10876nam 22004933 450 991059009920332120220818080222.01-119-77198-61-119-77196-X(MiAaPQ)EBC7074400(Au-PeEL)EBL7074400(CKB)24655623100041(EXLCZ)992465562310004120220818d2022 uy 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierCognitive Intelligence and Big Data in HealthcareNewark :John Wiley & Sons, Incorporated,2022.©2022.1 online resource (415 pages)Artificial Intelligence and Soft Computing for Industrial Transformation Ser.Print version: Sumathi, D. Cognitive Intelligence and Big Data in Healthcare Newark : John Wiley & Sons, Incorporated,c2022 9781119768883 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).Artificial Intelligence and Soft Computing for Industrial Transformation Ser.Electronic books.Sumathi D1254794Poongodi T1254793Balamurugan B1254795Ramasamy Lakshmana Kumar1254819MiAaPQMiAaPQMiAaPQBOOK9910590099203321Cognitive Intelligence and Big Data in Healthcare2908962UNINA03605nam 2200721 450 991048375720332120211008223223.03-540-76725-810.1007/978-3-540-76725-1(CKB)1000000000490897(SSID)ssj0000319631(PQKBManifestationID)11222547(PQKBTitleCode)TC0000319631(PQKBWorkID)10338735(PQKB)10131895(DE-He213)978-3-540-76725-1(MiAaPQ)EBC4976613(MiAaPQ)EBC5577491(MiAaPQ)EBC6511603(Au-PeEL)EBL4976613(CaONFJC)MIL135491(OCoLC)1024246647(Au-PeEL)EBL5577491(OCoLC)261324719(Au-PeEL)EBL6511603(OCoLC)1135613825(PPN)123736196(EXLCZ)99100000000049089720211008d2007 uy 0engurnn#008mamaatxtccrProgress in pattern recognition, image analysis and applications 12th iberoamerican congress on pattern recognition, ciarp 2007,valpariso, chile, november 13-16, 2007, proceedings /edited by Luis Rueda, Domingo Mery, Josef Kittler1st ed. 2007.Berlin, Germany :Springer,[2007]©20071 online resource (XXI, 972 p.)Image Processing, Computer Vision, Pattern Recognition, and Graphics ;4756Bibliographic Level Mode of Issuance: Monograph3-540-76724-X Includes bibliographical references and index.Keynote Lectures -- Signal Processing and Analysis -- Image Coding, Processing and Analysis -- Shape and Texture Analysis -- Computer Vision -- Pattern Recognition Principles -- Artificial Intelligence Techniques and Recognition -- Logical Combinatorial Pattern Recognition -- Neural Networks -- Kernel Machines -- Bioinformatics -- Data Mining -- Natural Language Processing and Recognition -- Industrial and Medical Applications of Pattern Recognition -- Robotics and Remote Sensing Applications of Pattern Recognition -- Document Processing and Recognition -- Fuzzy and Hybrid Techniques in Pattern Recognition.This book constitutes the refereed proceedings of the 12th Iberoamerican Congress on Pattern Recognition, CIARP 2007, held in Valparaiso, Chile, November 13-16, 2007. The 97 revised full papers presented together with 4 keynote articles were carefully reviewed and selected from 200 submissions. The papers cover ongoing research and mathematical methods for pattern recognition, image analysis, and applications in such diverse areas as computer vision, robotics and remote sensing, industry, health, space exploration, data mining, document analysis, natural language processing and speech recognition.Image Processing, Computer Vision, Pattern Recognition, and Graphics ;4756Computer visionOptical pattern recognitionArtificial intelligenceComputer vision.Optical pattern recognition.Artificial intelligence.006.4Mery DomingoKittler Josef1946-Rueda LuisMiAaPQMiAaPQMiAaPQBOOK9910483757203321Progress in Pattern Recognition, Image Analysis and Applications771919UNINA