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1
PORNPITAKPAN, Chanthika. "AI-Proof & SEO-Smart CVs: Tackling Socio-Legal Contexts in 2025." Global Empirical Marketing Studies 1, no. 1 (2025): e6.2025.05.02. https://doi.org/10.5281/zenodo.15320293.
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Citing This Article: APA Style: Pornpitakpan, C. (2025). AI-proof & SEO-smart CVs: Tackling socio-legal contexts in 2025. Global Empirical Marketing Studies, 1(1), Article e6.2025.05.02. https:p//doi.org/10.5281/zenodo.15320293 In the rapidly evolving job market of 2025, Artificial Intelligence (AI) and Search Engine Optimization (SEO) are transforming how candidates are discovered and evaluated, while socio-legal contexts add new challenges. Applicant Tracking Systems (ATS) parse CVs, search engines rank online portfolios, recruiters access applications across diverse devices, and legal rulings shape workplace policies. This practical advice offers a step-by-step guide to optimize CVs for ATS compatibility, SEO visibility, cross-device adaptability, and alignment with socio-legal contexts like the United Kingdom’s 2025 Supreme Court ruling on biological sex. Job seekers in marketing, international business, and beyond can maximize their interview prospects by following these evidence-informed best practices, bridging academic insights with real-world application in a digital-first job landscape. <strong>Keywords:</strong> CV optimization, applicant tracking systems (ATS), search engine optimization (SEO), AI-driven recruitment, cross-device adaptability, job search strategies, digital-first job market, personal branding, keyword integration, tagged PDFs, career development, GEMS2025, Global Empirical Marketing Studies
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Nasrin, Tamanna, and Divya K. Murthy. "An Analysis on Leveraging Technology in Recruitment Enhancing Sourcing Efficiency and Effectiveness in Dabster Consultancy Private Limited in BTM Layout, Bangalore." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40221.
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In this study, the Bangalore-based recruiting agency Dabster Consultancy Private Limited examines how technology might improve the efficacy and sourcing efficiency of its hiring procedures. Utilizing technology tools like applicant tracking systems (ATS), artificial intelligence (AI) for screening candidates, social media platforms, candidate relationship management (CRM) systems, and video interviewing platforms has become essential for enhancing recruitment results in today's cutthroat talent market. To analyse the research used a T-Test and Correlation as a statistical tool. The study looks at the time-consuming procedures, restricted reach, and expensive hiring expenses that Dabster Consultancy is currently facing in its recruitment efforts. Keywords: Recruitment Technology, Sourcing Efficiency, Application Tracking System (ATS), AI in Recruitment, Social Media Recruitment.
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Bevara, Ravi Varma Kumar, Nishith Reddy Mannuru, Sai Pranathi Karedla, et al. "Resume2Vec: Transforming Applicant Tracking Systems with Intelligent Resume Embeddings for Precise Candidate Matching." Electronics 14, no. 4 (2025): 794. https://doi.org/10.3390/electronics14040794.
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Conventional Applicant Tracking Systems (ATSs) encounter considerable constraints in accurately aligning resumes with job descriptions (JD), especially in handling unstructured data and intricate qualifications. We provide Resume2Vec, an innovative method that utilizes transformer-based deep learning models, including encoders (BERT, RoBERTa, and DistilBERT) and decoders (GPT, Gemini, and Llama), to create embeddings for resumes and job descriptions, employing cosine similarity for evaluation. Our methodology integrates quantitative analysis via embedding-based evaluation with qualitative human assessment across several professional fields. Experimental findings indicate that Resume2Vec outperformed conventional ATS systems, achieving enhancements of up to 15.85% in Normalized Discounted Cumulative Gain (nDCG) and 15.94% in Ranked Biased Overlap (RBO) scores, especially within the mechanical engineering and health and fitness domains. Although conventional the ATS exhibited slightly superior nDCG scores in operations management and software testing, Resume2Vec consistently displayed a more robust alignment with human preferences across the majority of domains, as indicated by the RBO metrics. This research demonstrates that Resume2Vec is a powerful and scalable method for matching resumes to job descriptions, effectively overcoming the shortcomings of traditional systems, while preserving a high alignment with human evaluation criteria. The results indicate considerable promise for transformer-based methodologies in enhancing recruiting technology, facilitating more efficient and precise candidate selection procedures.
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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 analytics, improving candidate selection and optimizing recruitment operations.
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P, Guru, Sivakami T, Bhuvaneswari J, Panneerselvam K, Gopalakrishnan S, and Rajandran K.V. R. "Intelligent applicant tracking: leveraging machine learning for recruitment automation." Revista Gestão & Tecnologia 25, no. 2 (2025): 126–46. https://doi.org/10.20397/2177-6652/2025.v25i2.3175.
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Recruitment is a crucial, time consuming process in talent acquisition, which begins with the scouring of the talent pool in pursuit of the best candidates. In traditional applicant tracking systems (ATS), searching is usually based on keywords, which could result in any system that filters out applications using these keywords leading to a biased or an inefficient shortlisting. In this research, we explore the development of an intelligent applicant tracking system using ML to automate the recruitment process. In the proposed system, natural language processing (NLP) is used to analyze and rank resumes based on the job description, skill relevance and experience alignment. The candidate suitability prediction and hidden patterns in applicant data are predicted by advanced ML algorithms such as ensemble method such as catboost. The system is able to predict resume effectiveness through an ensemble learning models trained on a wide variety of resumes and generate actioned insights. In general, KNN model has proved itself to be effective in automating resume screening process by 92.5% accuracy. The developed system is both accurate, and explains what leads to model decisions, giving users an idea of the factors used in model decisions. By doing so, the system can help job seekers and employers alike achieve better matches of candidate qualifications to job requirements.
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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 minimizes recruiter workload while improving accuracy in candidate selection. Furthermore, it enhances the efficiency of applicant shortlisting by filtering out irrelevant job applications. The system leverages machine learning models trained on diverse resume datasets to improve extraction accuracy and adaptability to various resume formats. Additionally, it integrates with applicant tracking systems (ATS) for seamless recruitment workflow automation. Experimental results demonstrate that our system achieves high precision in entity recognition, making it a valuable tool for modern recruitment platforms. The proposed solution not only optimizes the hiring process but also contributes to fair and unbiased candidate evaluation. Key Words: Automated Resume Parsing, Named Entity Recognition, Natural Language Processing, Recruitment Automation, Applicant Tracking System, Resume Screening, Machine Learning
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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, qualifications, and experience from job descriptions and compares them with resumes to identify gaps. By providing automated keyword suggestions, ATS optimization insights, and personalized resume recommendations, the model improves resume-job relevance and significantly increases the likelihood of passing ATS filters. The results demonstrate that integrating AI in the resume screening process enhances job application success rates, reduces manual effort for both job seekers and recruiters, and accelerates the hiring process.
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Ligi-Goryaev, V. V., G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, and V. V. Dzhakhnaev. "Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems." Sovremennaya nauka i innovatsii, no. 1 (45) (2024): 32–41. http://dx.doi.org/10.37493/2307-910x.2024.1.3.
