Ключевые слова
Аннотация
Ushbu maqolada biopotensial signallarni, xususan elektromiyogramma (EMG) va elektroensefalogramma (EEG) signallarini qayta ishlashda mashinaviy o‘qitish algoritmlarining samaradorligi tahlil qilinadi. Tadqiqotlar SVM, random forest va konvolyutsion neyron tarmoqlari kabi algoritmlarning signallarni klassifikatsiya qilish va patologik naqshlarni aniqlashdagi samaradorligini ko‘rsatdi. EMG va EEG signallarining murakkab tabiati, shovqin va artefaktlarga moyilligi ularni qayta ishlash jarayonini qiyinlashtiradi. Shu sababli, xususiyatlarni ekstraktsiya qilish (RMS, spektral energiya, dominant chastota) algoritmlarning aniqlik va sezgirligini oshirishda muhim rol o‘ynaydi. Tadqiqot natijalari mashinaviy o‘qitish algoritmlari biopotensial signallarni qayta ishlashda yuqori samaradorlikka ega ekanligini tasdiqladi, shuningdek, inson-kompyuter interfeyslari, neyroprostetik qurilmalar va nevrorehabilitatsiya jarayonlarida amaliy qo‘llanilishi mumkinligini ko‘rsatdi. Ushbu ish tibbiyot va biomeditsina muhandisligi sohalarida sun’iy intellektga asoslangan tizimlarni rivojlantirish uchun ilmiy asos yaratadi.
Библиографические ссылки
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