Computational Intelligence and Healthcare Informatics. (Record no. 28408)
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| fixed length control field | 11350nam a22005293i 4500 |
| 001 - CONTROL NUMBER | |
| control field | EBC6715215 |
| 003 - CONTROL NUMBER IDENTIFIER | |
| control field | MiAaPQ |
| 005 - DATE AND TIME OF LATEST TRANSACTION | |
| control field | 20240724115229.0 |
| 006 - FIXED-LENGTH DATA ELEMENTS--ADDITIONAL MATERIAL CHARACTERISTICS | |
| fixed length control field | m o d | |
| 007 - PHYSICAL DESCRIPTION FIXED FIELD--GENERAL INFORMATION | |
| fixed length control field | cr cnu|||||||| |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION | |
| fixed length control field | 240724s2021 xx o ||||0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| International Standard Book Number | 9781119818700 |
| Qualifying information | (electronic bk.) |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER | |
| Canceled/invalid ISBN | 9781119818687 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (MiAaPQ)EBC6715215 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (Au-PeEL)EBL6715215 |
| 035 ## - SYSTEM CONTROL NUMBER | |
| System control number | (OCoLC)1266908223 |
| 040 ## - CATALOGING SOURCE | |
| Original cataloging agency | MiAaPQ |
| Language of cataloging | eng |
| Description conventions | rda |
| -- | pn |
| Transcribing agency | MiAaPQ |
| Modifying agency | MiAaPQ |
| 050 #4 - LIBRARY OF CONGRESS CALL NUMBER | |
| Classification number | R859.7.A78 C667 2021 |
| 082 0# - DEWEY DECIMAL CLASSIFICATION NUMBER | |
| Classification number | 610.28563 |
| 100 1# - MAIN ENTRY--PERSONAL NAME | |
| Personal name | Jena, Om Prakash. |
| 245 10 - TITLE STATEMENT | |
| Title | Computational Intelligence and Healthcare Informatics. |
| 250 ## - EDITION STATEMENT | |
| Edition statement | 1st ed. |
| 264 #1 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Place of production, publication, distribution, manufacture | Newark : |
| Name of producer, publisher, distributor, manufacturer | John Wiley & Sons, Incorporated, |
| Date of production, publication, distribution, manufacture, or copyright notice | 2021. |
| 264 #4 - PRODUCTION, PUBLICATION, DISTRIBUTION, MANUFACTURE, AND COPYRIGHT NOTICE | |
| Date of production, publication, distribution, manufacture, or copyright notice | ©2022. |
| 300 ## - PHYSICAL DESCRIPTION | |
| Extent | 1 online resource (448 pages) |
| 336 ## - CONTENT TYPE | |
| Content type term | text |
| Content type code | txt |
| Source | rdacontent |
| 337 ## - MEDIA TYPE | |
| Media type term | computer |
| Media type code | c |
| Source | rdamedia |
| 338 ## - CARRIER TYPE | |
| Carrier type term | online resource |
| Carrier type code | cr |
| Source | rdacarrier |
| 490 1# - SERIES STATEMENT | |
| Series statement | Machine Learning in Biomedical Science and Healthcare Informatics Series |
| 505 0# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Cover -- Half-Title Page -- Series Page -- Title Page -- Copyright Page -- Contents -- Preface -- Part I: INTRODUCTION -- 1 Machine Learning and Big Data: An Approach Toward Better Healthcare Services -- 1.1 Introduction -- 1.2 Machine Learning in Healthcare -- 1.3 Machine Learning Algorithms -- 1.3.1 Supervised Learning -- 1.3.2 Unsupervised Learning -- 1.3.3 Semi-Supervised Learning -- 1.3.4 Reinforcement Learning -- 1.3.5 Deep Learning -- 1.4 Big Data in Healthcare -- 1.5 Application of Big Data in Healthcare -- 1.5.1 Electronic Health Records -- 1.5.2 Helping in Diagnostics -- 1.5.3 Preventive Medicine -- 1.5.4 Precision