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Artificial Intelligence – based Multiple Regression Algorithm to Indicate Human Development in the Workplace
Muthuvelayutham C,
Pages: 1-11 | First Published: 05 Dec 2023
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Abstact
There are particular difficulties in integrating human growth into the human workplace. Potential, poverty, and productivity are the three fundamental obstacles to human development. Maintaining employee skill development and training is a problem that human resource development frequently tackles. Employees must broaden their knowledge and acquire new skills as demands and trends change. In this study, an Artificial Intelligence-based Multiple Regression Algorithm (MRA) is employed to indicate human development in the workplace and is termed as AI-MRA. This MRA is compared with the existing models such as Random Forest, Naïve Bayes, and a hybrid model of Convolutional Neural Network withVariable-Length Markov Modelling (CNN-VMM) based on the parameters such as accuracy, sensitivity, specificity, precision, and recall. It is observed from the results that the proposed model has outperformed other existing algorithms.
Keywords: Human Development, Artificial Intelligence-based Multiple Regression Algorithm (AI-MRA), Random Forest, Naïve Bayes, Convolutional Neural Network withVariable-Length Markov Modelling (CNN-VMM)

 References

E. Rahayu, I. Parlina, and Z. A. Siregar, “Application of Multiple Linear Regression Algorithm for Motorcycle Sales Estimation,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, no. 1, pp. 1–10, Mar. 2022, doi: 10.55123/jomlai.v1i1.142.
2. L. Zhao, L. Wang, and D. Cui, “Hoeffding bound based evolutionary algorithm for symbolic regression,” Engineering Applications of Artificial Intelligence, no. 5, pp. 945–957, Aug. 2012, doi:
10.1016/j.engappai.2012.04.005.
Artificial Intelligence – based Multiple Regression Algorithm to Indicate Human Development in the
Workplace
Emperor International Journal of Management 10
3. J. Abdullahi, A. Iravanian, V. Nourani, and G. Elkiran, “Application of artificial intelligence based and multiple regression techniques for monthly precipitation modeling in coastal and inland stations,” DESALINATION AND WATER TREATMENT, pp. 338–349, 2020, doi:
10.5004/dwt.2020.24954.
4. Q. Jiang, M. Jia, L. Bi, Z. Zhuang, and K. Gao, “Development of a core feature identification application based on the Faster R-CNN algorithm,” Engineering Applications of Artificial Intelligence, p. 105200, Oct. 2022, doi: 10.1016/j.engappai.2022.105200.
5. D. J. Pradeep, M. M. Noel, and N. Arun, “Nonlinear control of a boost converter using a robust regression based reinforcement learning algorithm,” Engineering Applications of Artificial Intelligence, pp. 1–9, Jun. 2016, doi: 10.1016/j.engappai.2016.02.007.
6. S. H. Samareh Moosavi and V. K. Bardsiri, “Poor and rich optimization algorithm: A new human-based and multi populations algorithm,” Engineering Applications of Artificial Intelligence, pp. 165–181, Nov. 2019, doi: 10.1016/j.engappai.2019.08.025.
7. P. Singh, “A neutrosophic-entropy based adaptive thresholding segmentation algorithm: A special application in MR images of Parkinson’s disease,” Artificial Intelligence in Medicine, p. 101838, Apr. 2020, doi: 10.1016/j.artmed.2020.101838.
8. W. A. L. Sinaga, S. Sumarno, and I. P. Sari, “The Application of Multiple Linear Regression Method for Population Estimation Gunung Malela District,” JOMLAI: Journal of Machine Learning and Artificial Intelligence, no. 1, pp. 55–64, Mar. 2022, doi: 10.55123/jomlai.v1i1.143.
9. Chen, Xiaowei & Yu, Lei & Wang, Tian & Liu, Anfeng & Wu, Xiaofeng & Zhang, Benhong & Lv, Zhiguo & Sun, Zeyu. (2020). Artificial Intelligence-Empowered Path Selection: A Survey of Ant Colony Optimization for Static and Mobile Sensor Networks. IEEE Access. PP. 1-1. 10.1109/ACCESS.2020.2984329.
10. D. Senol and M. Ozturan, “Stock Price Direction Prediction Using Artificial Neural Network Approach: The Case of Turkey,” Journal of Artificial Intelligence, no. 2, pp. 70–77, Jun. 2008, doi: 10.3923/jai.2008.70.77.
11. A. Saha and S. Saha, “Integrating the artificial intelligence and hybrid machine learning algorithms for improving the accuracy of spatial prediction of landslide hazards in Kurseong Himalayan Region,” Artificial Intelligence in Geosciences, pp. 14–27, Dec. 2022, doi: 10.1016/j.aiig.2022.06.002.
12. B. Alhnaity and M. Abbod, “A new hybrid financial time series prediction model,” Engineering Applications of Artificial Intelligence, p. 103873, Oct. 2020, doi: 10.1016/j.engappai.2020.103873.
13. R. S. Peres, X. Jia, J. Lee, K. Sun, A. W. Colombo and J. Barata, "Industrial Artificial Intelligence in Industry 4.0 - Systematic Review, Challenges and Outlook," in IEEE Access, vol. 8, pp. 220121-220139, 2020, doi: 10.1109/ACCESS.2020.3042874.
14. P. Bangalore and L. B. Tjernberg, "An Artificial Neural Network Approach for Early Fault Detection of Gearbox Bearings," in IEEE Transactions on Smart Grid, vol. 6, no. 2, pp. 980-987, March 2015, doi: 10.1109/TSG.2014.2386305.

