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                <text>Conference Papers</text>
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              <text>Human Resource Management in the Power Industry Using Fuzzy Data Mining Algorithm</text>
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              <text>Fuzzy data mining; Human resource management; Power industry</text>
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              <text>Currently, database and information technology's frontier study area is data mining. It is acknowledged as one of the essential technologies with the greatest potential. Numerous technologies with a comparatively high level of technical substance are used in data mining, including artificial intelligence, neural networks, fuzzy theory, and mathematical statistics. The realization is challenging as well. Job satisfaction is one of several factors that cause employees to leave or switch jobs, and it is also closely tied to the organization's human resource management (HRM) procedures. It is continuously difficult and at times beyond the HR office's control to keep their profoundly qualified and talented specialists, yet data mining can assume a part in recognizing those labourers who are probably going to leave an association, permitting the HR division to plan a mediation methodology or search for options. We have analysed the major thoughts, techniques, and calculations of affiliation rule mining innovation in this article. They effectively finished affiliation broadcasting, acknowledged perception, and eventually revealed valuable data when they were coordinated into the human resource management arrangement of schools and colleges.  2023 IEEE.</text>
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              <text>Tripathi M.A.; Chacko E.; Jalaja V.; Srivastava A.; Rehana Banu S.; Dwivedi V.K.</text>
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              <text>Proceedings of the 2nd IEEE International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2023</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ACCAI58221.2023.10200599" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ACCAI58221.2023.10200599&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170043174&amp;amp;doi=10.1109%2FACCAI58221.2023.10200599&amp;amp;partnerID=40&amp;amp;md5=95c43d53927c7ad72b8a63c22449630e" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85170043174&amp;amp;doi=10.1109%2fACCAI58221.2023.10200599&amp;amp;partnerID=40&amp;amp;md5=95c43d53927c7ad72b8a63c22449630e&lt;/a&gt;</text>
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835031590-5</text>
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              <text>Tripathi M.A., Motilal Nehru National Institute of Technology, Department of Humanities and Social Sciences, Allahabad, India; Chacko E., Christ University, Dharmaram College Post, Bengaluru, India; Jalaja V., Reva University, School of Management Studies, Bengaluru, India; Srivastava A., G L.Bajaj Institute of Technology and Management, Uttar Pradesh, Greater Noida, 201306, India; Rehana Banu S., Yogi Vemana University, Andhra Pradesh, Kadapa, India; Dwivedi V.K., Vishwavidyalaya Engineering College, Department of Mathematics, Ambikapur, India</text>
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