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This paper reviews studies and developments in recruitment techniques interact with computer science. And novel approach proposed as Intelligent Recruitment System (IRS) which consist of resume classification and ranking with deep learning alongside with NLP techniques, Automatic question generation(AQG) system which measure technical proficiency of an applicant using the knowledge base of merged ontology designed using the web and local sources and soft skills measuring with question answering about different skill sets and matching the similarity of the predefined answers against the applicant given answer by using both the syntactical similarity measurement and semantic similarity measurements. The final output comes from a combination of the above input parameters. The purpose of this model is to propose a new framework based on the fuzzy inference system (FIS) and Mamdani's method. The decision-making mechanism of the IRS is based on the final total score of each parameter and maximum quota. If two or more candidates receive the same final total score, it is difficult to make a definitive decision. Therefore, a FIS approach is proposed to help decision makers choose the most qualified applicants and avoid unnecessary problems.
Maddumage et al. (Fri,) studied this question.