Advertisement

A Deep Learning-Based Approach for Predicting the Outcome of H-1B Visa Application

  • Anay Dombe
  • Rahul Rewale
  • Debabrata SwainEmail author
Conference paper
  • 3 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1101)

Abstract

The H-1B is a visa that allows US employers to employ foreign workers in specialty occupations. The number of H-1B visa applicants is growing drastically. Due to a heavy increment in the number of applications, the lottery system has been introduced, since only a certain number of visas can be issued every year. But, before a Labor Condition Application (LCA) enters the lottery pool, it has to be approved by the US Department of Labor (DOL). The approval or denial of this visa application depends on a number of factors such as salary, work location, full-time employment, etc. The purpose of this research is to predict the outcome of an applicant’s H-1B visa application using artificial neural networks and to compare the results with other machine learning approaches.

Keywords

H-1B Labor condition application Deep learning Artificial neural networks 

References

  1. 1.
    Boyd, Monica. 2014. Recruiting high skill labour in North America: Policies, outcomes and futures. International Migration 52 (3): 40–54.CrossRefGoogle Scholar
  2. 2.
    Dreher, Axel, and Panu Poutvaara. 2005. Student flows and migration: An empirical analysis.Google Scholar
  3. 3.
    Jing-Lin. H-1B Visa Data Analysis and Prediction by using K-means Clustering and Decision Tree Algorithms. [Online] Available: https://github.com/Jinglin-LI/H1B-Visa-Prediction-by-Machine-Learning-Algorithm/blob/master/H1B%20Prediction%20Research%20Report.pdf.
  4. 4.
    H-1B Visa Petitions 2011–2016—Kaggle. [Online]. Available: https://www.kaggle.com/nsharan/h-1b-visa/data. Accessed October 20, 2017.
  5. 5.
    Seo, Songwon. 2006. A review and comparison of methods for detecting outliers in univariate data sets. Master’s Thesis, University of Pittsburgh (Unpublished).Google Scholar
  6. 6.
    Glorot, Xavier, Antoine Bordes and Y. Bengio. 2010. Deep sparse rectifier neural networks. Journal of Machine Learning Research 15.Google Scholar
  7. 7.
    Kingma, Diederik P., Ba Adam Jimmy. A method for stochastic optimization.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Vishwakarma Institute of TechnologyPuneIndia

Personalised recommendations