Calorie Estimation of Food and Beverages using DeepLearning

This project aims to provide an automated system for accurately estimating the calorie content of food and beverages using advanced deep learning algorithms. With the increasing demand for health-conscious individuals, there is a need for a reliable, efficient, and easy-to-use tool that can help users make informed dietary choices. The project utilizes image processing techniques and deep learning models, such as Convolutional Neural Networks (CNN), to analyze food images and predict the corresponding calorie content. The system works by first capturing an image of the food or beverage, which is then processed and passed through a pre-trained deep learning model. The model is trained on a large dataset containing images of various food items along with their nutritional information. After preprocessing the input image, the model classifies the food and estimates the calorie count by leveraging its learned features. The estimated calorie value is then displayed to the user in real-time. This project leverages key technologies, including image recognition, deep learning, and nutrition analysis. It is designed to be integrated into mobile applications or web platforms, allowing users to track their daily caloric intake efficiently. The system's accuracy is continuously improved through training on a diverse dataset, ensuring reliable calorie estimation across different food items. This tool has the potential to revolutionize personal health management by promoting healthier eating habits.

Calorie estimation, deep learning, image recognition, food classification, Convolutional Neural Networks, health management, nutrition analysis, real-time prediction.

Lee, H., & Kim, H. (2018). “Food recognition with deep learning.” Proceedings of the International Conference on Image Processing (ICIP), 2220-2224.
Sharma, P., & Gupta, R. (2022). Machine Learning Techniques for Chronic Kidney Disease Detection. Journal of Healthcare Engineering, 2022, 1-9.
Tambi, V. K., & Singh, N. Evaluation of Web Services using Various Metrics for Mobile Environments and Multimedia Conferences based on SOAP and REST Principles.
Kumar, T. V. (2024). A Comparison of SQL and NO-SQL Database Management Systems for Unstructured Data.
Kumar, T. V. (2024). A Comprehensive Empirical Study Determining Practitioners’ Views on Docker Development Difficulties: Stack Overflow Analysis.
Kumar, T. V. (2024). Developments and Uses of Generative Artificial Intelligence and Present Experimental Data on the Impact on Productivity Applying Artificial Intelligence that is Generative.
Kumar, T. V. (2024). A New Framework and Performance Assessment Method for Distributed Deep Neural NetworkBased Middleware for Cyberattack Detection in the Smart IoT Ecosystem.
Sharma, S., & Dutta, N. (2024). Examining ChatGPT’s and Other Models’ Potential to Improve the Security Environment using Generative AI for Cybersecurity.
Tambi, V. K., & Singh, N. (2019). Development of a Project Risk Management System based on Industry 4.0 Technology and its Practical Implications. Development, 7(11).
Tambi, V. K., & Singh, N. Blockchain Technology and Cybersecurity Utilisation in New Smart City Applications.
Arora, P., & Bhardwaj, S. Mitigating the Security Issues and Challenges in the Internet of Things (IOT) Framework for Enhanced Security.
Arora, P., & Bhardwaj, S. (2017). A Very Safe and Effective Way to Protect Privacy in Cloud Data Storage Configurations.
Arora, P., & Bhardwaj, S. (2019). The Suitability of Different Cybersecurity Services to Stop Smart Home Attacks.
Arora, P., & Bhardwaj, S. (2020). Research on Cybersecurity Issues and Solutions for Intelligent Transportation Systems.
Arora, P., & Bhardwaj, S. (2021). Methods for Threat and Risk Assessment and Mitigation to Improve Security in the Automotive Sector. Methods, 8(2).
Arora, P., & Bhardwaj, S. Research on Various Security Techniques for Data Protection in Cloud Computing with Cryptography Structures.
Arora, P., & Bhardwaj, S. Examining Cloud Computing Data Confidentiality Techniques to Achieve Higher Security in Cloud Storage.
Arora, P., & Bhardwaj, S. Designs for Secure and Reliable Intrusion Detection Systems using Artificial Intelligence Techniques.
Shreyas, S. K., Katgar, S., Ramaji, M., Goudar, Y., & Srikanteswara, R. (2017). Efficient Food Storage Using Sensors, Android and IoT. Student BE, Department of CS&E Assistant Professor, Department of CS&E, ramya. srikanteswara@ nmit. ac. in NitteMeenakshi Institute Of Technology, Bengaluru.