Smart Job Recruitment Automation: Bridging Industry and University

Yadav, Vijay; Gewali, Ujjwal; Khatri, Suman; Rauniyar, Shree Ram; Shakya, Aman

doi:10.1109/aitb48515.2019.8947445

Cited by 13 publications

(2 citation statements)

References 6 publications

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“…Their accuracy performance was compared for the best model and results showed that DT J48 outperformed others with an accuracy of 66.0651%. Yadav et al [23] highlighted the need for an online job board system for colleges and its effectiveness in bridging the gap between college students and career opportunities. The purpose of the work was to help students in their final year class find better jobs and employers find suitable candidates for the job.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach

Imianvan,

Robinson,

Asuquo

et al. 2024

JAMCS

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Personnel recruitment processes in various government agencies, ministries, boards, and parastatals encounter challenges in effectively selecting candidates who meet specified requirements for job placement on time. Moreover, human resource (HR) managers face the additional burden of appeasing top government officials while also mitigating issues of nepotism and bias during recruitment. The success or failure of any organization heavily relies on the recruitment and retention of its workforce. Consequently, the decision to select suitable candidates for job positions is of utmost importance to management in every organization. This work develops a structured intelligent system that selects the best machine learning (ML) classification model for predicting applicants’ employability based on their attributes using the industry job selection criteria. A dataset of 16240 applicants’ records collected from Akwa State Universal Basic Education Board (AKSUBEB) was used to train and test the performance of the ML models. Naïve Bayes (NB), Logistic Regression (LR), Support Vector Machines (SVM), Random Forest (RF), and Decision Tree (DT) classifiers were deployed where results indicate that DT emerged the most effective classifier with a 98% prediction accuracy followed by RF with accuracy of 97.59% while LR recorded the least accuracy of 79.43%. This outcome indicates that tree-based ML structures can significantly help HR personnel to efficiently select suitable candidates for given job positions with reduced overhead in the recruitment process.

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Section: Related Workmentioning

confidence: 99%

Enhancing Job Recruitment Prediction through Supervised Learning and Structured Intelligent System: A Data Analytics Approach

Imianvan,

Robinson,

Asuquo

et al. 2024

JAMCS

View full text Add to dashboard Cite

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“…This paper is an extension of work originally presented in a conference, Artificial Intelligence for Transforming Business and Society (AITB), 2019 [1]. It contains an extensive explanation of the system using various diagrams.…”

Section: Literature Reviewmentioning

confidence: 99%

Need of E-Recruitment System for Universities: Case of Pulchowk Campus, Nepal

Yadav¹,

Khatri²,

Rauniyar³

et al. 2020

Adv. sci. technol. eng. syst. j.

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This paper highlights the importance of on-campus online job recruitment system and its role in helping students grab the available job opportunities. It highlights the problems associated with the traditional way of hiring, especially for college students. It also presents some findings and results obtained through various surveys conducted within the campus before and after the deployment of this system. The work presented in this paper is based on an e-recruitment system built for one of the leading engineering institute in Nepal, Pulchowk campus. With some features like job recommendation based on various levels of skill, smart multi-criteria search, graduate tracking, this system proves to be useful for all i.e. companies, students and the campus as well.

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