Academic literature on the topic 'Applicant tracking systems ATS'
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Journal articles on the topic "Applicant tracking systems ATS"
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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.
Dissertations / Theses on the topic "Applicant tracking systems ATS"
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Čevorová, Nina. "Problematika ROI v oblasti získávání a výběru zaměstnanců." Master's thesis, Vysoká škola ekonomická v Praze, 2010. http://www.nusl.cz/ntk/nusl-77881.
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The paper brings answers on the the question whether it is possible to measure the field of recruitment. Via quantifying the administrative process of recrutment, the paper reflects the savings of time and costs by using e-recruitment application (applicant tracking system ATS) and it searchs for the relationship between quality of hire and increase of company value and its profit.
Books on the topic "Applicant tracking systems ATS"
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O'Donnell, Neil. Résumé Essentials : Do's, Don'ts and It Depends: With Applicant Tracking Systems and LinkedIn Updates. Independently Published, 2017.
Find full textBook chapters on the topic "Applicant tracking systems ATS"
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Zhang, Chenyang. "Enforcement." In Win in Chinese Courts. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-3342-6_10.
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AbstractIn China, if the losing party does not take the initiative to satisfy an effective court judgment, arbitral award and other legal documents, the winning party may apply to the court for enforcement. Then the court may deduct the deposit and/or auction the property of the losing party and use the money/proceeds obtained therefrom to repay the winning party. However, this enforcement power is exclusively vested in the court, and the winning party cannot seize and/or dispose of the property of the losing party on its own. Due to the large number of enforcement cases, the difficulty in tracking down enforceable property and the insufficient punishment against dishonest judgment debtors, China has been plagued by the “difficulty in enforcement” for a long time. In China, the main reason for the “difficulty in enforcement” is that it is difficult to track down the enforceable property of the party subject to enforcement. In order to resolve this problem, Chinese courts have vigorously promoted the information system interconnection between different government departments, which has greatly improved the efficiency of tracking down and/or freezing the property of the party subject to enforcement through the information network system. Another reason for the “difficulty in enforcement” is that it is difficult to sell off non-cash properties. Chinese courts have carried out judicial auctions through the Internet, which has greatly improved the deal closing rate and increased the transaction price of the property auctioned, and greatly improved the possibility of the applicant being compensated. Other than the above approaches, Chinese courts may also impose various restrictions and punishments on dishonest judgment debtors. There are various restrictions and punishments and the implementation thereof depends on the interconnection of different information network systems. If the circumstances in which the party subject to enforcement evades or resists enforcement are serious, the Chinese court may also hold it criminally responsible. In addition, being a contracting state to the United Nations Convention on the Recognition and Enforcement of Foreign Arbitral Awards (hereinafter referred to as the “New York Convention”), China has been holding a quite friendly attitude towards the recognition and enforcement of foreign arbitral awards. In practice, most foreign arbitral awards can be recognized and enforced in China. Compared with the recognition and enforcement of foreign arbitral awards, there are more requirements and preconditions for the recognition and enforcement of foreign court judgments. Nevertheless, China is now relaxing these requirements and preconditions, and more and more foreign court judgments are being recognized and enforced. With continuous efforts of relevant departments, the enforcement conducted by Chinese courts is getting increasingly effective. We firmly believe that Chinese courts can further improve the enforcement effectiveness as day goes by.
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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. Organizations must be formed to provide high-quality products through effective HRM. E-HRM as an implementation support system (ISS) improves human resource utilization and creativity. E-HRM connects communication and talent identification for speedy skill evaluation and identification. Many companies use this new technology to manage HR processes, operations, and data online. The chapter addresses new HRM recruitment practices, their advantages and cons, and HRM's future.
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Collins, Samuel. "The Secret Lives of ePortfolios." In Applying Data Science and Learning Analytics Throughout a Learner’s Lifespan. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-7998-9644-9.ch012.
