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Prediction of Various Job Opportunities in IT Companies Using Enhanced Integrated Gated Recurrent Unit (EIGRU)

Author

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  • R. Santhosh Kumar

    (B.S. Abdur Rahman Crescent Institute of Science and Technology)

  • N. Prakash

    (B.S. Abdur Rahman Crescent Institute of Science and Technology)

Abstract

The fresh engineering graduates are looking only for the popular jobs where the competition is high and the number of job openings is minimal, but they fail to look for the other job openings. The major problem is that the graduates fail to look at the number of requirements needed for a job role in the present and future. So there is a need for a prediction model that provides the number of job opportunities in a job role in the future. Many research studies have been carried out to predict the placement status of students, but they have not predicted the number of job opportunities in a job role. Many existing prediction models focus on improving prediction accuracy but fail to consider the handling of data fluctuations. When there is a data fluctuation, the predicted value deviates from the actual value. This paper presents a hybrid time-series prediction model called the enhanced integrated gated recurrent unit (EIGRU) Model to predict the number of job opportunities in a job role based on the company, salary, and experience. The proposed EIGRU model tries to minimize the divergence in the predicted value. The proposed time series prediction model is achieving a prediction accuracy of 98%. Based on the experimental evaluation of the Job dataset, the proposed model’s mean absolute percentage error and mean absolute error values are lower than the baseline models. As a result, the graduates will know about the number of job opportunities in their job role and make an effective decision.

Suggested Citation

  • R. Santhosh Kumar & N. Prakash, 2024. "Prediction of Various Job Opportunities in IT Companies Using Enhanced Integrated Gated Recurrent Unit (EIGRU)," Annals of Data Science, Springer, vol. 11(6), pages 2001-2018, December.
    • Handle: RePEc:spr:aodasc:v:11:y:2024:i:6:d:10.1007_s40745-023-00495-z
    DOI: 10.1007/s40745-023-00495-z
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    References listed on IDEAS

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    1. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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