Results 291 to 300 of about 1,429,068 (340)

Impact of meteorological factors on the incidence of hand, foot and mouth disease in Ningbo from 2014 to 2019: a causal convolutional neural networks. [PDF]

BMC Public Health
Du B, Ren Z, Song Z, Yuan M, Li Z.
europepmc   +1 more source

Comparative analysis of convolutional neural networks and transformer architectures for breast cancer histopathological image classification. [PDF]

Front Med (Lausanne)
Yuan B, Hu Y, Liang Y, Zhu Y, Zhang L, Cai S, Peng R, Wang X, Yang Z, Hu J.   +9 more
europepmc   +1 more source

Music genre classification with parallel convolutional neural networks and capuchin search algorithm. [PDF]

Sci Rep
Zhang Y, Li T.
europepmc   +1 more source

Corrigendum to "Utilization of convolutional neural networks to analyze microscopic images for high-throughput screening of mesenchymal stem cells". [PDF]

Open Life Sci
Liu M, Du X, Hu J, Liang X, Wang H.
europepmc   +1 more source

Convolutional Neural Networks [PDF]

, 2017
Artificial neural networks have flourished in recent years in the processing of unstructured data, especially images, text, audio, and speech. Convolutional neural networks (CNNs) work best for such unstructured data. Whenever there is a topology associated with the data, convolutional neural networks do a good job of extracting the important features ...
Ragav Venkatesan, Baoxin Li
  +8 more sources

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Convolutional Neural Network

2021
Convolutional neural network (CNN) is a (Agrawal and Roy, IEEE Trans Magn 55:1–7, 2019) class of deep neural network. CNNs are what we call the most representative supervised model in the theory of deep learning is the technique that nowadays (Akinaga and Shima, Proc IEEE 98:2237–2251, 2010) is producing a lot of outstanding results especially in the ...
Y. V. R. Nagapawan, Kolla Bhanu Prakash, G. R. Kanagachidambaresan   +2 more
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Convolutional neural networks

2021
In this chapter we introduce the convolutional neural network theory including concepts such as convolution operator, kernel, stride, padding and pooling.
Ullo S. L., Sebastianelli A., Rosso M. P. D., Spiller D., Puglisi E., Nowakowski A., Bernardi M. L., Cimitile M.   +7 more
openaire   +4 more sources

Convolutional Neural Networks

2023
Dieses Kapitel führt in Convolutional Neural Networks (CNNs) ein und beschreibt, wie diese im Kontext der Sportanalyse verwendet werden können. Insbesondere eignen sich CNNs für das End-to-End-Lernen auf Bildern oder ähnlich strukturierten Daten. Dabei können CNNs Merkmale von Bildern anhand der Pixelwerte effizient lernen und beispielsweise sehr gute ...
Rudolph, Yannick, Brefeld, Ulf
openaire   +2 more sources