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@article{164365,
        author = {N. MARINBI and N. Hima sai and A. Ajay and K. Manasa and D. Sravani},
        title = {Resume screening and analysis},
        journal = {International Journal of Innovative Research in Technology},
        year = {},
        volume = {10},
        number = {12},
        pages = {945-947},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=164365},
        abstract = {Hiring the right talent is a challenge for all businesses. This challenge is magnified by the high volume of applicants if the business is labour-intensive, growing, and facing high attrition rates. companies often receive thousands of resumes for each job posting and employ dedicated screening officers to screen qualified candidates. The main aim of the resume screening is “task of selecting the best talent among many others is known as Resume Screening”. The complex endeavour of selecting the most suitable talent from a vast pool of applicants Therefore, standardized automated screening methods categorize qualified from unqualified candidates based on their background, education and professional experience faster, with more efficiency and more accurate results to streamline hiring processes. Artificial intelligence, along with text mining and natural language processing algorithms, can be applied for the development of programs capable of screening objectively thousands of resumes in few minutes without bias to identify the best fit for a job opening based on thresholds, specific criteria or scores. Typically, large companies do not have enough time to open each CV, so they use machine learning algorithms for the Resume Screening.These research' experimental findings show that the suggested strategies are more effective than conventional ones. The results of this study can help human resource managers and recruiters automate the hiring process and efficiently and impartially identify viable applicants},
        keywords = {},
        month = {},
        }