Please use this identifier to cite or link to this item:
http://hdl.handle.net/10603/564032
Title: | Hybrid soft computing technique with deep feature extraction to predict breast cancer using mammography images |
Researcher: | Vani kumari,S |
Guide(s): | K. Usha Rani |
Keywords: | Immunology Life Sciences Transplantation |
University: | Sri Padmavathi Womens University |
Completed Date: | 2023 |
Abstract: | newlineIn traditional control systems the complex problems cannot be accurately described by mathematical models and difficult to control using such existing methods. So, a purely digital system i.e., Soft Computing can be a very attractive alternative but there are many traps waiting for researchers. Soft Computing, as opposed to traditional computing, deals with approximate models and give solutions to complex real-life problems. Unlike hard computing, soft computing is tolerant of imprecision, uncertainty, partial truth and approximations. In effect, the role model for soft computing is the human mind. newlineSoft Computing can be considered as a combination of both Computer Science and Mathematics. It helps to deal with real-life situations. The different components of soft computing are required in the development of expert systems that work automatically. These systems can easily perform difficult tasks without the need of human beings. These systems are trained using different soft computing techniques. The different components of soft computing can provide solutions to the challenges faced in connected healthcare systems. |
Pagination: | 115 |
URI: | http://hdl.handle.net/10603/564032 |
Appears in Departments: | Department of Computer Science |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
02_ prelimnary pages .pdf | Attached File | 1.26 MB | Adobe PDF | View/Open |
03_table of contents-1-3.pdf | 338.82 kB | Adobe PDF | View/Open | |
04_abstract.pdf | 223.51 kB | Adobe PDF | View/Open | |
05_chapter-1.pdf | 1.06 MB | Adobe PDF | View/Open | |
07_chapter-3.pdf | 1.13 MB | Adobe PDF | View/Open | |
10_chapter-6.pdf | 1.54 MB | Adobe PDF | View/Open | |
11_chapter-7.pdf | 347.5 kB | Adobe PDF | View/Open | |
12_annexures.pdf.pdf | 415.44 kB | Adobe PDF | View/Open | |
80_recommendation.pdf | 902.75 kB | Adobe PDF | View/Open |
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