Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/459988
Title: Soft computing approches for medical diagnosis and healthcare management
Researcher: Das, Sunit
Guide(s): Sanyal, Manas K
Keywords: Engineering
Engineering and Technology
Engineering Biomedical
University: University of Kalyani
Completed Date: 2020
Abstract: newline Soft Computing is an up-and-coming field of current research that aims at vent artificial newlineintelligence through computational intelligence methods. Computational intelligence support newlineefficient knowledge extraction from datasets using logic programming to acquire and predict the newlineessential outcome. The intelligent medical diagnosis in our society is extremely prevalent newlinenowadays, because the number of doctors in our society, especially in village areas is inadequate newlineduring the past or present years in India. Based on this above fact soft computing approaches newlinealong with artificial intelligence techniques are suitable for intelligent medical diagnosis. The newlineinference mechanism used for the provision of diagnosis to patients in collaboration with the newlineinference framework developed to support the medical diagnosis.
Pagination: xxii, 185
URI: http://hdl.handle.net/10603/459988
Appears in Departments:Engineering & Technological Studies

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01_title.pdfAttached File260.07 kBAdobe PDFView/Open
02_declaration.pdf290.36 kBAdobe PDFView/Open
03_certificate.pdf852.51 kBAdobe PDFView/Open
04_acknowledgement.pdf307.37 kBAdobe PDFView/Open
05_content.pdf418.35 kBAdobe PDFView/Open
06_abbreviations.pdf252.96 kBAdobe PDFView/Open
07_list of graph and table.pdf341.89 kBAdobe PDFView/Open
08_chapter 1.pdf9.44 MBAdobe PDFView/Open
09_chapter 2.pdf19.87 MBAdobe PDFView/Open
10_chapter 3.pdf14.49 MBAdobe PDFView/Open
11_chapter 4.pdf18.75 MBAdobe PDFView/Open
12_chapter 5.pdf17.53 MBAdobe PDFView/Open
13_chapter 6.pdf20.97 MBAdobe PDFView/Open
14_chapter 7.pdf15.7 MBAdobe PDFView/Open
16_bibliography.pdf18.16 MBAdobe PDFView/Open
17_abstract.pdf277.58 kBAdobe PDFView/Open
80_recommendation.pdf8.8 MBAdobe PDFView/Open
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