Update cookies preferences

Cognitive Intelligence and Big Data in Healthcare [Hardback]

Edited by , Edited by , Edited by , Edited by
Other books in subject:
  • Hardback
  • Price: 202,60 €
  • This book is not in stock. Book will arrive in about 2-4 weeks. Please allow another 2 weeks for shipping outside Estonia.
  • Quantity:
  • Add to basket
  • Delivery time 4-6 weeks
  • Add to Wishlist
  • For Libraries
Cognitive Intelligence and Big Data in HealthcareLarger Image
Other books in subject:
Permanent link: https://www.kriso.ee/db/9781119768883.html
Keywords:
COGNITIVE INTELLIGENCE AND BIG DATA IN HEALTHCARE Applications of cognitive intelligence, advanced communication, and computational methods can drive healthcare research and enhance existing traditional methods in disease detection and management and prevention.

As health is the foremost factor affecting the quality of human life, it is necessary to understand how the human body is functioning by processing health data obtained from various sources more quickly. Since an enormous amount of data is generated during data processing, a cognitive computing system could be applied to respond to queries, thereby assisting in customizing intelligent recommendations. This decision-making process could be improved by the deployment of cognitive computing techniques in healthcare, allowing for cutting-edge techniques to be integrated into healthcare to provide intelligent services in various healthcare applications.

This book tackles all these issues and provides insight into these diversified topics in the healthcare sector and shows the range of recent innovative research, in addition to shedding light on future directions in this area.

