Results 271 to 280 of about 34,789 (293)

Image Captioning using Convolutional Neural Networks and Recurrent Neural Network

2021 6th International Conference for Convergence in Technology (I2CT), 2021
Image Caption is a concept of gathering the right description of the given image on the internet use Computer Vision and natural language processing. The following is achieved using the Deep learning techniques called as convolution neural network and recurrent neural network. The dataset used for implementation is called as the Flickr8_k Dataset.
Rachel Calvin, Shravya Suresh
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Mixed convolutional recurrent neural networks

Proceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing, 2019
In this work, we design a neural network named MCRNN for classifying signals with slight distinction. This model combines advantages of both Convolutional Neural Network and Recurrent Neural Network, allowing the network to distinguish between long signals with long time-frequency maps.
Wentao Wang, Canhui Liao, Quan Cheng, Pengju Wang   +3 more
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GCRNN: Group-Constrained Convolutional Recurrent Neural Network

IEEE Transactions on Neural Networks and Learning Systems, 2018
In this paper, we propose a new end-to-end deep neural network model for time-series classification (TSC) with emphasis on both the accuracy and the interpretation. The proposed model contains a convolutional network component to extract high-level features and a recurrent network component to enhance the modeling of the temporal characteristics of TS ...
Sangdi Lin, George C. Runger
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Convolutional Recurrent Neural Networks for Text Classification

Journal of Database Management, 2021
Recurrent neural network (RNN) and convolutional neural network (CNN) are two prevailing architectures used in text classification. Traditional approaches combine the strengths of these two networks by straightly streamlining them or linking features extracted from them. In this article, a novel approach is proposed to maintain the strengths of RNN and
Shengfei Lyu, Jiaqi Liu
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Automatic playlist generation using Convolutional Neural Networks and Recurrent Neural Networks

2019 27th European Signal Processing Conference (EUSIPCO), 2019
Nowadays, a great part of music consumption on music streaming services are based on playlists. Playlists are still mainly manually generated by expert curators, or users, process that in several cases is not a feasible with huge amount of music to deal with. There is the need of effective automatic playlist generation techniques.
Irene R. T., Borrelli C., Zanoni M., Buccoli M., Sarti A.   +4 more
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Convolutional decoding using recurrent neural networks

IJCNN'99. International Joint Conference on Neural Networks. Proceedings (Cat. No.99CH36339), 2003
We show how recurrent neutral network (RNN) convolutional decoders can be derived. As an example, we derive the RNN decoder for 1/2 rate code with constraint length 3. The derived RNN decoder is tested in Gaussian channel and the results are compared to results of optimal Viterbi decoder.
A. Hamalainen, J. Henriksson
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Epileptic seizure prediction with recurrent convolutional neural networks

2017 25th Signal Processing and Communications Applications Conference (SIU), 2017
In this paper, the use of recurrent convolutional neural networks for predicting epileptic seizures is proposed. Effective methods for predicting epileptic seizures need to be developed for the design of diagnostic and therapeutic techniques that will prevent or mitigate epileptic seizures.
Özcan, Ahmet Remzi, Erturk, Sarp
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Convolutional Recurrent Neural Networks for Knowledge Tracing

2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2020
Knowledge Tracing (KT) is a task that aims to assess students' mastery level of knowledge and predict their performance over questions, which has attracted widespread attention over the years. Recently, an increasing number of researches have applied deep learning techniques to knowledge tracing and have made a huge success over traditional Bayesian ...
Wei Wang, Tieyuan Liu, Liang Chang, Tianlong Gu, Xuemei Zhao   +4 more
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Recurrent convolutional neural network for object recognition

2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015
In recent years, the convolutional neural network (CNN) has achieved great success in many computer vision tasks. Partially inspired by neuroscience, CNN shares many properties with the visual system of the brain. A prominent difference is that CNN is typically a feed-forward architecture while in the visual system recurrent connections are abundant ...
null Ming Liang, null Xiaolin Hu
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DnRCNN: Deep Recurrent Convolutional Neural Network for HSI Destriping

IEEE Transactions on Neural Networks and Learning Systems, 2023
In spite of achieving promising results in hyperspectral image (HSI) restoration, deep-learning-based methodologies still face the problem of spectral or spatial information loss due to neglecting the inner correlation of HSI. To address this issue, we propose an innovative deep recurrent convolution neural network (DnRCNN) model for HSI destriping. To
Juntao Guan, Rui Lai, Huanan Li, Yintang Yang, Lin Gu   +4 more
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