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This chapter looks at student ePortfolios as a potential resource for graduate careers through text network analysis. The chapter begins with a critical examination of the current state of applicant tracking systems (ATS) and the way they utilize ranking algorithms to reduce graduates to a bundle of fungible skills. As a complementary corrective to these systems, the essay suggests text network analysis of ePortfolios, arguing that this would be one way to hire graduates for the future by opening the possibility for latent networked skills and meanings to re-define jobs. Network applications allow for prospective employers to quickly analyze ePortfolio content and see potential connections and innovations. Moreover, a text network analysis would be one way to develop more team-based approaches that would focus less on the individual than on the way that graduates might combine with each other in innovative teams. ePortfolios emerge here as a way of bringing back complexity into what is fast becoming an entirely automated hiring process.
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"Applicant Tracking Systems." In Encyclopedia of Electronic HRM. De Gruyter Oldenbourg, 2020. http://dx.doi.org/10.1515/9783110633702-033.
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Morris, Mark A. "Recruiting Technology." In Becoming a Talent Magnet. Oxford University PressNew York, 2024. https://doi.org/10.1093/oso/9780190938512.003.0005.
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Abstract This chapter addresses the core elements of technology used in talent acquisition, leveraging findings from a recent survey of senior talent acquisition leaders across the United States. A special-focus deep dive includes the core of most company’s talent acquisition technology, the applicant tracking system. Other topics discussed include social media tools, virtual interviews, artificial intelligence, and other types of technology tools used throughout the hiring lifecycle. Recommendations on when to use each type with different industries/candidates/human resources systems are offered and risks identified. Technology recommendations should be considered within the context of rapid change in this field.
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Shoenfelt, Elizabeth L. "Getting a Job." In Mastering the Job Market. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780190071172.003.0004.
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This chapter provides readers with critical insights into the nuts and bolts of conducting a job search. Specific actions that industrial-organizational master’s students can take to execute a successful job search are provided. Actions in graduate school that lay a foundation for a successful job search include developing an elevator speech, pursuing internships (paid or unpaid), participating in applied projects, and ensuring a professional social media image. Strategies for completing an application, using applicant tracking systems, writing résumés and cover letters, responding to phone screens, preparing for interviews, and demonstrating job offer etiquette are provided. These strategies enable most job seekers to land one or multiple job offers within six months of graduation.
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Divya J, Ms, and Ms Kajal J.Mehta. "ENHANCING HUMAN RESOURCES MANAGEMENT THROUGH AI-DRIVEN TALENT ACQUISITION AND EMPLOYEE ENGAGEMENT." In Futuristic Trends in Network & Communication Technologies Volume 3 Book 3. Iterative International Publishers, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3binc3p9ch1.
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Integration of artificial intelligence (AI) technologies in the field of Human Resources (HR) to optimize talent acquisition and management processes is the way forward. The advancements in AI have presented HR professionals with new tools and opportunities to streamline recruitment, enhance candidate assessment, and improve employee engagement and retention. The various applications of AI in HR, include AI-powered applicant tracking systems, predictive analytics for talent sourcing, chatbots for candidate screening, AI-based employee performance evaluation and personalized learning and development programs. Using AI has potential benefits, challenges, and ethical considerations too. So, it is important to understand the best practices and strategies to ensure the responsible and effective implementation of AI technologies in HR processes.
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Monje Amor, Ariadna. "Reinventing Human Resources Through Digitalization." In Advances in Human Resources Management and Organizational Development. IGI Global, 2023. http://dx.doi.org/10.4018/978-1-6684-6745-9.ch007.
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Human resource management (HRM) in organizations is rapidly being transformed by information technologies. The COVID-19 pandemic has intensified the pace of digitalization of HR processes, which has become particularly important in the context of the digital transformation of human resource activities. Those organizations that take advantage of digital HRM technologies are better positioned than those that pay less attention to them. Additionally, organizations have had to adapt to new work models such as hybrid or remote work. This chapter describes how digitization is reshaping human resources by introducing digital HR transformation in different activities, comparing digital recruitment to traditional recruitment, focusing on the role of applicant tracking systems in recruitment and selection, and examining digital trends and work models. Finally, several practical implications will be discussed.