Audience

The book will be very useful to a wide range of specialists including researchers, engineers, and postgraduate students in artificial intelligence, bioinformatics, information technology, as well as those in biomedicine.
Preface xv
1 Era of Computational Cognitive Techniques in Healthcare Systems
1(40)
Deependra Rastogi
Varun Tiwari
Shobhit Kumar
Prabhat Chandra Gupta
1.1 Introduction
2(1)
1.2 Cognitive Science
3(1)
1.3 Gap Between Classical Theory of Cognition
4(2)
1.4 Cognitive Computing's Evolution
6(1)
1.5 The Coming Era of Cognitive Computing
7(2)
1.6 Cognitive Computing Architecture
9(4)
1.6.1 The Internet-of-Things and Cognitive Computing
10(1)
1.6.2 Big Data and Cognitive Computing
11(2)
1.6.3 Cognitive Computing and Cloud Computing
13(1)
1.7 Enabling Technologies in Cognitive Computing
13(4)
1.7.1 Reinforcement Learning and Cognitive Computing
13(2)
1.7.2 Cognitive Computing with Deep Learning
15(1)
1.7.2.1 Relational Technique and Perceptual Technique
15(1)
1.7.2.2 Cognitive Computing and Image Understanding
16(1)
1.8 Intelligent Systems in Healthcare
17(15)
1.8.1 Intelligent Cognitive System in Healthcare (Why and How)
20(12)
1.9 The Cognitive Challenge
32(2)
1.9.1 Case Study: Patient Evacuation
32(1)
1.9.2 Case Study: Anesthesiology
32(2)
1.10 Conclusion
34(7)
References
35(6)
2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics
41(26)
Ana Carolina Borges Monteiro
Reinaldo Padilha Franca
Rangel Arthur
Yuzo Iano
2.1 Introduction
42(2)
2.2 Literature Concept
44(11)
2.2.1 Cognitive Computing Concept
44(3)
2.2.2 Neural Networks Concepts
47(2)
2.2.3 Convolutional Neural Network
49(3)
2.2.4 Deep Learning
52(3)
2.3 Materials and Methods (Metaheuristic Algorithm Proposal)
55(2)
2.4 Case Study and Discussion
57(3)
2.5 Conclusions with Future Research Scopes
60(7)
References
61(6)
3 Convergence of Big Data and Cognitive Computing in Healthcare
67(30)
R. Sathiyaraj
U. Rahamathunnisa
M. V. Jagannatha Reddy
T. Parameswaran
3.1 Introduction
68(2)
3.2 Literature Review
70(6)
3.2.1 Role of Cognitive Computing in Healthcare Applications
70(3)
3.2.2 Research Problem Study by IBM
73(1)
3.2.3 Purpose of Big Data in Healthcare
74(1)
3.2.4 Convergence of Big Data with Cognitive Computing
74(1)
3.2.4.1 Smart Healthcare
74(1)
3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare
75(1)
3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification
76(7)
3.3.1 EEG Pathology Diagnoses
76(1)
3.3.2 Cognitive-Big Data-Based Smart Healthcare
77(2)
3.3.3 System Architecture
79(1)
3.3.4 Detection and Classification of Pathology
80(1)
3.3.4.1 EEG Preprocessing and Illustration
80(1)
3.3.4.2 CNN Model
80(1)
3.3.5 Case Study
81(2)
3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud
83(9)
3.4.1 Cloud Computing with Big Data in Healthcare
86(1)
3.4.2 Heart Diseases
87(1)
3.4.3 Healthcare Big Data Techniques
88(1)
3.4.3.1 Rule Set Classifiers
88(1)
3.4.3.2 Neuro Fuzzy Classifiers
89(2)
3.4.3.3 Experimental Results
91(1)
3.5 Conclusion
92(5)
References
93(4)
4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging
97(24)
R. Indrakumari
Nilanjana Pradhan
Shrddha Sagar
Kiran Singh
4.1 Introduction
98(1)
4.2 The Role of Technology in an Aging Society
99(1)
4.3 Literature Survey
100(1)
4.4 Health Monitoring
101(4)
4.5 Nutrition Monitoring
105(1)
4.6 Stress-Log: An IoT-Based Smart Monitoring System
106(2)
4.7 Active Aging
108(1)
4.8 Localization
108(3)
4.9 Navigation Care
111(2)
4.10 Fall Monitoring
113(2)
4.10.1 Fall Detection System Architecture
114(1)
4.10.2 Wearable Device
114(1)
4.10.3 Wireless Communication Network
114(1)
4.10.4 Smart IoT Gateway
115(1)
4.10.5 Interoperability
115(1)
4.10.6 Transformation of Data
115(1)
4.10.7 Analyzer for Big Data
115(1)
4.11 Conclusion
115(6)
References
116(5)
5 Influence of Cognitive Computing in Healthcare Applications
121(24)
Lucia Agnes Beena T.
Vinolyn Vijaykumar
5.1 Introduction
122(2)
5.2 Bond Between Big Data and Cognitive Computing
124(2)
5.3 Need for Cognitive Computing in Healthcare
126(2)
5.4 Conceptual Model Linking Big Data and Cognitive Computing
128(5)
5.4.1 Significance of Big Data
128(1)
5.4.2 The Need for Cognitive Computing
129(1)
5.4.3 The Association Between the Big Data and Cognitive Computing
130(2)
5.4.4 The Advent of Cognition in Healthcare
132(1)
5.5 IBM's Watson and Cognitive Computing
133(4)
5.5.1 Industrial Revolution with Watson
134(1)
5.5.2 The IBM's Cognitive Computing Endeavour in Healthcare
135(2)
5.6 Future Directions
137(4)
5.6.1 Retail
138(1)
5.6.2 Research
139(1)
5.6.3 Travel
139(1)
5.6.4 Security and Threat Detection
139(1)
5.6.5 Cognitive Training Tools
140(1)
5.7 Conclusion
141(4)
References
141(4)
6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems
