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Journal articles on the topic 'Resume Analysis'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Saurabh Yadav, Sushrut ursal, Aadesh Thade, Sonali Tate, and Prof. Minal Nerkar. "Resume Analysis Using NLP and ATS Algorithm." International Journal of Latest Technology in Engineering Management & Applied Science 14, no. 4 (2025): 761–67. https://doi.org/10.51583/ijltemas.2025.140400090.

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Abstract: In today’s competitive job market, efficient and accurate resume screening is crucial for recruiters and hiring managers. Traditional manual resume review processes are time-consuming and prone to human error, which can lead to overlooking qualified candidates. This project aims to develop an automated system for resume analysis using Python, Natural Language Processing (NLP), and Applicant Tracking System (ATS) algorithms. The proposed solution leverages NLP techniques to extract key information from resumes, such as personal details, educational background, work experience, and ski

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Karhale, Prof Shivani. "Resume Analysis System Using Natural Language Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49872.

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Abstract : In the era of digital recruitment, organizations face the challenge of processing thousands of resumes efficiently and accurately. Manual resume screening is not only time-consuming but also prone to human error and bias. To address these issues, this project presents a Resume Analysis System using Natural Language Processing (NLP), which automates the process of analyzing and filtering resumes based on job requirements. The system leverages advanced NLP techniques such as tokenization, part-of-speech tagging, named entity recognition, and TF-IDF (Term Frequency-Inverse Document Fre

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Pawade, Premchand. "Resume Analysis System Using Natural Language Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49553.

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Abstract This project develops a resume analysis system using Natural Language Processing (NLP) to streamline hiring. Companies often receive large volumes of resumes, making it challenging to identify the best candidates quickly. Manually screening resumes is time-consuming, can be inconsistent, and may overlook key details. By automating this process with NLP, our system reads and evaluates resumes efficiently. It extracts key information like skills, experience, and education to match candidates to job requirements. For example, if a job requires a specific skill, the system can highlight c

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Karunamurthy, Dr A. "AI-Powered Resume Analysis Using SpaCy for Skill Extraction and Job Matching." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03528.

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Abstract -In today's competitive job market, recruiters face the challenge of efficiently sifting through vast volumes of resumes to identify the best candidates for open positions. Traditional keyword-based filtering methods are often inadequate in identifying the nuances of skills and experiences required for specific job roles. This project presents an AI-powered resume analysis system using the SpaCy natural language processing (NLP) library to enhance the accuracy of resume screening and job matching. Leveraging SpaCy's advanced capabilities, including Named Entity Recognition (NER), sema

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S, Siva Sakthii U., Neena Murali, Pradeepa M, Puspha Kumari R, and Janani D. "AI-Powered Resume Analyzer." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 07 (2025): 1–9. https://doi.org/10.55041/ijsrem51340.

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In the competitive job market, aligning resumes with job requirements is critical for job seekers. This project presents an AI-powered Resume Analyzer that automatically parses resumes, evaluates their quality, and compares them with job descriptions to suggest improvements. Key features include skill extraction, job role recommendations, resume scoring, and AI-powered gap analysis using Google Gemini API. The application is built using Flask and deployed locally, offering a user-friendly interface for both candidates and administrators. Keywords— Resume Parsing, Flask, Gemini API, Skill Gap A

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Prasad, Potu Ram, Mutyala Uday Kiran, and V. Subapriya. "AI Integrated Holistic Resume Analysis." IOSR Journal of Computer Engineering 27, no. 1 (2025): 01–06. https://doi.org/10.9790/0661-2701010106.

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Holistic resume analysis is a comprehensive evaluation process that examines a candidate's entire resume, rather than focusing solely on individual components such as education, experience, or skills. This approach integrates various elements to paint a complete picture of the candidate's professional identity and potential. By considering factors such as formatting, language, and personal branding alongside traditional qualifications, holistic analysis provides valuable insights into how effectively a resume communicates the candidate's value proposition to potential employers. It emphasizes

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Samruddhi, Farsole Sagar Darne Kunal Barahate Vaishnavi Bhute Rhutik Khode Minal Pazare Prof. R. V. Chaudhari. "Modern Real - Time Resume Analysis and Job Suggestion System Using NLP and Machine Learning Algorithm." International Journal of Advanced Innovative Technology in Engineering 10, no. 2 (2025): 134–37. https://doi.org/10.5281/zenodo.15422895.

