Decision Support System for Evaluating Suitable Job Applicants

Walek, Bogdan; Pektor, Ondrej; Farana, Radim

doi:10.3390/math9151773

Cited by 3 publications

(2 citation statements)

References 34 publications

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“…There are a variety of applications based on a broad palette of methods ranging from standard symbolic artificial intelligence methods ones to connectionist approaches. In addition, the broad applications in a variety of domains are growing from health care [16], through the industry to human resources [17].…”

Section: Related Workmentioning

confidence: 99%

Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

et al. 2022

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This article focuses on developing an expert system applicable to the area of neurocognitive rehabilitation. The benefit of this interdisciplinary research is to propose an expert system that has been adapted based on real patients’ results from the Addenbrooke’s cognitive examination (ACE-R). One of this research’s main results is a unique proposal to transfer the ACE-R result to the CHC (Cattell–Horn–Carroll) intelligence model. This unique approach enables transforming the CHC model domains according to the modified ACE-R factor analysis, which has never been used before. The expert system inference results allow the automated optimized design of a neurorehabilitation plan to train patients’ cognitive functions according to the CHC model. A set of tasks in 6 difficulty levels (Level 1–Level 6) was proposed for each of the nine CHC model domains. For each patient, the ACE-R results helped determine specific CHC domains to be rehabilitated as well as the starting game level for the rehabilitation within each domain. The proposed expert system has been verified on real data of 705 patients and achieved an average error of 5.94% for all CHC model domains. The proposed system is to be included in the outcomes of the research project of the Technology Agency of the Czech Republic as a verified procedure for healthcare providers.

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Section: Related Workmentioning

confidence: 99%

Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

et al. 2022

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“…With the development of science and information technology, the most optimal allocation of human resources asks to employ a person to work in the position corresponding to his/her ability [14]. en, it is much necessary to investigate intelligent algorithms to find out a shift balance between the in-school cultivated ability and the requirement of working ability from the companies.…”

Section: Introductionmentioning

confidence: 99%

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

Hong

Yang

Wen

et al. 2022

Computational Intelligence and Neuroscience

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Optimal human resources allocation asks to employ a person to work in the position corresponding to his/her ability. Employment competence is the key feedback to the cultivation of college students’ working ability. The data relationship needs to analyze between the in-school cultivation items and the working abilities required by the companies. Machine learning framework is introduced to study the companies’ responses to the cultivation of college students. In this work, a dual-network architecture is built up for statistical modeling evaluation of college graduates’ working ability in consistence with their job position and remuneration. A requirement network and a cultivation network are constructed for extracting features from the original working ability data required by companies and cultivated ever in school. The networks are fully trained by adaptively tuning the linking weights. The extracted features are fused together to estimate the working competence of each target sample/person. To evaluate the dual-network model, a modeling index system is designed, including proposing a total evaluation index calculus for the dual-network model, and a variable importance index from the original data. The samples are consequently ranked by the model predicted index and by the variable importance index, respectively. The ranking difference is used to evaluate the prediction efficiency of the dual-network model. Experimental results show that the dual network architecture is feasible to establish statistical models for the evaluation of college graduates’ in-school cultivated working ability in consistence with the company’s required working ability at their job position and their deserved remuneration.

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Improving the quality of hires via the use of machine learning and an expansion of the person–environment fit theory

Artar,

Balcioglu,

Erdil

2024

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PurposeOur proposed machine learning model contributes to improving the quality of Hire by providing a more nuanced and comprehensive analysis of candidate attributes. Instead of focusing solely on obvious factors, such as qualifications and experience, our model also considers various dimensions of fit, including person-job fit and person-organization fit. By integrating these dimensions of fit into the model, we can better predict a candidate’s potential contribution to the organization, hence enhancing the Quality of Hire.Design/methodology/approachWithin the scope of the investigation, the competencies of the personnel working in the IT department of one in the largest state banks of the country were used. The entire data collection includes information on 1,850 individual employees as well as 13 different characteristics. For analysis, Python’s “keras” and “seaborn” modules were used. The Gower coefficient was used to determine the distance between different records.FindingsThe K-NN method resulted in the formation of five clusters, represented as a scatter plot. The axis illustrates the cohesion that exists between things (employees) that are similar to one another and the separateness that exists between things that have their own individual identities. This shows that the clustering process is effective in improving both the degree of similarity within each cluster and the degree of dissimilarity between clusters.Research limitations/implicationsEmployee competencies were evaluated within the scope of the investigation. Additionally, other criteria requested from the employee were not included in the application.Originality/valueThis study will be beneficial for academics, professionals, and researchers in their attempts to overcome the ongoing obstacles and challenges related to the securing the proper talent for an organization. In addition to creating a mechanism to use big data in the form of structured and unstructured data from multiple sources and deriving insights using ML algorithms, it contributes to the debates on the quality of hire in an entire organization. This is done in addition to developing a mechanism for using big data in the form of structured and unstructured data from multiple sources.

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Decision Support System for Evaluating Suitable Job Applicants

Cited by 3 publications

References 34 publications

Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

Expert System for Neurocognitive Rehabilitation Based on the Transfer of the ACE-R to CHC Model Factors

A Dual-Network Modeling Architecture for Statistical Evaluation of College Graduates’ Working Ability in Consistence with Their Job Position and Remuneration

Improving the quality of hires via the use of machine learning and an expansion of the person–environment fit theory

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