145(24)
Reinaldo Padilha Franca
Ana Carolina Borges Monteiro
Rangel Arthur
Yuzo Lano
6.1 Introduction
146(2)
6.2 Literature Concept
148(14)
6.2.1 Cognitive Computing Concept
148(3)
6.2.1.1 Application Potential
151(2)
6.2.2 Cognitive Computing in Healthcare
153(4)
6.2.3 Deep Learning in Healthcare
157(3)
6.2.4 Natural Language Processing in Healthcare
160(2)
6.3 Discussion
162(1)
6.4 Trends
163(1)
6.5 Conclusions
164(5)
References
165(4)
7 Protecting Patient Data with 2F- Authentication
169(28)
G. S. Pradeep Ghantasala
Anu Radha Reddy
R. Mohan Krishna Ayyappa
7.1 Introduction
170(5)
7.2 Literature Survey
175(2)
7.3 Two-Factor Authentication
177(4)
7.3.1 Novel Features of Two-Factor Authentication
178(1)
7.3.2 Two-Factor Authentication Sorgen
178(1)
7.3.3 Two-Factor Security Libraries
179(1)
7.3.4 Challenges for Fitness Concern
180(1)
7.4 Proposed Methodology
181(5)
7.5 Medical Treatment and the Preservation of Records
186(3)
7.5.1 Remote Method of Control
187(1)
7.5.2 Enabling Healthcare System Technology
187(2)
7.6 Conclusion
189(8)
References
190(7)
8 Data Analytics for Healthcare Monitoring and Inferencing
197(32)
Gend Lai Prajapati
Rachana Raghuwanshi
Rambabu Raghuwanshi
8.1 An Overview of Healthcare Systems
198(1)
8.2 Need of Healthcare Systems
198(1)
8.3 Basic Principle of Healthcare Systems
199(1)
8.4 Design and Recommended Structure of Healthcare Systems
199(3)
8.4.1 Healthcare System Designs on the Basis of these Parameters
200(1)
8.4.2 Details of Healthcare Organizational Structure
201(1)
8.5 Various Challenges in Conventional Existing Healthcare System
202(1)
8.6 Health Informatics
202(1)
8.7 Information Technology Use in Healthcare Systems
203(1)
8.8 Details of Various Information Technology Application Use in Healthcare Systems
203(1)
8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below
204(1)
8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems
205(1)
8.11 Healthcare Data Analytics
206(1)
8.12 Healthcare as a Concept
206(1)
8.13 Healthcare's Key Technologies
207(1)
8.14 The Present State of Smart Healthcare Application
207(1)
8.15 Data Analytics with Machine Learning Use in Healthcare Systems
208(2)
8.16 Benefit of Data Analytics in Healthcare System
210(1)
8.17 Data Analysis and Visualization: COVID-19 Case Study in India
210(12)
8.18 Bioinformatics Data Analytics
222(2)
8.18.1 Notion of Bioinformatics
222(1)
8.18.2 Bioinformatics Data Challenges
222(1)
8.18.3 Sequence Analysis
222(1)
8.18.4 Applications
223(1)
8.18.5 COVID-19: A Bioinformatics Approach
224(1)
8.19 Conclusion
224(5)
References
225(4)
9 Features Optimistic Approach for the Detection of Parkinson's Disease
229(28)
R. Shantha Selva Kumari
L. Vaishalee
P. Malavikha
9.1 Introduction
230(2)
9.1.1 Parkinson's Disease
230(1)
9.1.2 Spect Scan
231(1)
9.2 Literature Survey
232(1)
9.3 Methods and Materials
233(15)
9.3.1 Database Details
233(1)
9.3.2 Procedure
234(1)
9.3.3 Pre-Processing Done by PPMI
235(1)
9.3.4 Image Analysis and Features Extraction
235(1)
9.3.4.1 Image Slicing
235(2)
9.3.4.2 Intensity Normalization
237(2)
9.3.4.3 Image Segmentation
239(1)
9.3.4.4 Shape Features Extraction
240(1)
9.3.4.5 SBR Features
241(1)
9.3.4.6 Feature Set Analysis
242(1)
9.3.4.7 Surface Fitting
242(1)
9.3.5 Classification Modeling
243(3)
9.3.6 Feature Importance Estimation
246(1)
9.3.6.1 Need for Analysis of Important Features
246(1)
9.3.6.2 Random Forest
247(1)
9.4 Results and Discussion
248(4)
9.4.1 Segmentation
248(1)
9.4.2 Shape Analysis
249(1)
9.4.3 Classification
249(3)
9.5 Conclusion
252(5)
References
253(4)
10 Big Data Analytics in Healthcare
257(46)
Akanksha Sharma
Rishabha Malviya
Ramji Gupta
10.1 Introduction
258(2)
10.2 Need for Big Data Analytics
260(4)
10.3 Characteristics of Big Data
264(3)
10.3.1 Volume
264(1)
10.3.2 Velocity
265(1)
10.3.3 Variety
265(1)
10.3.4 Veracity
265(1)
10.3.5 Value
265(1)
10.3.6 Validity
265(1)
10.3.7 Variability
266(1)
10.3.8 Viscosity
266(1)
10.3.9 Virality
266(1)
10.3.10 Visualization
266(1)
10.4 Big Data Analysis in Disease Treatment and Management
267(12)
10.4.1 For Diabetes
267(1)
10.4.2 For Heart Disease
268(2)
10.4.3 For Chronic Disease
270(1)
10.4.4 For Neurological Disease
271(1)
10.4.5 For Personalized Medicine
271(8)
10.5 Big Data: Databases and Platforms in Healthcare
279(6)
10.6 Importance of Big Data in Healthcare
285(1)
10.6.1 Evidence-Based Care
285(1)
10.6.2 Reduced Cost of Healthcare
285(1)
10.6.3 Increases the Participation of Patients in the Care Process
285(1)
10.6.4 The Implication in Health Surveillance
285(1)
10.6.5 Reduces Mortality Rate
285(1)
10.6.6 Increase of Communication Between Patients and Healthcare Providers
286(1)
10.6.7 Early Detection of Fraud and Security Threats in Health Management
