Computational Intelligence for Machine Learning and Healthcare Informatics / / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey.
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, refl...
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| Superior document: | Title is part of eBook package: De Gruyter DG Ebook Package English 2020 |
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| Place / Publishing House: | Berlin ;, Boston : : De Gruyter, , [2020] ©2020 |
| Year of Publication: | 2020 |
| Language: | English |
| Series: | Intelligent Biomedical Data Analysis ,
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| Physical Description: | 1 online resource (XV, 331 p.) |
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Computational Intelligence for Machine Learning and Healthcare Informatics / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey. Berlin ; Boston : De Gruyter, [2020] ©2020 1 online resource (XV, 331 p.) text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Intelligent Biomedical Data Analysis , 2629-7140 ; 1 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 http://purl.org/coar/access_right/c_16ec online access with authorization star 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) Artificial intelligence Medical applications. Machine learning. Algorithmus. Big Data. Digitalmedizin. Künstliche Intelligenz. Maschinelles Lernen. COMPUTERS / Intelligence (AI) & Semantics. bisacsh Bansal, Shweta, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bharti, Vishal, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Bhoi, Dhaval, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Choubey, Arvind, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ekatpure, Purva, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Gaba, Divya, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Gupta, Medha, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Jana, Premananda, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Joshi, Dhanashri P., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kaur, Prabhsimar, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kole, Dipak K., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kumar Mallick, Pradeep, editor. edt http://id.loc.gov/vocabulary/relators/edt Kumar, Yogesh, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kumari, Sneha, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Kumari, Sweta, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Lourdes Sánchez, María de, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Luis O., Gónzalez-Salcedo, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mahajan, Manish, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Maji, Srabanti, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mittal, Nitin, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mohanty, Vihal, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Mukherjee, Saswati, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Naveenkumar, J., contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Pandey, Manjusha, editor. edt http://id.loc.gov/vocabulary/relators/edt Raj, Sandeep, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Ramaswamy, Vanaja, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Rodríguez, Andrea, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Sarkar, Dhrubasish, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Selvanambi, Ramani, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Shaik, Daiyaan Ahmed, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Shivam,, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Singh, Harbinder, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Singh, Simrandeep, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Sinha, Shweta, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Srivastava, Rajshree, editor. edt http://id.loc.gov/vocabulary/relators/edt Srivastva, Rajshree, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Swarup Rautaray, Siddharth, editor. edt http://id.loc.gov/vocabulary/relators/edt Thakkar, Amit, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Thakur, Diksha, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Will, Adrián, contributor. ctb https://id.loc.gov/vocabulary/relators/ctb Title is part of eBook package: De Gruyter DG Ebook Package English 2020 9783110696288 Title is part of eBook package: De Gruyter DG Plus DeG Package 2020 Part 1 9783110696271 Title is part of eBook package: De Gruyter De Gruyter English eBooks 2020 - UC 9783110659061 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2020 English 9783110704716 Title is part of eBook package: De Gruyter EBOOK PACKAGE COMPLETE 2020 9783110704518 ZDB-23-DGG Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2020 English 9783110704815 Title is part of eBook package: De Gruyter EBOOK PACKAGE Engineering, Computer Sciences 2020 9783110704617 ZDB-23-DEI EPUB 9783110649277 print 9783110647822 https://doi.org/10.1515/9783110648195 https://www.degruyter.com/isbn/9783110648195 Cover https://www.degruyter.com/document/cover/isbn/9783110648195/original |
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Bansal, Shweta, |
| title |
Computational Intelligence for Machine Learning and Healthcare Informatics / |
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Computational Intelligence for Machine Learning and Healthcare Informatics / Intelligent Biomedical Data Analysis , 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_full |
Computational Intelligence for Machine Learning and Healthcare Informatics / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey. |
| title_fullStr |
Computational Intelligence for Machine Learning and Healthcare Informatics / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey. |
| title_full_unstemmed |
