الكشف عن اضطراب طيف التوحد باستخدام شبكة عصبية تلافيفية أحادية البعد
محتوى المقالة الرئيسي
الملخص
اضطراب طيف التوحد، المعروف أيضًا باسم ASD، هو مرض نمائي عصبي يضعف الكلام والتفاعل الاجتماعي والسلوك. التعلم الآلي هو مجال من مجالات الذكاء الاصطناعي يركز على إنشاء خوارزميات يمكنها تعلم الأنماط وتصنيف ASD بناءً على بيانات الإدخال. كانت نتائج استخدام خوارزميات التعلم الآلي لتصنيف ASD غير متسقة. هناك حاجة إلى مزيد من البحث لتحسين دقة تصنيف ASD. لمعالجة هذا الأمر، تم اقتراح التعلم العميق مثل 1D-CNN كبديل لتصنيف اكتشاف ASD. يتم تقييم التقنيات المقترحة على ثلاث مجموعات مختلفة من بيانات ASD المتاحة للجمهور (الأطفال والبالغون والمراهقون). تشير النتائج بقوة إلى أن 1D-CNN أظهرت دقة محسّنة في تصنيف ASD مقارنة بخوارزميات التعلم الآلي التقليدية، في كل مجموعات البيانات هذه بدقة أعلى تبلغ 99.45٪ و98.66٪ و 90٪ لفحص اضطراب طيف التوحد في البيانات للبالغين والأطفال والمراهقون على التوالي لأن النموذج المقترح أكثر ملاءمة لتحليل بيانات السلاسل الزمنية التي يشيع استخدامها في تشخيص هذا الاضطراب.
Received 10/02/2023,
Revised 23/04/2023,
Accepted 25/04/2023,
Published 20/06/2023
تفاصيل المقالة
هذا العمل مرخص بموجب Creative Commons Attribution 4.0 International License.
كيفية الاقتباس
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