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Neural Network Based Prediction Model for Job Applicants

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Predictive analytics, a division of the advanced analytics that uses various techniques like machine learning, data mining and so on, to predict the future events. Predictive analytics is summarized with the data collection, modelling, statistics and deployment. It can be used to predict the future possibilities in different areas like business, healthcare, telecom, finance. An effective technique for prediction is Artificial Neural Network. The model accuracy for prediction can be enhanced using neural networks. The model can also be used easily for prediction of output parameters because of its ability to solve the complex computation which are difficult to be solved by other techniques. In this paper, a brief review of Artificial Neural Network used for prediction analysis is presented with various techniques like Multi-Layer Perceptron, T-S Fuzzy Neural Networks, Support Vector Machine, Radial Basis Function Network, Levenberg-Marquardt Algorithm and Back Propagation and their applications are also presented. This paper also presents the neural network-based prediction model for job applicants which is used to predict the jobs of various applicants based on certain parameter ratings.
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Keywords: Back-Propagation; Data Mining; Neural Network; Predictive Analytics

Document Type: Research Article

Affiliations: Department of Computer Science and Information Technology, Central University of Jammu, Rahya Suchani (Bagla), District Samba 181143, Jammu and Kashmir, India

Publication date: September 1, 2019

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  • Journal of Computational and Theoretical Nanoscience is an international peer-reviewed journal with a wide-ranging coverage, consolidates research activities in all aspects of computational and theoretical nanoscience into a single reference source. This journal offers scientists and engineers peer-reviewed research papers in all aspects of computational and theoretical nanoscience and nanotechnology in chemistry, physics, materials science, engineering and biology to publish original full papers and timely state-of-the-art reviews and short communications encompassing the fundamental and applied research.
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