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E-raamat: Handbook on Intelligent Healthcare Analytics: Knowledge Engineering with Big Data

Edited by (Poornima Institute of Engineering & Technology, Jaipur, India), Edited by (Vels Institute of Science, Technology and Advanced Studies, Chennai, India), Edited by (B. S. Abdur Rahman Crescent Institute of Science and Technology, India), Edited by (Liverpool John Moo)
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HANDBOOK OF INTELLIGENT HEALTHCARE ANALYTICS The book explores the various recent tools and techniques used for deriving knowledge from healthcare data analytics for researchers and practitioners.

The power of healthcare data analytics is being increasingly used in the industry. Advanced analytics techniques are used against large data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful information.

A Handbook on Intelligent Healthcare Analytics covers both the theory and application of the tools, techniques, and algorithms for use in big data in healthcare and clinical research. It provides the most recent research findings to derive knowledge using big data analytics, which helps to analyze huge amounts of real-time healthcare data, the analysis of which can provide further insights in terms of procedural, technical, medical, and other types of improvements in healthcare.

In addition, the reader will find in this Handbook:





Innovative hybrid machine learning and deep learning techniques applied in various healthcare data sets, as well as various kinds of machine learning algorithms existing such as supervised, unsupervised, semi-supervised, reinforcement learning, and guides how readers can implement the Python environment for machine learning; An exploration of predictive analytics in healthcare; The various challenges for smart healthcare, including privacy, confidentiality, authenticity, loss of information, attacks, etc., that create a new burden for providers to maintain compliance with healthcare data security. In addition, this book also explores various sources of personalized healthcare data and the commercial platforms for healthcare data analytics.

Audience Healthcare professionals, researchers, and practitioners who wish to figure out the core concepts of smart healthcare applications and the innovative methods and technologies used in healthcare will all benefit from this book.
Preface xvii
1 An Introduction to Knowledge Engineering and Data Analytics 1(20)
D. Karthika
K. Kalaiselvi
1.1 Introduction
2(3)
1.1.1 Online Learning and Fragmented Learning Modeling
2(3)
1.2 Knowledge and Knowledge Engineering
5(1)
1.2.1 Knowledge
5(1)
1.2.2 Knowledge Engineering
5(1)
1.3 Knowledge Engineering as a Modelling Process
6(1)
1.4 Tools
7(1)
1.5 What are KBSs?
8(5)
1.5.1 What is KBE?
8(2)
1.5.2 When Can KBE Be Used?
10(2)
1.5.3 CAD or KBE?
12(1)
1.6 Guided Random Search and Network Techniques
13(1)
1.6.1 Guide Random Search Techniques
13(1)
1.7 Genetic Algorithms
14(5)
1.7.1 Design Point Data Structure
15(1)
1.7.2 Fitness Function
15(1)
1.7.3 Constraints
16(1)
1.7.4 Hybrid Algorithms
16(1)
1.7.5 Considerations When Using a GA
16(1)
1.7.6 Alternative to Genetic-Inspired Creation of Children
17(1)
1.7.7 Alternatives to GA
18(1)
1.7.8 Closing Remarks for GA
18(1)
1.8 Artificial Neural Networks
19(1)
1.9 Conclusion
19(1)
References
20(1)
2 A Framework for Big Data Knowledge Engineering 21(18)
T. Devi
