Relevant bibliographies by topics / Enhance Applicant Tracking System (ATS)

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  3. Conference papers

Academic literature on the topic 'Enhance Applicant Tracking System (ATS)'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Enhance Applicant Tracking System (ATS)"

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Rasika, Patil. "Transitioning from Workday Recruiting to Eightfold ATS: Implementation Strategies and Best Practices." International Journal of Leading Research Publication 3, no. 11 (2022): 1–8. https://doi.org/10.5281/zenodo.14646753.

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Organizations face numerous challenges when transitioning from one Applicant Tracking System (ATS) to another, especially when moving to more advanced, AI-powered systems. This paper outlines the process of migrating from Workday Recruiting to Eightfold ATS, with a focus on implementation phases, strategies for a smooth transition, and best practices for leveraging Eightfold's capabilities to enhance the recruitment process. Additionally, lessons learned from migration are discussed. Eightfold’s AI-driven platform is designed to modernize recruitment through machine learning and deep ana
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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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N, Sasikumar. "AI-Powered Opportunity Crafter: Unlocking Smart Career Possibilities." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 5032–37. https://doi.org/10.22214/ijraset.2025.68844.

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The job market is highly competitive, making it challenging for job seekers, including students, fresh graduates, and professionals, to secure employment. Traditional job search methods lack efficiency, personalization, and real-time feedback, often leaving candidates unprepared for Applicant Tracking Systems (ATS) and interviews. To address these challenges, we propose an AI-Powered Opportunity Crafter, an intelligent job assistant that leverages Artificial Intelligence (AI), Natural Language Processing (NLP), and Machine Learning (ML) to enhance job seekers' success rates., The system consis
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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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Bhavsar, Dr S. A. "Automated Resume Parsing using Name Entity Recognition." International Scientific Journal of Engineering and Management 04, no. 05 (2025): 1–7. https://doi.org/10.55041/isjem03365.

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Abstract - The traditional hiring process often involves manually reviewing numerous resumes, making recruitment time-consuming and costly. To address this challenge, we propose an Automated Resume Parsing System using Named Entity Recognition (NER), an advanced Natural Language Processing (NLP) technique. Our system efficiently extracts key information, such as candidate names, skills, education, and work experience, from unstructured resume data, enabling structured representation and faster decision-making. By automating resume screening, our approach significantly reduces hiring costs and
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Sujit Das, Dr, Ananya S Nair, and Pattela Aneesh. "AI Resume Analyzer: Smart Resume Evaluation and Enhancement." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44548.

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This paper presents an AI-driven resume analyzer designed to streamline and enhance the job candidate assessment process. The system utilizes Natural Language Processing (NLP) techniques and machine learning algorithms to process and evaluate resumes efficiently. By extracting key information, calculating an ATS compatibility score, identifying grammar errors, and providing improvement suggestions, the analyzer aims to assist both job seekers and recruiters optimize the resume screening process. The platform also evaluates resumes against job-specific requirements and offers curated resources,
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Rajeswari, Dr PVN. "An Artificial Intelligence Powered Interview Preparation System using MERN Stack." International Journal for Research in Applied Science and Engineering Technology 13, no. 4 (2025): 7018–22. https://doi.org/10.22214/ijraset.2025.70056.

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The Artificial Intelligence Preparation System is designed to support job seekers by enhancing their aptitude, data structures, and algorithm (DSA) skills while also providing AI-powered resume optimization and mock interviews. In today’s competitive job market, candidates often face challenges in finding structured preparation resources, refining their resumes, and gaining realistic interview practice. This system tackles these issues using Machine Learning (ML) and Natural Language Processing (NLP) technologies. It features three main components: a Prebuilt Question Bank, an AI-Powered Resum
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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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Dubey, Ankit. "INTELLIPREP: AI Powered Interview Preparation and Resume Evaluator." International Scientific Journal of Engineering and Management 04, no. 04 (2025): 1–7. https://doi.org/10.55041/isjem02939.

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In today’s highly competitive job market, students face significant challenges in resume preparation and interview readiness due to the lack of structured tools and personalized feedback. Traditional methods are often time- consuming, inefficient, and fail to meet modern hiring expectations. Recruiters also encounter difficulties in resume screening, evaluation consistency, and interview scheduling. To address these challenges, IntelliPrep offers an AI-powered solution that automates key aspects of the interview preparation process. The platform integrates resume parsing and evaluation, utiliz
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Pandey, Shweta, and Sumit Pandey. "Bridging the Gap in Recruitment Integration: A Comprehensive Approach to Syncing Workday and Greenhouse for Effective Position Management." International Journal of Business Quantitative Economics and Applied Management Research 7, no. 4 (2022): 12–22. https://doi.org/10.5281/zenodo.14176030.

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In the era of digital transformation, organizations increasingly adopt cloud technologies such as 'Software as a Service' (SaaS) to enhance agility, lower costs, scale effectively, and reduce administrative efforts. SaaS solutions often come with built-in integrations with other SaaS offerings, minimizing the need for custom integrations. However, these integrations do not always address the real-world scenarios faced by different organizations. Workday and Greenhouse are two leading SaaS solutions used for Human Capital Management (HCM) and Applicant Tracking System (ATS), respectively. Many
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Book chapters on the topic "Enhance Applicant Tracking System (ATS)"

1

Pendse, Meenal K., Mihir Vaidya, and Mansi Kotak. "Present-Day Human Resource Management (HRM) Hiring Processes." In Prioritizing Skills Development for Student Employability. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-3571-0.ch003.

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Human resource management (HRM) hiring practices have changed substantially to accommodate new technologies, company needs, and applicant expectations. Technology-enabled methods have superseded or simplified traditional ones. This includes applicant tracking systems (ATS) to manage applications via AI-driven resume screening to find qualified candidates and video interviews for remote evaluation. To attract more talent, employer branding, application experience, and diversity and inclusion are prioritized. Current HRM hiring practices prioritize speed, diversity, and digital compatibility. Or
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Conference papers on the topic "Enhance Applicant Tracking System (ATS)"

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Leony, Rifma Dwi, Sisco Mataheru, Kevin Sanjay Sirait, and Teguh Prasandy. "Evaluation Analysis Of Applicant Tracking System (ATS) Implementation In Companies And Recruitment Agency." In 2024 Ninth International Conference on Informatics and Computing (ICIC). IEEE, 2024. https://doi.org/10.1109/icic64337.2024.10957063.

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AL-Qassem, Amer Hani, Kakul Agha, Mohit Vij, Hamzah Elrehail, and Ruchi Agarwal. "Leading Talent Management: Empirical investigation on Applicant Tracking System (ATS) on e-Recruitment Performance." In 2023 International Conference on Business Analytics for Technology and Security (ICBATS). IEEE, 2023. http://dx.doi.org/10.1109/icbats57792.2023.10111172.

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B, Jeevan, and Suguna A. "Resume Building Web Application." In International Conference on Recent Trends in Computing & Communication Technologies (ICRCCT’2K24). International Journal of Advanced Trends in Engineering and Management, 2024. http://dx.doi.org/10.59544/tmov9106/icrcct24p119.

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These days, landing a job is tough, and creating the perfect resume is often the biggest challenge for job seekers. A resume is usually the first thing employers look at when considering an applicant, and they use it to narrow down candidates before moving to interviews. This paper aims to present an easy and user friendly way to build resumes. We are introducing an app that helps users create resumes simply by providing their information. The resume builder app lets users log in or sign up using OTP verification and allow them to create, update, delete, view, and save their resumes in PDF for
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Journal articles
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