
11.8 Discussion and Analysis
240(1)
11.9 Conclusion
241(14)
References
242(13)
12 A Novel Usage of Artificial Intelligence and Internet of Things in Remote-Based Healthcare Applications
255(20)
V. Arulkumar
D. Mansoor Hussain
S. Sridhar
P. Vivekanandan
12.1 Introduction Machine Learning
256(1)
12.2 Importance of Machine Learning
256(9)
12.2.1 ML vs. Classical Algorithms
258(1)
12.2.2 Learning Supervised
259(2)
12.2.3 Unsupervised Learning
261(2)
12.2.4 Network for Neuralism
263(1)
12.2.4.1 Definition of the Neural Network
263(1)
12.2.4.2 Neural Network Elements
263(2)
12.3 Procedure
265(1)
12.3.1 Dataset and Seizure Identification
265(1)
12.3.2 System
265(1)
12.4 Feature Extraction
266(1)
12.5 Experimental Methods
266(3)
12.5.1 Stepwise Feature Optimization
266(2)
12.5.2 Post-Classification Validation
268(1)
12.5.3 Fusion of Classification Methods
268(1)
12.6 Experiments
269(1)
12.7 Framework for EEG Signal Classification
269(1)
12.8 Detection of the Preictal State
270(1)
12.9 Determination of the Seizure Prediction Horizon
271(1)
12.10 Dynamic Classification Over Time
272(1)
12.11 Conclusion
273(2)
References
273(2)
13 Use of Machine Learning in Healthcare
275(20)
V. Lakshman Narayana
R. S. M. Lakshmi Patibandla
B. Tarakeswara Rao
Arepalli Peda Gopi
13.1 Introduction
276(1)
13.2 Uses of Machine Learning in Pharma and Medicine
276(5)
13.2.1 Distinguish Illnesses and Examination
277(1)
13.2.2 Drug Discovery and Manufacturing
277(1)
13.2.3 Scientific Imaging Analysis
278(1)
13.2.4 Twisted Therapy
278(1)
13.2.5 AI to Know-Based Social Change
278(1)
13.2.6 Perception Wellness Realisms
279(1)
13.2.7 Logical Preliminary and Exploration
279(1)
13.2.8 Publicly Supported Perceptions Collection
279(1)
13.2.9 Better Radiotherapy
280(1)
13.2.10 Incidence Forecast
280(1)
13.3 The Ongoing Preferences of ML in Human Services
281(3)
13.4 The Morals of the Use of Calculations in Medicinal Services
284(4)
13.5 Opportunities in Healthcare Quality Improvement
288(2)
13.5.1 Variation in Care
288(1)
13.5.2 Inappropriate Care
289(1)
13.5.3 Prevents Care-Associated Injurious and Death for Carefrontation
289(1)
13.5.4 The Fact That People Are Unable to do What They Know Works
289(1)
13.5.5 A Waste
290(1)
13.6 A Team-Based Care Approach Reduces Waste
290(1)
13.7 Conclusion
291(4)
References
292(3)
14 Methods of MRI Brain Tumor Segmentation
295(10)
Amit Verma
14.1 Introduction
295(1)
14.2 Generative and Descriptive Models
296(6)
14.2.1 Region-Based Segmentation
300(1)
14.2.2 Generative Model With Weighted Aggregation
300(2)
14.3 Conclusion
302(3)
References
303(2)
15 Early Detection of Type 2 Diabetes Mellitus Using Deep Neural Network-Based Model
305(14)
Varun Sapra
Luxmi Sapra
15.1 Introduction
306(1)
15.2 Data Set
307(3)
15.2.1 Data Insights
308(2)
15.3 Feature Engineering
310(2)
15.4 Framework for Early Detection of Disease
312(2)
15.4.1 Deep Neural Network
313(1)
15.5 Result
314(1)
15.6 Conclusion
315(4)
References
315(4)
16 A Comprehensive Analysis on Masked Face Detection Algorithms
319(16)
Pranjali Singh
Amitesh Garg
Amritpal Singh
16.1 Introduction
320(1)
16.2 Literature Review
321(4)
16.3 Implementation Approach
325(3)
16.3.1 Feature Extraction
325(1)
16.3.2 Image Processing
325(1)
16.3.3 Image Acquisition
325(1)
16.3.4 Classification
325(1)
16.3.5 MobileNetV2
326(1)
16.3.6 Deep Learning Architecture
326(1)
16.3.7 LeNet-5, AlexNet, and ResNet-50
326(1)
16.3.8 Data Collection
326(1)
16.3.9 Development of Model
327(1)
16.3.10 Training of Model
328(1)
16.3.11 Model Testing
328(1)
16.4 Observation and Analysis
328(4)
16.4.1 CNN Algorithm
328(2)
16.4.2 SSDNETV2 Algorithm
330(1)
16.4.3 SVM
331(1)
16.5 Conclusion
332(3)
References
333(2)
17 IoT-Based Automated Healthcare System
335(12)
Darpan Anand
Aashish Kumar
17.1 Introduction
335(6)
17.1.1 Software-Defined Network
336(1)
17.1.2 Network Function Virtualization
337(1)
17.1.3 Sensor Used in IoT Devices
338(3)
17.2 SDN-Based IoT Framework
341(2)
17.3 Literature Survey
343(1)
17.4 Architecture of SDN-IoT for Healthcare System
344(1)
17.5 Challenges
345(2)
17.6 Conclusion
347(1)
References 347(4)
Index 351
Rohit Tanwar, PhD (Kurukshetra University, Kurukshetra, India) is an assistant professor in the School of Computer Science at UPES Dehradun, India.

S. Balamurugan, PhD, SMIEEE, ACM Distinguished Speaker, received his PhD from Anna University, India. He has published 57 books, 300+ international journals/conferences, and 100 patents. He is the Director of the Albert Einstein Engineering and Research Labs. He is also the Vice-Chairman of the Renewable Energy Society of India (RESI). He is serving as a research consultant to many companies, startups, SMEs, and MSMEs. He has received numerous awards for research at national and international levels.

R. K. Saini, PhD (DIT University, Dehradun, India) is an assistant professor in the Department of Computer Science & Applications at DIT University, Dehradun (Uttarakhand).

Vishal Bharti, PhD is a professor in the Department of Computer Science and Engineering, Chandigarh University, India. He has published more than 75 research papers in both national & international journals.

Premkumar Chithaluru, PhD is an assistant professor in the Department of SCS at the University of Petroleum and Energy Studies (UPES), Dehradun, India.
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