A. Ramachandran
2.1 Introduction
22(4)
2.1.1 Knowledge Engineering in AI and Its Techniques
23(2)
2.1.1.1 Supervised Model
23(1)
2.1.1.2 Unsupervised Model
23(1)
2.1.1.3 Deep Learning
24(1)
2.1.1.4 Deep Reinforcement Learning
24(1)
2.1.1.5 Optimization
25(1)
2.1.2 Disaster Management
25(1)
2.2 Big Data in Knowledge Engineering
26(4)
2.2.1 Cognitive Tasks for Time Series Sequential Data
27(1)
2.2.2 Neural Network for Analyzing the Weather Forecasting
27(1)
2.2.3 Improved Bayesian Hidden Markov Frameworks
28(2)
2.3 Proposed System
30(2)
2.4 Results and Discussion
32(1)
2.5 Conclusion
33(3)
References
36(3)
3 Big Data Knowledge System in Healthcare 39(28)
P. Sujatha
K. Mahalakshmi
P. Sripriya
3.1 Introduction
40(1)
3.2 Overview of Big Data
41(2)
3.2.1 Big Data: Definition
41(1)
3.2.2 Big Data: Characteristics
42(1)
3.3 Big Data Tools and Techniques
43(2)
3.3.1 Big Data Value Chain
43(2)
3.3.2 Big Data Tools and Techniques
45(1)
3.4 Big Data Knowledge System in Healthcare
45(14)
3.4.1 Sources of Medical Big Data
51(2)
3.4.2 Knowledge in Healthcare
53(2)
3.4.3 Big Data Knowledge Management Systems in Healthcare
55(1)
3.4.4 Big Data Analytics in Healthcare
56(3)
3.5 Big Data Applications in the Healthcare Sector
59(3)
3.5.1 Real Time Healthcare Monitoring and Altering
59(1)
3.5.2 Early Disease Prediction with Big Data
59(2)
3.5.3 Patients Predictions for Improved Staffing
61(1)
3.5.4 Medical Imaging
61(1)
3.6 Challenges with Healthcare Big Data
62(2)
3.6.1 Challenges of Big Data
62(1)
3.6.2 Challenges of Healthcare Big Data
62(2)
3.7 Conclusion
64(1)
References
64(3)
4 Big Data for Personalized Healthcare 67(26)
R. Dhanalakshmi
Jose Anand
4.1 Introduction
68(3)
4.1.1 Objectives
68(1)
4.1.2 Motivation
69(1)
4.1.3 Domain Description
70(1)
4.1.4 Organization of the
Chapter
70(1)
4.2 Related Literature
71(4)
4.2.1 Healthcare Cyber Physical System Architecture
71(1)
4.2.2 Healthcare Cloud Architecture
71(1)
4.2.3 User Authentication Management
72(1)
4.2.4 Healthcare as a Service (HaaS)
72(1)
4.2.5 Reporting Services
73(1)
4.2.6 Chart and Trend Analysis
73(1)
4.2.7 Medical Data Analysis
73(1)
4.2.8 Hospital Platform Based On Cloud Computing
74(1)
4.2.9 Patient's Data Collection
74(1)
4.2.10 H-Cloud Challenges
75(1)
4.2.11 Healthcare Information System and Cost
75(1)
4.3 System Analysis and Design
75(8)
4.3.1 Proposed Solution
76(1)
4.3.2 Software Components
76(1)
4.3.3 System Design
76(1)
4.3.4 Architecture Diagram
77(1)
4.3.5 List of Modules
78(3)
4.3.6 Use Case Diagram
81(1)
4.3.7 Sequence Diagram
81(1)
4.3.8 Class Diagram
82(1)
4.4 System Implementation
83(5)
4.4.1 User Interface
83(1)
4.4.2 Storage Module
84(1)
4.4.3 Notification Module
85(1)
4.4.4 Middleware
86(1)
4.4.5 OTP Module
87(1)
4.5 Results and Discussion
88(2)
4.6 Conclusion
90(1)
References
90(3)
5 Knowledge Engineering for AI in Healthcare 93(22)
A. Thirumurthi Raja
B. Mahalakshmi
5.1 Introduction
94(1)
5.2 Overview
95(11)
5.2.1 Knowledge Representation
95(1)
5.2.2 Types of Knowledge in Artificial Intelligence
96(1)
5.2.3 Relation Between Knowledge and Intelligence
97(1)
5.2.4 Approaches to Knowledge Representation
97(1)
5.2.5 Requirements for Knowledge Representation System
98(1)
5.2.6 Techniques of Knowledge Representation
98(3)
5.2.6.1 Logical Representation
99(1)
5.2.6.2 Semantic Network Representation
99(1)
5.2.6.3 Frame Representation
99(1)
5.2.6.4 Production Rules
100(1)
5.2.7 Process of Knowledge Engineering
