Cognitive intelligence and big data in healthcare / / edited by D. Sumathi [and three others]

• Pubblicazione: Hoboken, New Jersey ; ; Beverly, Massachusetts : , : Wiley : , : Scrivener Publishing, , [2022]; ©2022
• Descrizione fisica: 1 online resource (415 pages)
• Disciplina: 005.7
• Soggetto topico: Big data; Medical informatics; Soft computing
• Persona (resp. second.): SumathiD
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: Cover -- Half-Title Page -- Title Page -- Copyright Page -- Contents -- Preface -- 1 Era of Computational Cognitive Techniques in Healthcare Systems -- 1.1 Introduction -- 1.2 Cognitive Science -- 1.3 Gap Between Classical Theory of Cognition -- 1.4 Cognitive Computing's Evolution -- 1.5 The Coming Era of Cognitive Computing -- 1.6 Cognitive Computing Architecture -- 1.6.1 The Internet-of-Things and Cognitive Computing -- 1.6.2 Big Data and Cognitive Computing -- 1.6.3 Cognitive Computing and Cloud Computing -- 1.7 Enabling Technologies in Cognitive Computing -- 1.7.1 Reinforcement Learning and Cognitive Computing -- 1.7.2 Cognitive Computing with Deep Learning -- 1.7.2.1 Relational Technique and Perceptual Technique -- 1.7.2.2 Cognitive Computing and Image Understanding -- 1.8 Intelligent Systems in Healthcare -- 1.8.1 Intelligent Cognitive System in Healthcare (Why and How) -- 1.9 The Cognitive Challenge -- 1.9.1 Case Study: Patient Evacuation -- 1.9.2 Case Study: Anesthesiology -- 1.10 Conclusion -- References -- 2 Proposal of a Metaheuristic Algorithm of Cognitive Computing for Classification of Erythrocytes and Leukocytes in Healthcare Informatics -- 2.1 Introduction -- 2.2 Literature Concept -- 2.2.1 Cognitive Computing Concept -- 2.2.2 Neural Networks Concepts -- 2.2.3 Convolutional Neural Network -- 2.2.4 Deep Learning -- 2.3 Materials and Methods (Metaheuristic Algorithm Proposal) -- 2.4 Case Study and Discussion -- 2.5 Conclusions with Future Research Scopes -- References -- 3 Convergence of Big Data and Cognitive Computing in Healthcare -- 3.1 Introduction -- 3.2 Literature Review -- 3.2.1 Role of Cognitive Computing in Healthcare Applications -- 3.2.2 Research Problem Study by IBM -- 3.2.3 Purpose of Big Data in Healthcare -- 3.2.4 Convergence of Big Data with Cognitive Computing -- 3.2.4.1 Smart Healthcare. ; 3.2.4.2 Big Data and Cognitive Computing-Based Smart Healthcare -- 3.3 Using Cognitive Computing and Big Data, a Smart Healthcare Framework for EEG Pathology Detection and Classification -- 3.3.1 EEG Pathology Diagnoses -- 3.3.2 Cognitive-Big Data-Based Smart Healthcare -- 3.3.3 System Architecture -- 3.3.4 Detection and Classification of Pathology -- 3.3.4.1 EEG Preprocessing and Illustration -- 3.3.4.2 CNN Model -- 3.3.5 Case Study -- 3.4 An Approach to Predict Heart Disease Using Integrated Big Data and Cognitive Computing in Cloud -- 3.4.1 Cloud Computing with Big Data in Healthcare -- 3.4.2 Heart Diseases -- 3.4.3 Healthcare Big Data Techniques -- 3.4.3.1 Rule Set Classifiers -- 3.4.3.2 Neuro Fuzzy Classifiers -- 3.4.3.3 Experimental Results -- 3.5 Conclusion -- References -- 4 IoT for Health, Safety, Well-Being, Inclusion, and Active Aging -- 4.1 Introduction -- 4.2 The Role of Technology in an Aging Society -- 4.3 Literature Survey -- 4.4 Health Monitoring -- 4.5 Nutrition Monitoring -- 4.6 Stress-Log: An IoT-Based Smart Monitoring System -- 4.7 Active Aging -- 4.8 Localization -- 4.9 Navigation Care -- 4.10 Fall Monitoring -- 4.10.1 Fall Detection System Architecture -- 4.10.2 Wearable Device -- 4.10.3 Wireless Communication Network -- 4.10.4 Smart IoT Gateway -- 4.10.5 Interoperability -- 4.10.6 Transformation of Data -- 4.10.7 Analyzer for Big Data -- 4.11 Conclusion -- References -- 5 Influence of Cognitive Computing in Healthcare Applications -- 5.1 Introduction -- 5.2 Bond Between Big Data and Cognitive Computing -- 5.3 Need for Cognitive Computing in Healthcare -- 5.4 Conceptual Model Linking Big Data and Cognitive Computing -- 5.4.1 Significance of Big Data -- 5.4.2 The Need for Cognitive Computing -- 5.4.3 The Association Between the Big Data and Cognitive Computing -- 5.4.4 The Advent of Cognition in Healthcare. ; 5.5 IBM's Watson and Cognitive Computing -- 5.5.1 Industrial Revolution with Watson -- 5.5.2 The IBM's Cognitive Computing Endeavour in Healthcare -- The IBM Watson Health and Watson Health Cloud -- Usage of Cognitive Application to Augment the Electronic Medical Record -- 5.6 Future Directions -- 5.6.1 Retail -- 5.6.2 Research -- 5.6.3 Travel -- 5.6.4 Security and Threat Detection -- 5.6.5 Cognitive Training Tools -- 5.7 Conclusion -- References -- 6 An Overview of the Computational Cognitive from a Modern Perspective, Its Techniques and Application Potential in Healthcare Systems -- 6.1 Introduction -- 6.2 Literature Concept -- 6.2.1 Cognitive Computing Concept -- 6.2.1.1 Application Potential -- 6.2.2 Cognitive Computing in Healthcare -- 6.2.3 Deep Learning in Healthcare -- 6.2.4 Natural Language Processing in Healthcare -- 6.3 Discussion -- 6.4 Trends -- 6.5 Conclusions -- References -- 7 Protecting Patient Data with 2FAuthentication -- 7.1 Introduction -- 7.2 Literature Survey -- 7.3 Two-Factor Authentication -- 7.3.1 Novel Features of Two-Factor Authentication -- 7.3.2 Two-Factor Authentication Sorgen -- 7.3.3 Two-Factor Security Libraries -- 7.3.4 Challenges for Fitness Concern -- 7.4 Proposed Methodology -- 7.5 Medical Treatment and the Preservation of Records -- 7.5.1 Remote Method of Control -- 7.5.2 Enabling Healthcare System Technology -- 7.6 Conclusion -- References -- 8 Data Analytics for Healthcare Monitoring and Inferencing -- 8.1 An Overview of Healthcare Systems -- 8.2 Need of Healthcare Systems -- 8.3 Basic Principle of Healthcare Systems -- 8.4 Design and Recommended Structure of Healthcare Systems -- 8.4.1 Healthcare System Designs on the Basis of these Parameters -- 8.4.2 Details of Healthcare Organizational Structure -- 8.5 Various Challenges in Conventional Existing Healthcare System -- 8.6 Health Informatics. ; 8.7 Information Technology Use in Healthcare Systems -- 8.8 Details of Various Information Technology Application Use in Healthcare Systems -- 8.9 Healthcare Information Technology Makes it Possible to Manage Patient Care and Exchange of Health Information Data, Details are Given Below -- 8.10 Barriers and Challenges to Implementation of Information Technology in Healthcare Systems -- 8.11 Healthcare Data Analytics -- 8.12 Healthcare as a Concept -- 8.13 Healthcare's Key Technologies -- 8.14 The Present State of Smart Healthcare Application -- 8.15 Data Analytics with Machine Learning Use in Healthcare Systems -- 8.16 Benefit of Data Analytics in Healthcare System -- 8.17 Data