Results 31 to 40 of about 449,358 (260)

Machine learning methods as an aid in planning orthodontic treatment on the example of Cone-Beam Computed Tomography analysis: a literature review

Journal of Education, Health and Sport, 2021
Convolutional neural networks (CNNs) are used in many areas of computer vision, such as object tracking and recognition, security, military, and biomedical image analysis.
Szymon Płotka, Tomasz Włodarczyk, Ryszard Szczerba, Anna Chomiak, Oskar Komisarek   +4 more
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CONVOLUTIONAL DEEP LEARNING NEURAL NETWORK FOR STROKE IMAGE RECOGNITION: REVIEW

Вестник КазНУ. Серия математика, механика, информатика, 2021
Deep learning is one of the developing area of articial intelligence research. It includes machine learning methods based on articial neural networks. One method that has been widely used and researched in recent years is convolution neural networks (CNN)
Azhar Toilybaikyzy Tursynova, B. S. Omarov, O. A. Postolache, M. Zh. Sakypbekova   +3 more
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Temporal refinement of 3D CNN semantic segmentations on 4D time-series of undersampled tomograms using hidden Markov models

Scientific Reports, 2021
Recently, several convolutional neural networks have been proposed not only for 2D images, but also for 3D and 4D volume segmentation. Nevertheless, due to the large data size of the latter, acquiring a sufficient amount of training annotations is much ...
Dimitrios Bellos, Mark Basham, Tony Pridmore, Andrew P. French   +3 more
doaj   +1 more source

Learning flexible representations of stochastic processes on graphs

, 2018
Graph convolutional networks adapt the architecture of convolutional neural networks to learn rich representations of data supported on arbitrary graphs by replacing the convolution operations of convolutional neural networks with graph-dependent linear ...
Balan, Radu, Bohannon, Addison, Sadler, Brian   +2 more
core   +1 more source

Dual-channel deep graph convolutional neural networks

Frontiers in Artificial Intelligence
The dual-channel graph convolutional neural networks based on hybrid features jointly model the different features of networks, so that the features can learn each other and improve the performance of various subsequent machine learning tasks.
Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhonglin Ye, Zhuoran Li, Zhuoran Li, Zhuoran Li, Zhuoran Li, Gege Li, Gege Li, Gege Li, Gege Li, Haixing Zhao, Haixing Zhao, Haixing Zhao, Haixing Zhao   +15 more
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Classification methods for handwritten digit recognition: A survey

Vojnotehnički Glasnik, 2023
Introduction/purpose: This paper provides a survey of handwritten digit recognition methods tested on the MNIST dataset. Methods: The paper analyzes, synthesizes and compares the development of different classifiers applied to the handwritten digit ...
Ira M. Tuba, Una M. Tuba, Mladen Đ. Veinović   +2 more
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Location Dependency in Video Prediction

, 2018
Deep convolutional neural networks are used to address many computer vision problems, including video prediction. The task of video prediction requires analyzing the video frames, temporally and spatially, and constructing a model of how the environment ...
Azizi, Niloofar, Behnke, Sven, Farazi, Hafez   +2 more
core   +1 more source

An Improved Convolutional Neural Networks: Quantum Pseudo-Transposed Convolutional Neural Networks

IEEE Access
Recent advancements in quantum machine learning have spurred the development of hybrid quantum-classical convolutional neural networks (HQCCNNs), which have demonstrated promising potential for image classification tasks.
Li Hai, Chen Liang, Hao Yaming, Yu Wenli, Shi Fengquan   +4 more
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A Particle Swarm Optimization-based Flexible Convolutional Auto-Encoder for Image Classification

, 2018
Convolutional auto-encoders have shown their remarkable performance in stacking to deep convolutional neural networks for classifying image data during past several years.
Sun, Yanan, Xue, Bing, Yen, Gary G., Zhang, Mengjie   +3 more
core   +1 more source

Convolutional Neural Networks: A Survey

Computers, 2023
Artificial intelligence (AI) has become a cornerstone of modern technology, revolutionizing industries from healthcare to finance. Convolutional neural networks (CNNs) are a subset of AI that have emerged as a powerful tool for various tasks including ...
Moez Krichen
doaj   +1 more source