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The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. Various machine learning algorithms can be employed to address this issue. Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. One approach to building such a model involves identifying and collecting relevant attributes or features that can help distinguish fraudulent job advertisements from legitimate ones. Some features that could be useful in detecting fraudulent job ads include job location, job description, job requirements, job responsibilities, company information, and recruiter data. Subsequently, different machine learning algorithms can be trained on prepared datasets using standard methods such as cross-validation to assess their performance. The performance of the trained models can be evaluated using various metrics such as accuracy, precision, and recall. Ultimately, the most effective model can be selected based on these evaluation metrics and deployed in a production environment, where it can classify job advertisements as fraudulent or legitimate. It's important to note that the model should also undergo continuous evaluation and updates over time to ensure its reliability and effectiveness. Based on the evaluation metrics, it was concluded that the GBT classifier exhibits higher performance and accuracy compared to the LinearSVC and RF classifiers on the given dataset. However, it should be considered that the GBT classifier requires more time for training and prediction; GBT takes 208.738579 seconds, while LSVC and RF take 64.267132 and 71.024914 seconds, respectively. Taking into account the evaluation results, the GBT model was utilized for the operational aspect of the program. For implementation of the prediction, machine learning was performed on GBT, RF, and LSVC using a custom dataset called "Job_Fraud," created based on the publicly available EMSCAD dataset. To address the significant data imbalance, an implementation of the Synthetic Minority Over-sampling Technique (SMOTE) from a library was utilized. Initially, a model was obtained and trained on the data using a classifier, removing stop-words through TFIDFVectorizer in the vector space. Then, after reducing the dimensionality of the data, the data was reloaded, and both the model and vectorizer were retrained before being used for prediction. The tkinter module was used for the graphical interface. The predict() function utilizes the trained model for predictions based on the feature vector.
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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 consists of three core modules: ATS Evaluation, Interview Preparation, and Mock Interviews. The ATS Evaluation module analyzes resumes for formatting, keyword relevance, and structure, providing an ATS score to improve application effectiveness. The Interview Preparation module generates AI-driven, job-specific questions to help candidates practice effectively. The Mock Interview module simulates real interviews using speech-to-text processing and sentiment analysis, offering feedback on clarity, tone, and confidence. By integrating AI-driven resume optimization, personalized interview coaching, and real-time simulations, this system enhances employability, increases confidence, and improves interview performance, making career opportunities more accessible and effective for job seekers.
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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 relevance, job compatibility, and formatting guidelines. It offers role-specific keyword analysis, skill-gap identification, and even recommends relevant online courses to help bridge those gaps. Additionally, the system includes a visually intuitive dashboard that enhances user interaction and comprehension. Designed with accessibility and effectiveness in mind, the application helps users build optimized, professional, and ATS-friendly resumes with minimal effort. This paper discusses the design, implementation, and future potential of the Smart AI Resume Analyzer in transforming how job seekers prepare for employment opportunities.
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Ligi-Goryaev, V. V., G. A. Mankaeva, T. B. Goldvarg, S. S. Muchkaeva, and E. N. Dzhakhnaeva. "Assessment of binary prediction of fraudulent advertisements in ATS candidate tracking cloud systems." Sovremennaya nauka i innovatsii, no. 1 (49) (2025): 51–62. https://doi.org/10.37493/2307-910x.2025.1.4.
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The abstract describes the construction of a binary classification model for predicting the type of job advertisement in cloud-based ATS (Applicant Tracking Systems) as either legitimate or fraudulent. Various machine learning algorithms can be employed to address this issue. Traditional classification algorithms, including LSVC (Support Vector Machine), GBT (Gradient Boosting Tree), and RF (Random Forest), have been chosen for this study. One approach to building such a model involves identifying and collecting relevant attributes or features that can help distinguish fraudulent job advertisements from legitimate ones. Some features that could be useful in detecting fraudulent job ads include job location, job description, job requirements, job responsibilities, company information, and recruiter data. Subsequently, different machine learning algorithms can be trained on prepared datasets using standard methods such as cross-validation to assess their performance. The performance of the trained models can be evaluated using various metrics such as accuracy, precision, and recall. Ultimately, the most effective model can be selected based on these evaluation metrics and deployed in a production environment, where it can classify job advertisements as fraudulent or legitimate. It's important to note that the model should also undergo continuous evaluation and updates over time to ensure its reliability and effectiveness. Based on the evaluation metrics, it was concluded that the GBT classifier exhibits higher performance and accuracy compared to the LinearSVC and RF classifiers on the given dataset. However, it should be considered that the GBT classifier requires more time for training and prediction; GBT takes 208.738579 seconds, while LSVC and RF take 64.267132 and 71.024914 seconds, respectively. Taking into account the evaluation results, the GBT model was utilized for the operational aspect of the program. For implementation of the prediction, machine learning was performed on GBT, RF, and LSVC using a custom dataset called "Job_Fraud," created based on the publicly available EMSCAD dataset. To address the significant data imbalance, an implementation of the Synthetic Minority Over-sampling Technique (SMOTE) from a library was utilized. Initially, a model was obtained and trained on the data using a classifier, removing stop-words through TFIDFVectorizer in the vector space. Then, after reducing the dimensionality of the data, the data was reloaded, and both the model and vectorizer were retrained before being used for prediction. The tkinter module was used for the graphical interface. The predict() function utilizes the trained model for predictions based on the feature vector.
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K, ChethanKumar, and SHRUTHI MURTHY. "AN ANALYSIS OFTHE DIGITAL TOOLS IMPACT ON HR PROCESS WITH SPECIAL REFERENCE TO SARTORIUS." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40184.
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This study examines the impact of digital tools on HR processes at Sartorius, a global leader in biopharmaceutical solutions, focusing on areas such as recruitment, onboarding, training, performance management, and employee retention. By leveraging quantitative surveys and qualitative interviews with HR professionals at Sartorius Nelamangala, the research highlights how tools like applicant tracking systems (ATS), human resource information systems (HRIS), and learning management systems (LMS) enhance operational efficiency, decision-making, and employee engagement. While digital tools streamline processes and foster collaboration, challenges such as technology integration, user adoption, and data privacy remain significant. To analyze the research used a T-TEST as a statistical tool. The study concludes with recommendations to address these issues, including targeted training, robust data security measures, and aligning technology implementation with organizational goals, emphasizing the transformative role of digital tools in driving HR excellence at Sartorius. Keywords: Digital tools, HR process, HR efficiency, Sartorius NelMangala, HR automation, talent management.
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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, to help users develop relevant skills and stay competitive in the job market. By automating the resume review process, the project enables job seekers to enhance their profiles, increasing their chances of success in today's dynamic hiring environment. Designed with scalability, accuracy, and user-friendliness in mind, this application serves as a valuable tool for job seekers, recruiters, and educational institutions. The system is implemented using Python, FastAPI for the backend, and Streamlit for a user-friendly interface. KEYWORDS: AI Resume Analyzer, Natural Language Processing, Machine Learning, Resume Screening, Applicant Tracking Systems (ATS), TF-IDF, Fuzzy Matching, FastAPI, Streamlit.
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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 a series of form fields, and provides a real-time preview of the resume design as the user inputs their data. The resume generator uses JavaScript to dynamically populate the preview with the user's input, and allows users to select from a range of pre-designed templates and color schemes to customize the look and feel of their resume. It processes the user input and generates a downloadable Portable Document Format (PDF) of the final resume. The platform analyzes user-provided information through ML models, offering suggestions on skill extraction, keyword matching, and action verb usage. This combination empowers users to create impactful resumes that are more likely to pass through ATS filters and reach human reviewers.
