| Issue |
BIO Web Conf.
Volume 172, 2025
International Conference on Nurturing Innovative Technological Trends in Engineering – BIOscience (NITTE-BIO 2025)
|
|
|---|---|---|
| Article Number | 02002 | |
| Number of page(s) | 18 | |
| Section | Bioinformatics / Computational Biology | |
| DOI | https://doi.org/10.1051/bioconf/202517202002 | |
| Published online | 10 April 2025 | |
Quantum AI: A Cognitive Machine Learning Technique based on Nurturing Food Security Sustainability Predictive Analysis for Life Science - Bioengineering in Healthcare
1 Department of Information Technology, Agni College of Technology, Chennai, India. senthilga@gmail.com
2 Department of Computer Science and Engineering, Vellore Institute of Technology Chennai Campus, Chennai, India. monica.km@vit.ac.in
3 Department of Electronics and Communication Engineering, Sri Sai Ram Institute of Technology, Chennai, India. r.praba05@gmail.com
4 Department of Computer Science and Engineering, Agni College of Technology, Chennai, India. prins19911128@gmail.com
5 Department of Information Technology, Agni College of Technology, Chennai, India. elavarasi.mtec@gmail.com
* Corresponding author: senthilga@gmail.com
Individualized and accurate evaluation of nutrient intake is essential for good health. Disease prevention and increased food security This article combines image analysis with quantum algorithms for precise food insights, introducing an advanced quantum-enhanced AI system. It is designed to predict the nutritional content of foods consumed. The system starts with taking photos of food using a Convolutional Neural Network (CNN) is processed by it has a classification accuracy of 91.87%. User-specific information such as age, weight, height, and BMI are also used to calculate individual nutritional needs. It enables tailored dietary recommendations. Quantum Support Vector Machines (QSVM), Quantum Neural Network (QNN), and Quantum Reinforcement Learning (QRL). The system's Leveraging (QRL) has high prediction accuracies of 90%, 92%, and 93%, ensuring efficient nutritional analysis in different foods. Integrating quantum computer models will greatly improve predictive performance and scalability. This has led to advances in bioengineering applications related to personalized nutrition. The proposed approach has the potential to be widely applied in health care. By helping with personal nutrition planning and supporting nutritional decision-making at a granular level.
Key words: Quantum AI / Convolutional Neural Network (CNN) / Quantum Support Vector Machines (QSVM) / Quantum Neural Networks (QNN) / Quantum Reinforcement Learning (QRL) / Nutritional Prediction / Health Optimization / Quantum Computing
© The Authors, published by EDP Sciences, 2025
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