Exploring Convolutional Neural Networks and Recurrent Neural Networks for Arabic Question Classification

2020 ◽  
pp. 233-242
Author(s):  
Alami Hamza ◽  
Noureddine En-Nahnahi ◽  
Mostafa Ezziyyani ◽  
Said El Alaoui Ouatik
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Question Classification

scholarly journals Real-time classification of hand movements as a basis for intuitive control of grasp neuroprostheses

2020 ◽  
Vol 6 (2) ◽  
Author(s):  
Dmitry Amelin ◽  
Ivan Potapov ◽  
Josep Cardona Audí ◽  
Andreas Kogut ◽  
Rüdiger Rupp ◽  
...  
Keyword(s):  
Neural Networks ◽  
Standard Deviation ◽  
Real Time ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Healthy Subjects ◽  
Hand Movements ◽  
Cord Injury ◽  
Field Programmable

AbstractThis paper reports on the evaluation of recurrent and convolutional neural networks as real-time grasp phase classifiers for future control of neuroprostheses for people with high spinal cord injury. A field-programmable gate array has been chosen as an implementation platform due to its form factor and ability to perform parallel computations, which are specific for the selected neural networks. Three different phases of two grasp patterns and the additional open hand pattern were predicted by means of surface Electromyography (EMG) signals (i.e. Seven classes in total). Across seven healthy subjects, CNN (Convolutional Neural Networks) and RNN (Recurrent Neural Networks) had a mean accuracy of 85.23% with a standard deviation of 4.77% and 112 µs per prediction and 83.30% with a standard deviation of 4.36% and 40 µs per prediction, respectively.

scholarly journals Rception: Wide and Deep Interaction Networks for Machine Reading Comprehension (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13987-13988
Author(s):  
Xuanyu Zhang ◽  
Zhichun Wang
Keyword(s):  
Neural Networks ◽  
Reading Comprehension ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Question Answering ◽  
Local Information ◽  
Interaction Networks ◽  
Layer By Layer ◽  
Global Context ◽  
Machine Reading

Most of models for machine reading comprehension (MRC) usually focus on recurrent neural networks (RNNs) and attention mechanism, though convolutional neural networks (CNNs) are also involved for time efficiency. However, little attention has been paid to leverage CNNs and RNNs in MRC. For a deeper understanding, humans sometimes need local information for short phrases, sometimes need global context for long passages. In this paper, we propose a novel architecture, i.e., Rception, to capture and leverage both local deep information and global wide context. It fuses different kinds of networks and hyper-parameters horizontally rather than simply stacking them layer by layer vertically. Experiments on the Stanford Question Answering Dataset (SQuAD) show that our proposed architecture achieves good performance.

scholarly journals Deep learning in Emergency Medicine: Recent contributions and methodological challenges

2020 ◽  
Vol 16 (1) ◽  
Author(s):  
Francesco Faita
Keyword(s):  
Artificial Intelligence ◽  
Neural Networks ◽  
Emergency Medicine ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Computational Models ◽  
Clinical Usefulness ◽  
Human Knowledge ◽  
Deep Convolutional Neural Networks

In the last few years, artificial intelligence (AI) technology has grown dramatically impacting several fields of human knowledge and medicine in particular. Among other approaches, deep learning, which is a subset of AI based on specific computational models, such as deep convolutional neural networks and recurrent neural networks, has shown exceptional performance in images and signals processing. Accordingly, emergency medicine will benefit from the adoption of this technology. However, a particular attention should be devoted to the review of these papers in order to exclude overoptimistic results from clinically transferable ones. We presented a group of studies recently published on PubMed and selected by keywords ‘deep learning emergency medicine’ and ‘artificial intelligence emergency medicine’ with the aim of highlighting their methodological strengths and weaknesses, as well as their clinical usefulness.

scholarly journals Convolutional neural networks for modeling and forecasting nonlinear nonstationary processes

ScienceRise ◽  
2021 ◽  
pp. 12-20
Author(s):  
Andrii Belas ◽  
Petro Bidyuk
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Convolutional Neural Networks ◽  
Recurrent Neural Networks ◽  
Time Series Data ◽  
Autoregressive Models ◽  
Series Data ◽  
Nonstationary Processes ◽  
Modeling And Forecasting ◽  
Technological Product

The object of research. The object of research is modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Investigated problem. There are several popular approaches to solving the problems of adequate model constructing and forecasting nonlinear nonstationary processes, such as autoregressive models and recurrent neural networks. However, each of them has its advantages and drawbacks. Autoregressive models cannot deal with the nonlinear or combined influence of previous states or external factors. Recurrent neural networks are computationally expensive and cannot work with sequences of high length or frequency. The main scientific result. The model for forecasting nonlinear nonstationary processes presented in the form of the time series data was built using convolutional neural networks. The current study shows results in which convolutional networks are superior to recurrent ones in terms of both accuracy and complexity. It was possible to build a more accurate model with a much fewer number of parameters. It indicates that one-dimensional convolutional neural networks can be a quite reasonable choice for solving time series forecasting problems. The area of practical use of the research results. Forecasting dynamics of processes in economy, finances, ecology, healthcare, technical systems and other areas exhibiting the types of nonlinear nonstationary processes. Innovative technological product. Methodology of using convolutional neural networks for modeling and forecasting nonlinear nonstationary processes presented in the form of time-series data. Scope of the innovative technological product. Nonlinear nonstationary processes presented in the form of time-series data.

scholarly journals Classifying Behaviours in Videos with Recurrent Neural Networks

2017 ◽  
Vol 7 (4) ◽  
pp. 1-15 ◽  
Author(s):  
Javier Abellan-Abenza ◽  
Alberto Garcia-Garcia ◽  
Sergiu Oprea ◽  
David Ivorra-Piqueres ◽  
Jose Garcia-Rodriguez
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Networks ◽  
Video Surveillance ◽  
Activity Recognition ◽  
Human Activity ◽  
Recurrent Neural Networks ◽  
Low Cost ◽  
Video Images ◽  
Employee Monitoring

This article describes how the human activity recognition in videos is a very attractive topic among researchers due to vast possible applications. This article considers the analysis of behaviors and activities in videos obtained with low-cost RGB cameras. To do this, a system is developed where a video is input, and produces as output the possible activities happening in the video. This information could be used in many applications such as video surveillance, disabled person assistance, as a home assistant, employee monitoring, etc. The developed system makes use of the successful techniques of Deep Learning. In particular, convolutional neural networks are used to detect features in the video images, meanwhile Recurrent Neural Networks are used to analyze these features and predict the possible activity in the video.

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