Sub-Millisecond Phase Retrieval for Phase-Diversity Wavefront Sensor
Sensors, 2020 We propose a convolutional neural network (CNN) based method, namely phase diversity convolutional neural network (PD-CNN) for the speed acceleration of phase-diversity wavefront sensing.
Yu Wu +3 more
doaj +1 more source
, 2021 Modeling nonadiabatic dynamics in complex molecular or condensed-phase systems has been challenging especially for the long-time dynamics. In this work, we propose a time series machine learning scheme based on the hybrid convolutional neural network ...
Jiebo, Li +3 more
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Convolutional neural networks: an overview and application in radiology
Insights into Imaging, 2018 Convolutional neural network (CNN), a class of artificial neural networks that has become dominant in various computer vision tasks, is attracting interest across a variety of domains, including radiology.
Rikiya Yamashita +3 more
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Perbandingan algoritma Convolutional Neural Network (CNN) Dan Faster Region Based Convolutional Neural Network (Faster R-CNN) pada pengenalan bahasa isyarat [PDF]
, 2022 Bahasa isyarat merupakan bahasa komunikasi non verbal yang penyampaiannya menggunakan simbol-simbol dari pergerakan anggota badan, seperti tangan, mimik wajah, gerak bibir, dan anggota badan lainnya.
Putra, Revaldo Pratama
core
Deep Learning Convolutional Neural Network for SARS-CoV-2 Detection Using Chest X-Ray Images
, 2023 The COVID-19 coronavirus illness is caused by a newly discovered species of coronavirus known as SARS-CoV-2. Since COVID-19 has now expanded across many nations, the World Health Organization (WHO) has designated it a pandemic.
Salam Abdulkhaleq Noaman +2 more
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AAR-CNNs: Auto Adaptive Regularized Convolutional Neural Networks [PDF]
Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence, 2018 In order to address the overfitting problem caused by the small or simple training datasets and the large model’s size in Convolutional Neural Networks (CNNs), a novel Auto Adaptive Regularization (AAR) method is proposed in this paper. The relevant networks can be called AAR-CNNs. AAR is the first method using the “abstraction extent” (predicted by AE
Yao Lu 0008 +3 more
openaire +1 more source
SPEAKER IDENTIFICATION SYSTEM USING AUDIO SIGNAL AND DEEP LEARNING METHOD [PDF]
Proceedings on Engineering SciencesAutomatic Speaker Identification (ASI) does not result in high accuracy, so it is essential to develop a highly accurate Speaker Identification (SI) system. Artificial Intelligence has shown remarkable improvement in the development of such systems using
Neelam Nehra +2 more
doaj +1 more source
Research on epileptic EEG recognition based on improved residual networks of 1-D CNN and indRNN
BMC Medical Informatics and Decision Making, 2021 Background Epilepsy is one of the diseases of the nervous system, which has a large population in the world. Traditional diagnosis methods mostly depended on the professional neurologists’ reading of the electroencephalogram (EEG), which was time ...
Mengnan Ma +4 more
doaj +1 more source
Optimasi Convolutional Neural Network dan K-Fold Cross Validation pada Sistem Klasifikasi Glaukoma
Jurnal Elkomika, 2022 ABSTRAK Pada penelitian ini dilakukan perancangan arsitektur Convolutional Neural Network (CNN) yang terdiri dari 5 layer konvolusi dan 1-fully connected layer untuk mengklasifikasikan citra fundus kedalam kondisi normal, early, moderate, deep, dan ...
YUNENDAH NUR FUADAH +5 more
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SF-ICNN: Spectral–Fractal Iterative Convolutional Neural Network for Classification of Hyperspectral Images [PDF]
One primary concern in the field of remote-sensing image processing is the precise classification of hyperspectral images (HSIs). Lately, deep-learning models have demonstrated cutting-edge results in HSI classification.
Akbari, Vahid +5 more
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