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    <journal>
        <name>International Journal of Research and Development in Engineering Sciences</name>
        <website>https://www.ijrdes.com</website>
    </journal>

    <metadata>
        <title>Automated Resume Screening  And Segmentation Using Natural Language Processing</title>

        <authors>
			<author><name>Peethala Sowjanya Yamini</name><name>D. Lalitha Bhaskari</name>     </author>
        </authors>

        <volume>8</volume>
        <issue>5 (September - October)</issue>

        <publication>
            <year>2026</year>
			<month>09</month>
			
			<period>September-October</period>
        </publication>

		<language>en</language><keywords><keyword>NLP</keyword><keyword>TF-IDF</keyword><keyword>Sentence-BERT</keyword><keyword>Applicant Tracking System</keyword><keyword>Explainable AI</keyword><keyword>Job Role Prediction.</keyword></keywords> 
    </metadata>

    <abstract>An AIbased system designed to simplify and improve the recruitment process through Artificial Intelligence and Natural Language Processing NLP techniques The system automatically extracts information from PDF resumes and compares it with a job description to identify the most suitable candidates It uses Term FrequencyInverse Document Frequency TFIDF for keyword extraction SentenceBERT SBERT for semantic similarity analysis and Applicant Tracking System ATS scoring for keywordbased evaluation A weighted scoring mechanism combines these measures to generate a final candidate score and rank applicants accordingly The system further segments resume content into sections such as skills education experience projects and certifications and includes an Explainable AI module that highlights missing skills and gives transparent reasons for each candidate score An AI Resume Suggestion module recommends improvements to resume quality and ATS compatibility while a Resume Quality Checker verifies the completeness of essential details such as contact information skills projects LinkedIn GitHub and certifications A classification model further predicts the most suitable job role from resume content All outputs are presented through an interactive Streamlit dashboard showing candidate rankings performance metrics and graphical visualizations The prototype was implemented using Python Streamlit spaCy SentenceTransformers Scikitlearn Pandas NumPy Matplotlib and pdf plumber and testing confirms that it reduces manual screening effort improves ranking accuracy over keywordonly ATS filtering and supports fair transparent and datadriven hiring decisions </abstract>

    <copyright>
        <statement>
            Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved.
        </statement>
        
            <year>2026</year>
        <license>All Rights Reserved</license>
    </copyright>

</article>