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Mapuranga, Raymond, Kebiat Mukuze, and Abigail Maravanyika. "Talent Management in the Digital Age." In Advances in Human Resources Management and Organizational Development. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2889-7.ch014.
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Using the integrative literature review as the research methodology, this chapter explores the role of technology in talent management within the public sector, specifically focusing on recruitment and retention strategies. The chapter highlights the unique challenges faced by public sector organizations in talent acquisition and retention, such as limited budgets, bureaucratic processes, and the perception of a less dynamic work environment. The chapter revealed that various technologies can be utilized to improve recruitment efforts in the public sector including the use of applicant tracking systems, online job portals, and social media platforms. The study concluded that technology could contribute to talent retention in the public sector through the role of digital platforms in fostering employee engagement, facilitating knowledge sharing and collaboration, and providing continuous learning opportunities. Lastly, the chapter provides practical recommendations for public sector organizations looking to leverage technology for recruitment and retention.
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Bhargava, Dr Shikha, and Dr Shiva Johri. "TALENT ACQUISITION AND RETENTION: A CRITICAL REVIEW OF THE LATEST INNOVATIONS & CHALLENGES IN TALENT MANAGEMENT." In Futuristic Trends in Management Volume 3 Book 28. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bhma28p5ch2.
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In today's competitive business landscape, organizations recognize the paramount significance of talent acquisition and retention as key drivers of sustained success. This research review paper aims to provide a critical examination of the latest innovations and challenges in talent management, with a particular focus on talent acquisition and retention strategies. The review commences by exploring the latest innovations in talent acquisition. It examines how technology has transformed the recruitment landscape, with the advent of AI-driven applicant tracking systems, social media recruiting, and virtual job fairs. The subsequent section of the review concentrates on the challenges faced by organizations in retaining their valuable human capital. Furthermore, this research review explores the intersection of talent acquisition and retention, emphasizing the importance of aligning these functions within a cohesive talent management framework. It highlights the significance of on boarding and employee development in enhancing retention rates and fostering long-term employee commitment. Throughout the analysis, the paper discusses the impact of the COVID-19 pandemic on talent management practices, including remote on boarding, virtual talent development initiatives, and strategies for supporting employee well-being in challenging times. In conclusion, this research review provides valuable insights into the contemporary landscape of talent acquisition and retention, identifying the latest innovations and acknowledging the challenges faced by organizations. By synthesizing current literature and research, this paper offers a comprehensive understanding of the best practices required to attract and retain top talent in an ever-changing business environment. As organizations navigate the complexities of talent management, the findings from this review will serve as a roadmap to optimize talent acquisition and foster a thriving, engaged, and productive workforce.
Conference papers on the topic "Applicant tracking systems 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 format.[3] This project introduces a web based resume building application designed to simplify the resume creation process. This tool is aimed at making resume building more accessible, efficient, and user friendly for individuals at any career stage.[1] With the rise of online recruitment, traditional hiring methods are becoming less effective. In India, especially in the IT sector, there are plenty of jobs, but many candidates don’t meet the requirements. Resumes are often poorly organized, and the skills listed aren’t verified, making it hard for recruiters to choose the right person for the job. Big tech companies use an Application Tracking System (ATS) to filter through many resumes, but this still leaves challenges.[4] To solve this problem, we propose building a machine learning web app that creates well structured, verified resumes and portfolios. The platform, called “Real Resume,” will verify the candidate’s skills through mock tests and make sure the resume meets ATS guidelines. Verified resumes will be shown to recruiters and categorized according to their needs, making the hiring process easier by ensuring that only qualified candidates are presented for each job role.[2] A resume is a document job seekers use to highlight their qualifications for a position, summarizing their experience and education to show why they’re a good fit for a job. For new graduates, creating a resume can be challenging because they have limited practical experience. Research shows that around 75% of resumes don’t even make it past the first stage at companies, and the numbers are worse for fresh graduates. [5] Other common problems with resumes include poor organization Incorrect formatting, and not knowing which sections to include or leave out.[5]
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Jei, Arvind, Ushashish Chandra, and Anand Madasamy. "Natural Language Processing Based Screening for Applicant Tracking Systems." In International Conference on Recent Trends in Data Science and its Applications (ICRTDA 2023). River Publishers, 2023. http://dx.doi.org/10.13052/rp-9788770040723.113.
