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Mohanan, Vasuky
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Humanities Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media

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Cat Body Language Recognition Using Computer Vision in an Android Application Thor, Wen Zheng; Mohanan, Vasuky
Journal of International Conference Proceedings Vol 8, No 1 (2025): 2025 ICPM Malaysia Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v8i1.3999

Abstract

Understanding cat behaviour is essential for fostering healthy human-cat relationships, but its inherent complexity frequently leads to misunderstandings. This study introduces Emeowtions, an innovative Android application employing artificial intelligence (AI) to decipher cat emotions and body language in real-time. Addressing market gaps for comprehensive tools, Emeowtions integrates the YOLOv8n object detection model with a custom-trained multi-label classification model for cat emotion and body language analysis. The custom model was developed based on the CRoss Industry Standard for Data Mining (CRISP-DM) framework and trained using transfer learning with MobileNetV3 on a custom curated dataset of annotated cat images. Built using the Waterfall methodology, the application allows users to obtain real-time, AI-driven insights via their smartphone camera. Beyond that, it provides a hybrid recommendation system suggesting tailored behaviour suggestions, a user feedback loop for model refinement, and a direct chat interface for veterinary consultations. Technical evaluation showed the AI model achieved a recall of 0.742. Overall, Emeowtions offers a valuable, practical tool that demonstrates AI's capability to reduce misinterpretations of cat behaviour, ultimately fostering healthier human-animal relationships and contributing to improved cat welfare.
MealCompass: A Food Recommendation System with Machine Learning Mah, Chun-Hoe; Mohanan, Vasuky
Journal of International Conference Proceedings Vol 8, No 1 (2025): 2025 ICPM Malaysia Proceeding
Publisher : AIBPM Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.32535/jicp.v8i1.3998

Abstract

Post-pandemic, Malaysians face “choice overload” when eating out. Additionally, the rising incidence of diabetes and obesity in Malaysia emphasizes the need for healthier eating options. To address these problems, MealCompass recommends food to users based on different user-defined criteria. Moreover, it aims to enable users to find healthier options and allow restaurant owners to provide nutritional information on food items served, which studies have proved an increase in selection of healthier choices by 13.5%. A hybrid recommendation system is proven to be more effective compared to using traditional methods alone. MealCompass is developed in Java, with Firebase as the backend. The hybrid recommendation system is trained on Google Colaboratory, and recommendations are shown in the application through a Flask server and Retrofit client. Waterfall model is used throughout the whole project. User feedback such as cuisine preferences, diet preferences and allergy issues, as well as the ratings of recommendations from users of the application will continuously refine and enhance the recommendations, ensuring more personalized suggestions over time. User acceptance testing among 16 respondents showed satisfaction and capability to deliver accurate and diverse recommendations. Despite these successes, limitations are noted, laying the groundwork for future enhancements, such as deploying the recommendation system to the cloud.
Co-Authors Mah, Chun-Hoe Thor, Wen Zheng