Medicine -- 1.5.5 Medical Research -- 1.5.6 Cost Reduction -- 1.5.7 Population Health -- 1.5.8 Telemedicine -- 1.5.9 Equipment Maintenance -- 1.5.10 Improved Operational Efficiency -- 1.5.11 Outbreak Prediction -- 1.6 Challenges for Big Data -- 1.7 Conclusion -- References -- Part II: MEDICAL DATA PROCESSING AND ANALYSIS -- 2 Thoracic Image Analysis Using Deep Learning -- 2.1 Introduction -- 2.2 Broad Overview of Research -- 2.2.1 Challenges -- 2.2.2 Performance Measuring Parameters -- 2.2.3 Availability of Datasets -- 2.3 Existing Models -- 2.4 Comparison of Existing Models -- 2.5 Summary -- 2.6 Conclusion and Future Scope -- References -- 3 Feature Selection and Machine Learning Models for High-Dimensional Data: State-of-the-Art -- 3.1 Introduction -- 3.1.1 Motivation of the Dimensionality Reduction -- 3.1.2 Feature Selection and Feature Extraction -- 3.1.3 Objectives of the Feature Selection -- 3.1.4 Feature Selection Process -- 3.2 Types of Feature Selection -- 3.2.1 Filter Methods -- 3.2.2 Wrapper Methods -- 3.2.3 Embedded Methods -- 3.2.4 Hybrid Methods -- 3.3 Machine Learning and Deep Learning Models -- 3.3.1 Restricted Boltzmann Machine -- 3.3.2 Autoencoder -- 3.3.3 Convolutional Neural Networks. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 3.3.4 Recurrent Neural Network -- 3.4 Real-World Applications and Scenario of Feature Selection -- 3.4.1 Microarray -- 3.4.2 Intrusion Detection -- 3.4.3 Text Categorization -- 3.5 Conclusion -- References -- 4 A Smart Web Application for Symptom-Based Disease Detection and Prediction Using State-of-the-Art ML and ANN Models -- 4.1 Introduction -- 4.2 Literature Review -- 4.3 Dataset, EDA, and Data Processing -- 4.4 Machine Learning Algorithms -- 4.4.1 Multinomial Naïve Bayes Classifier -- 4.4.4 K-Nearest Neighbor Classifier -- 4.4.5 Decision Tree Classifier -- IG(A,S) H(S) p(t)H(t) -- 4.4.6 Logistic Regression Classifier -- 4.4.7 Multilayer Perceptron Classifier -- 4.5 Work Architecture -- 4.6 Conclusion -- References -- 5 Classification of Heart Sound Signals Using Time-Frequency Image Texture Features -- 5.1 Introduction -- 5.1.1 Motivation -- 5.2 Related Work -- 5.3 Theoretical Background -- 5.3.1 Pre-Processing Techniques -- 5.3.2 Spectrogram Generation -- 5.3.2 Feature Extraction -- 5.3.4 Feature Selection -- 5.3.5 Support Vector Machine -- 5.4 Proposed Algorithm -- 5.5 Experimental Results -- 5.5.1 Database -- 5.5.2 Evaluation Metrics -- 5.5.3 Confusion Matrix -- 5.5.4 Results and Discussions -- 5.6 Conclusion -- References -- 6 Improving Multi-Label Classification in Prototype Selection Scenario -- 6.1 Introduction -- 6.2 Related Work -- 6.3 Methodology -- 6.3.1 Experiments and Evaluation -- 6.4 Performance Evaluation -- 6.5 Experiment Data Set -- 6.6 Experiment Results -- 6.7 Conclusion -- References -- 7 A Machine Learning-Based Intelligent Computational Framework for the Prediction of Diabetes Disease -- 7.1 Introduction -- 7.2 Materials and Methods -- 7.2.1 Dataset -- 7.2.2 Proposed Framework for Diabetes System -- 7.2.3 Pre-Processing of Data -- 7.3 Machine Learning Classification Hypotheses -- 7.3.1 K-Nearest Neighbor. