Artificial and Convolutional Neural Network Architectures for Childhood Stunting Classification: Design, Evaluation, and Optimization

Childhood stunting is a global health challenge, affecting 148 million children under 5 in 2022. It is a key indicator of chronic malnutrition, often driven by inadequate nutrition, recurrent infections, and socio-economic disparities. In Egypt, 22% of children under 5 are stunted, leading to cognit...

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Vydáno v:	SAGE open Ročník 15; číslo 3
Hlavní autoři:	Hendy, Abdelaziz, Ibrahim, Rasha Kadri, Abdelaliem, Sally Mohammed Farghaly, Alenezi, Ibrahim Naif, Sinnokrot, Shaban Majed, Aldosari, Nasser, Alshammari, Afrah Madyan, Hendy, Ahmed
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Los Angeles, CA SAGE Publications 01.09.2025 SAGE PUBLICATIONS, INC SAGE Publishing
Témata:	Accuracy Body height Child development Child Health Childhood Children Childrens health Classification Dropping out Early intervention Educational Objectives Health care industry Health services Health status Health surveys Interdisciplinary aspects Learning outcomes Machine learning Malnutrition National security Networks Neural networks Nutrition Optimization Outcomes of Education Outcomes of Treatment Recurrent Resource allocation Socioeconomic factors Egypt artificial neural networks autoencoders child nutrition disorders convolutional neural networks stunting Egypt
ISSN:	2158-2440, 2158-2440
On-line přístup:	Získat plný text
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