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Computational Intelligence for Machine Learning and Healthcare Informatics / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey.

Contributor(s): Bansal, Shweta [contributor.] | Bharti, Vishal [contributor.] | Bhoi, Dhaval [contributor.] | Choubey, Arvind [contributor.] | Ekatpure, Purva [contributor.] | Gaba, Divya [contributor.] | Gupta, Medha [contributor.] | Jana, Premananda [contributor.] | Joshi, Dhanashri P [contributor.] | Kaur, Prabhsimar [contributor.] | Kole, Dipak K [contributor.] | Kumar Mallick, Pradeep [editor.] | Kumar, Yogesh [contributor.] | Kumari, Sneha [contributor.] | Kumari, Sweta [contributor.] | Lourdes Sánchez, María de [contributor.] | Luis O., Gónzalez-Salcedo [contributor.] | Mahajan, Manish [contributor.] | Maji, Srabanti [contributor.] | Mittal, Nitin [contributor.] | Mohanty, Vihal [contributor.] | Mukherjee, Saswati [contributor.] | Naveenkumar, J [contributor.] | Pandey, Manjusha [editor.] | Raj, Sandeep [contributor.] | Ramaswamy, Vanaja [contributor.] | Rodríguez, Andrea [contributor.] | Sarkar, Dhrubasish [contributor.] | Selvanambi, Ramani [contributor.] | Shaik, Daiyaan Ahmed [contributor.] | Shivam [contributor.] | Singh, Harbinder [contributor.] | Singh, Simrandeep [contributor.] | Sinha, Shweta [contributor.] | Srivastava, Rajshree [editor.] | Srivastva, Rajshree [contributor.] | Swarup Rautaray, Siddharth [editor.] | Thakkar, Amit [contributor.] | Thakur, Diksha [contributor.] | Will, Adrián [contributor.].
Material type: materialTypeLabelBookSeries: Intelligent Biomedical Data Analysis , 1.Publisher: Berlin ; Boston : De Gruyter, [2020]Copyright date: ©2020Description: 1 online resource (XV, 331 p.).Content type: text Media type: computer Carrier type: online resourceISBN: 9783110648195.Subject(s): Artificial intelligence -- Medical applications | Machine learning | Algorithmus | Big Data | Digitalmedizin | Künstliche Intelligenz | Maschinelles Lernen | COMPUTERS / Intelligence (AI) & SemanticsAdditional physical formats: No title; No titleDDC classification: 610.28563 Other classification: XF 1277 Online resources: Click here to access online | Click here to access online | Cover Issued also in print.
Contents:
Frontmatter -- Preface -- Contents -- List of contributors -- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction -- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope -- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM -- 4. Computational intelligence approach to address the language barrier in healthcare -- 5. Recent advancement of machine learning and deep learning in the field of healthcare system -- 6. Predicting psychological disorders using machine learning -- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines -- 8. Machine learning approach for exploring computational intelligence -- 9. Classification of various image fusion algorithms and their performance evaluation metrics -- 10. Recommender system in healthcare: an overview -- 11. Dense CNN approach for medical diagnosis -- 12. Impact of sentiment analysis tools to improve patients' life in critical diseases -- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm -- 14. Machine learning in healthcare -- 15. Computational health informatics using evolutionary-based feature selection -- Index
Title is part of eBook package:DG Ebook Package English 2020Title is part of eBook package:DG Plus DeG Package 2020 Part 1Title is part of eBook package:De Gruyter English eBooks 2020 - UCTitle is part of eBook package:EBOOK PACKAGE COMPLETE 2020 EnglishTitle is part of eBook package:EBOOK PACKAGE COMPLETE 2020Title is part of eBook package:EBOOK PACKAGE Engineering, Computer Sciences 2020 EnglishTitle is part of eBook package:EBOOK PACKAGE Engineering, Computer Sciences 2020Summary: This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.
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Frontmatter -- Preface -- Contents -- List of contributors -- 1. A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction -- 2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope -- 3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM -- 4. Computational intelligence approach to address the language barrier in healthcare -- 5. Recent advancement of machine learning and deep learning in the field of healthcare system -- 6. Predicting psychological disorders using machine learning -- 7. Automatic analysis of cardiovascular diseases using EMD and support vector machines -- 8. Machine learning approach for exploring computational intelligence -- 9. Classification of various image fusion algorithms and their performance evaluation metrics -- 10. Recommender system in healthcare: an overview -- 11. Dense CNN approach for medical diagnosis -- 12. Impact of sentiment analysis tools to improve patients' life in critical diseases -- 13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm -- 14. Machine learning in healthcare -- 15. Computational health informatics using evolutionary-based feature selection -- Index

restricted access online access with authorization star

http://purl.org/coar/access_right/c_16ec

This book presents a variety of techniques designed to enhance and empower multi-disciplinary and multi-institutional machine learning research in healthcare informatics. It is intended to provide a unique compendium of current and emerging machine learning paradigms for healthcare informatics, reflecting the diversity, complexity, and depth and breadth of this multi-disciplinary area.

Issued also in print.

Mode of access: Internet via World Wide Web.

In English.

Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)

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