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              <text>A Predictive Modelling of Factors Influencing Job Satisfaction Through a CNN-BiGRU Algorithm</text>
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              <text>Bidirectional Gating Recurrent Unit (BiGRU); Convolutional Neural Network (CNN); Long short Term Memory (LSTM)</text>
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              <text>The fields of humanities, psychology, and sociology are where the word 'job satisfaction' originated. According to psychology, it is a condition in which a worker experiences his circumstances emotionally and responds by experiencing either pleasure or suffering. It is regarded as a variable in various sociological categories pertaining to how each employee assesses and thinks about his work. Because a satisfied employee contributes to and builds upon an organization's success, job satisfaction is intimately tied to an employee's performance and the quality of the work they do. As a result, job satisfaction directly correlates to an organization's success. The proposed strategy incorporates data preprocessing, feature selection, and model training. The missing value is a common feature of data preparation. Feature selection is chosen using the ANOVA F-Test Filter, the Chi-Square Filter, and the full Data Set Construction procedure. The model's efficacy can be evaluated with the help of CNN-BiGRU. The proposed technique is compared to two more models: BiGRU and CNN. It has been shown that our proposed technique outperforms two other models.  2023 IEEE.</text>
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              <text>Kar R.; Renjith Krishnan K.; Antony P.; Rashmi B.H.; Padmapriya S.; Unni M.V.</text>
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              <text>International Conference on Self Sustainable Artificial Intelligence Systems, ICSSAS 2023 - Proceedings, pp. 68-73.</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>2023-01-01</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICSSAS57918.2023.10331757" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICSSAS57918.2023.10331757&lt;/a&gt;
&lt;br /&gt;&lt;br /&gt;&lt;a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181163473&amp;amp;doi=10.1109%2FICSSAS57918.2023.10331757&amp;amp;partnerID=40&amp;amp;md5=2993a1258d288e148441506558f66966" target="_blank" rel="noreferrer noopener"&gt;https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181163473&amp;amp;doi=10.1109%2fICSSAS57918.2023.10331757&amp;amp;partnerID=40&amp;amp;md5=2993a1258d288e148441506558f66966&lt;/a&gt;</text>
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              <text>ISBN: 979-835030085-7</text>
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              <text>Kar R., St. Claret College, Department of Management, Bangalore, India; Renjith Krishnan K., College of Economics and Business Administration, University of Technology and Applied Sciences, Shinas, Oman; Antony P., Jyoti Nivas College Autonomous, Department of Mba, Bangalore, India; Rashmi B.H., School of Business and Management, Christ University, Bangalore, India; Padmapriya S., Christ University, Department of Commerce, Bangalore, India; Unni M.V., St. Claret College, Department of Management, Bangalore, India</text>
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