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This paper is about in today's highly competitive job market job seekers face significant challenges in optimizing their resumes to pass Applicant Tracking Systems (ATS) and align with job requirements. Many resumes are rejected due to missing keywords, improper formatting, or a lack of ATS-friendly structures, making it difficult for qualified candidates to secure interviews. To address this issue, we present an AI-powered resume analysis system that enhances job matching efficiency by leveraging natural language processing (NLP) and machine learning. This system extracts key skills, qualific

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Sinha, Arvind Kumar, Md Amir Khusru Akhtar, and Mohit Kumar. "Break Down Resumes into Sections to Extract Data and Perform Text Analysis using Python." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 6s (2023): 391–400. http://dx.doi.org/10.17762/ijritcc.v11i6s.6945.

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The objective of AI-based resume screening is to automate the screening process, and text, keyword, and named entity recognition extraction are critical. This paper discusses segmenting resumes in order to extract data and perform text analysis. The raw CV file has been imported, and the resume data cleaned to remove extra spaces, punctuation and stop words. To extract names from resumes, regular expressions are used. We have also used the spaCy library which is considered the most accurate natural language processing library. It includes already-trained models for entity recognition, parsing,

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M.L, Pravesh, Samhita R, Mythili M, and Suprith S. "RESUME ANALYZER." International Research Journal of Computer Science 9, no. 8 (2022): 250–55. http://dx.doi.org/10.26562/irjcs.2022.v0908.19.

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Resume analysis is the process in which a machine analyses a resume based on given requirements of the job description. With the flood of resumes received by companies, it is not effective and also not possible for a person to go through a number of resumes to select a candidate. They have become very popular among the companies in the process of determining candidate selection. The main objective of the project is to be able to match the requirements and skills from a job description to the resume applied. This gives an instantaneous result on whether the resume is accepted or rejected. The e

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Chai-Adisaksopha, Chatree, Christopher M. Hillis, Daniel M. Witt, Sam Schulman, Mark Crowther, and Alfonso Iorio. "Warfarin Resumption after Intracranial Hemorrhage: A Systematic Review and Meta-Analysis." Blood 128, no. 22 (2016): 3821. http://dx.doi.org/10.1182/blood.v128.22.3821.3821.

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Abstract Objective: To assess the effect of warfarin resumption in patients who experienced warfarin-associated intracranial hemorrhage. Study design: Systematic review and meta-analysis Data sources:We searched MEDLINE (1966 to July 2016), EMBASE (1980 to July 2016) and Cochrane Library electronic database (up to July 2016). Inclusion criteria:Studies were eligible for inclusion if (1) they were randomized controlled trials, prospective cohort or retrospective cohort studies, (2) studies that included adult patients (≥18 years), (3) studies that investigated the patients who experienced warfa

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Prakash, Sakshi,. "Resume Analysis Using Machine Learning and Natural Language Processing." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31638.

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The recent recruitment process has changed from manually to computerized. The majority of resume analysis is carried out utilizing Natural Language Processing and Machine learning. It deals with selecting the best candidates from an enormous pool of applicants who have the necessary skills for a certain job description.By leveraging these NLP capabilities provided by NLTK, resume analysis systems can automate the extraction, classification, and evaluation of information from resumes, enabling recruiters to efficiently screen and evaluate candidates based on their qualifications, skills, experi

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Levina, Tatiana M., Elina I. Almukhametova, Polina S. Kobzeva, Maria V. Troshina, and Almas V. Gafarov. "SOFTWARE MODULE FOR PROCESSING JOB APPLICANTS’ RESUMES." Electrical and data processing facilities and systems 21, no. 1 (2025): 141–48. https://doi.org/10.17122/1999-5458-2025-21-1-141-148.

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Relevance In the conditions of the modern labor market, there is a constant increase in the number of job seekers. At the same time, companies are faced with a huge flow of resumes. Manual processing of such a volume of information becomes a difficult task due to limited time and resources. Increasing competition in the labor market forces enterprises to focus on effective recruitment, which includes not only the selection of suitable candidates, but also the fast and high-quality processing of their resumes. Resume processing is often done manually, which requires significant time. This appro

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Sruthi, Patlolla, P. N. V. K. G. Adithya, M. D. Suleman, Pallerla Kunal, and Surya Prakash Gairola. "Smart Resume Analyser: A Case Study using RNN-based Keyword Extraction." E3S Web of Conferences 430 (2023): 01023. http://dx.doi.org/10.1051/e3sconf/202343001023.

