Results 81 to 90 of about 3,882,996 (203)

Lattice gauge equivariant convolutional neural networks

, 2022
We propose Lattice gauge equivariant Convolutional Neural Networks (L-CNNs) for generic machine learning applications on lattice gauge theoretical problems.
Favoni, Matteo; orcid:, Ipp, Andreas; orcid:, Schuh, Daniel; orcid:, Müller, David I.   +3 more
core   +1 more source

Diagnosis of Alzheimer’s Disease and Mild Cognitive Impairment Using Convolutional Neural Networks

Journal of Alzheimer's Disease Reports
Background: Alzheimer’s disease and mild cognitive impairment are common diseases in the elderly, affecting more than 50 million people worldwide in 2020.
Sara Ghasemi Dakdareh, Karim Abbasian
doaj   +1 more source

VC dimensions of group convolutional neural networks

, 2022
We study the generalization capacity of group convolutional neural networks. We identify precise estimates for the VC dimensions of simple sets of group convolutional neural networks.
Petersen, Philipp Christian, Sepliarskaia, Anna   +1 more
core  

Convolutional neural networks for detection of selective sweeps.

, 2023
Convolutional neural networks for detection of selective sweeps.
Lauren A. Sugden (15927527), Ryan M. Cecil (17458456)   +1 more
core   +1 more source

A study of combination of autoencoders and boosted Big-Bang crunch theory architectures for Land-Use classification using remotely sensed imagery

Scientific Reports
The research introduced a new method for land-use classification by merging deep convolutional neural networks with a modified variant of a metaheuristic optimization technique.
Qiongbing Xiong, Xuecheng Wu, Cizhen Yu, Hasan Hosseinzadeh   +3 more
doaj   +1 more source

Toward Audio Beehive Monitoring: Deep Learning vs. Standard Machine Learning in Classifying Beehive Audio Samples

Applied Sciences, 2018
Electronic beehive monitoring extracts critical information on colony behavior and phenology without invasive beehive inspections and transportation costs.
Vladimir Kulyukin, Sarbajit Mukherjee, Prakhar Amlathe   +2 more
doaj   +1 more source

Predicting the Demand in Bitcoin Using Data Charts: A Convolutional Neural Networks Prediction Model

, 2020
Traditional time series modeling techniques emphasize on predicting cryptocurrencies using classically structured data representation as numerical features to present the time-series datasets.
Kashef, R., Corrigan, L., Ibrahim, A.
core   +1 more source

Characteristic of famous convolutional neural networks architectures.

, 2018
Characteristic of famous convolutional neural networks architectures.
JaeYeon Park (5184392), JeongGil Ko (5184395), Jeongyeup Paek (5184398), Donghwan Park (5184386), Jungmo Ahn (5184389)   +4 more
core   +1 more source

Wavelet Convolutional Neural Networks

CoRR, 2018
Spatial and spectral approaches are two major approaches for image processing tasks such as image classification and object recognition. Among many such algorithms, convolutional neural networks (CNNs) have recently achieved significant performance improvement in many challenging tasks. Since CNNs process images directly in the spatial domain, they are
Shin Fujieda, Kohei Takayama, Toshiya Hachisuka   +2 more
openaire   +3 more sources

Application of 1D convolutional neural networks for anomaly detection in resistance measurements of railway wheelsets

Pojazdy Szynowe
The article presents the application of convolutional neural networks (CNN) for the classification of electrical resistance measurements of railway wheelsets.
Tomasz Paweł Olejniczak
doaj   +1 more source