Exemplifying Workflow Sequencing and Analysis in Artificial Neural Networks

Abstract

Artificial Neural networks have seen an flare-up of attention over the most recent years and are being productively functional across an astonishing variety of problem domains, varied as science, finance, medicine, engineering, physics and biology. The exhilaration track from the fact that these networks are cracked to model with the competence of the human brain. From a statistical viewpoint artificial neural networks are fascinating because of their prospective use in prediction, regression and classification tribulations. This paper advocates the significance of Artificial Neural Networks by highlighting its advancements, trends and challenges. In addition, this study aims to magnetize research appetizers with a road map towards application solving and psychiatry in a methodical approach.

Keywords: Artificial Neural Networks; Supervised learning; Unsupervised learning; Workflow Sequencing; Real-time datasets