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Malarmannan, Arjun. "Enhancing Workforce Diversity: Integrating Inclusivity Parameters into Applicant Tracking Systems." In Global Symposium on Leadership and Project Management 2023. Global Symposium on Leadership and Project Management, 2023. http://dx.doi.org/10.52202/070182-0002.
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Kota, Srinath, and Mo Jamshidi. "ATS: Aerial Tracking and Surveillance System." In 2008 IEEE International Conference on System of Systems Engineering (SoSE). IEEE, 2008. http://dx.doi.org/10.1109/sysose.2008.4724193.
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Chy, John. "Application of Virtualization to Implement the Automatic Train Supervision System of a Communications Based Train Control System." In 2020 Joint Rail Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/jrc2020-8085.
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Abstract Capacity improvement and obsolescence management are the primary reasons for deploying Communications Based Train Control (CBTC) technology to replace conventional track circuit-based signaling systems like in New York City Transit (NYCT), Baltimore Maryland Transit Administration (MTA) or the San Francisco Bay Area Rapid Transit District (BART). Resignaling projects without stopping revenue operations are highly complex and are referred as brownfield train control projects. The Automatic Train Supervision (ATS) subsystem in a CBTC System is responsible for monitoring and regulating train operations. The ATS’ responsibilities include functions such as identifying trains, tracking and displaying trains, setting speed restrictions and work zones, automatic and manual routing capabilities. In addition, the ATS serves as the Human Machine Interface (HMI) between Train Controllers at the Operations Control Center (OCC). One of the challenges in brownfield train control projects is fitting a CBTC ATS subsystem into an already existing Operations Control Center (OCC). The console in the operating theater will need to host both the existing system and the new CBTC ATS workstation. Similarly, the technical rooms may already be at capacity but still need to accommodate the CBTC system in addition to the legacy system for the transition period. Transferring the OCC to a new building is often part of the modernization program and is the ideal method to mitigate space constraints. However, CBTC deployment is not always associated with transfer to a new larger building with a large OCC theater. Transfer to the new OCC with more space may be done before the CBTC deployment or at the same time as the CBTC revenue service. When there is no new larger OCC, solutions need to be investigated to accommodate both the legacy system and the new CBTC at the existing OCC. Advancements in virtual technology provides a more efficient solution that reduces the amount of physical space an ATS needs in the OCC without compromising communication and processing speed or capabilities. This paper describes the different equipment and functionalities of an ATS subsystem and the challenge of fitting each piece into an existing OCC while keeping the legacy system. The paper then discusses the basic technology behind networking, defining the concept of machine virtualization at a high level, and how all these technologies may be used to solve the ATS challenges faced during CBTC resignaling projects.
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Thiele, Frederik, Iñigo Fernandez Imaña, Xavier Manuel Juanpere, and Harald Pfifer. "Adaptive Control for Vibration Attenuation of a Laser Communication Terminal." In ESA 12th International Conference on Guidance Navigation and Control and 9th International Conference on Astrodynamics Tools and Techniques. ESA, 2023. http://dx.doi.org/10.5270/esa-gnc-icatt-2023-106.
