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Handbook of research on engineering,...
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Patil, Bhushan, (1993-)
Handbook of research on engineering, business, and healthcare applications of data science and analytics
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Handbook of research on engineering, business, and healthcare applications of data science and analyticsBhushan Patil and Manisha Vohra, editors.
其他題名:
Research on engineering, business, and healthcare applications of data science and analytics
其他作者:
Patil, Bhushan,
出版者:
Hershey, Pennsylvania :IGI Global,2020.
面頁冊數:
33 PDFs (583 pages)
標題:
Information resourcesData processing.
電子資源:
http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-3053-5
ISBN:
9781799830542 (ebk.)
Handbook of research on engineering, business, and healthcare applications of data science and analytics
Handbook of research on engineering, business, and healthcare applications of data science and analytics
[electronic resource] /Research on engineering, business, and healthcare applications of data science and analyticsBhushan Patil and Manisha Vohra, editors. - Hershey, Pennsylvania :IGI Global,2020. - 33 PDFs (583 pages)
Includes bibliographical references and index.
Chapter 1. Introduction to data science -- Chapter 2. A walk through the world of data analytics -- Chapter 3. A survey on tools for data analytics and data science -- Chapter 4. Tools, technologies, and methodologies to support data science: support technologies for data science -- Chapter 5. A survey on intelligence tools for data analytics -- Chapter 6. Data analysis in context-based statistical modeling in predictive analytics -- Chapter 7. Evaluating business performance using data envelopment analysis and grey relational analysis -- Chapter 8. Comparison of European option pricing models at multiple periods -- Chapter 9. Use of data analytics to increase the efficiency of last mile logistics for ecommerce deliveries -- Chapter 10. Review of big data applications in finance and economics -- Chapter 11. Role of data science and data analytics in forensic accounting and fraud detection -- Chapter 12. Machine learning and its application in monitoring diabetes mellitus -- Chapter 13. Opportunistic edge computing architecture for smart healthcare systems -- Chapter 14. Risk analysis of diabetic patient using map-reduce and machine learning algorithm -- Chapter 15. Integrated big data e-healthcare solutions to a fragmented health information system in Namibia -- Chapter 16. Technology in healthcare: vision of smart hospitals -- Chapter 17. Gait abnormality detection using deep convolution network -- Chapter 18. Exploration of computational intelligence insights and data analytics to combat COVID-19 -- Chapter 19. Computer-aided diagnosis of knee osteoarthritis from radiographic images using random forest classifier -- Chapter 20. Healthcare conversational chatbot for medical diagnosis -- Chapter 21. A literature review on thyroid hormonal problems in women using data science and analytics: healthcare applications -- Chapter 22. Influence of some sociodemographic factors on causes of death among South African youth -- Chapter 23. Information technology act 2000 and the potential use of data analytics in reducing cybercrime in India -- Chapter 24. Clustering by K-means method and K-medoids method: an application with statistical regions of Turkey -- Chapter 25. Descriptive data analytics on dinesafe data for food assessment and evaluation using R programming language: a case study on Toronto's dinesafe inspection and disclosure system.
"This book explores the application of data science and analytics in the engineering, healthcare, and business sectors"--
ISBN: 9781799830542 (ebk.)Subjects--Topical Terms:
900152
Information resources
--Data processing.
LC Class. No.: ZA3075 / .H36 2020
Dewey Class. No.: 001.4/33
Handbook of research on engineering, business, and healthcare applications of data science and analytics
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Chapter 1. Introduction to data science -- Chapter 2. A walk through the world of data analytics -- Chapter 3. A survey on tools for data analytics and data science -- Chapter 4. Tools, technologies, and methodologies to support data science: support technologies for data science -- Chapter 5. A survey on intelligence tools for data analytics -- Chapter 6. Data analysis in context-based statistical modeling in predictive analytics -- Chapter 7. Evaluating business performance using data envelopment analysis and grey relational analysis -- Chapter 8. Comparison of European option pricing models at multiple periods -- Chapter 9. Use of data analytics to increase the efficiency of last mile logistics for ecommerce deliveries -- Chapter 10. Review of big data applications in finance and economics -- Chapter 11. Role of data science and data analytics in forensic accounting and fraud detection -- Chapter 12. Machine learning and its application in monitoring diabetes mellitus -- Chapter 13. Opportunistic edge computing architecture for smart healthcare systems -- Chapter 14. Risk analysis of diabetic patient using map-reduce and machine learning algorithm -- Chapter 15. Integrated big data e-healthcare solutions to a fragmented health information system in Namibia -- Chapter 16. Technology in healthcare: vision of smart hospitals -- Chapter 17. Gait abnormality detection using deep convolution network -- Chapter 18. Exploration of computational intelligence insights and data analytics to combat COVID-19 -- Chapter 19. Computer-aided diagnosis of knee osteoarthritis from radiographic images using random forest classifier -- Chapter 20. Healthcare conversational chatbot for medical diagnosis -- Chapter 21. A literature review on thyroid hormonal problems in women using data science and analytics: healthcare applications -- Chapter 22. Influence of some sociodemographic factors on causes of death among South African youth -- Chapter 23. Information technology act 2000 and the potential use of data analytics in reducing cybercrime in India -- Chapter 24. Clustering by K-means method and K-medoids method: an application with statistical regions of Turkey -- Chapter 25. Descriptive data analytics on dinesafe data for food assessment and evaluation using R programming language: a case study on Toronto's dinesafe inspection and disclosure system.
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http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/978-1-7998-3053-5
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