This paper presents novel GA-ANFIS expert system prototype for dermatological disease detection by using dermatological features and diagnoses collected in real conditions. Nine dermatological features are used as inputs to classifiers that are based on Adaptive Neuro-Fuzzy Inference Systems (ANFIS) for the first level of fuzzy model optimization. After that, they are used as inputs in Genetic ...

This paper compares the accuracy of three hybrid intelligent systems in forecasting ten international stock market indices; namely the CAC40, DAX, FTSE, Hang Seng, KOSPI, NASDAQ, NIKKEI, S&P500, Taiwan stock market price index, and the Canadian TSE. In particular, genetic algorithms (GA) are used to optimize the topology and parameters of the adaptive time delay neural networks (ATNN) and the t...

This study investigates the ability of a new hybrid neuro-fuzzy model by combining (ANFIS) approach with marine predators’ algorithm (MPA) in predicting short-term (from 1 h ahead to day ahead) significant wave heights. Data from two stations, Cairns and Palm Beach buoy, were used assessing considered methods. The ANFIS-MPA was compared other methods, ANFIS genetic (ANFIS-GA) particle swarm opt...

Given the growing use of e-learning and expansion internet-based infrastructure during COVID-19 epidemic, need for a resilient approach to systems is deeply felt. This article introduces combined technique utilizing adaptive neuro-fuzzy inference system (ANFIS) genetic algorithm (GA), named ANFIS-GA, evaluate resilience. In proposed ANFIS model, 22 features from five main factors including indi...

This paper proposes two different approaches for the prediction of type2 diabetes. Many different techniques have been used for the prediction of chronic diseases by different researchers. Among them Adaptive Neuro Fuzzy Inference system (ANFIS) is very popular and already used for the prediction of type 2 diabetes. In this paper, the proposed system is optimization of ANFIS using Genetic Algor...

A model of Adaptive Neuro-Fuzzy Inference System (ANFIS) trained with an evolutionary algorithm, namely Genetic Algorithm (GA) is presented in this paper. Further, the tested on NASDAQ stock market indices which among most widely followed United States. Empirical results show that by determining parameters ANFIS (premise and consequent parameters) using GA, we can improve performance terms Mean...

This paper presents a hybrid adaptive network based fuzzy inference system (ANFIS), computer simulation and time series algorithm to estimate and predict electricity consumption estimation. The difficulty with electricity consumption estimation modeling approach such as time series is the reason for proposing the hybrid approach of this study. The algorithm is ideal for uncertain, ambiguous and...

Abstract Energy forecasting is crucial for efficient energy management and planning future needs. Previous studies have employed hybrid modeling techniques, but insufficient attention has been given to hyper-parameter tuning parameter selection. In this study, we present a model, which combines fuzzy c-means clustered adaptive neuro-fuzzy inference system (ANFIS) genetic algorithm (GA), named G...

This document presents an energy forecast methodology using Adaptive Neuro-Fuzzy Inference System (ANFIS) and Genetic Algorithms (GA). The GA has been used for the selection of the training inputs of the ANFIS in order to minimize the training result error. The presented algorithm has been installed and it is being operating in an automotive manufacturing plant. It periodically communicates wit...