In the structure of ANFIS, there are two different parameter groups: premise and
consequence. Training ANFIS means determination of these parameters using an …

Deep neural networks (DNN) have remarkably progressed in applications involving large
and complex datasets but have been criticized as a black-box. This downside has recently …

With the rapid growth in foreign trade business and the continuous expansion of customs
functions, the amount of data obtained by customs monitoring systems has drastically …

The emergence of new sites for wind energy exploration in South Africa requires an
accurate prediction of the potential power output of a typical utility-scale wind turbine in such …

The objective of the present work is to find out the optimal level as well as the influence of
the electrical discharge machining (EDM) process on material removal rate (MRR), surface …

In recent years, adaptive network-based fuzzy inference systems (ANFIS) applied in various
engineering fields have demonstrated their ability to combine the learning capability of …

Forecasting is a part of statistical modelling that is widely used in various fields because of
its benefits in decision-making. The purpose of forecasting is to predict the future values of …

The fact that conventional energy sources are exhaustive and limited are increasingly
encouraging research in the field of alternative and renewable energy sources. The …

The impact of innovated Neuro-Fuzzy System (NFS) has emerged as a dominant technique
for addressing various difficult research problems in business. ANFIS (Adaptive Neuro …

Artificial intelligence is the branch of computer science that attempts to model cognitive
processes such as learning, adaptability and perception to generate intelligent behavior …