Results 51 to 60 of about 293,560 (163)
Voicing classification of visual speech using convolutional neural networks [PDF]
, 2015 The application of neural network and convolutional neural net- work (CNN) architectures is explored for the tasks of voicing classification (classifying frames as being either non-speech, unvoiced, or voiced) and voice activity detection (VAD) of vi ...
Le Cornu, Thomas, Milner, Ben
core
, 2015 In this work we describe a Convolutional Neural Network (CNN) to accurately predict the scene illumination. Taking image patches as input, the CNN works in the spatial domain without using hand-crafted features that are employed by most previous methods.
Bianco, Simone +2 more
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CNN-MGP: Convolutional Neural Networks for Metagenomics Gene Prediction [PDF]
Interdisciplinary Sciences: Computational Life Sciences, 2018 Accurate gene prediction in metagenomics fragments is a computationally challenging task due to the short-read length, incomplete, and fragmented nature of the data. Most gene-prediction programs are based on extracting a large number of features and then applying statistical approaches or supervised classification approaches to predict genes.
Amani Al-Ajlan, Achraf El Allali
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Using Convolutional Neural Networks for Blocking Prediction in Elastic Optical Networks
Applied SciencesThis paper presents a study on connection-blocking prediction in Elastic Optical Networks (EONs) using Convolutional Neural Networks (CNNs). In EONs, connections are established and torn down dynamically to fulfill the instantaneous requirements of the ...
Farzaneh Nourmohammadi +3 more
doaj +1 more source
Deep spatiotemporal human activity recognition using an optimized 3D CNN model [PDF]
PeerJ Computer ScienceSurveillance video systems have become indispensable in modern societies for monitoring human activity and detecting abnormal behavior across both public and private environments.
Hamada I. AbdulWakel +5 more
doaj +2 more sources
Information, 2020 Traditional time series forecasting techniques can not extract good enough sequence data features, and their accuracies are limited. The deep learning structure SeriesNet is an advanced method, which adopts hybrid neural networks, including dilated ...
Yepeng Cheng +2 more
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Advancements in Image Classification using Convolutional Neural Network
, 2019 Convolutional Neural Network (CNN) is the state-of-the-art for image classification task. Here we have briefly discussed different components of CNN. In this paper, We have explained different CNN architectures for image classification.
Dutta, Paramartha +2 more
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Strategies in Jpeg Compression Using Convolutional Neural Network(Cnn) [PDF]
SSRN Electronic Journal, 2021 Interests in digital image processing are growing enormously in recent decades. As a result, different data compression techniques have been proposed which are concerned mostly with the minimization of information used for the representation of images. With the advances of deep neural networks, image compression can be achieved to a higher degree. This
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Research on a Bearing Fault Diagnosis Method Based on a CNN-LSTM-GRU Model
MachinesIn view of the problem of the insufficient performance of deep learning models in time series prediction and poor comprehensive space–time feature extraction, this paper proposes a diagnostic method (CNN-LSTM-GRU) that integrates convolutional neural ...
Kaixu Han, Wenhao Wang, Jun Guo
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Particular object retrieval with integral max-pooling of CNN activations [PDF]
, 2016 Recently, image representation built upon Convolutional Neural Network (CNN) has been shown to provide effective descriptors for image search, outperforming pre-CNN features as short-vector representations.
Jégou, Hervé +2 more
core +1 more source