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The pursuit of a reputable position in a company is a common goal shared by many people. For making this desire into reality their resume is the key to it. Making these resumes is a challenging task for many and modifying it is another time-consuming and burdensome task. Taking these problems into consideration and to make eye-catching resumes we have developed a smart resume analyser that can analyse the user’s resume, it intelligently identifies their skills and qualifications, enabling us to suggest the best-suited job titles for their profile. Furthermore, based on this analysis, our syste

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Rebrii, Maksymilian S., and Svitlana L. Zinovatna. "Candidates resume analyzer using artificial intelligence." Informatics. Culture. Technology 1, no. 1 (2024): 139–46. http://dx.doi.org/10.15276/ict.01.2024.20.

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In the modern recruitment process, where the number of candidates and job vacancies constantly increases, automating resume analysis becomes critically important for efficient selection. This paper focuses on developing a system for automated resume analysis using artificial intelligence, specifically the OpenAI model (ChatGPT). The paper thoroughly examines the system's components, structure, and functional capabilities. The system consists of several key modules: the user interface ensures interaction with users, the Application Programming Interface Gateway facilitates data exchange between

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DiMarco, John, and Sofia Fasos. "Resume content research across disciplines: an analysis of ProQuest from 1984-2018." Electronic Library 38, no. 1 (2019): 81–94. http://dx.doi.org/10.1108/el-07-2019-0175.

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Purpose The purpose of this study is to offer original analysis to examine the prevalence of publication titles, subtopics and methods present in peer-reviewed articles containing the search term “resume content” in ProQuest Central. Design/methodology/approach As a means for understanding better the scope of empirical studies in resumes, a limited search was conducted in ProQuest to build a data set of research articles under the limited search heading of “resume content”. Using ProQuest Central, a popular repository of peer reviewed, indexed articles for database searches in academic and ins

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Jafari, Faizan Ali. "Automated Resume Screening System Using NLP and Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48082.

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Abstract—Modern recruitment struggles with the inefficiencies of manual resume screening, a process often slow, error-prone, and biased. We present an AI-powered system that integrates natural language processing (NLP) and machine learning (ML) with a MERN stack platform to automate resume extraction, analysis, and ranking. Using a dataset of resumes from diverse sources, we employed advanced NLP techniques—such as named entity recognition—and ML models like logistic regression and random forests to rank candidates efficiently. Integrated with a scalable MERN stack, the system offers recruiter

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Pardesi, M. A. "Resume Analyzer with a Mentor." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 02 (2025): 1–9. https://doi.org/10.55041/ijsrem41475.

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Resume Analyzer with Mentor is designed to analyse resumes and provide recommendations to users. By analysing the resume, it offers personalized suggestions for improvement. Additionally, we provide a mentor for users to receive the right guidance. We use various machine learning libraries to enhance the accuracy of the analysis. For the mentor system, we created a chat application using the MERN stack, allowing users to interact with mentors. We offer two plans for users: free and paid. In the free plan, users receive accurate analysis and recommendations. In the paid plan, users receive both

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Afifah, Zahra Nur. "The Incomplete Filling in The Medical Resume Form of Inpatients at The Regional General Hospital." Jurnal Ilmiah Kesehatan Media Husada 10, no. 2 (2021): 142–47. http://dx.doi.org/10.33475/jikmh.v10i2.272.

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Preliminary: A medical resume is a summary of medical service activities provided by health workers, especially doctors during the treatment period until the patient is discharged either in good health or in death. Incomplete filling of medical resumes can be caused by negligence of officers or lack of understanding of the importance of completing patient data. This research is a descriptive research with a qualitative approach. In this study there were 4 medical recorder and 50 medical resume form. Method: The sampling technique used is the total sample. The data analysis used is descriptive

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Raghupathi Kanala, Meghana Vijaya Raghavan, Gundala Jathin Kumar, Sushruth Bommagoni, and Arun Kumar Onteddu. "Context aware intelligence resume analyser using deep learning algorithms." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 738–46. https://doi.org/10.30574/wjaets.2025.15.2.0588.

