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.
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Dombe, A., Rewale, R., Swain, D. (2020). A Deep Learning-Based Approach for Predicting the Outcome of H-1B Visa Application. In: Swain, D., Pattnaik, P., Gupta, P. (eds) Machine Learning and Information Processing. Advances in Intelligent Systems and Computing, vol 1101. Springer, Singapore. https://doi.org/10.1007/978-981-15-1884-3_18
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DOI: https://doi.org/10.1007/978-981-15-1884-3_18
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