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Effectiveness of Using Artificial Intelligence for Early Child Development Screening

This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various mach...

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Main Authors: Gau, Michael-Lian, Ting, Huong-Yong, Toh, Teck-Hock, Wong, Pui-Ying, Woo, Pei Jun *, Wo, Su-Woan, Tan, Gek-Ling
Format: Article
Language:English
Published: Tecno Scientifica 2023
Subjects:
BF Psychology
Q Science (General)
Online Access:http://eprints.sunway.edu.my/2343/1/134.pdf
http://eprints.sunway.edu.my/2343/
https://doi.org/10.53623/gisa.v3i1.229
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spelling my.sunway.eprints.23432023-08-30T03:11:39Z http://eprints.sunway.edu.my/2343/ Effectiveness of Using Artificial Intelligence for Early Child Development Screening Gau, Michael-Lian Ting, Huong-Yong Toh, Teck-Hock Wong, Pui-Ying Woo, Pei Jun * Wo, Su-Woan Tan, Gek-Ling BF Psychology Q Science (General) This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results. Tecno Scientifica 2023 Article PeerReviewed text en cc_by_nc_4 http://eprints.sunway.edu.my/2343/1/134.pdf Gau, Michael-Lian and Ting, Huong-Yong and Toh, Teck-Hock and Wong, Pui-Ying and Woo, Pei Jun * and Wo, Su-Woan and Tan, Gek-Ling (2023) Effectiveness of Using Artificial Intelligence for Early Child Development Screening. Green Intelligent Systems and Applications, 3 (1). pp. 1-13. ISSN 2809-1116 https://doi.org/10.53623/gisa.v3i1.229 10.53623/gisa.v3i1.229
institution Sunway University
building Sunway Campus Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Sunway University
content_source Sunway Institutional Repository
url_provider http://eprints.sunway.edu.my/
language English
topic BF Psychology
Q Science (General)
spellingShingle BF Psychology
Q Science (General)
Gau, Michael-Lian
Ting, Huong-Yong
Toh, Teck-Hock
Wong, Pui-Ying
Woo, Pei Jun *
Wo, Su-Woan
Tan, Gek-Ling
Effectiveness of Using Artificial Intelligence for Early Child Development Screening
description This study presents a novel approach to recognizing emotions in infants using machine learning models. To address the lack of infant-specific datasets, a custom dataset of infants' faces was created by extracting images from the AffectNet dataset. The dataset was then used to train various machine learning models with different parameters. The best-performing model was evaluated on the City Infant Faces dataset. The proposed deep learning model achieved an accuracy of 94.63% in recognizing positive, negative, and neutral facial expressions. These results provide a benchmark for the performance of machine learning models in infant emotion recognition and suggest potential applications in developing emotion-sensitive technologies for infants. This study fills a gap in the literature on emotion recognition, which has largely focused on adults or children and highlights the importance of developing infant-specific datasets and evaluating different parameters to achieve accurate results.
format Article
author Gau, Michael-Lian
Ting, Huong-Yong
Toh, Teck-Hock
Wong, Pui-Ying
Woo, Pei Jun *
Wo, Su-Woan
Tan, Gek-Ling
author_facet Gau, Michael-Lian
Ting, Huong-Yong
Toh, Teck-Hock
Wong, Pui-Ying
Woo, Pei Jun *
Wo, Su-Woan
Tan, Gek-Ling
author_sort Gau, Michael-Lian
title Effectiveness of Using Artificial Intelligence for Early Child Development Screening
title_short Effectiveness of Using Artificial Intelligence for Early Child Development Screening
title_full Effectiveness of Using Artificial Intelligence for Early Child Development Screening
title_fullStr Effectiveness of Using Artificial Intelligence for Early Child Development Screening
title_full_unstemmed Effectiveness of Using Artificial Intelligence for Early Child Development Screening
title_sort effectiveness of using artificial intelligence for early child development screening
publisher Tecno Scientifica
publishDate 2023
url http://eprints.sunway.edu.my/2343/1/134.pdf
http://eprints.sunway.edu.my/2343/
https://doi.org/10.53623/gisa.v3i1.229
_version_ 1775626103752228864
score 13.211869

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