Results 41 to 50 of about 431,572 (197)
BERT-Based Combination of Convolutional and Recurrent Neural Network for Indonesian Sentiment Analysis [PDF]
Applied Soft Computing, 2022 Sentiment analysis is the computational study of opinions and emotions ex-pressed in text. Deep learning is a model that is currently producing state-of-the-art in various application domains, including sentiment analysis.
H. Murfi +4 more
semanticscholar +1 more source
Convolutional Recurrent Neural Networks for Electrocardiogram Classification
Computing in Cardiology Conference (CinC), 2017 We propose two deep neural network architectures for classification of arbitrary-length electrocardiogram (ECG) recordings and evaluate them on the atrial fibrillation (AF) classification data set provided by the PhysioNet/CinC Challenge 2017. The first architecture is a deep convolutional neural network (CNN) with averaging-based feature aggregation ...
Martin Zihlmann +2 more
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Inception recurrent convolutional neural network for object recognition [PDF]
Machine Vision and Applications, 2021 Deep convolutional neural network (DCNN) is an influential tool for solving various problems in machine learning and computer vision. Recurrent connectivity is a very important component of visual information processing within the human brain. The idea of recurrent connectivity is rarely applied within convolutional layers, the exceptions being a ...
Md. Zahangir Alom +4 more
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Important Trading Point Prediction Using a Hybrid Convolutional Recurrent Neural Network
Applied Sciences, 2021 Stock performance prediction plays an important role in determining the appropriate timing of buying or selling a stock in the development of a trading system.
Xinpeng Yu, Dagang Li
semanticscholar +1 more source
Recurrent Convolutional Neural Networks for Scene Parsing
CoRR, 2013 Scene parsing is a technique that consist on giving a label to all pixels in an image according to the class they belong to. To ensure a good visual coherence and a high class accuracy, it is essential for a scene parser to capture image long range dependencies.
Pedro H. O. Pinheiro, Ronan Collobert
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Convolutional unitary or orthogonal recurrent neural networks
CoRR, 2023 Recurrent neural networks are extremely powerful yet hard to train. One of their issues is the vanishing gradient problem, whereby propagation of training signals may be exponentially attenuated, freezing training. Use of orthogonal or unitary matrices, whose powers neither explode nor decay, has been proposed to mitigate this issue, but their ...
openaire +2 more sources
Convolutional recurrent neural networks for bird audio detection [PDF]
2017 25th European Signal Processing Conference (EUSIPCO), 2017 Publication in the conference proceedings of EUSIPCO, Kos island, Greece ...
Emre Cakir +4 more
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Sound event detection using spatial features and convolutional recurrent neural network [PDF]
IEEE International Conference on Acoustics, Speech, and Signal Processing, 2017 This paper proposes to use low-level spatial features extracted from multichannel audio for sound event detection. We extend the convolutional recurrent neural network to handle more than one type of these multichannel features by learning from each of ...
Sharath Adavanne +2 more
semanticscholar +1 more source
, 2020 Detail of the hyperparameters retained for Multi-layer Perceptron (MLP), convolutional neural network (ConvNet), recurrent neural network (RNN) and DeepConvLSTM.
Tomoya Suzuki (562515) +2 more
core +1 more source
Smart IoT Network Based Convolutional Recurrent Neural Network with Element-Wise Prediction System [PDF]
, 2021 An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network.
Al-Raweshidy, HS, Al-Jamali, NAS
core +1 more source