101(5)
5.2.8 Knowledge Discovery Process
106(1)
5.3 Applications of Knowledge Engineering in AI for Healthcare
106(7)
5.3.1 AI Supports in Clinical Decisions
107(1)
5.3.2 AI-Assisted Robotic Surgery
107(1)
5.3.3 Enhance Primary Care and Triage
108(1)
5.3.4 Clinical Judgments or Diagnosis
108(1)
5.3.5 Precision Medicine
109(1)
5.3.6 Drug Discovery
109(1)
5.3.7 Deep Learning to Diagnose Diseases
110(1)
5.3.8 Automating Administrative Tasks
111(1)
5.3.9 Reducing Operational Costs
112(1)
5.3.10 Virtual Nursing Assistants
113(1)
5.4 Conclusion
113(1)
References
114(1)
6 Business Intelligence and Analytics from Big Data to Healthcare 115(32)
P. Maheswari
A. Jaya
Joao Manuel R.S. Tavares
6.1 Introduction
116(2)
6.1.1 Impact of Healthcare Industry on Economy
116(1)
6.1.2 Coronavirus Impact on the Healthcare Industry
117(1)
6.1.3 Objective of the Study
117(1)
6.1.4 Limitations of the Study
117(1)
6.2 Related Works
118(2)
6.3 Conceptual Healthcare Stock Prediction System
120(4)
6.3.1 Data Source
122(1)
6.3.2 Business Intelligence and Analytics Framework
122(2)
6.3.2.1 Simple Machine Learning Model
122(1)
6.3.2.2 Time Series Forecasting
123(1)
6.3.2.3 Complex Deep Neural Network
123(1)
6.3.3 Predicting the Stock Price
124(1)
6.4 Implementation and Result Discussion
124(17)
6.4.1 Apollo Hospitals Enterprise Limited
125(1)
6.4.2 Cadila Healthcare Ltd
125(3)
6.4.3 Dr. Reddy's Laboratories
128(2)
6.4.4 Fortis Healthcare Limited
130(1)
6.4.5 Max Healthcare Institute Limited
131(1)
6.4.6 Opto Circuits Limited
131(4)
6.4.7 Panacea Biotec
135(1)
6.4.8 Poly Medicure Ltd
136(2)
6.4.9 Thyrocare Technologies Limited
138(1)
6.4.10 Zydus Wellness Ltd
138(3)
6.5 Comparisons of Healthcare Stock Prediction Framework
141(2)
6.6 Conclusion and Future Enhancement
143(1)
References
143(2)
Books
145(1)
Web Citation
145(2)
7 Internet of Things and Big Data Analytics for Smart Healthcare 147(20)
K. Sathish Kumar
P.G. Om Prakash
N. Alangudi Balaji
Robertas Damageviaus
7.1 Introduction
148(1)
7.2 Literature Survey
149(2)
7.3 Smart Healthcare Using Internet of Things and Big Data Analytics
151(8)
7.3.1 Smart Diabetes Prediction
151(3)
7.3.2 Smart ADHD Prediction
154(5)
7.4 Security for Internet of Things
159(5)
7.4.1 K(Binary) ECC FSM
159(1)
7.4.2 NAF Method
160(1)
7.4.3 K-NAF Multiplication Architecture
161(1)
7.4.4 K(NAF) ECC FSM
161(3)
7.5 Conclusion
164(1)
References
165(2)
8 Knowledge-Driven and Intelligent Computing in Healthcare 167(22)
R. Mervin
Dinesh Mavalaru
Tintu Thomas
8.1 Introduction
168(3)
8.1.1 Basics of Health Recommendation System
169(1)
8.1.2 Basics of Ontology
169(1)
8.1.3 Need of Ontology in Health Recommendation System
170(1)
8.2 Literature Review
171(4)
8.2.1 Ontology in Various Domain
172(2)
8.2.2 Ontology in Health Recommendation System
174(1)
8.3 Framework for Health Recommendation System
175(7)
8.3.1 Domain Ontology Creation
176(2)
8.3.2 Query Pre- Processing
178(1)
8.3.3 Feature Selection
179(1)
8.3.4 Recommendation System
180(2)
8.4 Experimental Results
182(1)
8.5 Conclusion and Future Perspective
183(1)
References
183(6)
9 Secure Healthcare Systems Based on Big Data Analytics 189(24)
A. Angel Cerli
K. Kalaiselvi
Vijayakumar Varadarajan
9.1 Introduction
190(3)
9.2 Healthcare Data
193(2)
9.2.1 Structured Data
193(1)
9.2.2 Unstructured Data
194(1)
9.2.3 Semi-Structured Data
194(1)
9.2.4 Genomic Data
194(1)
9.2.5 Patient Behavior and Sentiment Data
194(1)
9.2.6 Clinical Data and Clinical Notes
