Identification of Players in the Database of Public and Private Banks with a Meta-Heuristic Algorithm

Document Type : Original Research Manuscripts

Authors

1 Adaptive Management, Department of Management,Dehaghan Branch,Islamic Azad University ,Dehaghan, Isfahan, Iran.

2 Associate Professor, Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

3 Assistant Professor, Department of Management, Dehaghan Branch, Islamic Azad University, Dehaghan, Iran.

Abstract
The research aims to identify players in the database of public and private banks using a meta-heuristic algorithm. This issue pertains to enhancing the human resources management system to ensure consistent stability in the bank's operations. In this database analysis process, Poisson distribution and artificial intelligence are utilized to analyze data with an exponential distribution. For this purpose, the VIS, CNSGA-II, NSGA-II, MISA, NNIA, and NRGA algorithms were implemented using MATLAB software. The VIS algorithm showed the best performance in most criteria. Algorithms CNSGA-II and MISA are both ranked second and exhibit similar performances. NSGA-II algorithm is ranked second. The NNIA algorithm performs the best, while the NRGA algorithm performs the worst. These analyses are conducted to assess the performance of algorithms based on various criteria. The results obtained from these analyses show that the VIS algorithm generally demonstrates the best performance. This means that VIS is known as an identification of players in the databases of public and private banks. In addition to the Variable in Neighborhood Search (VIS) algorithm, other algorithms like CNSGA-II and MISA are also closely ranked and share the second position in various criteria. These algorithms have similar functions and can make comparable enhancements in identifying players in the databases of public and private banks.

