INTELLIGENT PET CARE MANAGEMENT SYSTEM WITH LSTM-BASED PERSONALIZED RECOMMENDATIONS

Authors

  • Shyam Mahara Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Sumit Pandey Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Suraj Tiwari Computer Science and Engineering, Echelon Institute of Technology, Faridabad
  • Charu Rohilla Computer Science and Engineering, Echelon Institute of Technology, Faridabad

DOI:

https://doi.org/10.29121/ijetmr.v10.i7.2023.1601

Keywords:

Intelligent., Pet, System, Care, Lstm

Abstract

The Pet Care Management System is a comprehensive platform designed to streamline the process of pet care for owners, enabling them to efficiently manage their pets' dietary, health, and training needs. The system provides a single interface for pet owners to select appropriate pet food, schedule veterinary appointments, track vaccinations, and even access training resources. Built with ReactJS, ViteJS, Node.js, and MongoDB, the platform ensures an intuitive and responsive user experience while leveraging modern technologies for real-time data handling and seamless operations.
Incorporating Long Short-Term Memory (LSTM) models, the system optimizes personalized recommendations for pet care based on historical data, user preferences, and pet-specific needs. LSTM is used to predict the most suitable pet food and healthcare options by learning from previous user interactions and patterns, ensuring that each recommendation becomes more accurate and contextually relevant over time. The system’s backend is powered by Node.js, with MongoDB storing vital user and pet data, while JSON Web Token (JWT) provides secure authentication for users. This intelligent system not only reduces the time and effort required by pet owners but also contributes to smarter pet management, enhancing overall pet well-being

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Published

2023-07-31

How to Cite

Mahara, S., Pandey, S., Tiwari, S., & Rohilla, C. (2023). INTELLIGENT PET CARE MANAGEMENT SYSTEM WITH LSTM-BASED PERSONALIZED RECOMMENDATIONS. International Journal of Engineering Technologies and Management Research, 10(7), 45–59. https://doi.org/10.29121/ijetmr.v10.i7.2023.1601