Autor:

Abbas, Sidra ; Ojo, Stephen ; Krichen, Moez ; Alamro, Meznah A. ; Mihoub, Alaeddine ; Vilcekova, Lucia

Współtwórca:

Woźniak, Marcin - ed. ; Kumar, Yogesh - ed. ; Ijaz, Muhammad Fazal - ed.

Tytuł:

Autism spectrum disorder detection in toddlers and adults using deep learning

Podtytuł:

.

Tytuł publikacji grupowej:

AMCS, volume 34 (2024)

Temat i słowa kluczowe:

autism spectrum disorder (ASD) ; deep learning ; feature fusion ; feature prediction ; healthcare

Abstract:

Autism spectrum disorder includes symptoms like anxiety, depressive disorders, and epilepsy because of its impact on relationships, learning, and employment. Since no confirmed treatment and diagnosis are available, the emphasis is on improving an individual?s capacities through symptom mitigation. This work investigates autism screening for adults and toddlers utilizing deep learning. We investigated models for feature prediction and fused these predictions with the original dataset to be trained with deep long short-term memory (DLSTM). Features are fused from the training and testing sets and then combined with the original dataset. Data analysis is carried out to detect anomalies and outliers, and a label encoding technique is utilized to convert the categorical data into numerical values. We hyper-tuned the DLSTM model parameters to optimize and assess significant outcomes. Experimental analysis and results revealed that the proposed approach worked better than the other techniques, yielding 99.9% accuracy for toddlers and 99% for adults.

Wydawca:

Zielona Góra: Uniwersytet Zielonogórski

Data wydania:

2024

Typ zasobu:

artykuł

DOI:

10.61822/amcs-2024-0042

Strony:

631-645

Źródło:

AMCS, volume 34, number 4 (2024) ; kliknij tutaj, żeby przejść

Jezyk:

eng

Licencja CC BY 4.0:

kliknij tutaj, żeby przejść

Prawa do dysponowania publikacją:

Biblioteka Uniwersytetu Zielonogórskiego