286(1)
10.6.8 Improvement in the Care Quality
286(1)
10.7 Application of Big Data Analytics
286(7)
10.7.1 Image Processing
286(1)
10.7.2 Signal Processing
287(1)
10.7.3 Genomics
288(1)
10.7.4 Bioinformatics Applications
289(2)
10.7.5 Clinical Informatics Application
291(2)
10.8 Conclusion
293(10)
References
294(9)
11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery
303(24)
V. Sathananthavathi
G. Indumathi
11.1 Introduction
304(2)
11.1.1 Glaucoma
304(2)
11.2 Literature Survey
306(3)
11.3 Methodology
309(8)
11.3.1 Sclera Segmentation
310(1)
11.3.1.1 Fully Convolutional Network
311(2)
11.3.2 Pupil/Iris Ratio
313(1)
11.3.2.1 Canny Edge Detection
314(1)
11.3.2.2 Mean Redness Level (MRL)
315(1)
11.3.2.3 Red Area Percentage (RAP)
316(1)
11.4 Results and Discussion
317(7)
11.4.1 Feature Extraction from Frontal Eye Images
318(1)
11.4.1.1 Level of Mean Redness (MRL)
318(1)
11.4.1.2 Percentage of Red Area (RAP)
318(1)
11.4.2 Images of the Frontal Eye Pupil/Iris Ratio
318(1)
11.4.2.1 Histogram Equalization
319(1)
11.4.2.2 Morphological Reconstruction
319(1)
11.4.2.3 Canny Edge Detection
319(1)
11.4.2.4 Adaptive Thresholding
320(1)
11.4.2.5 Circular Hough Transform
321(1)
11.4.2.6 Classification
322(2)
11.5 Conclusion and Future Work
324(3)
References
325(2)
12 State of Mental Health and Social Media: Analysis, Challenges, Advancements
327(22)
Atul Pankaj Patil
Kusutn Lata Jain
Smaranika Mohapatra
Suyesha Singh
12.1 Introduction
328(1)
12.2 Introduction to Big Data and Data Mining
328(2)
12.3 Role of Sentimental Analysis in the Healthcare Sector
330(2)
12.4 Case Study: Analyzing Mental Health
332(11)
12.4.1 Problem Statement
332(1)
12.4.2 Research Objectives
333(1)
12.4.3 Methodology and Framework
333(1)
12.4.3.1 Big 5 Personality Model
333(1)
12.4.3.2 Openness to Explore
334(1)
12.4.3.3 Methodology
335(5)
12.4.3.4 Detailed Design Methodologies
340(1)
12.4.3.5 Work Done Details as Required
341(2)
12.5 Results and Discussion
343(2)
12.6 Conclusion and Future
345(4)
References
346(3)
13 Applications of Artificial Intelligence, Blockchain, and Internet-of-Things in Management of Chronic Disease
349(18)
Geetanjali
Rishabha Malviya
Rajendra Awasthi
Pramod Kumar Sharma
Nidhi Kala
Vinod Kumar
Sanjay Kumar Yadav
13.1 Introduction
350(1)
13.2 Artificial Intelligence and Management of Chronic Diseases
351(3)
13.3 Blockchain and Healthcare
354(4)
13.3.1 Blockchain and Healthcare Management of Chronic Disease
355(3)
13.4 Internet-of-Things and Healthcare Management of Chronic Disease
358(2)
13.5 Conclusions
360(7)
References
360(7)
14 Research Challenges and Future Directions in Applying Cognitive Computing in the Healthcare Domain
367(24)
BKSP Kumar Raju Alluri
14.1 Introduction
367(4)
14.2 Cognitive Computing Framework in Healthcare
371(1)
14.3 Benefits of Using Cognitive Computing for Healthcare
372(2)
14.4 Applications of Deploying Cognitive Assisted Technology in Healthcare Management
374(3)
14.4.1 Using Cognitive Services for a Patient's Healthcare Management
375(1)
14.4.2 Using Cognitive Services for Healthcare Providers
376(1)
14.5 Challenges in Using the Cognitive Assistive Technology in Healthcare Management
377(3)
14.6 Future Directions for Extending Heathcare Services Using CATs
380(4)
14.7 Addressing CAT Challenges in Healthcare as a General Framework
384(1)
14.8 Conclusion
384(7)
References
385(6)
Index 391
D. Sumathi, PhD, is an associate professor at VIT-AP University, Andhra Pradesh. She has an overall experience of 21 years out of which six years in the industry, and 15 years in the teaching field. Her research interests include cloud computing, network security, data mining, natural language processing, and the theoretical foundations of computer science.

T. Poongodi, PhD, is an associate professor in the Department of Computer Science and Engineering at Galgotias University, Delhi NCR, India. She has more than 15 years of experience working in teaching and research.

B. Balamurugan, PhD, is a professor in the School of Computing Science and Engineering at Galgotias University, Delhi NCR, India. His focus is on engineering education, blockchain, and data sciences. He has published more than 30 books on various technologies and more than 150 research articles in SCI journals, conferences, and book chapters.

Lakshmana Kumar Ramasamy, PhD, is leading the Machine Learning for Cyber Security team at Hindusthan College of Engineering and Technology, Coimbatore. Tamil Nadu, India. He is also allied with a company conducting specific training for Infosys Campus Connect, Oracle WDP, and Palo Alto Networks. He holds the Gold level partnership award from Infosys, India for bridging the gap between industry and academia in 2017.