Full textAbstract:
Laser Communication is the next generation of communication technology in the space industry, finding use in near earth as well as deep space application. They provide secure connections with significant higher data rates and less power consumption compared to conventional methods. Moreover, they address the ongoing problem of frequency saturation in radio communication technology. A challenge for optical systems is the high pointing requirement necessary to establish reliable communication links, which increase with higher data rates or distance. In this work a control method using estimation and adaptive algorithms is presented to attenuate vibrations as the main contributors for depointing in a standard Laser Communication Terminal (LCT) setup. Higher tracking performance compared to a baseline controller is achieved and the potential for decreasing hardware requirements, and thus enabling cost reduction, is analysed. The investigated standard LCT has a rigid telescope and focal plane assembly. A Fine Pointing Mechanism (FPM), which shows flexible behaviour, provides pointing angle adjustments, while an Acquisition and Tracking Sensor (ATS) determines the depointing measurement based on the transmitted and received signal. The entire terminal is connected to the satellite with a flexible mounting structure, which includes a Coarse Pointing Mechanism (CPM) to change the telescope's attitude. Establishing and maintaining an optical communication link requires a complex sequence and multiple LCT operational modes. After an initial acquisition and alignment phase the data transmission is conducted, which imposes the highest constraints for performance and robustness on the system. Those guarantee a continuous communication link and ensure specified data rates. Therefore, only the transmission mode is investigated within this paper. During this operational mode the CPM holds its position and can be considered quasi-static. The pointing adjustment is provided by the FPM in closed-loop operation. The system experiences several external disturbances, which can deteriorate pointing performance. They are transferred from the satellite to the terminal through the CPM. Typical sources are thermoelastic behaviour, solar array rotation and various micro-vibrations (MVs). Thereof MVs created by the reaction wheels for the satellite’s attitude control are identified as the main contributor, as their frequencies can coincide with the CPM-Modes. Hence only the impact of the reaction wheels is considered within the presented study. At any given time, multiple superimposed MVs from the reaction wheels are present within the system. They can each be modelled as a sinusoid with variable frequency and amplitude. After CPM transfer, they act on the closed-loop as an output disturbance during transmission mode. The general envelope for frequencies and amplitudes is known due to the reaction wheels specifications. However, the exact values at a specific time are considered unknown as no communication between the satellite and the LCT is assumed. The CPM orientation and thus its mode is indefinite as well. Only the FPM and ATS transfers are considered known. The required pointing performance can be provided using high bandwidth controllers and high sample rates, needing expensive hardware for their implementation. Within this work an alternative control method using estimation and adaptive algorithms is presented. It is based on the principle of destructive interference and particularly suited to attenuate sinusoidal disturbances with the potential to decrease hardware requirements compared to high bandwidth controllers. An observer is defined to provide an estimation of the disturbances imposed on the system after CPM transfer. It uses the residual disturbance measurement by the ATS as well as the ATS and FPM transfer. The synthesized control signal for the FPM is also fed back into the observer, allowing the estimation of the original disturbance despite attenuation. The observer provides an estimation of the disturbance in its entirety, however synthesizing the appropriate control input requires the specific properties, such as frequency, amplitude and phase-offset of each present sinusoid separately. A Fast Fourier Transformation (FFT) of a discrete signal section and analysis of the obtained frequency spectrum provides the current frequency of the most dominant sinusoid in the disturbance. Then a parameterized signal is created adaptively, which matches the amplitude and phase-offset of the disturbance part corresponding to the frequency provided by the FFT. The implemented gradient algorithm uses the solution of two differential equations minimizing a cost function, which represents the residual disturbance. A cascade setup of the described method is used to handle all parts of the disturbance. An intermediate signal processing step is added to prevent duplication in the frequency estimation by removing already identified signals from the further investigated estimated disturbance. In a concluding step the parameters are adjusted according to the FPM transfer and an additional phase-offset of 180° is introduced to synthesize the necessary control signal to attenuates the disturbance at FPM output. A nonlinear simulation is used to evaluate the pointing performance for a realistic scenario with multiple MVs. A Monte-Carlo study for several CPM-Modes, which also includes sensor noise and FPM transfer uncertainty, showed a significant improvement in pointing performance, especially during CPM resonance, compared to a simple high bandwidth controller provided as a baseline reference by Airbus. Moreover, preliminary investigations about the impact of different sample rates are conducted. The presented work was developed in cooperation between the Technische Universität Dresden and Airbus Defence and Space as part of the ESA-funded project No. AO/111017/21/NL/MGu entitled Adaptable Control and Estimation with Guaranteed Robust Performance (ACE).