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Emogan Label-changing Approach for Emotional State Analysis in Mobile Communication Using Monkey Algorithm


Affiliations
1 Department of Computer Science, College of Engineering and Technology, Wollega University, Ethiopia
2 Department of Computer Science and Engineering, CMR University, India
3 Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India
4 Department of Artificial Intelligence, DVR and Dr. HS MIC College of Technology, India
     

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In mobile communication, understanding and analyzing emotional states plays a pivotal role in enhancing user experience and communication dynamics. Existing emotional state analysis methods often face challenges in accurately capturing dynamic changes in users' emotions during mobile communication. The lack of adaptability and real-time responsiveness hinders the effectiveness of these methods, highlighting the need for a novel approach. Despite the advancements in emotion analysis techniques, there is a gap in addressing real-time label-changing requirements in mobile communication. Existing methods lack the flexibility to adjust emotional labels dynamically, limiting their applicability in capturing the nuances of evolving emotional states. This research addresses the need for an efficient emotional state analysis approach by introducing the EmoGAN Label-Changing Methodology, utilizing the innovative Monkey Algorithm. The EmoGAN Label-Changing Approach integrates Generative Adversarial Networks (GANs) with the Monkey Algorithm to enable real-time label adjustments based on evolving emotional cues. This hybrid methodology leverages GANs for generating diverse emotional labels and employs the Monkey Algorithm for adaptive learning and quick adjustments, ensuring the model's responsiveness to changing emotional states. The experimental results demonstrate the superior performance of the EmoGAN Label-Changing Approach compared to traditional emotion analysis methods. The model successfully adapts to real-time emotional fluctuations, providing more accurate and timely insights into users' emotional states during mobile communication.

Keywords

EmoGAN, Emotional State Analysis, Mobile Communication, Monkey Algorithm, Real-time Label Changing.
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Abstract Views: 54

PDF Views: 1




  • Emogan Label-changing Approach for Emotional State Analysis in Mobile Communication Using Monkey Algorithm

Abstract Views: 54  |  PDF Views: 1

Authors

P. Ramesh Babu
Department of Computer Science, College of Engineering and Technology, Wollega University, Ethiopia
R. Nandhi Kesavan
Department of Computer Science and Engineering, CMR University, India
A. Sivaramakrishnan
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, India
G. Sai Chaitanya Kumar
Department of Artificial Intelligence, DVR and Dr. HS MIC College of Technology, India

Abstract


In mobile communication, understanding and analyzing emotional states plays a pivotal role in enhancing user experience and communication dynamics. Existing emotional state analysis methods often face challenges in accurately capturing dynamic changes in users' emotions during mobile communication. The lack of adaptability and real-time responsiveness hinders the effectiveness of these methods, highlighting the need for a novel approach. Despite the advancements in emotion analysis techniques, there is a gap in addressing real-time label-changing requirements in mobile communication. Existing methods lack the flexibility to adjust emotional labels dynamically, limiting their applicability in capturing the nuances of evolving emotional states. This research addresses the need for an efficient emotional state analysis approach by introducing the EmoGAN Label-Changing Methodology, utilizing the innovative Monkey Algorithm. The EmoGAN Label-Changing Approach integrates Generative Adversarial Networks (GANs) with the Monkey Algorithm to enable real-time label adjustments based on evolving emotional cues. This hybrid methodology leverages GANs for generating diverse emotional labels and employs the Monkey Algorithm for adaptive learning and quick adjustments, ensuring the model's responsiveness to changing emotional states. The experimental results demonstrate the superior performance of the EmoGAN Label-Changing Approach compared to traditional emotion analysis methods. The model successfully adapts to real-time emotional fluctuations, providing more accurate and timely insights into users' emotional states during mobile communication.

Keywords


EmoGAN, Emotional State Analysis, Mobile Communication, Monkey Algorithm, Real-time Label Changing.

References