Analysis and Visualization: COVID-19 Case Study in India -- 8.18 Bioinformatics Data Analytics -- 8.18.1 Notion of Bioinformatics -- 8.18.2 Bioinformatics Data Challenges -- 8.18.3 Sequence Analysis -- 8.18.4 Applications -- 8.18.5 COVID-19: A Bioinformatics Approach -- 8.19 Conclusion -- References -- 9 Features Optimistic Approach for the Detection of Parkinson's Disease -- 9.1 Introduction -- 9.1.1 Parkinson's Disease -- 9.1.2 Spect Scan -- 9.2 Literature Survey -- 9.3 Methods and Materials -- 9.3.1 Database Details -- 9.3.2 Procedure -- 9.3.3 Pre-Processing Done by PPMI -- 9.3.4 Image Analysis and Features Extraction -- 9.3.4.1 Image Slicing -- 9.3.4.2 Intensity Normalization -- 9.3.4.3 Image Segmentation -- 9.3.4.4 Shape Features Extraction -- 9.3.4.5 SBR Features -- 9.3.4.6 Feature Set Analysis -- 9.3.4.7 Surface Fitting -- 9.3.5 Classification Modeling -- 9.3.6 Feature Importance Estimation -- 9.3.6.1 Need for Analysis of Important Features -- 9.3.6.2 Random Forest -- 9.4 Results and Discussion -- 9.4.1 Segmentation -- 9.4.2 Shape Analysis -- 9.4.3 Classification -- 9.5 Conclusion -- References -- 10 Big Data Analytics in Healthcare -- 10.1 Introduction. ; 10.2 Need for Big Data Analytics -- 10.3 Characteristics of Big Data -- 10.3.1 Volume -- 10.3.2 Velocity -- 10.3.3 Variety -- 10.3.4 Veracity -- 10.3.5 Value -- 10.3.6 Validity -- 10.3.7 Variability -- 10.3.8 Viscosity -- 10.3.9 Virality -- 10.3.10 Visualization -- 10.4 Big Data Analysis in Disease Treatment and Management -- 10.4.1 For Diabetes -- 10.4.2 For Heart Disease -- 10.4.3 For Chronic Disease -- 10.4.4 For Neurological Disease -- 10.4.5 For Personalized Medicine -- 10.5 Big Data: Databases and Platforms in Healthcare -- 10.6 Importance of Big Data in Healthcare -- 10.6.1 Evidence-Based Care -- 10.6.2 Reduced Cost of Healthcare -- 10.6.3 Increases the Participation of Patients in the Care Process -- 10.6.4 The Implication in Health Surveillance -- 10.6.5 Reduces Mortality Rate -- 10.6.6 Increase of Communication Between Patients and Healthcare Providers -- 10.6.7 Early Detection of Fraud and Security Threats in Health Management -- 10.6.8 Improvement in the Care Quality -- 10.7 Application of Big Data Analytics -- 10.7.1 Image Processing -- 10.7.2 Signal Processing -- 10.7.3 Genomics -- 10.7.4 Bioinformatics Applications -- 10.7.5 Clinical Informatics Application -- 10.8 Conclusion -- References -- 11 Case Studies of Cognitive Computing in Healthcare Systems: Disease Prediction, Genomics Studies, Medical Image Analysis, Patient Care, Medical Diagnostics, Drug Discovery -- 11.1 Introduction -- 11.1.1 Glaucoma -- 11.2 Literature Survey -- 11.3 Methodology -- 11.3.1 Sclera Segmentation -- 11.3.1.1 Fully Convolutional Network -- 11.3.2 Pupil/Iris Ratio -- 11.3.2.1 Canny Edge Detection -- 11.3.2.2 Mean Redness Level (MRL) -- MBP Mean Blue Mean S m S -- 11.3.2.3 Red Area Percentage (RAP) -- 11.4 Results and Discussion -- 11.4.1 Feature Extraction from Frontal Eye Images -- 11.4.1.1 Level of Mean Redness (MRL). ; 11.4.1.2 Percentage of Red Area (RAP).
• Titolo autorizzato: Cognitive intelligence and big data in healthcare
• ISBN: 1-119-77198-6; 1-119-77196-X
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910830680403321
• Lo trovi qui: Univ. Federico II
• Serie: Artificial Intelligence and Soft Computing for Industrial Transformation