194(1)
9.2.7 Clinical Reference and Health Publication Data
195(1)
9.2.8 Administrative and External Data
195(1)
9.3 Recent Works in Big Data Analytics in Healthcare Data
195(2)
9.4 Healthcare Big Data
197(1)
9.5 Privacy of Healthcare Big Data
198(2)
9.6 Privacy Right by Country and Organization
200(1)
9.7 How Blockchain is Big Data Usable for Healthcare
200(7)
9.7.1 Digital Trust
200(2)
9.7.2 Smart Data Tracking
202(1)
9.7.3 Ecosystem Sensible
202(1)
9.7.4 Switch Digital
202(1)
9.7.5 Cybersecurity
203(1)
9.7.6 Sharing Interoperability and Data
203(3)
9.7.7 Improving Research and Development (R&D)
206(1)
9.7.8 Drugs Fighting Counterfeit
206(1)
9.7.9 Patient Mutual Participation
206(1)
9.7.10 Internet Access by Patient to Longitudinal Data
206(1)
9.7.11 Data Storage into Off Related to Confidentiality and Data Scale
207(1)
9.8 Blockchain Threats and Medical Strategies Big Data Technology
207(1)
9.9 Conclusion and Future Research
208(1)
References
208(5)
10 Predictive and Descriptive Analysis for Healthcare Data 213(20)
Pritam R. Ahire
Rohini Hanchate
10.1 Introduction
214(1)
10.2 Motivation
215(14)
10.2.1 Healthcare Analysis
215(2)
10.2.2 Predictive Analytics
217(1)
10.2.3 Predictive Analytics Current Trends
217(1)
10.2.3.1 Importance of PA
217(1)
10.2.4 Descriptive Analysis
218(3)
10.2.4.1 Descriptive Statistics
218(1)
10.2.4.2 Categories of Descriptive Analysis
219(2)
10.2.5 Method of Modeling
221(1)
10.2.6 Measures of Data Analytics
221(2)
10.2.7 Healthcare Data Analytics Platforms and Tools
223(2)
10.2.8 Challenges
225(1)
10.2.9 Issues in Predictive Healthcare Analysis
226(1)
10.2.9.1 Integrating Separate Data Sources
226(1)
10.2.9.2 Advanced Cloud Technologies
226(1)
10.2.9.3 Privacy and Security
227(1)
10.2.9.4 The Fast Pace of Technology Changes
227(1)
10.2.10 Applications of Predictive Analysis
227(9)
10.2.10.1 Improving Operational Efficiency
227(1)
10.2.10.2 Personal Medicine
228(1)
10.2.10.3 Population Health and Risk Scoring
228(1)
10.2.10.4 Outbreak Prediction
228(1)
10.2.10.5 Controlling Patient Deterioration
228(1)
10.2.10.6 Supply Chain Management
228(1)
10.2.10.7 Potential in Precision Medicine
229(1)
10.2.10.8 Cost Savings From Reducing Waste and Fraud
229(1)
10.3 Conclusion
229(1)
References
229(4)
11 Machine and Deep Learning Algorithms for Healthcare Applications 233(22)
K. France
A. Jaya
Doru Tiliute
11.1 Introduction
234(1)
11.2 Artificial Intelligence, Machine Learning, and Deep Learning
234(2)
11.3 Machine Learning
236(3)
11.3.1 Supervised Learning
236(2)
11.3.2 Unsupervised Learning
238(1)
11.3.3 Semi-Supervised
238(1)
11.3.4 Reinforcement Learning
238(1)
11.4 Advantages of Using Deep Learning on Top of Machine Learning
239(1)
11.5 Deep Learning Architecture
239(3)
11.6 Medical Image Analysis using Deep Learning
242(1)
11.7 Deep Learning in Chest X-Ray Images
243(3)
11.8 Machine Learning and Deep Learning in Content-Based Medical Image Retrieval
246(3)
11.9 Image Retrieval Performance Metrics
249(1)
11.10 Conclusion
250(1)
References
250(5)
12 Artificial Intelligence in Healthcare Data Science with Knowledge Engineering 255(30)
S. Asha
V. Kanchana Devi
G. Sahaja Vaishnavi
12.1 Introduction
256(4)
12.2 Literature Review
260(6)
12.3 AI in Healthcare
266(2)
12.4 Data Science and Knowledge Engineering for COVID-19
268(2)
12.5 Proposed Architecture and Its Implementation
270(8)
12.5.1 Implementation
270(15)
12.5.1.1 Data Collection
270(1)