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 7.3.2 Decision Tree -- 7.3.3 Random Forest -- 7.3.4 Logistic Regression -- 7.3.5 Naïve Bayes -- 7.3.6 Support Vector Machine -- 7.3.7 Adaptive Boosting -- 7.3.8 Extra-Tree Classifier -- 7.4 Classifier Validation Method -- 7.4.1 K-Fold Cross-Validation Technique -- 7.5 Performance Evaluation Metrics -- 7.6 Results and Discussion -- 7.6.1 Performance of All Classifiers Using 5-Fold CV Method -- 7.6.2 Performance of All Classifiers Using the 7-Fold Cross-Validation Method -- 7.6.3 Performance of All Classifiers Using 10-Fold CV Method -- 7.7 Conclusion -- References -- 8 Hyperparameter Tuning of Ensemble Classifiers Using Grid Search and Random Search for Prediction of Heart Disease -- 8.1 Introduction -- 8.2 Related Work -- 8.3 Proposed Method -- 8.3.1 Dataset Description -- 8.3.2 Ensemble Learners for Classification Modeling -- 8.3.3 Hyperparameter Tuning of Ensemble Learners -- 8.4 Experimental Outcomes and Analyses -- 8.4.1 Characteristics of UCI Heart Disease Dataset -- 8.4.2 Experimental Result of Ensemble Learners and Performance Comparison -- 8.4.3 Analysis of Experimental Result -- 8.5 Conclusion -- References -- 9 Computational Intelligence and Healthcare Informatics Part III- Recent Development and Advanced Methodologies -- 9.1 Introduction: Simulation in Healthcare -- 9.2 Need for a Healthcare Simulation Process -- 9.3 Types of Healthcare Simulations -- 9.4 AI in Healthcare Simulation -- 9.4.1 Machine Learning Models in Healthcare Simulation -- 9.4.2 Deep Learning Models in Healthcare Simulation -- 9.5 Conclusion -- References -- 10 Wolfram's Cellular Automata Model in Health Informatics -- 10.1 Introduction -- 10.2 Cellular Automata -- 10.3 Application of Cellular Automata in Health Science -- 10.4 Cellular Automata in Health Informatics -- 10.5 Health Informatics-Deep Learning-Cellular Automata -- 10.6 Conclusion -- References. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | Part III: MACHINE LEARNING AND COVID PROSPECTIVE -- 11 COVID-19: Classification of Countries for Analysis and Prediction of Global Novel Corona Virus Infections Disease Using Data -- 11.1 Introduction -- 11.2 Literature Review -- 11.3 Data Pre-Processing -- 11.4 Proposed Methodologies -- 11.4.1 Simple Linear Regression -- 11.4.2 Association Rule Mining -- 11.4.3 Back Propagation Neural Network -- 11.5 Experimental Results -- 11.6 Conclusion and Future Scopes -- References -- 12 Sentiment Analysis on Social Media for Emotional Prediction During COVID-19 Pandemic Using Efficient Machine Learning Approac -- 12.1 Introduction -- 12.2 Literature Review -- 12.3 System Design -- 12.3.1 Extracting Feature With WMAR -- 12.4 Result and Discussion -- 12.5 Conclusion -- References -- 13 Primary Healthcare Model for Remote Area Using Self-Organizing Map Network -- 13.1 Introduction -- 13.2 Background Details and Literature Review -- 13.3 Methodology -- 13.3.1 Severity_Factor of Patient -- 13.3.2 Clustering by Self-Organizing Mapping -- 13.4 Results and Discussion -- 13.5 Conclusion -- References -- 14 Face Mask Detection in Real-Time Video Stream Using Deep Learning -- 14.1 Introduction -- 14.2 Related Work -- 14.3 Proposed Work -- 14.3.1 Dataset Description -- 14.3.2 Data Pre-Processing and Augmentation -- 14.3.3 VGG19 Architecture and Implementation -- 14.3.4 Face Mask Detection From Real-Time Video Stream -- 14.4 Results and Evaluation -- 14.5 Conclusion -- References -- 15 A Computational Intelligence Approach for Skin Disease Identification Using Machine/Deep Learning Algorithms -- 15.1 Introduction -- 15.2 Research Problem Statements -- 15.3 Dataset Description -- 15.4 Machine Learning Technique Used for Skin Disease Identification -- 15.4.1 Logistic Regression -- 15.4.2 SVM -- 15.4.3 Recurrent Neural Networks. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 15.4.4 Decision Tree Classification Algorithm -- 15.4.6 Random Forest -- 15.5 Result and Analysis -- 15.6 Conclusion -- References -- 16 Asymptotic Patients' Healthcare Monitoring and Identification of Health Ailments in Post COVID-19 Scenario -- 16.1 Introduction -- 16.1.1 Motivation -- 16.1.2 Contributions -- 16.1.3 Paper Organization -- 16.1.4 System Model Problem Formulation -- 16.1.5 Proposed Methodology -- 16.2 Material Properties and Design Specifications -- 16.2.1 Hardware Components -- 16.2.2 Sensors -- 16.2.3 Software Components -- 16.3 Experimental Methods and Materials -- 16.3.1 Simulation Environment -- 16.4 Simulation Results -- 16.5 Conclusion -- 16.6 Abbreviations and Acronyms -- References -- 17 COVID-19 Detection System Using Cellular Automata-Based Segmentation Techniques -- 17.1 Introduction -- 17.2 Literature Survey -- 17.2.1 Cellular Automata -- 17.2.2 Image Segmentation -- 17.2.3 Deep Learning Techniques -- 17.3 Proposed Methodology -- 17.4 Results and Discussion -- 17.5 Conclusion -- References -- 18 Interesting Patterns From COVID-19 Dataset Using Graph-Based Statistical Analysis for Preventive Measures -- 18.1 Introduction -- 18.2 Methods -- 18.2.1 Data -- 18.3 GSA Model: Graph-Based Statistical Analysis -- 18.4 Graph-Based Analysis -- 18.4.1 Modeling Your Data as a Graph -- 18.4.2 RDF for Knowledge Graph -- 18.4.3 Knowledge Graph Representation -- 18.4.4 RDF Triple for KaTrace -- 18.4.5 Cipher Query Operation on Knowledge Graph -- 18.5 Machine Learning Techniques -- 18.5.1 Apriori Algorithm -- 18.5.2 Decision Tree Classifier -- 18.5.3 System Generated Facts on Pandas -- 18.5.4 Time Series Model -- 18.6 Exploratory Data Analysis -- 18.6.1 Statistical Inference -- 18.7 Conclusion -- 18.8 Limitations -- Acknowledgments -- Abbreviations -- References -- Part IV: PROSPECTIVE OF COMPUTATIONAL INTELLIGENCE IN HEALTHCARE. |
| 505 8# - FORMATTED CONTENTS NOTE | |
| Formatted contents note | 19 Conceptualizing Tomorrow's Healthcare Through Digitization. |
| 588 ## - SOURCE OF DESCRIPTION NOTE | |
| Source of description note | Description based on publisher supplied metadata and other sources. |
| 590 ## - LOCAL NOTE (RLIN) | |
| Local note | Electronic reproduction. Ann Arbor, Michigan : ProQuest Ebook Central, 2024. Available via World Wide Web. Access may be limited to ProQuest Ebook Central affiliated libraries. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM | |
| Topical term or geographic name entry element | Artificial intelligence-Medical applications. |
| 655 #4 - INDEX TERM--GENRE/FORM | |
| Genre/form data or focus term | Electronic books. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Tripathy, Alok Ranjan. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Elngar, Ahmed A. |
| 700 1# - ADDED ENTRY--PERSONAL NAME | |
| Personal name | Polkowski, Zdzislaw. |
| 776 08 - ADDITIONAL PHYSICAL FORM ENTRY | |
| Relationship information | Print version: |
| Main entry heading | Jena, Om Prakash |
| Title | Computational Intelligence and Healthcare Informatics |
| Place, publisher, and date of publication | Newark : John Wiley & Sons, Incorporated,c2021 |
| International Standard Book Number | 9781119818687 |
| 797 2# - LOCAL ADDED ENTRY--CORPORATE NAME (RLIN) | |
| Corporate name or jurisdiction name as entry element | ProQuest (Firm) |
| 830 #0 - SERIES ADDED ENTRY--UNIFORM TITLE | |
| Uniform title | Machine Learning in Biomedical Science and Healthcare Informatics Series |
| 856 40 - ELECTRONIC LOCATION AND ACCESS | |
| Uniform Resource Identifier | <a href="https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6715215">https://ebookcentral.proquest.com/lib/orpp/detail.action?docID=6715215</a> |
| Public note | Click to View |
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