Demographic change

Radant, Olaf; Colomo‐Palacios, Ricardo; Stantchev, Vladimir

doi:10.1145/2808580.2808676

Proceedings of the 3rd International Conference on Technological Ecosystems for Enhancing Multiculturality 2015

DOI: 10.1145/2808580.2808676

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Demographic change

Olaf Radant

Ricardo Colomo‐Palacios

Vladimir Stantchev

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2020

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Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Prathan

2020

IET softw.

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A data mining-based technique is proposed for the selection and employment of the best-fit programmers to meet the needs of software companies. The proposed technique incorporates Bayes' theorem and artificial neural network (ANN). The datasets used were from two software companies (Company 1 and Company 2) in India, covering the years 2010-2015. Bayes' theorem is used for identifying the prognostic attributes of the best-fit programmers, while the ANN classifier is used for predicting the best-fit programmers. Using a confusion matrix, the ANN classifier performance is 97.2 and 87.3%, 95.8 and 54.5%, and 100 and 75% with regard to accuracy, precision, and recall on the two test datasets of Company 1 and Company 2, respectively. The results show that the technique is effective for predicting the best-fit programmers. Software companies can use this technique in their recruitment and selection process to determine the best-fit employees for the programmer posts. The proposed technique can also be adapted for application in other disciplines such as sports, education, etc., to identify the most suitable person to fill a relevant position.

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Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Prathan

2020

IET softw.

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show abstract

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Demographic change

Cited by 1 publication

References 39 publications

Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

Determining the best‐fit programmers using Bayes’ theorem and artificial neural network

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