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Artificial intelligence techniques for classification of Alzheimer's disease using neuroimaging data: a review. [PDF]
Front Artif IntellNibila Y, Sivagami M.
europepmc +1 more source
Convolutional Recurrent Neural Networks forHyperspectral Data Classification [PDF]
Remote Sensing, 2017 Deep neural networks, such as convolutional neural networks (CNN) and stacked autoencoders, have recently been successfully used to extract deep features for hyperspectral data classification. Recurrent neural networks (RNN) are another type of neural networks, which are widely used for sequence analysis because they are constructed to extract ...
Hao Wu, Saurabh Prasad
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HCRNNIDS: Hybrid Convolutional Recurrent Neural Network-Based Network Intrusion Detection System
Processes, 2021 Nowadays, network attacks are the most crucial problem of modern society. All networks, from small to large, are vulnerable to network threats. An intrusion detection (ID) system is critical for mitigating and identifying malicious threats in networks ...
Muhammad Ashfaq Khan
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Convolutional Recurrent Neural Networks for Text Classification
Journal of Database Management, 2021Recurrent 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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Convolutional Recurrent Neural Networks for Text Classification
2019 International Joint Conference on Neural Networks (IJCNN), 2019Text classification is an important task in natural language processing with wide applications. Traditional text classification methods manually extract the features which are later fed into the classifier for training. Recent researchers have employed convolutional neural networks or recurrent neural networks for text classification motivated by the ...
Ruishuang Wang +4 more
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Convolutional Recurrent Neural Networks for Knowledge Tracing
2020 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), 2020Knowledge 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 0012 +4 more
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