数字通信与网络（英文）2026，Vol.12Issue(5)：755-764,10.DOI:10.1016/j.dcan.2024.11.011
AiCareNeonates:artificial intelligence powered adaptation of transfer learning models to classify neonate's sleep-wake states for pediatricians in the loop
AiCareNeonates:artificial intelligence powered adaptation of transfer learning models to classify neonate's sleep-wake states for pediatricians in the loop
Muhammad Awais 1Hemant Ghayvat 2Rebakah Geddam 3Lewis Nkenyereye 4Kapal Dev5
作者信息
- 1. Center for Intelligent Medical Electronics,School of Information Science and Technology at Fudan University,Shanghai,China
- 2. Computer Science Department,Faculty of Technology,Linnaeus University,Växjö,351 95,Sweden
- 3. Computer Science Department,Institute of Technology,Nirma University,Ahmedabad,382481,Gujarat,India
- 4. Department of Computer and Information Security,Sejong University,South Korea
- 5. ADAPT Centre and Department of Computer Science,Munster Technological University,Bishopstown,Cork,T12 P928,Ireland||Department of Electrical and Computer Engineering,Lebanese American University,Byblos,Lebanon||Centre for Research Impact & Outcome,Institute of Engineering and Technology,Chitkara University,Rajpura,Punjab,India
- 折叠
摘要
关键词
Deep convolutional neural network/Neonatal sleep classification/Transfer learning/Neonatal Red-Green-Blue(RGB)face database/Quadrupole exciton/PolaritonKey words
Deep convolutional neural network/Neonatal sleep classification/Transfer learning/Neonatal Red-Green-Blue(RGB)face database/Quadrupole exciton/Polariton引用本文复制引用
Muhammad Awais,Hemant Ghayvat,Rebakah Geddam,Lewis Nkenyereye,Kapal Dev..AiCareNeonates:artificial intelligence powered adaptation of transfer learning models to classify neonate's sleep-wake states for pediatricians in the loop[J].数字通信与网络（英文）,2026,12(5):755-764,10.