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Wadne, Dr Vinod S. "AI-Powered Interview Xpert: An Intelligent Platform for Interviews, Resumes, and Portfolios." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48010.
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In the modern job market, standing out requires more than just qualifications—it demands excellent interview skills, strong resumes, and an impressive portfolio. This project introduces an AI-powered platform aimed at helping job seekers and students excel in these critical areas. The platform combines three key components: a Mock Interview Assistant, a Resume Generator, and a Portfolio Creator—each enhanced with artificial intelligence to deliver tailored feedback and data-driven support. The Mock Interview module simulates realistic interview environments by customizing questions based on the user's desired role and industry. Leveraging natural language processing (NLP) and machine learning, it assesses user responses for relevance and clarity, providing feedback, ratings, and improvement tips for soft skills. It also integrates text-to-speech to simulate real-time conversations. The Resume Builder analyzes job postings using AI to help users optimize their resumes with appropriate keywords, phrasing, and content suggestions. It detects skill mismatches, offers enhancement suggestions aligned with industry norms, and ensures compatibility with Applicant Tracking Systems (ATS), improving the likelihood of passing early recruitment stages. The Portfolio Builder assists users in crafting visually compelling and content-rich digital portfolios. By utilizing computer vision and NLP, it evaluates structure, visual appeal, and presentation, while offering personalized tips on how to effectively display work and projects. Altogether, this platform delivers a well- rounded, AI-enhanced career toolkit designed to build confidence and improve employment outcomes. With an engaging and user-friendly interface, it seeks to significantly boost users' preparedness and success in the hiring process. Index Terms: Artificial Intelligence, Natural Language Processing, Mock Interviews, Resume Optimization, Portfolio Builder, Career Readiness, Applicant Tracking Systems
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Dimas Bagus Susanto and Said Hamzali. "The Role of Technology in Improving the Effectiveness of Employee Recruitment and Selection." Journal of Economic Education and Entrepreneurship Studies 5, no. 3 (2024): 421–34. http://dx.doi.org/10.62794/je3s.v5i3.3800.
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This research explores the role of technology in improving the effectiveness of employee recruitment and selection. Against the background of the importance of an effective recruitment and selection process for organizational performance, this research focuses on identifying the latest technologies that can optimize the process and evaluating their impact on speed, cost, and accuracy in employee selection. A qualitative approach with a literature study method was used to extract information from various relevant sources. The results show that the use of technologies such as applicant tracking systems (ATS), artificial intelligence (AI), and digital platforms can increase candidate reach, reduce recruitment time and costs, and improve accuracy and candidate experience. However, challenges such as start-up costs, training needs, and resistance to change were also identified. This research provides practical recommendations for organizations looking to adopt technology in employee recruitment and selection to achieve operational efficiency and competitive advantage.
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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. Furthermore, the integrating of NLP with applicant tracking systems (ATS) offers improved efficiency and accuracy in matching candidates to job requirements. The paper also examines challenges such as handling diverse resume formats and ensuring fairness in automated evaluations. By leveraging NLP, organizations can achieve a more streamlined and objective screening process, ultimately leading to better hiring outcomes and reduced bias in recruitment.
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UPENDRA, CH, and Dr PRK RAJU. "A STUDY ON RECRUITMENT AND SELECTION PRACTICES OF ANDHRA PRADESH PAPER MILLS LIMITED, UNIT: KADIYAM." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–6. https://doi.org/10.55041/ijsrem39557.
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Recruitment and selection are critical processes in human resource management that aim to identify, attract, and hire the best candidates for organizational roles. Effective recruitment ensures a diverse and qualified candidate pool, while a well-structured selection process helps organizations choose the most suitable individuals. The recruitment process involves job advertising, sourcing candidates, and engaging with potential employees through various channels, including digital platforms, job fairs, and networking. Selection, on the other hand, focuses on assessing candidates through interviews, tests, background checks, and reference verification to determine their skills, experience, and cultural fit. This process is essential for enhancing organizational performance, reducing turnover, and maintaining a positive work environment. The growing use of technology, such as applicant tracking systems (ATS) and artificial intelligence (AI) in recruitment, has streamlined the process, making it more efficient and data- driven. This study explores the significance of recruitment and selection, factors influencing these processes, and modern trends that shape talent acquisition strategies in today's competitive job market. *** Keywords: effective recruitment strategies, candidate satisfaction.
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Mukhtar, Afiah, and Masradin Masradin. "Bagaimana Teknologi Era 4.0 Menerapkan Rekrutmen? (Kajian Manajemen Sumber Daya Manusia)." Paraduta : Jurnal Ekonomi dan Ilmu-Ilmu Sosial 1, no. 2 (2023): 77–89. https://doi.org/10.56630/paraduta.v1i2.479.
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Tren teknologi digital akibat Industri 4.0 mempengaruhi bidang manajemen sumber daya manusia sehingga penelitian ini bertujuan untuk bagaimana teknologi canggih ini mengubah pendekatan dan pengelolaan aspek penting dari sumber daya manusia yang semakin terhubung dengan digital, serta memberikan wawasan, rekomendasi dan panduan praktis yang bernilai bagi organisasi dalam mengadopsi teknologi tersebut. Metode yang digunakan yaitu penelitian kepustakaan (library research) melibatkan evaluasi kritis dan mendalam terhadap sumber-sumber pustaka yang relevan dengan konten makalah, seperti jurnal dan buku  yang memiliki kualitas referensi yang baik. Penggunaan teknologi di Era 4.0 dalam perekrutan dapat dilakukan melalui : 1)Penggunaan Platform Digital, Sistem Manajemen Rekrutmen (Applicant Tracking Systems - ATS). 3) Penggunaan Big Data dan Analitika. 4) Video Interviewing. 5) Uji Kemampuan Online. 6) Kecerdasan Buatan (AI) dalam Seleksi Awal. 7) Chatbot dan Automasi Komunikasi. 8) Penilaian Berbasis Game. 9)Analisis Media Sosial. 10) Mobile Recruitment. Namum beberapa hal yang harus diperhatikan dalam penggunaan teknologi di Era 4.0 harus menyesuaikan dengan kebutuhan spesifik perusahaan agar sesuai dengan strategi rekrutmen, tingkatkan transparansi dan etika penggunaan teknologi juga harus menjadi perhatian dalam perekrutan. Keywords : Teknologi Era 4.0, Rekrutmen, MSDM
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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 Resume Analyzer, and an AI-Driven Interview Simulator. The Prebuilt Question Bank offers a curated set of aptitude and DSA questions categorized by difficulty level, helping users learn progressively while tracking their performance and receiving personalized study recommendations. The Resume Analyzer evaluates resumes by comparing them with job descriptions and suggests improvements in content and structure. It uses NLP for keyword extraction to ensure alignment with job roles, improving the chances of passing applicant tracking systems (ATS). The Interview Simulator replicates real-world technical interviews based on selected topics, analyzing user responses for technical accuracy, communication skills, and overall performance using AI models. Developed with the MERN (MongoDB, Express.js, React.js, Node.js) stack, the system integrates AI to improve resume assessment accuracy and enhance clarity in interview feedback. By offering structured learning, AIdriven insights, and realistic practice, this innovative platform empowers users to be more confident, competitive, and wellprepared for real-world hiring processes.
