Mazkur maqolada ta’lim muassasalarini boshqarish jarayonida innovatsion texnologiyalardan foydalanishning nazariy asoslari va amaliy jihatlari yoritilgan. Bugungi kunda ta’lim tizimida raqobatbardoshlikni ta’minlash, samaradorlikni oshirish va strategik maqsadlarga erishishda zamonaviy texnologiyalar muhim vosita sifatida qaralmoqda. Maqolada boshqaruv faoliyatiga raqamli platformalar, sun’iy intellekt asosidagi tizimlar, elektron hujjat aylanishi, masofaviy monitoring va tahlil dasturlarini joriy etishning afzalliklari tahlil qilinadi. Shuningdek, xorijiy tajribalar asosida milliy sharoitga mos innovatsion boshqaruv modellarini yaratish imkoniyatlari o‘rganiladi. Tadqiqot asosida innovatsion texnologiyalarni ta’lim menejmentiga integratsiya qilish orqali samarali, shaffof va tezkor boshqaruv tizimini shakllantirish bo‘yicha tavsiyalar ishlab chiqilgan.

Markaziy Osiyoda suv xavfsizligi tobora dolzarb masalaga aylanib bormoqda. Bunga iqlim o‘zgaruvchanligining kuchayishi, suvga bo‘lgan talabning ortishi hamda transchegaraviy daryolarni boshqarish bilan bog‘liq muammolar sabab bo‘lmoqda. Ushbu maqolada mintaqadagi transchegaraviy suv resurslarini boshqarish murakkabligi, milliy manfaatlar bilan mintaqaviy hamkorlik o‘rtasidagi o‘zaro bog‘liqlik asosida o‘rganiladi.

Рассмотрены виды тестовых заданий по филологическим дисциплинам и методика их создания, дано определение теста, пояснено понятие предметной области. Тестовые задания разделены на виды, приведены примеры. Показано, что компетентностные знания, умения и навыки студентов можно оценить на основе тестовых образцов.

This paper focuses on the selection and justification of deep learning models for emotion classification tasks. It provides a comprehensive analysis of various neural network architectures, including Convolutional Neural Networks, Recurrent Neural Networks, Long Short-Term Memory networks, and Transformer models, assessing their performance in recognizing and classifying human emotions from multimodal data sources. The study examines the strengths and limitations of each model with respect to data type, training efficiency, computational complexity, and generalization capabilities. Furthermore, criteria for optimal model selection tailored to real-world emotion recognition applications are discussed. The findings contribute to enhancing the accuracy and robustness of emotion classification systems and offer valuable guidelines for researchers and practitioners developing advanced affective computing solutions.

The food industry is a branch of the national economy that produces food products. The industry includes enterprises of meat and dairy, oil and fat, fish, flour and cereals, pasta, fruit and vegetable canning, dairy and cream, sugar, tea, confectionery, bakery, grape and champagne wines, alcohol, vodka, tobacco, brewing, thirst-quenching, soap and other industries.