12.5.1.2 Understanding Class and Dependencies
270(2)
12.5.1.3 Pre-Processing
272(1)
12.5.1.4 Sampling
273(1)
12.5.1.5 Model Fixing
273(1)
12.5.1.6 Analysis of Real-Time Datasets
273(3)
12.5.1.7 Machine Learning Algorithms
276(2)
12.6 Conclusions and Future Work
278(2)
References
280(5)
13 Knowledge Engineering Challenges in Smart Healthcare Data Analysis System 285(24)
S.J. Agasba Saroj
B. Saleena
B. Prakash
13.1 Introduction
285(4)
13.1.1 Motivation
287(2)
13.2 Ongoing Research on Intelligent Decision Support System
289(2)
13.3 Methodology and Architecture of the Intelligent Rule-Based System
291(4)
13.3.1 Proposed System Design
292(1)
13.3.2 Algorithms Used
293(18)
13.3.2.1 Forward Chaining
293(1)
13.3.2.2 Backward Chaining
294(1)
13.4 Creating a Rule-Based System using Prolog
295(9)
13.5 Results and Discussions
304(2)
13.6 Conclusion
306(1)
13.7 Acknowledgments
307(1)
References
307(2)
14 Big Data in Healthcare: Management, Analysis, and Future Prospects 309(18)
A. Akila
R. Parameswari
C. Jayakumari
14.1 Introduction
309(1)
14.2 Breast Cancer: Overview
310(1)
14.3 State-of-the-Art Technology in Treatment of Cancer
311(1)
14.3.1 Chemotherapy
311(1)
14.3.2 Radiotherapy
311(1)
14.4 Early Diagnosis of Breast Cancer: Overview
312(2)
14.4.1 Advantages and Risks Associated with the Early Detection of Breast Cancer
312(1)
14.4.2 Diagnosis the Breast Cancer
313(1)
14.5 Literature Review
314(1)
14.6 Machine Learning Algorithms
315(5)
14.6.1 Principal Component Analysis Algorithms
316(1)
14.6.2 K-Means Algorithm
317(1)
14.6.3 K-Nearest Neighbor Algorithm
317(1)
14.6.4 Logistic Regression Algorithm
318(1)
14.6.5 Support Vector Machine Algorithm
318(1)
14.6.6 AdaBoost Algorithm
319(1)
14.6.7 Neural Networks Algorithm
319(1)
14.6.8 Random Forest Algorithm
319(1)
14.7 Result and Discussion
320(2)
14.7.1 Performance Metrics
320(11)
14.7.1.1 ROC Curve
320(1)
14.7.1.2 Accuracy
321(1)
14.7.1.3 Precision and Recall
321(1)
14.7.1.4 F1-Score
322(1)
14.8 Experimental Result and Discussion
322(2)
14.9 Conclusion
324(1)
References
325(2)
15 Machine Learning for Information Extraction, Data Analysis and Predictions in the Healthcare System 327(18)
G. Jaculine Priya
S. Saradha
15.1 Introduction
327(2)
15.2 Machine Learning in Healthcare
329(2)
15.3 Types of Learnings in Machine Learning
331(3)
15.3.1 Supervised Learning
332(1)
15.3.2 Unsupervised Algorithms
333(1)
15.3.3 Semi-Supervised Learning
334(1)
15.3.4 Reinforcement Learning
334(1)
15.4 Types of Machine Learning Algorithms
334(6)
15.4.1 Classification
335(1)
15.4.2 Bayes Classification
335(1)
15.4.3 Association Analysis
335(1)
15.4.4 Correlation Analysis
336(1)
15.4.5 Cluster Analysis
336(1)
15.4.6 Outlier Analysis
336(1)
15.4.7 Regression Analysis
337(1)
15.4.8 K-Means
337(1)
15.4.9 Apriori Algorithm
337(1)
15.4.10 K Nearest Neighbor
337(1)
15.4.11 Naive Bayes
338(1)
15.4.12 AdaBoost
338(1)
15.4.13 Support Vector Machine
338(1)
15.4.14 Classification and Regression Trees
339(1)
15.4.15 Linear Discriminant Analysis
339(1)
15.4.16 Logistic Regression
339(1)
15.4.17 Linear Regression
339(1)
15.4.18 Principal Component Analysis
339(1)
15.5 Machine Learning for Information Extraction
340(1)
15.5.1 Natural Language Processing
340(1)
15.6 Predictive Analysis in Healthcare
341(1)
15.7 Conclusion
342(1)
References
342(3)
16 Knowledge Fusion Patterns in Healthcare 345(20)
N. Deepa
N. Kanimozhi
16.1 Introduction
346(2)
16.2 Related Work
348(1)
16.3 Materials and Methods