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TERENTIEVA, Nataliia, Denys PAVLOV, and Maksym TVERDOKHLIB. "INTEGRATION OF DIGITAL TOOLS AND ARTIFICIAL INTELLIGENCEINTO HR MANAGEMENT PROCESSES." Herald of Khmelnytskyi National University. Economic sciences 334, no. 5 (2024): 624–29. https://doi.org/10.31891/2307-5740-2024-334-94.
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Nowadays, companies are forced to quickly adapt to market changes and effectively use digital innovations to optimize operations and rationally use human resources. Given that human capital is the main asset of modern companies, they must apply the latest methods of personnel management to increase efficiency and adaptability in a changing business environment. DMS (document management system) acts as a single platform for storing, accessing and managing all company documents. DMS is easily integrated with HR systems, which simplifies the management of personnel processes and document flow in the company. Automation of onboarding (one of the key processes) allows you to minimize time spent on administrative tasks, providing a structured and standardized approach. In addition to onboarding, it is especially important to optimize the process of adaptation and training of personnel. Automated training systems allow corporate networks to quickly update employees' skills, providing them with access to up-to-date training materials, courses and certifications. Automation of work processes helps save time and reduce human errors. ATS (Applicant Tracking System) is a software for automating and optimizing the recruitment process. HRMS (Human Resource Management System) is a comprehensive centralized system for managing all aspects of HR. LMS (Learning Management System) is a software or web platform that allows organizations to create, manage, deliver and track training materials and programs. Therefore, AI-powered software solutions can significantly facilitate and accelerate the candidate selection process. However, it is worth remembering that the human factor is key. People can detect subtle details that AI can miss.
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Dhumal, Prof Jyoti. "Connect Edge: Employment Services Applications." International Journal for Research in Applied Science and Engineering Technology 13, no. 6 (2025): 756–68. https://doi.org/10.22214/ijraset.2025.72182.
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Abstract: Campus placements play a crucial role in bridging the gap between education and employment. Traditional placement systems often rely on manual processes, fragmented tools, and lack personalized interaction between stakeholders such as students, companies, and administrators. To address these limitations, we present Connect Edge—a comprehensive, full-stack web application designed to automate, streamline, and enhance the campus placement process using modern technologies and AI integration. Connect Edge features role-based dashboards for Admin, Student, and Company users, allowing secure, permission-based access to relevant functionalities. Companies can post job opportunities which are reviewed and approved by the Admin. Upon approval, students are notified via email and can view and apply for these positions. The platform also incorporates a community blogging section where students and alumni share placement insights and preparation tips, fostering peer learning and mentorship. A built-in resume builder helps students create professional resumes, while an integrated Gemini AI module evaluates resumes using ATS (Applicant Tracking System) standards and provides personalized improvement suggestions. This paper discusses the development lifecycle, architecture, and impact of Connect Edge in improving placement efficiency, student engagement, and recruitment transparency. The system not only bridges communication gaps but also supports career readiness by guiding students in building industry-aligned resumes and accessing relevant opportunities. With scalability and future enhancements in mind, Connect Edge sets a new benchmark for digitized placement systems in educational institutions.
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Pandey, Ritambhara. "The Impact of Artificial Intelligence on Talent Acquisition and Employee Experience in Modern Organizations." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem49805.
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ABSTRACT In the digital age, Artificial Intelligence (AI) has emerged as a transformative force in reshaping human resource management, particularly in the domains of talent acquisition and employee experience. This study investigates the impact of AI-powered tools and technologies on recruitment strategies, candidate engagement, and the overall experience of employees in modern organizations. The research aims to assess how AI enhances recruitment efficiency, reduces biases, and improves decision-making processes while also evaluating its influence on employee satisfaction, onboarding, engagement, and workplace support. A quantitative methodology was adopted, using a structured Google Form survey distributed among HR professionals, recruiters, and employees from various sectors. The survey collected insights into the use of AI tools such as applicant tracking systems (ATS), chatbots, automated screening platforms, and sentiment analysis tools in HR operations. The results indicate a growing reliance on AI to streamline hiring, improve job-candidate matching, and personalize employee support systems. However, concerns around ethical implications, data privacy, and the potential depersonalization of human interaction were also highlighted. This research contributes to the academic and practical understanding of AI's dual role in optimizing recruitment workflows and enriching the employee journey. It emphasizes the need for a balanced approach that integrates technological innovation with human- centered HR practices. The findings offer actionable insights for HR professionals, policymakers, and organizational leaders aiming to leverage AI for sustainable talent management and enhanced workplace experiences.
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Onukwugha, Chinwe, Christopher Ofoegbu, Obinna Aliche, and Chidi Betrand. "Resume Optimization Model Using Machine Learning Techniques." International Journal of Intelligent Information Systems 13, no. 5 (2024): 109–16. http://dx.doi.org/10.11648/j.ijiis.20241305.12.
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In the contemporary job market, where competition is fierce and employers are inundated with an ever-growing pool of resumes, the need for effective resume optimization has become paramount. Resumes serve as the first point of contact between job seekers and potential employers, playing a pivotal role in shaping initial perceptions. However, the traditional approach to resume crafting often lacks a systematic and data-driven methodology. A well-crafted resume plays a crucial role in securing employment opportunities. However, crafting an effective resume that resonates with both human recruiters and Applicant Tracking Systems (ATS) can be a daunting task. By employing natural language processing (NLP) and machine learning algorithms Multinomidal Naïve Bayes (MNB) and K Nearest Neighbour (KNN), this system extracts relevant features from resumes, such as keyword relevance, formatting styles, content organization, and overall readability. Through supervised learning models trained on a diverse dataset of resumes, the system can predict the effectiveness of a resume and generate actionable insights. Overall, the KNN model demonstrated effectiveness in automating the resume screening process, of 87% accuracy. The developed system not only provides accurate predictions but also offers interpretable explanations, enabling users to understand the factors contributing to the model&apos;s decisions. The system has the potential to benefit both job seekers and employers by facilitating better matches between candidates&apos; qualifications and job requirements.
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Bhavani, P. Ganga. "Find Your Dream Work." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem40987.
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Job searching is a multifaceted process that encompasses various strategies, tools, and techniques to secure employment. The modern job market demands a comprehensive understanding of digital platforms, effective networking, and the ability to tailor applications to specific roles. This abstract delves into the essential components of successful job searching, highlighting the importance of online job portals, resume optimization, networking, and interview preparation Key Components of Job Searching: Online Job Portals: The rise of technology has transformed job searching, with online job portals playing a pivotal role. These platforms offer extensive listings across various industries and locations, allowing job seekers to apply for multiple positions with ease. Advanced algorithms on these sites match candidates' skills and experiences with suitable job openings, increasing the chances of finding the right fit. Resume Optimization: Crafting a compelling resume is crucial in the job search process. An optimized resume should be tailored to the job description, highlighting relevant skills and achievements. Keywords from the job posting should be incorporated to pass through Applicant Tracking Systems (ATS) used by many employers. Networking: Building a professional network is vital for uncovering hidden job opportunities. Networking can occur both online, through platforms like LinkedIn, and offline, at industry events and job fairs. Personal connections and referrals often provide a significant advantage in the competitive job market. KeyWords: Industry-Specific,Job Titles,Skills and Competencies,Education and Training,Networking and Collaboration,Personal Branding,Buzzwords for Trending Skills
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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 organizations leverage this combination to support their HCM and recruiting processes. Greenhouse offers a built-in integration solution, 'HRIS Link,' for Workday, but it has its limitations. This article examines the job overlap feature, business process workflow, custom objects, studio integration, RaaS (Report as a Service) in Workday, and HRIS Link in Greenhouse to automate the synchronization process of positions (new or backfill) from Workday to Greenhouse, when job requisitions functionality is not enabled in Workday. By utilizing the existing functionalities and features within Workday and Greenhouse, organizations can avoid the need for custom solutions or new integration products, thereby reducing their application stack and costs.
