Advancement in E Recruitment Towards Expert Recruitment System (ERS)

Obayi Adaora Angela, Gregory Anichebe, Uzo Izuchukwu Uchenna, Ezema Modesta, Nnaemeka Ogbene, Ihedioha Uchechi Michael, Agbo Jonathan Chukwunweike

Abstract


The inspiration drawn from e-recruitment is in making the process to become more creative, formidable and as well to be cost effective. We are desirous of achieving a lot more attraction than it is currently in order to sustain the process. Before now, we have some existing systems which were traditional methods like employment agencies, doing adverts through the print media. This process was relatively very slow and stressful. In this work, we have developed a remolded ERS based on our former work to hire applicants by accepting applications online and conduction Test and interview through the expert systems knowledge base until the candidate is eventually hired. The expert system is of a recruitment process model where applicants don’t actually have direct interaction with the employers but with the expert system that makes decision. The system provides response to applicant request and also provides procedure for recruitment from start to finishing stage, when the job seeker is now called successful employee. We implored Water fall model in our design. In this model, each stage essentially completes before the next phase can activate so that there will be no overlapping in the phases.


Keywords


ERS, Recruitment, Human Resources, Knowledge base, Fuzzy System

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References


Aaron Smith, 2015 ‘U.S. Smartphone Use in 2015’ Pew research Internet and Research APRIL 1, 2015 [ONLINE] RETRIEVED 20 DECEMBER 2018 https://www.pewinternet org/2015/04/01/us-smartphone

Ihedioha Uchechi M and Modesta Ezema, 2020, “Design and Implementation of an Expert Recruitment System." IOSR Journal of Computer Engineering (IOSR-JCE), 22.1 (2020), pp. 48-55

McAfee, A., and E. Brynjolfsson. 2012. “Big Data: The management Revolution.” Harvard Business Review. 90: 60-68.

Bryan Dean Wright. 2016 ‘Robots are coming for your job’ Los Angeles Times, MAR 28 2016, [ONLINE] RETRIEVED 17 DECEMBER 2018 https://www.latimes.com/opinion/op-ed/la-oe-wright-robots-jobs-data-mining-20160328-story.html

Ron Miller 2017 ‘Technology can’t replace the human touch’ 2017, [ONLINE] RETRIEVED 17DECEMBER 2018,https://techcrunch.com/2017/01/15/technology-cant-replace-the-human-touch

Online at https://www.geeksforgeeks.org/difference-between-ai-and-expert-system/

Tuncay Bayraka and Bahadir Akcama, 2015, “Exploring Benefits of a Web Based Testing and Training Tool” Available online at www.sciencedirect.com Procedia - Social and Behavioral Sciences Published by Elsevier Ltd. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of Istanbul Univeristy. doi: 10.1016/j.sbspro.2015.06.146

ScienceDirect

Murphy, M. C., Sharma, A., & Rosso, M. (2012). Measuring Assurance of Learning Goals: Effectiveness of Computer Training and Assessment Tools. Information Systems Education Journal, 10(5), 87.

Mahdi Bohlouli , Nikolaos Mittas , George Kakarontzas , Theodosios Theodosiou , Lefteris Angelis , Madjid Fathi , Competence Assessment as an Expert System for Human Resource Management: A Mathematical Approach, Expert Systems With Applications (2016), doi: 10.1016/j.eswa.2016.10.046

Huang, C.-Y., Chen, C.-S., & Lai, C.-E. (2016). Evaluation and analysis of incorporating Fuzzy Expert System approach into test suite reduction. Information and Software Technology, 79, http://dx.doi.org/10.1016/j.infsof.2016.07.005

Wagner, W. P. (2017). Trends in expert system development: A longitudinal content analysis of over thirty years of expert system case studies. Expert Systems with Applications, 76, 85–96. doi:10.1016/j.eswa.2017.01.028

Leith, P. (2016). The rise and fall of the legal expert system†. International Review of Law, Computers & Technology, 30(3), 94–106. doi:10.1080/13600869.2016.1232465

Thong, P. H., & Son, L. H. (2015). A New Approach to Multi-variable Fuzzy Forecasting Using Picture Fuzzy Clustering and Picture Fuzzy Rule Interpolation Method. Knowledge and Systems Engineering, 679–690. doi:10.1007/978-3-319-11680-8_54

Ritu Kapur, 2019, “Towards a Knowledge warehouse and expert system for the automation of SDLC tasks” 2019 IEEE/ACM International Conference on Software and System Processes (ICSSP) https://doi.org/10.1109/ICSSP.2019.00011.

Prostejovsky, A. M., Brosinsky, C., Heussen, K., Westermann, D., Kreusel, J., & Marinelli, M. (2019). The future role of human operators in highly automated electric power systems. Electric Power Systems Research, 175, 105883. doi:10.1016/j.epsr.2019.105883

Bilal HMOUD and Varallyai LASZL, 2019 “WILL ARTIFICIAL INTELLIGENCE TAKE OVER HUMANRESOURCES RECRUITMENT AND SELECTION? “Network Intelligence Studies, Issue Year: VII/2019, Issue No: 13, Page Range: 21-30, online at https://www.ceeol.com/search/article-detail?id=840067

Dmytro Chumachenko, Victor Balitskii, Tetyana Chumachenko, Victoria Makarova and Maryna Railian, 2019,” Intelligent Expert System of Knowledge Examination of Medical Staff Regarding Infections Associated with the Provision of Medical Care”

Abdul-Kadar Masum, Loo-See Beh, Abul-Kalam Azad, and Kazi Hoque, 2018, “Intelligent Human Resource Information System (iHRIS): A Holistic Decision Support Framework for HR Excellence” The International Arab Journal of Information Technology, Vol. 15, No. 1, January 2018

Nazari, S., Fallah, M., Kazemipoor, H., & Salehipour, A. (2018). A fuzzy inference- fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. https://doi.org/10.1016/j.eswa.2017.11.001

Hani M. Sh Bakeer & Samy S. Abu-Naser, 2019, “An Intelligent Tutoring System for Learning TOEFL” International Journal of Academic Pedagogical Research (IJAPR) 12 (2):9-15 (2019)

Y. Liu, Z.X. Wang, X. Qiao, E.G. Hou, The monitoring and fault diag-nosis technology research of battery management system, in: ChineseAutomation Congress (CAC), 2017, IEEE, 2017, pp. 1187–1190

Friedman-hill, E., “JESS, The rule engine for the java platform”, from http://herzberg.ca.sandia.gov/jess/. Access date: November 28, 2009.

Pappis, C.P.; Siettos, C.I. Fuzzy reasoning. In Introductory Tutorials in Optimization and Decision Support Techniques; Burke, E.K., Kendall, G., Eds.; Kluwer: Boston, MA, USA, 2005.

Camastra, F.; Ciaramella, A.; Giovannelli, V.; Lener, M.; Rastelli, V.; Staiano, A.; Staiano, G.;Starace, A. A fuzzy decision system for genetically modified plant environmental risk assessment using Mamdani inference. Exp. Sys. App. 2015, 42, 1710–1716


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Copyright (c) 2020 Ihedioha Uchechi Michael, Gregory Anichebe, Uzo Izuchukwu Uchenna, Ezema Modesta, Nnaemeka Ogbene, Ihedioha Uchechi Michael

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