Wisenosh Quantum AI: Nutritional Information Integrated With Image API and Nano Robotic System in Chemical Industries
DOI:
https://doi.org/10.62486/978-9915-9851-0-7_202632Keywords:
Wisenosh Quantum AI, Nutritional analysis, Food recognition, Deep learning, Nanomaterial sensors, Personalized nutrition, Food chemical processing, Robotic monitoringAbstract
The Wisenosh Quantum AI platform offers a complete and smart nutrition analysis solution that combines food recognition through images, machine learning, and monitoring at the nanoscale level for making personalized dietary management easier. Deep learning models like ResNet-50 and EfficientNet are utilized in a way that the machine is able to recognize the dishes in the images uploaded by users, and at the same time, connect the serving size and nutrients to a secure RESTful API for exact nutrient research. The evaluation includes not only calories but also macronutrients, micronutrients, and vitamins, while Named Entity Recognition brings to light allergens and potential dietary issues. Individual recommendations are generated through collaborative filtering which considers users' likes, habits, and even their changing health goals. The combination of robot-assisted data collection, nanomaterial-based biological feedback, and chemical analysis of food components allows the system to provide very timely and contextually appropriate dietary guidance.AI, robotics, and quantum-inspired computing team up in ways that let them monitor processes closely and tweak them for better results. They handle important jobs like keeping food consumption safe and managing chemical operations, not to mention their work in industrial setups. That kind of collaboration adds a sharp level of accuracy to all these areas, which in turn boosts how well they run and how trustworthy they feel. The technologies lean on one another to pull off some really impressive achievements in a wide range of uses.Wisenosh Quantum AI is the pioneer of intelligent nutrition and health-controlled industrial processes going from the past to the future.
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Copyright (c) 2026 Kavitha Shanmugakani, P.Selvarani, P.Brinda, K. Harini, Sai Guru Prigeesh M, Yogesh D, VishwaKanna M, Akhatov Akmal Rustamovich (Author)
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The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
