A Joint Neuro-fuzzy Malaria Diagnosis System
| dc.contributor.author | Oladele, Tinuke Omolewa | |
| dc.contributor.author | Ogundokun, R. O. | |
| dc.contributor.author | Misra, S. | |
| dc.contributor.author | Adeniyi, J. K. | |
| dc.contributor.author | Jaglan, V. | |
| dc.date.accessioned | 2023-05-12T12:41:05Z | |
| dc.date.available | 2023-05-12T12:41:05Z | |
| dc.date.issued | 2021 | |
| dc.description.abstract | Diagnosis takes a definitive role in the course of determining about clarifying patients as either having or not having the disorder. This method is relatively sluggish and tedious. Various fact-finding and data-mining methods are part of the approach of this article. In the development of the Collaborative Neuro-Fuzzy Expert System diagnosis platform, Neural Networks and Fuzzy Logic, which are artificial intelligence methods, have been merged together. Oral interviews were conducted with medical professionals whose experience was caught in the Expertise Developed Fuzzy Proficient Scheme. With Microsoft Visual C # (C Sharp) Programming Language and Microsoft SQL (Structured Query Language) Server 2012 to handle the database, the Neuro-Fuzzy Expert Framework diagnostic software was introduced. To capture the predominant signs, questionnaires were administered to the patients and filled out by the doctors on behalf of the patients. | en_US |
| dc.identifier.uri | https://uilspace.unilorin.edu.ng/handle/20.500.12484/10169 | |
| dc.language.iso | en | en_US |
| dc.publisher | Journal of Physics: Conference Series | en_US |
| dc.title | A Joint Neuro-fuzzy Malaria Diagnosis System | en_US |
| dc.type | Article | en_US |
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