Keywords

Subjects

Abdi, A., Akbarian, M., and Pourhsinlou, M. (1394). Compilation of competency profile of strategic jobs in order to evaluate the strategic readiness of human capital. Human Resources Studies Quarterly, 18, pp. 71-94. [in Persian]
Aghabaghery, R., Golpayegani, A. H., & Esmaeili, L. (2020). A New Method for Organizational Process Model Discovery Through the Analysis of Workflows and Data Exchange Networks. Social Network Analysis and Mining, 10(1), 12. https://doi.org/10.1007/s13278-020-0623-5
Angorani, M. (2017). Analysis of the relationship between private and state banks. Consultants Institute, Tehran. [in Persian]
Appadoo, K., Soonnoo, M. B., & Mungloo-Dilmohamud, Z. (2020, December). Job recommendation system, machine learning, regression, classification, natural language processing. In 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE) (pp. 1-6). IEEE. https://doi.org/10.1109/CSDE50874.2020.9411584
Arbabi Manya, A., Reza Zainabadi, H., Akbar Hasanpour, A. (2018). Identifying and validating the competencies of the key jobs of the tax affairs organization in the implementation of the comprehensive tax plan. Tax Research Journal, Number 42. [in Persian]
Asgarnejad Nouri, B., & Mirmousavi, M. (2020). Meta-analysis of the key indicators of human resources management effective on improving the performance of employees. Management, improvement and transformation studies, 101, 126 to 160. [in Persian]
 Asplund, K. (2020). When profession trumps potential: The moderating role of professional identification in employees’ reactions to talent management. The International Journal of Human Resource Management, 31(4), 539-561. ‏ https://doi.org/10.1080/09585192.2019.1570307
Bamber, G. J., Bartram, T., & Stanton, P. (2017). HRM and workplace innovations: formulating research questions. Personnel Review, 46(7), 1216-1227. https://doi.org/10.1108/PR-10-2017-0292
 Chalutz-Ben Gal, H. (2023). Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook (pp. 833-866). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_36
D'Amato, A., & Roome, N. (2009). Toward an integrated model of leadership for corporate responsibility and sustainable development: a process model of corporate responsibility beyond management innovation. Corporate Governance: The international journal of business in society. https://doi.org/10.1108/14720700910984972
Dastyar, B., Kazemnejad, H., Sereshgi, A. A., & Jabalameli, M. A. (2017). Using Data Mining Techniques to Develop Knowledge Management in Organizations: A Review. Journal of Engineering, Project, and Production Management, 7(2), 80. https://doi.org/10.32738/JEPPM.201707.0004
Deloitte.2019. Managing Talent Flow. Available online: https://www.google.com/
Ghosh, A., & Sengupta, T. (2016). Predictive analytics for human resources. edited by J. Fitz-Enz and II John Mattox, Hoboken, NJ, John Wiley and Sons, 2014, pp. 1–149.
Graczyk-Kucharska, M., Olszewski, R., & Weber, G. W. (2023). The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region. Central European Journal of Operations Research, 31(1), 205-237. https://doi.org/10.1007/s10100-022-00805-5
Horváthová, P., Velčovská, Š., Kauerová, L., & Larsen, F. R. (2019). Evaluation of Key Positions and Employees Management Level in Manufacturing Industry—The Czech Case. Sustainability, 12(1), 242. https://doi.org/10.3390/su12010242
Kaliannan, M., Darmalinggam, D., Dorasamy, M., & Abraham, M. (2022). Inclusive talent development as a key talent management approach: A systematic literature review. Human Resource Management Review, 100926. ‏ https://doi.org/10.1016/j.hrmr.2022.100926
Kansal, J., & Jain, N. (2019). Development of competency model and mapping of employee's competencies for organizational development: A new approach. ‏
Lamba, D., Goyal, S., Chitresh, V., & Gupta, N. (2020, May). An integrated system for occupational category classification based on resume and job matching. In Proceedings of the International Conference on Innovative Computing & Communications (ICICC). https://doi.org/10.2139/ssrn.3607282
Ma, H., & Chen, M. (2022, June). Application of Data Mining Technology (DMT) in Human Resources Assessment Management System. In International Conference on Applications and Techniques in Cyber Intelligence (pp. 155-162). Cham: Springer International Publishing. https://doi.org/10.1007/978-3-031-29097-8_19
Malik, A. R., & Singh, P. (2014). ‘High potential’programs: Let's hear it for ‘B’players. Human Resource Management Review, 24(4), 330-346. ‏ https://doi.org/10.1016/j.hrmr.2014.06.001
Miao, R., Bozionelos, N., Zhou, W., & Newman, A. (2021). High-performance work systems and key employee attitudes: the roles of psychological capital and an interactional justice climate. The International Journal of Human Resource Management, 32(2), 443-477. ‏ https://doi.org/10.1080/09585192.2019.1710722