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G. Venkateshwaran,. "Artificial Intelligence in HR: Transforming Recruitment and Selection in IT Industry." Journal of Information Systems Engineering and Management 10, no. 17s (2025): 38–45. https://doi.org/10.52783/jisem.v10i17s.2705.
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e integration of Artificial Intelligence (AI) in Human Resource Management (HRM) has revolutionized traditional recruitment and selection processes, particularly in the IT industry, where rapid technological advancements demand a skilled and dynamic workforce. AI-driven recruitment systems leverage machine learning, natural language processing (NLP), and predictive analytics to enhance efficiency, reduce hiring biases, and improve decision-making. This study explores the transformative role of AI in recruitment and selection within the IT sector, highlighting its benefits, challenges, and future implications. AI-powered tools have streamlined various stages of the hiring process, from resume screening to candidate assessment. Automated applicant tracking systems (ATS) equipped with AI algorithms can efficiently scan thousands of resumes, filtering out the most relevant candidates based on predefined criteria. Additionally, AI-driven chatbots and virtual assistants engage with applicants, provide real-time responses, schedule interviews, and enhance the candidate experience. These tools reduce the time-to-hire and improve the quality of recruitment by identifying the best-fit candidates based on skills, experience, and cultural alignment. Another critical advantage of AI in recruitment is its potential to reduce human biases. Traditional hiring processes are often influenced by unconscious biases related to gender, ethnicity, or educational background. AI-driven assessments focus on skills-based evaluation, utilizing predictive analytics to match candidates with job roles based on competencies rather than demographic factors. Furthermore, video interview analysis tools can assess verbal and non-verbal cues using facial recognition and speech analysis, helping recruiters make data-driven hiring decisions. Despite these benefits, AI in recruitment comes with challenges. One significant concern is data privacy and ethical considerations. AI algorithms rely on vast datasets, raising questions about data security, transparency, and fairness. If trained on biased historical data, AI systems may perpetuate discrimination rather than eliminate it. Ensuring algorithmic fairness and regulatory compliance, such as adhering to General Data Protection Regulation (GDPR) and other data protection laws, is crucial for ethical AI deployment in HR. Additionally, the human touch in recruitment remains essential. While AI can handle administrative tasks and initial screenings, final hiring decisions still require human judgment. Organizations must strike a balance between AI automation and human intuition to ensure a holistic approach to talent acquisition. HR professionals must also undergo upskilling to effectively leverage AI tools and interpret AI-driven insights. The future of AI in HRM will see advancements in predictive hiring, sentiment analysis, and skill gap analysis. AI-powered platforms will not only match candidates to current job roles but also predict future skill requirements and recommend personalized learning paths for employees. In the IT industry, where skills evolve rapidly, AI-driven workforce planning will play a crucial role in talent retention and upskilling initiatives. This paper analyzes the impact of AI on recruitment and selection in the IT industry, focusing on efficiency, bias reduction, candidate experience, and decision-making improvements. It examines AI-driven tools like ATS, chatbots, and predictive analytics, discusses ethical concerns, and explores the future role of AI in workforce planning and talent acquisition.
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Chavan, Prasad R., Yash Chandurkar, Ankita Tidake, Gaurav Lavankar, Suhani Gaikwad, and Rohit Chavan. "Enhancing recruitment efficiency: An advanced Applicant Tracking System (ATS)." Industrial Management Advances 2, no. 1 (2024): 6373. http://dx.doi.org/10.59429/ima.v2i1.6373.
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The Applicant Tracking System (ATS), also known as a talent management system or job applicant tracking system, is a software application designed to facilitate more efficient recruitment processes for companies or selection agencies. The objective of ATS is to streamline various aspects of the recruiting process, from receiving applications to hiring employees and effectively manage recruitment needs electronically. Methodologies such as NLP and KNN models are used for automated resume parsing and classifying the resume from unstructured format to structured format. The final results found significant improvement in performance of functionalities such as candidate screening, applicant testing, interview scheduling, managing the hiring process, reference checks, and completing new-hire paperwork.
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Marlita, Devi, Sri Handayani, Erni Pratiwi Perwitasari, Muhammad Ridwan Azis, and Yosua Hamonangan. "Socialization Applicant Tracking System (ATS) and ATS Curriculum Vitae for ITL Trisakti and General Students." Asian Journal of Community Services 3, no. 2 (2024): 287–94. http://dx.doi.org/10.55927/ajcs.v3i2.8080.
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The method used for the program is through socialization and knowledge sharing about the Applicant Tracking System (ATS) in the form of case examples and tips online because the implementation of the events was still in the PPKM period. The Implementation of the activity was centered at ITL Trisakti, East Jakarta and participants could take part online. The purpose of the socialization is to prepare basic knowledge for students and the general public about ATS and its derivative applications. As a result of these activities, participants understand more about ATS. And start updating their profiles and curriculum vitae using ATS so that it is more accessible to recruiters of companies that need employees to fill the required positions.
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Омарова, Н. О., and А. А. Эчилова. "Research of problems of automation of personnel selection." Экономика и предпринимательство, no. 3(128) (May 13, 2021): 1079–81. http://dx.doi.org/10.34925/eip.2021.128.3.213.
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На сегодняшний день одним из актуальных направлений совершенствования рекрутинга является внедрение автоматизированной системы подбора (ATS), которая позволяет повысить качество подбора и одновременно снизить нагрузку на одного специалиста по подбору персонала, а так же рационально перераспределить объем работы между всеми специалистами отдела кадров. One of the topical directions of the improvement today is the introduction of an Applicant Tracking System (ATS), which will help improve the quality of selection and at the same time to reduce the load per recruiter, plus efficiently redistribute workload among all HR professionals.
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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 skills. Additionally, ATS algorithms are employed to score and rank resumes based on their relevance to specific job descriptions, facilitating a more streamlined and objective hiring process. The system is designed to enhance the efficiency of resume screening by reducing the time and effort required for initial resume screening while improving the accuracy of selection. This report details the development, implementation, and evaluation of the proposed resume analysis system, highlighting its potential benefits and limitations.
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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, utilizing AI to assess skills, experience, and educational qualifications while generating an ATS score to optimize candidate profiles. It also provides a versatile virtual interview platform that supports live and pre-recorded video interviews, text-based interactions, and voice-based assessments. IntelliPrep enhances preparation through NLP- driven response analysis, aptitude test modules, and real-time coding challenges, ensuring a holistic approach to interview readiness. Additional features such as group discussion analysis, AI- powered speech evaluation, and detailed progress tracking provide comprehensive insights to improve candidates' communication and technical skills. By leveraging advanced AI and gamified learning, IntelliPrep offers a structured, interactive, and personalized approach to job preparation. The platform empowers students with the tools needed to excel in recruitment processes, ultimately bridging the gap between traditional methods and modern hiring expectations. Keywords- Applicant Tracking System (ATS), Virtual Interview Platform, NLP-based Response Analysis, Leetcode Scraping, Gamified Learning & Assessment
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Saprit Anand and Abhijeet Kumar Giri. "Optimizing Resume Design for ATS Compatibility: A Large Language Model Approach." International Journal on Smart & Sustainable Intelligent Computing 1, no. 2 (2024): 49–57. https://doi.org/10.63503/j.ijssic.2024.33.
