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              <text>Efficient Method for Personality Prediction using Hybrid Method of Convolutional Neural Network and LSTM</text>
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              <text>Correlation Based Feature Subset; Personality Prediction; Social Network</text>
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              <text>Users' contributions and the emotions conveyed in status updates may prove invaluable to studies of human behavior and character. A number of other research have taken a similar approach, and the field itself is still growing. The goal of this proposed is to create a technique for deducing a user's personality traits based on their social media profiles. Among the many customer services now available on SNSs are media and recommendations of user involvement. The need to give internet users with more specialized and customized services that meet their specific requirements, which sometimes depend heavily on the users' inner personalities, is significant. However, there hasn't been much work done on the psychological analysis that's needed to deduce the user's inner nature from their outward activities. In this instance, LSTM-CNN was fed pre-processed and vectorized text documents. SNF is used for feature extraction. The proposed method employs CFS for the purpose of Feature Selection. Finally, LSTM-CNN was used to train the model. While CNN is good at extracting features that are independent of time, LSTM is better at capturing long-term dependencies. combination of features for personality prediction, the LSTM-CNN model is superior to the individual models.   2023 IEEE.</text>
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              <text>Sujatha N.; Pramod S.; Bhatla S.; Thulasimani T.; Kant R.; Chauhan A.</text>
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              <text>2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 959-964.</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/ICESC57686.2023.10193058" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICESC57686.2023.10193058&lt;/a&gt;
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              <text>ISBN: 979-835030009-3</text>
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              <text>Sujatha N., B. M. S. College of Engineering, Department of Mathematics, Karnataka, Bengaluru, India; Pramod S., Nitte Meenakshi Institute of Technology Yelahanka Bangalore, Department of Mathematics, Karnataka, India; Bhatla S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Thulasimani T., Bannari Amman Institute of Technology Sathyamangalam, Department of Mathematics, Tamilnadu, Erode, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India</text>
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