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This research introduces an AI-powered resume analyzer that makes it easier for people to evaluate and polish their resumes. Using cutting-edge Natural Language Processing (NLP), deep learning, and powerful Large Language Models (LLMs), the system quickly pulls out key details—like skills, education, work experience, and projects—from resumes in formats like PDF or DOCX. Unlike older resume tools, this one is faster and smarter, offering unique features like a Skill Gap Analysis to see how well your qualifications match a job’s needs. It also includes a neural network that ranks your resume’s

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Sanyal, Kaustav. "Intelligent Resume Parsing and Job Recommendation via Web-Based CV Analysis System." International Journal for Research in Applied Science and Engineering Technology 13, no. 5 (2025): 2291–300. https://doi.org/10.22214/ijraset.2025.70746.

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The process of matching job applicants to suitable job openings is typically inefficient due to unorganized resume formats and absence of customized job search facilities. This paper presents a light-weight web-based system that automatically extracts structured information from PDF resumes and recommends suitable job openings based on publicly available job search APIs. The system is based on natural language processing-based data extraction, stores the extracted information in a CSV database, and builds context-sensitive job queries. A basic web interface built on top of Flask supports PDF u

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Dr. J. JayaPriya, Mouleeswaran R, Kishore T, Praveen G, and Arjun N. "Smart AI Resume Analyzer." International Journal of Scientific Research in Science, Engineering and Technology 12, no. 3 (2025): 879–83. https://doi.org/10.32628/ijsrset2512147.

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In today’s technology-driven recruitment ecosystem, job seekers face increasing challenges due to the widespread use of Applicant Tracking Systems (ATS) by employers. These systems filter out resumes that do not meet specific formatting or keyword criteria, often discarding qualified candidates in the process. The Smart AI Resume Analyzer is an innovative solution developed to bridge this gap by providing an intelligent platform that evaluates and enhances resumes using Natural Language Processing (NLP) and machine learning. The platform performs real-time scoring of resumes based on keyword r

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Muhammad, Savad N., and T. Preethi. "Resume screener system." i-manager's Journal on Computer Science 11, no. 3 (2023): 47. http://dx.doi.org/10.26634/jcom.11.3.20482.

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This research paper introduces a state-of-the-art "Resume Screener System" aimed at revolutionizing and automating the labor-intensive task of resume analysis for recruitment purposes. Developed using Python, the system integrates Artificial Intelligence and Natural Language Processing techniques to streamline the hiring process. Utilizing a dataset sourced from Kaggle, comprising a thousand resumes converted into textual data, the system undergoes comprehensive model training and evaluation. Employing advanced machine learning methodologies such as the Support Vector Classifier (SVC) and Neig

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Anisafitri, Azizah. "THE RELATIONSHIP OF CHARACTERISTICS OF MEDICAL DOCTORS IN CHARGE OF SERVICE (DPJP) TO THE COMPLIANCE OF MEDICAL RESUME FILLING OF SOCIAL SECURITY HEALTH CARE AGENCY (BPJS) PATIENTS (Study at inpatient installation Jemursari Islamic Hospital Surabaya)." Indonesian Journal of Public Health 14, no. 1 (2019): 1. http://dx.doi.org/10.20473/ijph.v14i1.2019.1-12.

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A medical resume is an important document containing a summary of patient care while they are in hospital. The filling of the medical resume affects the claim process of the National Health Insurance (NHI) program. Medical resume is filled by Medical Doctor in Charge (MDiC). A preliminary study at private hospital “RSI Jemursari Surabaya” showed 86,67% incomplete BPJS claim files because there were no medical resumes. The purpose of this study was to analyze the level of medical obedience in filling medical resume and the factors that influenced based on individual characteristics. It was a de

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Yunita, Nirma, Galih Persadha, and Husin Husin. "Uji Validasi Rancangan Aplikasi Kelengkapan Resume Medis di Klinik Almeta Medika." Jurnal Kesehatan Indonesia 14, no. 1 (2024): 31. http://dx.doi.org/10.33657/jurkessia.v14i1.914.