Mirzaei, H., Qolipour, A., Seyedjavadin, R., & Hasanqolipour, T. (2019). Identification of vital and key job criteria to attract and retain talents in the National Iranian Tanker Company. 13th year public management research. Number 47. [in Persian]
Nabi Pourafrouzi, M. & Yazdanjo, M. (2022). Analyzing the effectiveness and efficiency of financial intermediation as a financial participation strategy in the banking industry, https://civilica.com/doc/[in Persian]
Papineni, S., Reddy, A.M., Yarlagadda, S., et al. (2021). An extensive analytical approach on human resources using random forest algorithm. Int. J. Eng. Trends Technol. 69(5), 119–127. https://doi.org/10.14445/22315381/IJETT-V69I5P217
Perrin, B. (2015). Bringing accountability up to date with the realities of public sector management in the 21st century. Canadian Public Administration, 58(1), 183-203. https://doi.org/10.1111/capa.12107
Pessach, D., Singer, G., Avrahami, D., Ben-Gal, H. C., Shmueli, E., & Ben-Gal, I. (2020). Employees recruitment: A prescriptive analytics approach via machine learning and mathematical programming. Decision Support Systems, 134, 113290. https://doi.org/10.1016/j.dss.2020.113290
Russell, C., & Bennett, N. (2015). Big data and talent management: Using hard data to make the soft stuff easy. Business Horizons, 58(3), 237-242. https://doi.org/10.1016/j.bushor.2014.08.001
Ryan, J., & Herleman, H. (2015). A big data platform for workforce analytics.The Data Science Revolution and Organizational Psychology, p. 19.
Sela, A., & Ben-Gal, H. C. (2018, December). Big data analysis of employee turnover in global media companies, google, facebook and others. In 2018 IEEE International Conference on the Science of Electrical Engineering in Israel (ICSEE) (pp. 1-5). IEEE. https://doi.org/10.1109/ICSEE.2018.8645991
Stone, D. L., Deadrick, D. L., Lukaszewski, K. M., & Johnson, R. (2015). The influence of technology on the future of human resource management. Human resource management review, 25(2), 216-231. https://doi.org/10.1016/j.hrmr.2015.01.002
Strah, N., Rupp, D. E., & Morris, S. B. (2022). Job analysis and job classification for addressing pay inequality in organizations: Adjusting our methods within a shifting legal landscape. Industrial and Organizational Psychology, 15(1), 1-45. ‏ https://doi.org/10.1017/iop.2021.94
Tanasescu, LG., Bologa, AR. (2022). Machine Learning and Data Mining Techniques for Human Resource Optimization Process—Employee Attrition. In: Ciurea, C., Boja, C., Pocatilu, P., Doinea, M. (eds) Education, Research and Business Technologies. Smart Innovation, Systems and Technologies, vol 276. Springer, Singapore. https://doi.org/10.1007/978-981-16-8866-9_22
Traicoff, D., Pope, A., Bloland, P., Lal, D., Bahl, J., Stewart, S., ... & Sandhu, H. (2019). Developing standardized competencies to strengthen immunization systems and workforce. Vaccine, 37(11), 1428-1435. https://doi.org/10.1016/j.vaccine.2019.01.047
 Wei, F. (2022). Performance evaluation of tourism human resource management based on fuzzy data mining. Journal of Mathematics, 2022. https://doi.org/10.1155/2022/3745377
Wiedmer, T. (2015). Generations do differ: Best practices in leading traditionalists, boomers, and generations X, Y, and Z. Delta Kappa Gamma Bulletin, 82(1), 51.
Xue, X., Feng, J., Gao, Y., Liu, M., Zhang, W., Sun, X., ... & Guo, S. (2019). Convolutional recurrent neural networks with a self-attention mechanism for personnel performance prediction. Entropy, 21(12), 1227. https://doi.org/10.3390/e21121227
Ye, Y. (2022). Assistant Teaching System of Human Resource Management Course Based on Data Mining. In: Fu, W., Sun, G. (eds) e-Learning, e-Education, and Online Training. eLEOT 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 453. Springer, Cham. https://doi.org/10.1007/978-3-031-21161-4_21
Zehir, C., Karaboğa, T., & Başar, D. (2020). The transformation of human resource management and its impact on overall business performance: big data analytics and AI technologies in strategic HRM. Digital Business Strategies in Blockchain Ecosystems: Transformational Design and Future of Global Business, 265-279. https://doi.org/10.1007/978-3-030-29739-8_12
Zhao, J., Wang, J., Sigdel, M., Zhang, B., Hoang, P., Liu, M., & Korayem, M. (2021). Embedding-based recommender system for job to candidate matching on scale. arXiv preprint arXiv:2107.00221. ‏
Zheng, F., Song, S., Xia, Y., Zhang, Y. (2023). Application of Fuzzy Data Mining Algorithm in Human Resource Management of Power Industry. In: Jan, M.A., Khan, F. (eds) Application of Big Data, Blockchain, and Internet of Things for Education Informatization. BigIoT-EDU 2022. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 467. Springer, Cham. https://doi.org/10.1007/978-3-031-23944-1_61
 
Volume 4, Issue 4 - Serial Number 12
Autumn 2024
Pages 112-127

  • Receive Date 12 April 2024
  • Revise Date 14 May 2024
  • Accept Date 20 June 2024