349(3)
16.3.1 Classification of Data Fusion
349(2)
16.3.2 Levels and Its Working in Healthcare Ecosystems
351(1)
16.3.2.1 Initial Level Data Access (ILA)
351(1)
16.3.2.2 Middle Level Access (MLA)
352(1)
16.3.2.3 High Level Access (HLA)
352(1)
16.4 Proposed System
352(3)
16.4.1 Objective
353(2)
16.4.2 Sample Dataset
355(1)
16.5 Results and Discussion
355(6)
16.6 Conclusion and Future Work
361(1)
References
362(3)
17 Commercial Platforms for Healthcare Analytics: Health Issues for Patients with Sickle Cells 365(22)
J.K. Adedeji
T.O. Owolabi
R.S. Fayose
17.1 Introduction
366(1)
17.2 Materials and Methods
367(10)
17.2.1 Data Acquisition and Pre-Processing
367(1)
17.2.2 Sickle Cells Normalization Image
368(1)
17.2.3 Gradient Calculation
369(2)
17.2.4 Gradient Descent Step
371(1)
17.2.5 Insight to Previous Methods Adopted in Convolutional Neural Networks
372(1)
17.2.6 Segments of Convolutional Neural Networks
372(2)
17.2.6.1 Convolutional Layer
372(1)
17.2.6.2 Pooling Layer
373(1)
17.2.6.3 Fully Connected Layer
374(1)
17.2.6.4 Softmax Layer
374(1)
17.2.7 Basic Transformations of Convolutional Neural Networks in Healthcare
374(2)
17.2.8 Algorithm Review and Comparison
376(1)
17.2.9 Feedforward
376(1)
17.3 Results and Discussion
377(6)
17.3.1 Results on Suitability for Applications in Healthcare
377(1)
17.3.2 Class Prediction
377(1)
17.3.3 The Model Sanity Checking
377(1)
17.3.4 Analysis of the Epoch and Training Losses
378(1)
17.3.5 Discussion and Healthcare Interpretations
379(1)
17.3.6 Load Data
379(1)
17.3.7 Image Pre-Processing
380(1)
17.3.8 Building and Training the Classifier
381(1)
17.3.9 Saving the Checkpoint Suitable for Healthcare
382(1)
17.3.10 Loading the Checkpoint
383(1)
17.4 Conclusion
383(1)
References
383(4)
18 New Trends and Applications of Big Data Analytics for Medical Science and Healthcare 387(26)
K. Niha
W. Aisha Banu
18.1 Introduction
388(1)
18.2 Related Work
389(1)
18.3 Convolutional Layer
389(1)
18.4 Pooling Layer
390(1)
18.5 Fully Connected Layer
390(1)
18.6 Recurrent Neural Network
391(1)
18.7 LSTM and GRU
392(5)
18.8 Materials and Methods
397(9)
18.8.1 Pre-Processing Strategy Selection
397(3)
18.8.2 Feature Extraction and Classification
400(6)
18.9 Results and Discussions
406(2)
18.10 Conclusion
408(1)
18.11 Acknowledgement
409(1)
References
409(4)
Index 413
A. Jaya, PhD, Professor in the Department of Computer Applications, B. S. Abdur Rahman Crescent Institute of Science and Technology, India. She has published more than 90 research articles in international journals.

K. Kalaiselvi, PhD, is a Professor and Head in the Department of Computer Science, School of Computing Sciences, Vels Institute of Science, Technology and Advanced Studies, Chennai, India. She has published more than 50 research articles in international journals.

Dinesh Goyal, PhD, is Principal at the Poornima Institute of Engineering & Technology, Jaipur, India. He has six patents published as well as six books and numerous articles.

Dhiya Al-Jumeily, PhD, is a professor of Artificial Intelligence and the Associate Dean of External Engagement for the Faculty of Engineering and Technology, Liverpool John Moores University, UK. He has published well over 200 peer-reviewed scientific publications, six books, and five book chapters. His current research is on decision support systems for self-management of health and disease.
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