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The quality of medical records becomes more valuable if the information written is complete, especially on the medical resume. Complete medical resume writing at Almeta Medika Clinic Banjarmasin has not been carried out, this causes patient information to be incomplete. So a complete medical resume application was designed. This research aims to validate the complete medical resume application at the Almeta Medika Clinic, Banjarmasin. This research method is descriptive qualitative, collecting data using questionnaires and checklists. The subjects in this research were medical records officers

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Rasal, Pratham. "Resume Parser Analysis Using Machine Learning and Natural Language Processing." International Journal for Research in Applied Science and Engineering Technology 11, no. 5 (2023): 2840–44. http://dx.doi.org/10.22214/ijraset.2023.52202.

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Abstract: With the rise of online job application processes, submitting a resume has become easier than ever before. As a result, a larger number of individuals are impacted by this change. Many organizations still accept resumes by mail, which can cause challenges for their human resources departments. Sorting through a large number of applications to find the most suitable candidates can be a time-consuming process for these agencies. Job applicants submit resumes in a wide range of formats, including various fonts, font sizes, colors, and other design elements. Human resources departments a

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Deeptha, R., Dey Abirup, G. S. G. R. Gowtham, and Tripathi Abhishek. "Resume screening using NLP." i-manager's Journal on Information Technology 13, no. 3 (2024): 37. https://doi.org/10.26634/jit.13.3.21339.

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Resume screening is a critical step in the recruitment process, traditionally relying on manual review to assess candidates' qualifications. The advent of Natural Language Processing (NLP) has introduced advanced techniques to enhance this process by automating and optimizing resume evaluation. This paper explores the application of NLP in resume screening, focusing on methods such as keyword extraction, semantic analysis, and machine learning models. It discusses how NLP algorithms can identify relevant skills, experiences, and qualifications by analyzing the textual content of resumes. Furth

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Rosadi, Imam, and Rindiani Indah Alysia. "Completeness of inpatient medical resume filling." KESANS : International Journal of Health and Science 1, no. 6 (2022): 611–24. http://dx.doi.org/10.54543/kesans.v1i6.67.

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Medical resume is an important and basic sheet in inpatient medical records so it needs to be filled out completely because it is important in the process of patient treatment, insurance claims, compliance with accreditation standards, and legal evidence. However, in reality there are still many medical resumes that have not been filled out completely. The purpose of this study was to determine and the causes of incomplete filling of inpatient medical resumes. This study uses a literature review method that analyzes the results of six previous studies with relevant themes. Based on the results

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Shivhare, Kratika. "ResumeCraft: A Machine Learning-powered Web Platform for Resume Building." International Journal for Research in Applied Science and Engineering Technology 12, no. 5 (2024): 1154–66. http://dx.doi.org/10.22214/ijraset.2024.61731.

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Abstract: The competitive job market necessitates well-crafted resumes that resonate with both human recruiters and Applicant Tracking Systems (ATS). This paper introduces ResumeCraft, a web-based platform empowering users to build strong resumes and optimize them for ATS compatibility. ResumeCraft leverages Machine Learning (ML) for data analysis and user guidance, while the user interface is built with Hypertext Markup Language (HTML), Cascading Style Sheets (CSS), and JavaScript for a user-friendly experience. The system allows users to input their personal and professional details through

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CHARAN, KONKI, and RONGALA RAJESH. "Automated Resume Screening Using Machine Learning." International Scientific Journal of Engineering and Management 04, no. 07 (2025): 1–9. https://doi.org/10.55041/isjem04860.

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The exponential growth of digital job applications has posed significant challenges for recruiters, who often face the daunting task of manually screening thousands of resumes to identify suitable candidates. This research addresses these challenges by proposing an automated resume classification system leveraging Natural Language Processing (NLP) and machine learning techniques. The proposed system integrates comprehensive text preprocessing, feature extraction using Term Frequency–Inverse Document Frequency (TF-IDF), and a One-vs-Rest K-Nearest Neighbors (KNN) classifier to categorize resume

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Gopi Krishna, A. K., P. Koursar Basha, Suneetha Yadav, and G. Kiran Kumar Reddy. "Assessment on recruiters judgements in the resumes of engineering students in campus recruitment." Journal of Management Research and Analysis 11, no. 2 (2024): 99–108. http://dx.doi.org/10.18231/j.jmra.2024.018.

