Vol. 4 No. 4 (2025): October
RESEARCH ARTICLES

AI-Driven Emotion Analytics For Emergency Management in Tourism Using Improved CNN

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  • P Chandra Obul Reddy,
  • P Charitha,
  • B Ganesh Kumar Reddy,
  • K Harshith,
  • M Anjali,
  • B Rohan
P Chandra Obul Reddy
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
P Charitha
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
B Ganesh Kumar Reddy
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
K Harshith
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.
M Anjali
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.M Anjali
B Rohan
Department of AI and Data Science, Annamacharya Institute of Technology and Sciences, Kadapa, Andhra Pradesh, India.M Anjali
DOI: https://doi.org/10.5281/zenodo.15251134

Published 2025-04-20

Keywords

  • Emotion Recognition,
  • Convolutional Neural Networks,
  • , Image Processing,
  • Emergency Response,
  • Tourism Industry

How to Cite

P Chandra Obul Reddy, P Charitha, B Ganesh Kumar Reddy, K Harshith, M Anjali, & B Rohan. (2025). AI-Driven Emotion Analytics For Emergency Management in Tourism Using Improved CNN. International Journal of Computational Learning & Intelligence, 4(4), 725–732. https://doi.org/10.5281/zenodo.15251134

Copyright (c) 2025 P Chandra Obul Reddy, P Charitha, B Ganesh Kumar Reddy, K Harshith, M Anjali, B Rohan

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Abstract

Emotion recognition plays a critical role in enhancing human-computer interactions, particularly in dynamic environments like the tourism industry. During emergency events, understanding tourists' emotions can aid in decision-making, safety measures, and overall experience management. This study leverages deep learning methodologies, particularly Convolutional Neural Networks (CNN), to classify and analyze emotional states. The proposed system integrates image preprocessing, feature extraction, and advanced classification techniques to improve accuracy and efficiency. By incorporating real-time emotion detection, the model enhances responsive management strategies, ensuring improved safety and customer satisfaction.
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