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              <text>The Effect of Prediction on Employee Engagement Organizational Commitment and Employee Performance Using Denoised Auto Encoder and SVM Based Model</text>
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              <text>auto encoder-support vector machine (AE-SVM); kernel density estimation (KDE); normalization; support vector machine (SVM)</text>
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              <text>The purpose of human resources is to ensure that the appropriate people are hired for open positions at appropriate times, that the system receive the necessary training, and that their performance is monitored and their perspective skills are secure through the use of evaluation methods. Despite the importance of this data to decision-makers, it can be difficult to glean useful insights from large datasets. Data mining has made it possible for human resources experts to automate the hitherto tedious task of manually processing enormous data sets. Finding almost perfect outcomes is the main goal of data mining, which is to discover hidden knowledge in data patterns and trends. The proposed method goes as follows: preprocessing is done by data cleaning and data normalization, feature selection using correlation and information theoretic ranking criteria. The last step in training and evaluating the model is using AE-SVM, which stands for Auto Encoder Support Vector Machine. The suggested model is more effective and performs better than two existing models: Support Vector Machine and AE-CNN. The suggested approach attains an accuracy rate of 94%.   2024 IEEE.</text>
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              <text>Manavadaria M.S.; Kumar R.; Devi S.P.; Narendra Kiran P.B.; Priyadharshini K.V.; Allah Khan M.</text>
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              <text>International Conference on Intelligent Algorithms for Computational Intelligence Systems, IACIS 2024</text>
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          <name>Publisher</name>
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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/IACIS61494.2024.10721902" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/IACIS61494.2024.10721902&lt;/a&gt;
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              <text>Restricted Access</text>
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              <text>ISBN: 979-835036066-0</text>
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              <text>Online</text>
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              <text>Manavadaria M.S., Cspit, Department of Ece, Gujarat, India; Kumar R., Vidya Vihar Institute of Technology, Department of Cse, Bihar, India; Devi S.P., Velammal College of Engineering and Technology, Department of Cse, Madurai, India; Narendra Kiran P.B., Christ University, School of Business and Management, Bangalore, India; Priyadharshini K.V., M. Kumarasamy College of Engineering, Department of Information Technology, Karur, India; Allah Khan M., Uiet Babasaheb Bhimrao Ambedkar University (A Central University), Department of Civil Engineering, Lucknow, India</text>
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