Learning Uncertainty with Artificial Neural Networks for Improved Remaining Time Prediction of Business Processes

Uncertainty of Artificial Neural Networks for Daily Evaporation Prediction (Case Study: Rasht and Manjil Stations)

This research uses the multilayer perceptron (MLP) model to predict daily evaporation at two synoptic stations located in Rasht and Manjil, Guilan province, in north-west of Iran. Initially the most important combinations of climatic parameters for both of the stations were identified using the gamma test; and daily evaporation were modeled based on the obtained optimal combination. The results...

Prediction the Return Fluctuations with Artificial Neural Networks' Approach

Time changes of return, inefficiency studies performed and presence of effective factors on share return rate are caused development modern and intelligent methods in estimation and evaluation of share return in stock companies. Aim of this research is prediction of return using financial variables with artificial neural network approach. Therefore, the statistical population of this study incl...

Prediction of Gas Lift Parameters Using Artificial Neural Networks

A Business Forecasting Competition Approach to Modeling Artificial Neural Networks for Time Series Prediction

Various heuristic approaches have been proposed to limit design complexity and computing time in artificial neural network modelling, parameterisation and selection for time series prediction. However, no single approach demonstrates robust superiority on arbitrary datasets, causing additional decision problems and a trial-and-error approach to network modelling. To reflect this, we propose an ...

Artificial neural networks versus bivariate logistic regression in prediction diagnosis of patients with hypertension and diabetes

Background: Diabetes and hypertension are important non-communicable diseases and their prevalence is important for health authorities. The aim of this study was to determine the predictive precision of the bivariate Logistic Regression (LR) and Artificial Neutral Network (ANN) in concurrent diagnosis of diabetes and hypertension. Methods: This cross-sectional study was performed with 12000 ...