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Recruitment of applicant is based on the personality perceptions. In this analysis we have examined the personality perceptions in resumes of engineering students within the completion of engineering course. This study focussed on the relation of impression management theory’s principle of self-presentations strategy with resumes. Specific personality perceptions of resumes were assessed to judge the hire-ability level of engineering students. In the process of assessment, this study focuses on three significant areas of resume such as educational qualifications, personal information and resum

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Serikov, A. E., and G. A. Abitova. "Information technology for personality prediction based on resume analysis for HR companies." Bulletin of Shakarim University. Technical Sciences, no. 2(10) (July 1, 2023): 45–50. http://dx.doi.org/10.53360/2788-7995-2023-2(10)-6.

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This article presents the development of an information technology solution for personality prediction based on resume analysis for HR companies. The purpose of this study is to investigate the feasibility of using machine learning techniques to analyze resumes and predict personality traits of candidates for recruitment purposes. The methodology involved collecting a large dataset of resumes and using natural language processing techniques to extract relevant features and train a deep learning model. The results show that the developed solution achieves high accuracy in predicting personality

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A .Tejaswini, Mrs. "AUTOMATED RESUME SCREENING USING NLP AND MACHINE LEARNING." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03751.

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ABSTRACT: This project presents a machine learning-based resume screening system that extracts IT hard skills, soft skills, education, and experience from resumes and job descriptions. Using algorithms like Support Vector Classifier (SVC) and Random Forest, the system ensures accurate skill extraction and semantic matching. It provides personalized recommendations by highlighting key skills and relevant experience, helping job seekers make informed career decisions.The system supports multiple resume formats and adapts to changing market trends. A bidirectional semantic matching approach is us

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Susanti, Ari, Atika Mima Amalin, Hanna Miftachul Khoir, Fifin Alfiatur Rosida, Ahmad Chudayfi, and Dominikus Sukma Adi. "Analisis Kelengkapan Kuantitatif Berkas Resume Medis di Rumah Sakit Angkatan Laut Dr. Ramelan Surabaya." Jurnal Kesehatan Indonesia 14, no. 1 (2024): 35. http://dx.doi.org/10.33657/jurkessia.v14i1.863.

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A complete medical record form is an indicator of good medical record management and directly affects service quality. Analysis of the completeness of medical record files at Dr. Ramelan Surabaya found that the completeness of the medical resume had not reached 100%, only 90% completeness. The purpose of this study was to analyze the completeness of medical resume files at Dr. Ramelan Surabaya with descriptive method. Data collection used the cross sectional method in September 2022 as many as 21 medical resume forms. The results showed that the identification review obtained 100% complete in

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Gopalakrishna, Suhas Tangadle, and Vijayaraghavan Varadharajan. "Automated Tool for Resume Classification Using Sementic Analysis." International Journal of Artificial Intelligence & Applications 10, no. 01 (2019): 11–23. http://dx.doi.org/10.5121/ijaia.2019.10102.

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Athawale, Deepa. "AUTOMATED TOOL FOR RESUME CODIFICATION USING SEMENTIC ANALYSIS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem31646.

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Agencies and colorful hıgh- position fırms must deal with a large number of new jobs seeking people with varıous resumes. still, managing large quantities of textbook data and opting the best-fit candıdate is more diffıcult and time- consuming. This paper provıdes an overvıew of an ongoing Informatıon Extractıon System design that helps recruiters in ıdentifying the stylish candıdate by rooting applicable informatıon from the capsule. This design presents a system that uses Natural Language Processing (NLP) technıques to prize nanosecond data from a capsule, similar as education, experıence, s

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Mohamed, Mimi Nahariah Azwani, Muhammad Nadzrin Khairuddin, and Mei Ph’ng Lee. "Exploring Students’ Written Discourse in Resumes: Unveiling Insights for Enhancing Social Intelligence." SHS Web of Conferences 182 (2024): 04007. http://dx.doi.org/10.1051/shsconf/202418204007.