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The modern hiring process increasingly relies on Application Tracking Systems (ATS) to sort and evaluate resumes based on organizational preferences. However, many skilled and competent candidates fail to craft resumes that are ATS optimized, leading to missed opportunities. This study explores the potential of Large Language Models (LLMs) to enhance ATS compatibility in resumes. Using Natural Language Processing (NLP) techniques, LLMs analyze resumes to identify errors, recommend suitable keywords, enhance semantic alignment, and format content to meet ATS requirements. Evaluation results demonstrate that imple menting the model's suggested changes significantly improves ATS scores. This ap proach bridges the gap between job seekers and automated recruiting systems, empowering individuals to enhance their resumes effectively. By leveraging LLMs, job seekers gain a powerful tool to align their resumes with employer expectations, increasing their chances of success in the hiring process. This study highlights how LLMs can transform the recruitment landscape, improving access to employment and redefining traditional hiring practices
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Rao, Akhila, Shailashri V. T, Molly Sanjay Chaudhuri, and Kondru Sudheer Kumar. "Contemporary Employee Recruitment Practices and Areas of Future Research in Indian Railways." Think India 22, no. 2 (2019): 745–63. http://dx.doi.org/10.26643/think-india.v22i2.8878.
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The modern business milieu is highly competitive due to vast technological advancement which makes employees a vital source of competitive advantage. Precisely, the recruitment process has become a key determinant of an organization’s success and a logistic capital resource to the human resource; thus, the process should be entirely modern. A conventional recruitment and selection process comprises of job analysis, manpower planning, and recruitment and selection. The current study seeks to explore employee recruitment practices and proposes areas of future research in Indian Railways using secondary data. It also gives recommendations on how to improve the recruitment practices in the government-owned Indian Railways. The trends investigated in the study include the applicant tracking software (ATS), use of video resumes, Chatbots, the utilization of social networks, and increased focus on passive candidates.
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Koshti, Harsh, Prathamesh Gosavi, Roshan Pagar, Prathamesh Khairnar, and Sopan Talekar. "AI-Powered Interview Preparation System: Integrating Resume Analysis, HR Simulation, and Technical Skill Assessment." Journal of Engineering Research and Reports 27, no. 5 (2025): 21–33. https://doi.org/10.9734/jerr/2025/v27i51489.
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This article presents an AI-powered interview preparation system designed to objectively collect and evaluate candidate responses. The solution automates key stages of the interview process, enhancing recruitment efficiency through real-time emotion detection using Convolutional Neural Networks (CNN), natural language processing (NLP) for resume analysis, and speech recognition for response evaluation. The system integrates with an Applicant Tracking System (ATS) for resume screening, an HR interview simulator focusing on communication and behavioural skills, and a technical assessment module for domain-specific knowledge evaluation. Extensive testing demonstrated significant improvements in candidate preparation efficiency and performance quality compared to traditional methods. The system also supports candidates by automatically generating questions, providing instant feedback, and offering detailed performance insights. This research delivers a scalable and unbiased approach, bridging traditional hiring techniques with modern AI capabilities.
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Erna Wulandari, Anggun Indriyani, Dwi Wahyu Ningrum, Rhelvia Ambarwati, and Rauly Sijabat. "Systematic Literature Review (SLR) : Pengaruh Pengembangan Digital E-Recruitment Terhadap Seleksi Penerimaan Karyawan Baru." Jejak digital: Jurnal Ilmiah Multidisiplin 1, no. 3 (2025): 385–93. https://doi.org/10.63822/sbqffc05.
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Perkembangan teknologi digital telah merevolusi berbagai aspek bisnis, termasuk dalam proses rekrutmen karyawan. Digital e-recruitment menjadi solusi inovatif dalam manajemen sumber daya manusia dengan memanfaatkan platform digital untuk menarik, menyeleksi, dan merekrut kandidat secara efisien. Penelitian ini menggunakan metode Systematic Literature Review (SLR) dengan pendekatan PRISMA untuk menganalisis pengaruh digital e-recruitment terhadap proses seleksi penerimaan karyawan baru. Data diperoleh dari berbagai jurnal dan buku yang relevan dalam lima tahun terakhir. Hasil kajian menunjukkan bahwa penerapan teknologi seperti Applicant Tracking System (ATS) dan kecerdasan buatan (AI) dapat mempercepat durasi rekrutmen, meningkatkan akurasi seleksi, serta memperluas jangkauan pencarian kandidat. Meskipun demikian, terdapat tantangan seperti kesenjangan digital dan berkurangnya interaksi interpersonal. Kajian ini menyarankan agar perusahaan memadukan teknologi dengan pendekatan humanis serta meningkatkan literasi digital dalam tim HR. Penelitian ini diharapkan menjadi referensi bagi pengembangan strategi rekrutmen digital yang efektif dan inklusif di masa depan.
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Suningsih, Sri, Lidya Ayuni Putri, Ernie Hendrawaty, Agrianti Komalasari, Susi Sarumpaet, and Winia Waziana. "Pelatihan Pembuatan Curriculum Vitae dalam Bahasa Inggris yang Berbasis Application Tracking System." Jurnal Nusantara Mengabdi 3, no. 2 (2024): 85–93. http://dx.doi.org/10.35912/jnm.v3i2.2979.
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Purpose: One of the most important aspects of creating professional human resources in a company is the recruitment process, in which the company selects the right candidates with the skills and qualifications needed by the company. Applicants who come from college graduates must be able to adapt and be competitive to enter the world of work, and must know and understand how to format a CV that can meet company standards. In line with this, this community service activity aims to provide training in making CVs in English based on the Application Tracking System (ATS). Methodology/approach: The method used was to conduct training in making Curriculum Vitae in English based on the Application Tracking System. Results: The results of this Community Service activity showed that participants experienced increased knowledge regarding how to write a good and appropriate CV based on the Applicant Tracking Platform. Thus, with this job application preparation training, participants can be confident and have a greater opportunity to be superior to their competitors during file selection during the job application process. Limitations: This community service activity cannot identify problems or weaknesses in documents or job application files such as CVs. This training activity contributes to the community, especially students, alumni, and graduates in Bandar Lampung, who are in the process of looking for jobs to broaden their insight and improve their soft skills in writing ideal CVs, and are ATS-friendly so that they have more opportunities to get work and compete for targeted jobs. Contribution: By carrying out community service activities at the Faculty of Economics and Business, University of Lampung, in 2023, it can be concluded that this training activity ran well and improved community skills in writing English CVs based on the Application Tracking System, which is different from the CV they used before participating in this training activity.from the CV they used before participating this training activity.
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Mohan, Anergha K., Thomas John, and Nikhil P S. "Contextual Conversational Intelligence : Leveraging Language Models For College Chatbot By Retrieval Augmented Generation." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 10 (2024): 1–6. http://dx.doi.org/10.55041/ijsrem38284.
