Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent

Temsamani Khallouk Yassine; Achchab Said

doi:doi:10.11648/j.ijiis.20241306.11

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Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent

Temsamani Khallouk Yassine^*

, Achchab Said

Published in International Journal of Intelligent Information Systems (Volume 13, Issue 6)

Received: 18 November 2024 Accepted: 3 December 2024 Published: 23 December 2024

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Abstract

Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.

Published in	International Journal of Intelligent Information Systems (Volume 13, Issue 6)
DOI	10.11648/j.ijiis.20241306.11
Page(s)	117-127
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Predictive Hiring, HR, Stochastic Gradient Descent, Optimization, Variable Selection

References

[1]	Z. H. Zhou, K. Chen, and X. Y. Dai, “Data-driven intelligence in hiring and employee selection,” International Journal of Data Science and Analytics, vol. 11, pp. 221–234, 2021.
[2]	I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. MIT Press, 2016.
[3]	T. A. Leopold, V. Ratcheva, and S. Zahidi, “The future of jobs report 2018,” World Economic Forum, 2018.
[4]	E. Faliagka, A. Tsakalidis, and G. Tzimas, “An integrated e-recruitment system for automated personality mining and applicant ranking,” Internet Research, vol. 22, pp. 551–568, 2012.
[5]	D. He, X. Yuan, and H. Li, “Ai and talent acquisition: The emerging frontier,” Journal of Business Research, vol. 112, pp. 140–148, 2020.
[6]	H. Heidari, A. Ferrario, and M. B. Zafar, “Fairness in machine learning: From statistical to causal definitions,” Data Mining and Knowledge Discovery, vol. 34, pp. 453–495, 2020.
[7]	F. S. Guenole, N. The Business Case for AI in HR: With Insights and Tips on Getting Started. IBM Smarter Workforce Institute, 2018.
[8]	D. G. W. S.. T. N. Weller, C., “Predictive hiring: Ai applications for predicting success in recruitment.,” Journal of Machine Learning Research,, pp. (110), 1–34, 2020.
[9]	M. M. E.. X. C. Chen, L., “Intelligent recruiting system by analyzing interview data based on machine learning algorithms.,” IEEE Access, 7, pp. 134539–134553, 2019.
[10]	D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” International Conference on Learning Representations (ICLR), 2014.
[11]	J. A. L. G. Nemirovski, A. and A. Shapiro, “Robust stochastic approximation approach to stochastic programming,” Siam Journal of Optimization, vol. 19, pp. 1574–1609, 2009.
[12]	P. Tseng, “An incremental gradient (-subgradient) method with momentum term and adaptive stepsize rule,” SIAM Journal on Optimization, vol. 10, pp. 323–345, 1998.
[13]	L. Bottou, “Large-scale machine learning with stochastic gradient descent.,” Proceedings of COMPSTAT’2010, Physica-Verlag HD,, pp. 177–186., 2010.
[14]	M. J. D. G.. H. G. Sutskever, I., “On the importance of initialization and momentum in deep learning,” Proceedings of the 30th International Conference on Machine Learning (ICML-13),, pp. 1139–1147, 2013.
[15]	H. Robbins and S. Monro, “A stochastic approximation method,” Ann. Math. Stat., vol. 22, pp. 400–407, 1951.
[16]	R. Johnson and T. Zhang, “Accelerating stochastic gradient descent using predictive variance reduction,” vol. 26, pp. 315–323, 2013.
[17]	L. Bottou and O. Bousquet, “The tradeoffs of large scale learning,” Advances in Neural Information Processing Systems (NIPS), vol. 20, pp. 161–168, 2008.