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Social intelligence empowers individuals not only to comprehend and interpret written discourse but also to respond effectively and appropriately. In the context of job applications, the ability to demonstrate the presence of social intelligence through documents such as resumes can significantly persuade recruiters’ interest. However, many future graduates are not concerned about producing impactful resumes. The way a job advertisement is presented can affect how a resume is crafted. This paper explores how future graduates interpret job advertisements and shape their resumes. One postgraduat

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Hatti, Santosh Kumar. "Efficient Resume Screening Tool." International Journal for Research in Applied Science and Engineering Technology 13, no. 1 (2025): 483–88. https://doi.org/10.22214/ijraset.2025.66307.

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The "Efficient Resume Screening Tool" employs Natural Language Processing (NLP) and Machine Learning (ML) to transform the traditional resume screening process. By automating the analysis of resumes, it identifies candidates best suited for specific job roles, significantly reducing the time and effort associated with manual screening while improving accuracy and consistency. The tool incorporates features such as skill categorization, web-based visualization, and automated report generation, catering to diverse recruitment needs. Using advanced NLP techniques, it extracts, analyzes, and ranks

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Handayuni, Linda, Ririn Afrima Yenni, Dewi Mardiawati, and Melda Yenti. "LITERATUR REVIEW TENTANG SIKAP DAN PENGETAHUAN PETUGAS MEDIS TERHADAP KELNGKAPAN PENGISIAN RESUME MEDIS RAWAT INAP." Ensiklopedia of Journal 4, no. 4 (2022): 9–13. http://dx.doi.org/10.33559/eoj.v4i4.1160.

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A medical resume is a summary of all services received by patients during treatment or treatment carried out by health workers. The incompleteness of filling in inpatient medical resumes in hospitals still exists such as lack of attitude and knowledge of health workers, busyness of doctors, absence of SOPs, disagreement with the time limit for filling out medical resumes, which is 1x24 hours. The purpose of this study was to describe the attitudes and knowledge of medical officers to the completeness of filling out an inpatient medical resume. The data used is secondary data, then the data is

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Grigor’ev, Dmitrij M., and Mariya A. Grigor’eva. "THE USAGE OF MATHEMATICAL METHODS IN ANALYSIS OF SUITABILITY OF AN APPLICANT FOR THE CONSIDERED POSITION." Technologies & Quality 64, no. 2 (2024): 51–58. http://dx.doi.org/10.34216/2587-6147-2024-2-64-51-58.

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Is it possible to predict the long-term nature of labor relations and correctly assess the expediency of investing material resources and non-material benefits in a candidate for a position? Is it possible to abandon the psychological aspect when considering an applicant and focus only on the mathematical and logical indicators of his resume? This article considers the possibility of usage of mathematical methods in the primary analysis of a candidate’s compliance with the requirements of the position. Current article deals exclusively with the mathematical aspect and proposes a methodology fo

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Tarun.B, Mohamed Fasidh, and Mrs.S. Nithya. "Job Screen AI – Automated Resume Screening system." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 03 (2025): 882–85. https://doi.org/10.47392/irjaem.2025.0143.

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This paper presents an Automated Resume Screening System utilizing AI model to enhance recruitment efficiency. The system automates the process of evaluating resumes against job descriptions, reducing manual effort and improving hiring accuracy. By leveraging AI for analysis, the system provides timely feedback to applicants and assists employers in selecting the most suitable candidates. The paper discusses the literature survey, methodology, and potential enhancements for AI-driven recruitment systems.

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Raut, Kishor. "Survey Paper on Resume Building Applications." International Journal for Research in Applied Science and Engineering Technology 10, no. 1 (2022): 380–81. http://dx.doi.org/10.22214/ijraset.2022.39779.

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Abstract: Nowadays getting a good job is a vigorous and vast competition and many fail in the first step i.e., Resume shortlisting due to either imperfect data in the resume or imperfect/wrong resume format. Recruiter hardly takes 10-15 seconds to judge you upon your resume. In this survey paper, we point out a comparative study on different methods used for resume building and which technology is used to build them. Some of the methods use Android applications, some use Desktop applications. This paper makes a detailed analysis and talks about the merits and demerits of various Resume buildin

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H M, Chandana. "Career Compass: Navigating to the Right Job with Machine Learning Precision." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49618.

