Our recent study using historic data of wheat yield and associated plantation area, rainfall,
and temperature has shown that incorporating statistics and artificial neural networks can …

Resource estimation techniques have upgraded over the past couple of years, thereby
improving resource estimates. The classical method of estimation is less used in ore grade …

Grade estimation is a critical issue in mineral resource evaluation, being extensively
investigated by data mining techniques. In this paper, a hybrid method composed of back …

Estimating the spatial distribution of ore grade is one of the most critical and important steps
to continue investment decision on the deposit. Kriging is the most widely used method to …

Abstract Artificial Neural Network (ANN) as a method of data processing and inspired by
studies of the nervous systems–has become a robust tool for modeling complex, non-linear …

The long-term mine planning model (LTMP) and short-term mine planning model (STMP)
are two approaches that describe the ore content in a mine; they are essential intangible …

This paper re-examines the Itakpe iron ore deposit using geostatistics and artificial neural
network techniques. Set of exploration information on the deposit are used to develop …

The present work deals with modeling and optimization of ultrasonic welding (USW) process
parameters including welding time, pressure, and vibration amplitude influencing strength of …

Spatial prediction of orebody characteristics can often be challenging given the commonly
complex geological structure of mineral deposits. For example, a high nugget effect can …

Artificial neural networks are powerful global approximators for mineral grade assessment.
The techniques are capable of retaining nonlinearity and spatial heterogeneity of a feature …