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По вашему запросу найдено документов: 16337

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Melanoma detection with deep neural networks

Shchetinin E.Y., Sevastianov L.A., Ayrjan E.A., Demidova A.V.

convolutional neural network architecture was developed, which was applied to the processing of dermoscopic

Artificial neural network technology for text recognition

Pyataeva, A. V., Genza, S. A.

The paper shows the use of artificial neural networks for the task of scene text recognition. A

Convolutional neural network with semantically meaningful activations for speech analysis

Vashkevich, R., Azarov, E. S., Азаров, И. С.

. The mathematical apparatus of convolutional neural networks (CNN) seems suitable to implement this kind

Convolutional neural network in the recognition of spatial images of sugarcane crops in the troncal region of the coast of Ecuador

Andryshchenko I.S., Cobeña Cevallos J.P., Atiencia Villagomez J.M.

networks. The deep convolution of neural networks is used both in the recognition as well

Convolutional neural network based pest and disease recognition

Ma, Shikuan, Liu, Zhixin, Cui, Mengjun

In recent years, convolutional neural network, as one of the most representative algorithms of deep

Multi-Branch Convolutional Neural Network Architecture for Glaucoma Diagnosis Using Optical Coherence Tomography Biomarkers and Synthetic Image Simulation

Usenko, P. V., Prudnik, A. M.

This paper presents a multi-branch convolutional neural network designed for glaucoma diagnosis

Convolutional neural networks and face recognition task

Sochenkov I., Makovetskii A., Vokhmintsev A., Melnikov A., Sochenkova A.

Convolutional neural networks and face recognition task

Computer-aided cholelithiasis diagnosis using explainable convolutional neural network

Kumar D., Mehta M.A., Kotecha K., Kulkarni A.

introduced in the literature, their use is limited because Convolutional Neural Network (CNN) models

Recognition of underlying surface using a convolutional neural network on a single-board compute

Paulenka, D. A., Kovalev, V. A., Snezhko, E. V., Liauchuk, V. A., Pechkovsky, E. I.

and uses convolutional neural network based on MobileNetV2 architecture for image classification

Advancing BPM detection in hip-hop and R&B through audio representations and convolutional neural networks

Tushynskaya, K. V., Zyranova, M. M., Asadchy, A. E.

audio representations with Convolutional Neural Networks (CNNs). Through analyzing tempograms

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