ICTACT Journal on Communication Technology

Open Access Open Access  Restricted Access Subscription Access
Open Access Open Access Open Access  Restricted Access Restricted Access Subscription Access

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
     

   Subscribe/Renew Journal


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.
Subscription Login to verify subscription
User
Notifications
Font Size

  • M.M. Joe, B. Ramakrishnan and R.S. Shaji, “Modeling GSM based Network Communication in Vehicular Network”, Proceedings of International Journal of Computer Network and Information Security, Vol. 6, No. 3, pp. 37-43, 2014.

  • J.P. Quadrat, J.B. Lasserre and J.B. Hiriart Urruty, “Pythagoras Theorem for Areas”, The American Mathematical Monthly, Vol. 108, No. 6, pp. 549-551, 2001.

  • N. Michelusi, M. Nokleby, U.I Mitra and R. Calderbank, “Multi-Scale Spectrum Sensing in Dense Multi-Cell Cognitive Networks”, IEEE Transactions on Wireless Communications, Vol. 67, No. 4, pp. 2673-2688, 2019.

  • J. Fan, L. Li and W. Chen, “Denoise-and-Forward Two-Path Successive Relaying with DBPSK Modulation”, IEEE Wireless Communication Letters, Vol. 6, No. 1, pp. 42-45, 2017.

  • V. Saravanan and A. Sumathi, “Handoff Mobiles with Low Latency in Heterogeneous Networks for Seamless Mobility: A Survey and Future Directions”, European Journal of Scientific Research, Vol. 81, No. 3, pp. 417-424, 2012.

  • C. Chandrasekar, “Qos-Continuous Live Media Streaming in Mobile Environment using Vbr and Edge Network”, International Journal of Computer Applications, Vol. 53, No. 6, pp. 1-8, 2012.

  • C.D. Kumar and V. Saravanan, “Weighted Multi-Objective Cluster Based Honey Bee Foraging Load Balanced Routing in Mobile Ad Hoc Network”, International Journal of Applied Engineering Research, Vol. 13, No. 12, pp. 10394-10405, 2018.

  • J. Jasmine, “DSQLR-A Distributed Scheduling and QoS Localized Routing Scheme for Wireless Sensor Network”, Recent Trends in Information Technology and Communication for Industry 4.0, Vol. 1, pp. 47-60, 2022.

  • S. Kato and D. Scott, “Relationships between Emotional States and Emoticons in Mobile Phone Email Communication in Japan”, International Journal on E-learning, Vol. 8, No. 3, pp. 385-401, 2009.

  • A. Mehrotra, R. Hendley and M. Musolesi, “MyTraces: Investigating Correlation and Causation between Users’ Emotional States and Mobile Phone Interaction”, Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, Vol. 1, No. 3, pp. 1-21, 2017.

  • O. Kwon, C.R. Kim and G. Kim, “Factors Affecting the Intensity of Emotional Expressions in Mobile Communications”, Online Information Review, Vol. 37, No. 1, pp. 114-131, 2013.

  • H. Lee and I.P. Park, “Towards Unobtrusive Emotion Recognition for Affective Social Communication”, Proceedings of IEEE Conference on Consumer Communications and Networking, pp. 260-264, 2012.

  • A.P. Plageras and K.E. Psannis, “IOT-based Health and Emotion Care System”, ICT Express, Vol. 9, No. 1, pp. 112-115, 2023.

  • H. Ahn and E. Park, “Motivations for User Satisfaction of Mobile Fitness Applications: An Analysis of User Experience based on Online Review Comments”, Humanities and Social Sciences Communications, Vol. 10, No. 1, pp. 1-7, 2023.


Abstract Views: 132

PDF Views: 1




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

Abstract Views: 132  |  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