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Abstract - Job recommendation systems are critical tools in bridging the gap between job seekers and employers by automatically suggesting suitable opportunities. This paper explores a resume- and skill-based job recommendation system using a hybrid approach combining machine learning, deep learning, and NLP techniques. The proposed system parses user resumes to extract structured skill sets and work experience using NLP. Then, it applies transformer-based models like BERT for contextual embedding of resume and job descriptions to calculate similarity scores. Additionally, knowledge graphs are

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Ponulak, Filip. "Analysis of the ReSuMe Learning Process For Spiking Neural Networks." International Journal of Applied Mathematics and Computer Science 18, no. 2 (2008): 117–27. http://dx.doi.org/10.2478/v10006-008-0011-1.

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Analysis of the ReSuMe Learning Process For Spiking Neural NetworksIn this paper we perform an analysis of the learning process with the ReSuMe method and spiking neural networks (Ponulak, 2005; Ponulak, 2006b). We investigate how the particular parameters of the learning algorithm affect the process of learning. We consider the issue of speeding up the adaptation process, while maintaining the stability of the optimal solution. This is an important issue in many real-life tasks where the neural networks are applied and where the fast learning convergence is highly desirable.

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Sujitha, M., and Dr T. V. .Ananthan. "Skill Gap Analysis Using Machine Learning." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem42529.

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The “Skill Gap Analysis Using Machine Learning” project aims to bridge the gap between user skillsets and industry requirements.It examines user resumes, finds skill gaps, and gives a path for skill improvement using Natural Language Processing (NLP) and machine learning techniques. Additionally, the system suggests resources for interview preparation based on the professional domains that the user has chosen. This platform supports firms in workforce development while providing users with practical insights for career growth. KEYWORDS: Skill Gap Analysis, Machine Learning, Natural Language Pr

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Kangude, Snehal, Aakansha Karwande, Samrudhi Kulkarni, and Rasika Pagare. "Aspire-Hub: All in one Career Readiness App." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 11 (2024): 1–8. http://dx.doi.org/10.55041/ijsrem38462.

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In today's competitive job market, effective preparation is crucial for securing desired roles. This project presents an innovative app designed to assist students and job seekers through every phase of their placement journey. Leveraging machine learning and AI, the platform offers a personalized experience, starting with seamless registration and skill gap analysis. Based on this, the app generates tailored learning modules and practice tests to enhance employability. A built-in resume builder helps users craft professional resumes, while the AI-driven mock interview feature evaluates respon

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Khaire, Prof Sneha A. "Review on Resume Analysis and Job Recommendation using AI." International Journal for Research in Applied Science and Engineering Technology 9, no. 5 (2021): 1221–24. http://dx.doi.org/10.22214/ijraset.2021.34461.

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Walraevens, Joris, Bart Steyaert, and Herwig Bruneel. "Analysis of a discrete-time preemptive resume priority buffer." European Journal of Operational Research 186, no. 1 (2008): 182–201. http://dx.doi.org/10.1016/j.ejor.2007.01.028.

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Alderham, Asrar Hussain, and Emad Sami Jaha. "Improved Candidate-Career Matching Using Comparative Semantic Resume Analysis." Advances in Science, Technology and Engineering Systems Journal 9, no. 1 (2024): 15–22. http://dx.doi.org/10.25046/aj090103.

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M., Mrs Khamar. "SKILL SYNC SELECTOR USING MACHINE LEARNING TECHNIQUES." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 04 (2024): 1–5. http://dx.doi.org/10.55041/ijsrem29959.

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Our study introduces an Automatic Resume Scanner Application that makes an advantage of advanced natural language processing (NLP). Together with performing thorough linguistic analysis and effectively standardizing resumes, it leverages state-of-the-art natural language processing (NLP) algorithms for sentiment analysis, context interpretation, and profile ranking. Using machine learning algorithms, it matches candidate qualifications with job demands through an intuitive interface that allows criteria to be customized. The application additionally manages bias detection and reduction in orde

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Roy, Ria, and Dr Nadindla Srividya. "A Enhanced Process for Examining Resumes and Organizing Candidates Through Statistical Methods." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38029.

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Selecting suitable applicants is an important part of the screening process in a recruitment consulting firm. In general, traditional recruiting practices are used by the majority of businesses and consulting organizations. The primary strategy is employing resume-based screening techniques to attract exceptionally skilled researchers and developers for high-tech enterprises. Typically, the organization evaluates the individual's qualifications, professional history, employment experience, motivations, specialist abilities, and past creative projects as shown in their résumé in order to select

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