This article investigates deep learning-based approaches for assessing the energy composition of food using images. Unlike traditional methods, deep learning techniques, particularly Convolutional Neural Network (CNN) models, enable the automated classification of food items and the estimation of their caloric values. The research highlights the significance of datasets and algorithms employed for food identification and volume estimation. Furthermore, the effectiveness of models used to determine energy composition is analyzed. The study underscores the potential of this approach to automate nutritional monitoring and its applications in the healthcare sector.
| Mualliflar | Norinov, Muhammad Yunus, Ibragimov, Ilkhomjon, Норинов, Мухаммад Юнус, Ибрагимов, Илхомжон, Norinov, Muhammad Yunus, Ibragimov, Ilxomjon |
|---|---|
| Jurnal | Жамият ва инновациялар / Общество и инновации / Society and innovations |
| Nashr sanasi | 2024-10-25 |
| Jild | 5 |
| Son | 10/S |
| Betlar | 356-362 |
| Til | O‘zbek |
| DOI | 10.47689/2181-1415-vol5-iss10/s-pp356-362 |
DOI: 10.47689/2181-1415-vol5-iss10/s-pp356-362 · Maqolaning asl sahifasi
Chuqur o'rganish, oziq-ovqat tasvirlari, energiya tarkibini baholash, Convolutional Neural Networks (CNN), kaloriyalarni hisoblash, oziq-ovqat tasnifi, hajmni baholash, oziq-ovqat monitoringi, sog'liqni saqlash texnologiyalari, sun'iy intellekt, глубокое обучение, изображения продуктов питания, оценка энергетического состава, сверточные нейронные сети (CNN), расчет калорий, классификация продуктов, оценка объема, мониторинг питания, технологии здравоохранения, искусственный интеллект, Deep learning, food images, energy composition assessment, Convolutional Neural Networks (CNN), calorie calculation, food classification, volume estimation, nutritional monitoring, healthcare technologies, artificial intelligence
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