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By harnessing the capabilities of Large Language Models (LLM), the proposed system evolves into a sophisticated conversational agent, capable of understanding nuanced user inputs, providing context-aware responses, and delivering an unparalleled level of engagement. This chatbot is also equipped to assess a student's likelihood of being accepted into a specific program at the college using a machine learning (ML) model. The model analyzes the student's rank and eligible quota (such as reserved categories) in conjunction with historical admission data to gauge their chances of acceptance. It also recommends alternative programs where the student may have a better chance of admission based on their rank. The chatbot also features a smart Applicant Tracking System (ATS) that assists students in evaluating their resumes. This system provides keyword matching suggestions based on job descriptions and generates an overview of the resume. Additionally, it highlights areas for improvement and highlights the resume's distinctiveness. The methodology involves the development and deployment of a website chat bot integrated with Lang Chain, with the potential of advanced language models in transforming website chat bot interactions using Retrieval augmented generation (RAG) technique to ground LLM to generate responses to user queries based on a custom knowledge-base. Key Words: Chatbot, Generative AI, Large Langauge Model, Website, Machine Learning, Retrieval-augmented generation , Web scrapping , Resume , ATS.
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Madanchian, Mitra. "From Recruitment to Retention: AI Tools for Human Resource Decision-Making." Applied Sciences 14, no. 24 (2024): 11750. https://doi.org/10.3390/app142411750.
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HR decision-making is changing as a result of artificial intelligence (AI), especially in the areas of hiring, onboarding, and retention. This study examines the use of AI tools throughout the lifecycle of an employee, emphasizing how they enhance the effectiveness, customization, and scalability of HR procedures. These solutions streamline employee setup, learning, and documentation. They range from AI-driven applicant tracking systems (ATSs) for applicant selection to AI-powered platforms for automated onboarding and individualized training. Predictive analytics also helps retention and performance monitoring plans, which lowers turnover, but issues such as bias, data privacy, and ethical problems must be carefully considered. This paper addresses the limitations and future directions of AI while examining its disruptive potential in HR.
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Navarro, Gloribeth. "Fair and Ethical Resume Screening: Enhancing ATS with JustScreen the ResumeScreeningApp." Journal of Information Technology, Cybersecurity, and Artificial Intelligence 2, no. 1 (2025): 1–7. https://doi.org/10.70715/jitcai.2024.v2.i1.001.
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Abstract— In today's fast-paced job market, the efficiency and fairness of the resume screening process are paramount. "JustScreen" emerges as a cutting-edge solution leveraging advanced Natural Language Processing (NLP) to automate resume evaluation, thus eliminating biases and promoting merit-based candidate selection. This thesis explores JustScreen's innovative approach to integrating NLP and machine learning algorithms to enhance the recruitment workflow, ensuring a more streamlined, unbiased, and efficient candidate assessment process. The methodology involves several key components: data preprocessing, NLP information extraction, fairness metrics calculation, bias mitigation, and interpretability techniques. By utilizing frameworks such as spaCy for NLP tasks, JustScreen aims to overcome the challenges of traditional manual screening processes, improving both accuracy and fairness. This thesis explores the transition from developing a full Application Tracking System (ATS) to creating a powerful enhancement for existing ATS systems. The ResumeScreeningApp/ JustScreen integrates generative AI to provide comprehensive resume analysis, adding significant value to traditional ATS functionalities. Initial evaluations indicate a significant advancement in talent acquisition practices, promoting equal opportunities and reducing the impact of potentially discriminatory factors. This research signifies a transformative shift in recruitment, setting new standards for ethical and efficient hiring practices using Generative AI.
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S, Vinod Kumar, and Dr Janardhan G. Shetty. "A Comparative Analysis on Recruitment and Selection Process at Chennai Shoes and Industrial Supplies LLP, T Nagar Chennai." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem40225.
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This study investigates the recruitment and selection practices at Chennai Shoes and Industrial Supplies LLP, located in T. Nagar, Chennai, with a focus on evaluating the effectiveness of their current strategies and identifying areas for improvement. The research combines both qualitative and quantitative methods to assess the organization's recruitment sources, selection techniques, and overall efficiency. A t-test is employed to compare the effectiveness of two key recruitment strategies: Traditional interviews and Online recruitment platforms. The objective is to determine whether there is a significant difference in employee satisfaction between candidates hired through these two methods. The results of the t-test reveal that there is no significant difference (p-value > 0.05) in employee satisfaction between the two recruitment methods, suggesting that both approaches yield similar levels of candidate satisfaction. The findings also highlight key areas for improvement, including the integration of technology in recruitment processes, the need for standardized interview techniques, and the importance of enhancing employee retention strategies. The study concludes by offering actionable recommendations, such as adopting an Applicant Tracking System (ATS) and improving employee development programs, to optimize recruitment, selection, and retention processes, thereby enhancing the company's competitive advantage. Keywords: Recruitment and Selection, T-test, Employee Satisfaction, Traditional Interviews, Online Recruitment Platforms, Chennai Shoes and Industrial Supplies LLP.
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Wu, Ling, Yongrong Sun, Kedong Zhao, and Xiyu Fu. "A Novel Vision-Based PRPL Multistage Image Processing Algorithm for Autonomous Aerial Refueling." Wireless Communications and Mobile Computing 2021 (November 10, 2021): 1–13. http://dx.doi.org/10.1155/2021/2778857.
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Autonomous aerial refueling (AAR) technology can increase the flight endurance of unmanned air vehicles (UAVs) effectively. Drogue detection and target tracking method are significant for probe-drogue refueling system in the docking stage. This paper proposes a novel vision-based multistage image processing algorithm of drogue detection and target tracking for AAR. This algorithm divides the whole task into four stages: preprocessor, recognizer, predictor, and locker (PRPL). The adaptive threshold segmentation (ATS) algorithm and support vector machine (SVM) classifier are utilized in preprocessor and recognizer for drogue detection. An improved kernelized correlation filter (IKCF) tracking algorithm and scale adaptive method by window position as well as image resolution adjusted are adopted in predictor and locker for target tracking in complex dynamic environments. Finally, the proposed PRPL multistage image processing strategy is tested using an autonomous aerial refueling testbed. The results indicate that the proposed algorithm achieves high precision, good reliability, and real-time capability compared with conventional algorithms. The average processing time is within 11 ms in various environments, which can meet the requirement for drogue detection and tracking in AAR.
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Laumer, Sven, Christian Maier, and Andreas Eckhardt. "The impact of business process management and applicant tracking systems on recruiting process performance: an empirical study." Journal of Business Economics 85, no. 4 (2014): 421–53. http://dx.doi.org/10.1007/s11573-014-0758-9.
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Eveleth, Daniel M., and Hayley Eveleth. "Job-Seeker Reactions to Rejection Emails." International Journal of Technology and Human Interaction 17, no. 3 (2021): 1–15. http://dx.doi.org/10.4018/ijthi.2021070101.
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The authors examined participant reactions to rejection emails. Those participants who evaluated emails that provided information in an interpersonally-sensitive manner with an opportunity for future interaction reported significantly higher attitudes toward the recruiter than did those who evaluated emails that were low in information sensitivity and interactivity. In addition, the effect of email type on word-of-mouth intentions toward the company was mediated by participant attitudes toward the recruiter. These results provide implications for organizations that are focusing on the efficiency-oriented benefits of using applicant tracking system at the expense of job-seekers' reactions and for individual recruiters who may be concerned about the effect of organization practices on their professional brand.
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Lacour, S., R. Dembet, R. Abuter, et al. "The GRAVITY fringe tracker." Astronomy & Astrophysics 624 (April 2019): A99. http://dx.doi.org/10.1051/0004-6361/201834981.
