MOBIL ILOVALARDA BASHORATLI MODELLAR SAMARADORLIGINI BAHOLASH
https://doi.org/10.5281/zenodo.17262872
Keywords:
Mobil ilovalar, bashoratli modellar, sun’iy intellekt, Machine Learning, Deep Learning, CNN, LSTM, Edge Computing, samaradorlik, foydalanuvchi tajribasiAbstract
Ushbu maqolada mobil ilovalarda qo‘llanilayotgan bashoratli modellar samaradorligi tahlil qilindi. Sun’iy intellekt texnologiyalarining (Machine Learning, Deep Learning, Natural Language Processing, Computer Vision) mobil ilovalarda qo‘llanilish sohasi, ularning foydalanuvchi tajribasini yaxshilash va tizim samaradorligini oshirishdagi o‘rni yoritildi. CNN, RNN, Random Forest, SVM va LSTM kabi algoritmlar samaradorligi aniqlik, precision, recall, F1-score, kechikish va energiya samaradorligi kabi mezonlar asosida baholandi. Tadqiqot natijalariga ko‘ra, bashoratli modellar foydalanuvchilarni ushlab qolish, resurslardan tejamkor foydalanish va ilovalarning tezkorligini oshirishda yuqori samaradorlikka ega ekani aniqlandi. Shuningdek, mobil qurilmalar imkoniyatlarini inobatga olgan holda Edge Computing va TensorFlow Lite texnologiyalarining qo‘llanilishi kechikishni kamaytirishi isbotlandi.
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