Neurofuzzy inference system for diagnosis of malaria

Author

Rastogi, Ayush ; Gupta, Neeraj K. ; Tyagi, Praveen Kumar

Author_Institution

Dept. of Electr. & Electron. Eng., KIET, Ghaziabad, India

fYear

2014

fDate

28-29 Nov. 2014

Firstpage

24

Lastpage

28

Abstract

In this paper, a structure of adaptive system is proposed with the help of Neurofuzzy System (NFS) for diagnosis of Malaria. Investigation of malaria using Neurofuzzy system has been used for decision making ability based on predefined rules and learning by the backpropagation algorithm. Mapping Network in backpropagation algorithm is applied to minimize the errors in the output. Investigation of malaria by the proposed system is illustrated and good performance is achieved with maximum instant error of 0.06144.

Keywords

1/f noise; backpropagation; decision making; diseases; fuzzy neural nets; fuzzy reasoning; learning systems; medical computing; minimisation; patient diagnosis; adaptive system structure; backpropagation algorithm; decision making ability; error minimization; learning; malaria diagnosis; mapping network; maximum instant error; neurofuzzy inference system; Computational intelligence; Diseases; Fuzzy logic; Medical diagnostic imaging; Neural networks; Training; Backpropagation algorithm; Malaria; Neurofuzzy inference system;

fLanguage

English

Publisher

ieee

Conference_Titel

Computational Intelligence on Power, Energy and Controls with their impact on Humanity (CIPECH), 2014 Innovative Applications of

Conference_Location

Ghaziabad

Type

conf

DOI

10.1109/CIPECH.2014.7019042

Filename

7019042