Computational Intelligence for Machine Learning and Healthcare Informatics / ed. by Rajshree Srivastava, Pradeep Kumar Mallick, Siddharth Swarup Rautaray, Manjusha Pandey. |
| title_auth |
Computational Intelligence for Machine Learning and Healthcare Informatics / |
| title_alt |
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_new |
Computational Intelligence for Machine Learning and Healthcare Informatics / |
| title_sort |
computational intelligence for machine learning and healthcare informatics / |
| series |
Intelligent Biomedical Data Analysis , |
| series2 |
Intelligent Biomedical Data Analysis , |
| publisher |
De Gruyter, |
| publishDate |
2020 |
| physical |
1 online resource (XV, 331 p.) 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 |
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2629-7140 ; |
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R - Medicine |
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R - General Medicine |
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R859 |
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R 3859.7 A78 |
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Not Illustrated |
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600 - Technology |
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610 - Medicine & health |
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610 - Medicine & health |
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610.28563 |
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A review of bone tissue engineering for the application of artificial intelligence in cellular adhesion prediction -- </subfield><subfield code="t">2. Implementation and classification of machine learning algorithms in healthcare informatics: approaches, challenges, and future scope -- </subfield><subfield code="t">3. Cardiac arrhythmia recognition using Stockwell transform and ABC-optimized twin SVM -- </subfield><subfield code="t">4. Computational intelligence approach to address the language barrier in healthcare -- </subfield><subfield code="t">5. Recent advancement of machine learning and deep learning in the field of healthcare system -- </subfield><subfield code="t">6. Predicting psychological disorders using machine learning -- </subfield><subfield code="t">7. Automatic analysis of cardiovascular diseases using EMD and support vector machines -- </subfield><subfield code="t">8. Machine learning approach for exploring computational intelligence -- </subfield><subfield code="t">9. Classification of various image fusion algorithms and their performance evaluation metrics -- </subfield><subfield code="t">10. Recommender system in healthcare: an overview -- </subfield><subfield code="t">11. Dense CNN approach for medical diagnosis -- </subfield><subfield code="t">12. Impact of sentiment analysis tools to improve patients’ life in critical diseases -- </subfield><subfield code="t">13. A fuzzy entropy-based multilevel image thresholding using neural network optimization algorithm -- </subfield><subfield code="t">14. Machine learning in healthcare -- </subfield><subfield code="t">15. Computational health informatics using evolutionary-based feature selection -- </subfield><subfield code="t">Index</subfield></datafield><datafield tag="506" ind1="0" ind2=" "><subfield code="a">restricted access</subfield><subfield code="u">http://purl.org/coar/access_right/c_16ec</subfield><subfield code="f">online access with authorization</subfield><subfield code="2">star</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">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.</subfield></datafield><datafield tag="530" ind1=" " ind2=" "><subfield code="a">Issued also in print.</subfield></datafield><datafield tag="538" ind1=" " ind2=" "><subfield code="a">Mode of access: Internet via World Wide Web.</subfield></datafield><datafield tag="546" ind1=" " ind2=" "><subfield code="a">In English.</subfield></datafield><datafield tag="588" ind1="0" ind2=" "><subfield code="a">Description based on online resource; title from PDF title page (publisher's Web site, viewed 28. Feb 2023)</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Artificial intelligence</subfield><subfield code="x">Medical applications.</subfield></datafield><datafield tag="650" ind1=" " ind2="0"><subfield code="a">Machine learning.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Algorithmus.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Big Data.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Digitalmedizin.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Künstliche Intelligenz.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Maschinelles Lernen.</subfield></datafield><datafield tag="650" ind1=" " ind2="7"><subfield code="a">COMPUTERS / Intelligence (AI) & Semantics.</subfield><subfield code="2">bisacsh</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bansal, Shweta, </subfield><subfield code="e">contributor.</subfield><subfield code="4">ctb</subfield><subfield code="4">https://id.loc.gov/vocabulary/relators/ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bharti, Vishal, </subfield><subfield code="e">contributor.</subfield><subfield code="4">ctb</subfield><subfield code="4">https://id.loc.gov/vocabulary/relators/ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Bhoi, Dhaval, </subfield><subfield code="e">contributor.</subfield><subfield code="4">ctb</subfield><subfield code="4">https://id.loc.gov/vocabulary/relators/ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Choubey, Arvind, </subfield><subfield code="e">contributor.</subfield><subfield code="4">ctb</subfield><subfield code="4">https://id.loc.gov/vocabulary/relators/ctb</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Ekatpure, Purva, 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