ARTIFICIAL INTELLIGENCE (AI) MODEL: ADAPTIVE NEURO-FUZZY INFERENCE SYSTEM (ANFIS) FOR DIAGNOSIS OF COVID-19 INFLUENZA
Computing and Informatics
; 41(4):1114-1135, 2022.
Article
in English
| Scopus | ID: covidwho-2236239
ABSTRACT
The COVID-19 influenza became a curse on the world. It has been around for two years, so no one needs to make a big introduction of it. It has became a significant challenge around the world. Owing to this, we made dynamic networks using an amalgamating of fuzzy logic and neural networks for the prediction of sufferers of COVID-19. These hybrid networks serve for the assessment of the COVID-19 victims and usefully serve for the assessment of the medical resources needed for future victims. This manuscript proposed Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction model for COVID-19 prediction in Andhra Pradesh, India. We gathered data on positive COVID-19 sufferers in Andhra Pradesh for this purpose. The data can be separated into three categories training set, testing set and checking set. We have utilized Root Mean Square Deviation (RMSD) for prediction precision. If the prediction model has a lower RMSD value, it is regarded as the best forecast. In this study, we concluded that the 3 Triangular MFns for each input were excellent with the extreme precision for all of the districts based on our expertise. In the end, we deployed seven SANFIS replicas in Andhra Pradesh, but we discovered that SANFIS6 and SANFIS7 provided excellent COVID-19 prediction results. These findings will assist the government, healthcare agencies, and medical organizations in planning for future COVID-19 victims' medical requirements. These sorts of Sugeno Adaptive Neuro-Fuzzy Inference System (SANFIS) prediction models based on Artificial Intelligence (AI) will be beneficial in overcoming the COVID-19. © 2022 Slovak Academy of Sciences. All rights reserved.
artificial intelligence (AI); COVID-19; membership function; neural networks (NN); root mean square deviation (RMSD); Sugeno adaptive neuro-fuzzy inference system (SANFIS); Diagnosis; Forecasting; Fuzzy inference; Fuzzy neural networks; Fuzzy systems; Membership functions; Adaptive neuro-fuzzy inference; Artificial intelligence; Memberships function; Neural network; Neural-networks; Neuro-fuzzy inference systems; Prediction modelling; Root mean square deviation; Root-mean-square deviations; Sugeno adaptive neuro-fuzzy inference system
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
Computing and Informatics
Year:
2022
Document Type:
Article
Similar
MEDLINE
...
LILACS
LIS