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Context. The GRAVITY instrument was commissioned on the VLTI in 2016 and is now available to the astronomical community. It is the first optical interferometer capable of observing sources as faint as magnitude 19 in K band. This is possible through the fringe tracker, which compensates the differential piston based on measurements of a brighter off-axis astronomical reference source. Aims. The goal of this paper is to describe the main developments made in the context of the GRAVITY fringe tracker. This could serve as basis for future fringe-tracking systems. Methods. The paper therefore covers all aspects of the fringe tracker, from hardware to control software and on-sky observations. Special emphasis is placed on the interaction between the group-delay controller and the phase-delay controller. The group-delay control loop is a simple but robust integrator. The phase-delay controller is a state-space control loop based on an auto-regressive representation of the atmospheric and vibrational perturbations. A Kalman filter provides the best possible determination of the state of the system. Results. The fringe tracker shows good tracking performance on sources with coherent K magnitudes of 11 on the Unit Telescopes (UTs) and 9.5 on the Auxiliary Telescopes (ATs). It can track fringes with a signal-to-noise ratio of 1.5 per detector integration time, limited by photon and background noises. During good seeing conditions, the optical path delay residuals on the ATs can be as low as 75 nm root mean square. The performance is limited to around 250 nm on the UTs because of structural vibrations.
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IJBTSR. "Impacts de la Digitalisation sur la fonction RH : Proposition d'un Modèle Conceptuel de Recherche." International Journal of Business and Technology Studies and Research 5, no. 2 (2024): 10 pages. https://doi.org/10.5281/zenodo.10527979.
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<em>La digitalisation a profondément transformé la Fonction des Ressources Humaines (FRH), jouant un rôle central dans l'efficacité organisationnelle. Cette révolution redéfinit la manière dont les entreprises gèrent leurs fonctions, des ressources humaines, et cela devient possible grâce à l'utilisation d'outils digitaux performants tels que, l'Intelligence Artificielle (IA), Applicant Tracking System (ATS), Robotic Process Automation (RPA), les Réseaux Sociaux (RS)... . La digitalisation de la FRH ne se limite pas à une simple tendance, mais représente une révolution incontournable. Son objectif est de rendre la FRH plus flexible, efficace et centrée sur les besoins des employés, libérant ainsi les professionnels des tâches répétitives. La digitalisation englobe une multitude de processus RH, notamment le recrutement, la gestion des performances, la formation, la communication interne et la rémunération. Cela offre un avantage concurrentiel aux entreprises qui adoptent ces technologies, en leur permettant de gérer plus efficacement leur capital humain dans un environnement en constante évolution. Cette étude vise à explorer en profondeur l'impact de la digitalisation sur la fonction RH, son influence sur la performance de la fonction elle-même et sur la performance globale de l'organisation. Nous développons également un cadre théorique et un modèle conceptuel pour soutenir notre recherche, offrant ainsi des perspectives claires sur cette transformation majeure.</em> <strong> </strong>
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Ilkin, Taghiyev, and Jae Heung Lee. "Detecting Fake Job Recruitment with a Machine Learning Approach." Korean Institute of Smart Media 12, no. 2 (2023): 36–41. http://dx.doi.org/10.30693/smj.2023.12.2.36.
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With the advent of applicant tracking systems, online recruitment has become more popular, and recruitment fraud has become a serious problem. This research aims to develop a reliable model to detect recruitment fraud in online recruitment environments to reduce cost losses and enhance privacy. The main contribution of this paper is to provide an automated methodology that leverages insights gained from exploratory analysis of data to distinguish which job postings are fraudulent and which are legitimate. Using EMSCAD, a recruitment fraud dataset provided by Kaggle, we trained and evaluated various single-classifier and ensemble-classifier-based machine learning models, and found that the ensemble classifier, the random forest classifier, performed best with an accuracy of 98.67% and an F1 score of 0.81.
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Kulkarni1, Niranjan. "AI-Powered Resume Parsing for Efficient Recruitment." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–9. https://doi.org/10.55041/ijsrem43332.
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The incorporation of Artificial Intelligence (AI) into Business Process Management Systems (BPMS) has significantly transformed multiple industries, particularly human resource management. A notable innovation in this domain is the AI-driven Resume Parser, which enhances the recruitment process by automating resume evaluation and candidate selection. Conventional hiring methods can be inefficient, requiring extensive time and effort while being susceptible to human bias, making it challenging to identify the best candidates effectively. The proposed system utilizes Natural Language Processing (NLP) and Machine Learning (ML) to extract, classify, and organize essential resume details, allowing recruiters to make informed, data-driven hiring decisions. This research explores the implementation of AI-driven resume parsing, highlighting its role in enhancing efficiency, accuracy, and fairness in recruitment. The system can process resumes in multiple formats, handle large-scale applicant pools. However, challenges such as contextual ambiguity, data privacy concerns, and algorithmic bias necessitate human oversight to ensure ethical and reliable decision-making. As organizations increasingly adopt AI-driven automation to optimize business processes, the AI Resume Parser represents a transformative solution that enhances recruitment efficiency while reducing operational workload. Key Words: Artificial Intelligence, Business Process Management, AI in Recruitment, Resume Parsing, Natural Language Processing, Machine Learning, HR Automation, Data Extraction, Applicant Tracking System, Recruitment Optimization.
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Lina, Manna Akter. "Human Resource Information System (HRIS): An Important Element of Modern Organization." Global Disclosure of Economics and Business 8, no. 2 (2019): 61–66. http://dx.doi.org/10.18034/gdeb.v8i2.98.
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Human resources are the most important factor for any organization working in the 21st century. With the increasing effect of globalization and technology, organizations have started to use information systems in various functions of human resources. Human resource information system (HRIS) is not a new concept. Its application is improving day by day with changing the environment. It is one kind of software for data entry, data tracking, and other information about Human Resources like payroll, management, and accounting functions within an organization. An HRIS generally provide the ability to make the plan more effectively, control and manage HR costs; attain better efficiency and excellence in HR decision making; and improve employee and managerial productivity and effectiveness. Lastly, I can say that HRIS has various benefits, but the primary benefit is HRIS stores plenty of data about the employees of the organizations. It also helps in the strategic activities of HR managers and more in training and development, applicant tracking in recruitment and selection and human resource planning, etc.
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Kashyap, Mr Harshith, Hariprasad Guttedar, Shashank H M, N. C. Rugved, and S. N. Vibudha Datta. "Survey Paper on Ensuring Inclusive Data Driven Hiring Practices Across All Jobs." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39657.
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The need for inclusive hiring across sectors has increased with the rapidly increasing numbers of diverse and rapidly evolving process markets. The current paper considers data-driven methods toward fair and unbiased recruitment. In fact, the goal of ensuring a level playing field among all applicants, regardless of gender, ethnicity, or background, drives this research. We examine ways to include complex algorithms to reduce unconscious biases. Bias, systems designed to anonymize applicant information, and instruments for developing inclusive job descriptions. We also evaluate data-driven tracking systems monitoring diversity metrics and areas for continuous improvement and innovative methodologies for the unbiased evaluation of governmental examination results. This paper synthesizes recent research and emerging solutions to focus attention on the importance of transparency, accountability, and inclusion in the hiring process. Practices across public and personal sectors. in the long run, this look at seeks to encourage groups to enforce actionable techniques that mirror and assist the range of the groups they serve