OVQATLANISH MONITORINGI TIZIMLARIDA SUN’IY INTELLEKT VA ASSOSIATIV QOIDALARNI KO‘RIB CHIQISH VA TAHLIL QILISH
Kalit so'zlar
https://doi.org/10.47390/ts-v4i2y2026N02Kalit so'zlar
sun’iy intellekt, ovqatlanish monitoringi, individual ratsion, assosativ qoidalar, Apriori algoritmi, FP-Growth algoritmi, ma’lumotlarni intellektual tahlil qilish.Annotasiya
Mazkur maqolada ovqatlanish monitoringi tizimlarida sun’iy intellekt texnologiyalari va assosativ qoidalarni izlash ko‘rib chiqish va tahlil qilishga asoslangan. Katta hajmdagi ovqatlanish ma’lumotlarini intellektual tahlil qilish sog‘lom ovqatlanish bo‘yicha shaxsga mos tavsiyalar ishlab chiqish imkonini beradi. Maqolada ovqatlanish monitoringi tizimlarining umumiy modeli, sun’iy intellektning individual ovqatlanishdagi o‘rni hamda assosativ qoidalarni izlash algoritmlari — Apriori va FP-Growth algoritmlarining nazariy asoslari ko‘rib chiqilgan. Shuningdek, sun’iy intellekt va assosativ qoidalarni integratsiyalash asosida individual ratsion shakllantirishning afzalliklari va amaliy qo‘llanilish jihatlari yoritilgan. Tadqiqot natijalari sog‘lom ovqatlanishni ta’minlash va ovqatlanish bilan bog‘liq kasalliklarni oldini olishda intellektual tizimlardan foydalanish samaradorligini ko‘rsatadi.
Manbalar
1. Ordovas J.M., Ferguson L.R., Tai E.S. Personalized nutrition and health. BMJ, 2020, Vol.369, pp. 118–125.
2. Zhang Y., Chen J., Wang X. Artificial intelligence in dietary monitoring systems. IEEE Access, 2021, Vol. 9, pp. 3–10.
3. Chen L., Yang S., Li H. AI-based food intake assessment. Sensors, 2022, Vol. 22(2), pp. 45–52.
4. Kaur P., Sharma M. Machine learning approaches for nutrition recommendation systems. Expert Systems with Applications, 2021, Vol. 168, pp. 302–310.
5. Torkamani A., Wineinger N.E., Topol E.J. The role of AI in personalized nutrition. Nature Reviews Genetics, 2020, Vol. 21, pp. 156–165.
6. Li J., Liu Y., Zhang D. Diet pattern analysis using association rule mining. Nutrition, 2021, Vol. 82, pp. 89–96.
7. Agrawal R., Imieliński T., Swami A. Association rule mining in health data. Knowledge-Based Systems, 2020, Vol. 195, pp. 11–20.
8. Han J., Pei J., Kamber M. Data mining techniques and applications. Information Sciences, 2020, Vol. 512, pp. 243–250.
9. Feng Q., Liu X., Zhang H. Apriori-based nutrition data analysis. Applied Soft Computing, 2021, Vol. 99, pp. 410–418.
10. Zhou K., Wang Y., Xu L. FP-Growth for nutrition datasets. Applied Soft Computing, 2022, Vol. 114, pp. 57–65.
11. Martínez S., López M., García J. AI-driven personalized diet systems. Computers in Biology and Medicine, 2021, Vol. 134, pp. 133–140.
12. Sun Y., Li P., Zhao H. Hybrid AI and association rule models for diet planning. Expert Systems, 2022, Vol. 39(4), pp. 410–420


