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              <text>Machine Learning Approach for Evaluating Industry-Based Employer Ranking and Financial Stability</text>
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              <text>Data-driven Assessment; Employer ranking; Employer Ranking; Financial Stability; Job Market Metrics; Longitudinal Studies; Machine Learning; Sentiment Analysis</text>
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              <text>Using the computational prowess of machine learning, this study presents a fresh method for assessing the relative standing and fiscal health of employers across different sectors. The research makes use of a wide variety of data, including financial reports, statistics on the labor market, employee evaluations, and indicators unique to the business, to arrive at in-depth judgements. The financial stability assessment applies a linear regression model, whereas employer ranking is predicted using a logistic regression model. Financial data, employment market dynamics, and sentiment research are used as foundational characteristics for these models. Company A is more financially stable than Company B, yet it is anticipated to be ranked lower as an employer. This highlights the difficulty of judging businesses. The implications of these results for job-seekers, investors, and businesses are varied. The study also highlights the significance of ethics, openness, and addressing biases in assessment. This study paves the way for future advancements in this crucial subject and provides a basis for data-driven, well-informed decision-making in the ever-changing landscapes of contemporary industrial evaluations.  2024 IEEE.</text>
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              <text>Swain P.C.; Balan S.; Lakshmi S.R.; Choudhary A.; Patjoshi P.K.; Raja J.</text>
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              <text>5th International Conference on Recent Trends in Computer Science and Technology, ICRTCST 2024 - Proceedings, pp. 111-115.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2024-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICRTCST61793.2024.10578454" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICRTCST61793.2024.10578454&lt;/a&gt;
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              <text>ISBN: 979-835035137-8</text>
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              <text>Swain P.C., Kalinga Institute of Industrial Technology (KIIT), School of Social, Financial &amp;amp; Human Sciences, Odisha, Bhubaneswar, India; Balan S., Rajalakshmi Engineering College, Department of Management Studies, Chennai, India; Lakshmi S.R., Christ University, Department of Business &amp;amp; Management, Karnataka, Bangalore, 560073, India; Choudhary A., Graphic Era Deemed to be University, Department of Computer Science &amp;amp; Engineering, Uttarakhand, Dehradun, 248002, India; Patjoshi P.K., Centurion University of Technology and Management, BBSR Campus, School of Management, India; Raja J., St. Martins Engineering College, Department of Computer Science and Engineering, Telangana, Secunderabad, India</text>
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