The infiltration process during irrigation is an essential variable for better water management and hence there a need to develop accurate model estimate the amount irrigation. However, fact that highly non-linear procedure required special modeling approach accurately mimic procedure. Therefore, ability of Adaptive Neuro-Fuzzy Interface System (ANFIS) models in estimating infiltrated furrow su...

The objective of this study is to investigate a potential application of the Adaptive Neuro-Fuzzy Inference System (ANFIS) and the Geographic Information System (GIS) as a relatively new approach for landslide susceptibility mapping in the Hoa Binh province of Vietnam. Firstly, a landslide inventory map with a total of 118 landslide locations was constructed from various sources. Then the lands...

This paper presents the application of the adaptive neuro fuzzy inference system (ANFIS) to a model of the flow field inside an in vitro arteriovenous (AV) graft-to-vein connection implanted to the kidney patients. A model based on ANFIS is proposed. Its relevant steps oriented to find the optimal AV graft angle are given. The advantage of this neuro-fuzzy hybrid approach is that it does not re...

Optimal sequence similarity of triplex capsid proteins of human herpes simplex virus (HHV) is a complex bioinformatics problem, which is controlled by alignment algorithms, substitution matrix, gap penalty and gap extension. A precise choice of mutation matrix is required to optimal the alignment similarity and appropriate computational approach required for similarity search. The present paper...

in this research, the physico-chemical water quality parameters and the effect of climate changes onwater quality is evaluated. during the observation period (5 months) physico-chemical parameterssuch as water temperature, turbidity, saturated oxygen, dissolved oxygen, ph, chlorophyll-a, salinity,conductivity, and concentration of total nitrogen (nutrient level) as main pollutant factor have be...

Abstract Improving the performance of machine learning (ML) algorithms is essential for accurately estimating water quality parameters (WQPs). For first time, a novel hybrid framework, namely adaptive neural fuzzy inference system–discrete wavelet transform–gradient-based optimization (ANFIS–DWT–GBO), estimation electrical conductivity (EC) and total dissolved solids (TDS), used. Before WQPs, A...

Present study investigates the capabilities of six distinct machine learning techniques such as ANFIS network with fuzzy c-means (ANFIS-FCM), grid partition (ANFIS-GP), subtractive clustering (ANFIS-SC), feed-forward neural (FNN), Elman (ENN), and long short-term memory (LSTM) in one-day ahead soil temperature (ST) forecasting. For this aim, daily ST data gathered at three different depths 5 cm...

in recent years, use of fuzzy collection theories for modeling of hydrological phenomenon's that is including complexity and uncertainly is considered scholars. so in this research, adaptive neuro-fuzzy inference system (anfis) is used for performance of river flow forecasting process. in this research, three parameters such as raining, temperature and daily discharge of lighvanchai basin ...

OBJECTIVES To develop and explore the predictability of patient perceptions of satisfaction through the hospital adoption of health information technology (HIT), leading to a better understanding of the benefits of increased HIT investment. DATA AND METHODS The solution proposed is based on comparing the predictive capability of artificial neural networks (ANNs) with the adaptive neuro-fuzzy ...

A neuro-fuzzy modeling tool (ANFIS) has been used to dynamically model cross flow ultrafiltration of milk. It aims to predict permeate flux and total hydraulic resistance as a function of transmembrane pressure, pH, temperature, fat, molecular weight cut off, and processing time. Dynamic modeling of ultrafiltration performance of colloidal systems (such as milk) is very important for design...