An Improved Performance of Convolutional Neural Network for Infant Pose Estimation by Evaluating Hyperparameter-Reference-Cited by-同舟云学术

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An Improved Performance of Convolutional Neural Network for Infant Pose Estimation by Evaluating Hyperparameter

Published:2023-07-26 Issue: Volume: Page:
ISSN:
Container-title:2023 International Seminar on Intelligent Technology and Its Applications (ISITIA)
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Author:

Ningrum Endah Suryawati¹,Yuniarno Eko Mulyanto²,Purnomo Mauridhi Hery²

Affiliation:

1. Institut Teknologi Sepuluh Nopember,Dept. of Electrical Engineering,Surabaya,Indonesia

2. University Center of Excellence on Artificial Intelligence for Healthcare and Society (UCE AIHeS),Institut Teknologi Sepuluh Nopember,Dept. of Electrical Engineering, Dept. of Computer Engineering,Surabaya,Indonesia

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10220947/10220436/10221125.pdf?arnumber=10221125

Reference21 articles.

1. Unsupervised Domain Adaptation Learning for Hierarchical Infant Pose Recognition with Synthetic Data

2. Deep learning-based quantitative analyses of spontaneous movements and their association with early neurological development in preterm infants;shin,2021

3. On hyperparameter optimization of machine learning algorithms: Theory and practice

4. Invariant Representation Learning for Infant Pose Estimation with Small Data

5. Infant Cerebral Palsy Risk Early Detection based on Pulse Estimation using OpenPose and Advanced Algorithms from Unbalanced and Limited Dataset;ningrum;IEEE Medical Measurement and Application (MeMeA) 18th,2023

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