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APA Style

Yassine, T. K., Said, A. (2024). Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. International Journal of Intelligent Information Systems, 13(6), 117-127. https://doi.org/10.11648/j.ijiis.20241306.11

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ACS Style

Yassine, T. K.; Said, A. Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. Int. J. Intell. Inf. Syst. 2024, 13(6), 117-127. doi: 10.11648/j.ijiis.20241306.11

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AMA Style

Yassine TK, Said A. Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. Int J Intell Inf Syst. 2024;13(6):117-127. doi: 10.11648/j.ijiis.20241306.11

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@article{10.11648/j.ijiis.20241306.11,
  author = {Temsamani Khallouk Yassine and Achchab Said},
  title = {Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {6},
  pages = {117-127},
  doi = {10.11648/j.ijiis.20241306.11},
  url = {https://doi.org/10.11648/j.ijiis.20241306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241306.11},
  abstract = {Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.
},
 year = {2024}
}

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TY  - JOUR
T1  - Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent

AU  - Temsamani Khallouk Yassine
AU  - Achchab Said
Y1  - 2024/12/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241306.11
DO  - 10.11648/j.ijiis.20241306.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 117
EP  - 127
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241306.11
AB  - Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.

VL  - 13
IS  - 6
ER  -

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Author Information

Temsamani Khallouk Yassine

Engineering School of Information Technology and System Analysis, Mohammed V University, Rabat, Morocco

Contact Email

http://orcid.org/0009-0000-5688-8836
Achchab Said

Engineering School of Information Technology and System Analysis, Mohammed V University, Rabat, Morocco

Contact Email

http://orcid.org/0000-0001-5752-8699

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Plain Text BibTeX RIS

APA Style

Yassine, T. K., Said, A. (2024). Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. International Journal of Intelligent Information Systems, 13(6), 117-127. https://doi.org/10.11648/j.ijiis.20241306.11

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ACS Style

Yassine, T. K.; Said, A. Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. Int. J. Intell. Inf. Syst. 2024, 13(6), 117-127. doi: 10.11648/j.ijiis.20241306.11

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AMA Style

Yassine TK, Said A. Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent. Int J Intell Inf Syst. 2024;13(6):117-127. doi: 10.11648/j.ijiis.20241306.11

Copy | Download

@article{10.11648/j.ijiis.20241306.11,
  author = {Temsamani Khallouk Yassine and Achchab Said},
  title = {Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent
},
  journal = {International Journal of Intelligent Information Systems},
  volume = {13},
  number = {6},
  pages = {117-127},
  doi = {10.11648/j.ijiis.20241306.11},
  url = {https://doi.org/10.11648/j.ijiis.20241306.11},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijiis.20241306.11},
  abstract = {Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.
},
 year = {2024}
}

Copy | Download

TY  - JOUR
T1  - Predictive Hiring and AI: Elevating Recruitment with Optimized Neural Networks and Gradient Descent

AU  - Temsamani Khallouk Yassine
AU  - Achchab Said
Y1  - 2024/12/23
PY  - 2024
N1  - https://doi.org/10.11648/j.ijiis.20241306.11
DO  - 10.11648/j.ijiis.20241306.11
T2  - International Journal of Intelligent Information Systems
JF  - International Journal of Intelligent Information Systems
JO  - International Journal of Intelligent Information Systems
SP  - 117
EP  - 127
PB  - Science Publishing Group
SN  - 2328-7683
UR  - https://doi.org/10.11648/j.ijiis.20241306.11
AB  - Efficient and accurate hiring processes are critical for organizational success, yet traditional recruitment methods often face challenges such as inefficiencies and delays. This study explores the application of artificial intelligence (AI) and machine learning (ML) techniques to enhance predictive hiring models. A hybrid framework is proposed, integrating neural networks with Stochastic Gradient Descent (SGD) optimization and feature selection methods, including Logistic Regression (LR) and Discriminant Analysis (DA). The approach demonstrates a marked improvement in prediction accuracy and efficiency, with Logistic Regression emerging as a more effective feature selection method for neural networks in this context. By leveraging these techniques, human resource teams can streamline candidate evaluations, enhance decision-making processes, and modernize recruitment workflows. This research underscores the transformative potential of AI in addressing the limitations of traditional hiring practices.

VL  - 13
IS  - 6
ER  -

Copy | Download