Notice of Violation of IEEE Publication Principles; Artificial Neural Networks: Its Techniques and Applications to Forecasting

Notice of Violation of IEEE Publication Principles

"Artificial Neural Networks: Its Techniques and Applications to Forecasting,"
by A. Gupta, A. O. Salau, P. Chaturvedi, S. A. Akinola and N. Ikechi Nwulu
in the Proceedings of the International Conference on Automation, Computational and Technology Management (ICACTM), London, United Kingdom, 2019, pp. 320-324.

This paper has been found to be in violation of IEEE's Publication Principles. The author credits include Nnamdi Nwulu, who was added without his knowledge or permission, and without contributing to the work. Due to the nature of this violation, reasonable effort should be made to modify all past and future references to this paper.

In the last few decades, Artificial Intelligence (AI) techniques have been used extensively in numerous applications. Amongst AIs numerous techniques, the Artificial Neural Networks (ANN) has dominated in its capability and applicability in different fields. Neural is a word derived from (animal) human nervous system which represents the nerve cells or neurons present in the brain. This paper however focuses on the review of ANN and its applications in forecasting. The basic concepts of ANN, its characteristics and applications were discussed. In addition, a comparison was made between ANN and other conventional methods. The conclusion of this review shows that ANN mostly performs better than other conventional techniques in solving real-life problems.