15. M. Hao, H. Li, X. Luo, G. Xu, H. Yang and S. Liu, "Efficient and Privacy-Enhanced Federated Learning for Industrial Artificial Intelligence," in IEEE Transactions on Industrial Informatics, vol. 16, no. 10, pp. 6532-6542, Oct. 2020, doi: 10.1109/TII.2019.2945367.
16. J. Wan, J. Yang, Z. Wang and Q. Hua, "Artificial Intelligence for Cloud-Assisted Smart Factory," in IEEE Access, vol. 6, pp. 55419-55430, 2018, doi: 10.1109/ACCESS.2018.2871724.
17. R. Narwal and H. Aggarwal, “Human Behavior Prediction and Artificial Intelligence,” in Artificial Intelligence and Global Society, Chapman and Hall/CRC, 2021, pp. 31–41.
18. D. Šuc, D. Vladušič, and I. Bratko, “Qualitatively faithful quantitative prediction,” Artificial Intelligence, no. 2, pp. 189–214, Oct. 2004, doi: 10.1016/j.artint.2004.05.002.
19. S. Mehrmolaei, “EPTs-TL: A two-level approach for efficient event prediction in healthcare,” Artificial Intelligence in Medicine, p. 101999, Jan. 2021, doi: 10.1016/j.artmed.2020.101999.
20. J. Wan, X. Li, H. -N. Dai, A. Kusiak, M. Martínez-García and D. Li, "Artificial-Intelligence-Driven Customized Manufacturing Factory: Key Technologies, Applications, and Challenges," in Proceedings of the IEEE, vol. 109, no. 4, pp. 377-398, April 2021, doi: 10.1109/JPROC.2020.3034808.
21. Zhang, J., Wang, P. and Gao, R.X., 2021. Hybrid machine learning for human action recognition and prediction in assembly. Robotics and Computer-Integrated Manufacturing, 72, p.102184.
22. Chakraborty, R., Mridha, K., Shaw, R.N. and Ghosh, A., 2021, September. Study and prediction analysis of the employee turnover using machine learning approaches. In 2021 IEEE 4th International Conference on Computing, Power and Communication Technologies (GUCON) (pp. 1-6). IEEE.