An Enhanced Application Oriented Predictive Analytic using Optimal Deep Learning Model

Authors

  • Vaishali Mate Bhagat Assistant Professor, Department of Computer Science & Engineering, P. R. Pote College of Engineering and Management, Amravati, Maharashtra, India
  • V.U. Deorankar Assistant Professor, Department of Computer Science, Tai Golwalkar Mahavidyalaya Ramtek, Nagpur, Maharashtra, India

Abstract

In order to presents better Predictiveanalytic models of different data using various machine learning algorithms. The data prediction is developed on various applications. These applications are facing many challenges while collecting data and data selection etc. The proposed prediction model involves several phases like (a) Data acquisition, (b) Data cleaning, (c) Data normalization, (d) Optimal Feature selection, and (e) Prediction. The optimal feature selection will be accomplished by a new variant of meta-heuristic algorithm like Self Adaptive-Spider Monkey Optimization (SA-SMO) [26]. The optimally selected features will be subjected to an improved deep learning algorithm termed as Recurrent Neural Network (RNN).

Keywords:Big data, Self Adaptive-Spider Monkey Optimization (SA-SMO), Recurrent Neural Network (RNN).

Cite this Article: V.B. Bhagat, V.U. Deorankar. An Enhanced Application Oriented Predictive Analytic using Optimal Deep Learning Model. Journal of Software Engineering Tools & Technology Trends. 2020; 7(2): 5–10p.

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Published

2020-08-21

Issue

Vol. 7 No. 2 (2020)

Section

Research Articles

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