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 …

Students' perception on course satisfaction through student surveys has become more
influential in institutional operations because their experience in study may affect not only …

In this paper, a class of Zhang neural networks (ZNNs) are developed and analyzed on
convergence properties. Different from conventional gradient-based neural networks …

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

The inverse problem of magnetic petrophysics is to determine magnetic contents of
rocks/ores provided with their susceptibility readings already known. This has not been …

This book has been written as a designated textbook for university students studying
engineering and some areas of applied sciences to continue knowledge building in …

This paper presents the research outcomes of a magnetic mineralogical study on Hamersley
iron ores. Thermal magnetic analysis shows that typical high-grade martite-microplaty …

This paper compares two vastly different methods of analysis–multiple regression and
neural networks, in supply chain flexibility assessment. Data of manufacturing firms …

Accurate modeling of wheat production in advance provides wheat growers, traders, and
governmental agencies with a great advantage in planning the distribution of wheat …

The models proposed in the field of time series forecasting are numerous. However,
accurate forecasting is still one of the main challenging problems faced by decision makers …