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Please use this identifier to cite or link to this item: https://repository.esi-sba.dz/jspui/handle/123456789/576
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dc.contributor.authorHAMDI, ASma-
dc.date.accessioned2023-10-19T14:11:12Z-
dc.date.available2023-10-19T14:11:12Z-
dc.date.issued2023-
dc.identifier.urihttps://repository.esi-sba.dz/jspui/handle/123456789/576-
dc.descriptionSupervisor : MAHAMMED Nadir / Co-supervisor : SAIDI Imeneen_US
dc.description.abstractAbstract : Resume screening is a critical component of the recruitment process, enabling the identification of qualified candidates for a given role. Advancements in technology have introduced AI-driven approaches to enhance resume screening through natural language processing (NLP) and machine learning algorithms. AI resume screening utilizes various techniques to analyze resumes and cover letters. Keyword matching involves scanning documents for keywords that match job requirements, quickly identifying candidates with the necessary skills and qualifications. Sentiment analysis, using NLP. Qualification matching employs machine learning algorithms to analyze educational backgrounds and work experiences, determining if candidates meet minimum qualifications. Objective scoring assigns scores based on the degree of match between candidates and job requirements, facilitating efficient prioritization of qualified candidates for further review. By leveraging AI and techniques such as keyword matching, sentiment analysis, qualification matching, and objective scoring, organizations can optimize their resume screening processes. These AI-driven methods streamline candidate evaluation, improve efficiency, and enhance the overall effectiveness of recruitment processes.*** Résumé : La s´election des CV est un ´el´ement essentiel du processus de recrutement, permettant d’identifier les candidats qualifi´es pour un poste donn´e. Les avanc´ees technologiques ont introduit des approches bas´ees sur l’intelligence artificielle pour am´eliorer la s´election des CV grˆace au traitement du langage naturel (NLP) et aux algorithmes d’apprentissage automatique. La s´election automatis´ee des CV utilise diverses techniques pour analyser les CV et les lettres de motivation. La correspondance des mots cl´es consiste `a analyser les documents `a la recherche de mots cl´es correspondant aux exigences du poste, ce qui permet d’identifier rapidement les candidats ayant les comp´etences et les qualifications n´ecessaires. L’analyse de sentiment, en utilisant le NLP. La correspondance des qualifications utilise des algorithmes d’apprentissage automatique pour analyser les parcours ´educatifs et les exp´eriences professionnelles, afin de d´eterminer si les candidats remplissent les qualifications minimales. L’attribution de scores objectifs attribue des scores en fonction du degr´e de correspondance entre les candidats et les exigences du poste, facilitant la priorisation efficace des candidats qualifi´es pour un examen ult´erieur. En tirant parti de l’IA et de techniques telles que la correspondance des mots cl´es, l’analyse de sentiment, la correspondance des qualifications et l’attribution de scores objectifs, les organisations peuvent optimiser leurs processus de s´election des CV. Ces m´ethodes bas´ees sur l’IA simplifient l’´evaluation des candidats, am´eliorent l’efficacit´e et renforcent l’efficacit´e globale des processus de recrutement.en_US
dc.language.isoenen_US
dc.subjectResume Screeningen_US
dc.subjectRecruitment with AIen_US
dc.subjectMachine Learningen_US
dc.subjectNLPen_US
dc.subjectKeyword Matchingen_US
dc.subjectSentiment Analysisen_US
dc.titleResume Screening Using NLP and Machine Learningen_US
dc.typeThesisen_US
Appears in Collections:Master

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