Molla MJH, Basak SK, Obaidullah SM, Debnath NC, Sen S (2024)
Publication Language: English
Publication Type: Journal article
Publication year: 2024
Book Volume: 31
Pages Range: 161-169
Journal Issue: 3
In modern day the success of majority of organizations depend on the efficiency of human resources. Measuring human resources and their performance are often challenging as there is no standard metric to measure how well a resource will fit in the organization. Moreover based on the job role the requirements of the skill-set of the candidates vary. For the newly joined candidates the requirements of most of the companies are same but different subset of skill are required as per the job role. As the number of subset of the skill set is high a standardized model is required to define a framework that will guide the recruitment process. The cost of recruitment goes high if a company organizes the recruitment from beginning to end phase. Generally the initial phases are same for a particular type of industry and this can be outsourced to a third party. A lattice based model named as Lattice-graph is proposed to manage the recruitment of newly joined candidates when the skill-set is static. However, when new skill(s) are added Lattice-graph can’t incorporate these dynamic skills. Henceforth a new dynamic model is defined named as Hybrid-graph Skill Set Model (HGSSM) to incorporate the challenges of including new skill as per the requirement of the recruiter. Classification algorithms are also deployed to measure the accuracy of the newly joined candidate’s recruitment based on the known skillset.
APA:
Molla, M.J.H., Basak, S.K., Obaidullah, S.M., Debnath, N.C., & Sen, S. (2024). A Lattice Based Service Oriented Framework for an Effective Human Resource Management. International Journal of Computers and their Applications, 31(3), 161-169.
MLA:
Molla, Md Jakir Hossain, et al. "A Lattice Based Service Oriented Framework for an Effective Human Resource Management." International Journal of Computers and their Applications 31.3 (2024): 161-169.
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