Machine translation using deep learning: An overview

Singh, Shashi Pal; Kumar, Ajai; Darbari, Hemant; Singh, Lenali; Rastogi, Anshika; Jain, Shikha

doi:10.1109/comptelix.2017.8003957

Cited by 144 publications

(77 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…All indicators point towards an even wider use of deep learning in various fields. Deep learning has already found its application in transportation and greenhouse-gas emission control [68], traffic control [69], text classification [8,70], object detection [71], speech detection [72,73], translation [74] and in other fields. These applications were not so represented in the past.…”

Section: Research Questionsmentioning

confidence: 99%

Deep Learning and Medical Diagnosis: A Review of Literature

Bakator

Radosav

2018

MTI

364

159

View full text Add to dashboard Cite

Abstract:In this review the application of deep learning for medical diagnosis is addressed. A thorough analysis of various scientific articles in the domain of deep neural networks application in the medical field has been conducted. More than 300 research articles were obtained, and after several selection steps, 46 articles were presented in more detail. The results indicate that convolutional neural networks (CNN) are the most widely represented when it comes to deep learning and medical image analysis. Furthermore, based on the findings of this article, it can be noted that the application of deep learning technology is widespread, but the majority of applications are focused on bioinformatics, medical diagnosis and other similar fields.

show abstract

Section: Research Questionsmentioning

confidence: 99%

Deep Learning and Medical Diagnosis: A Review of Literature

Bakator

Radosav

2018

MTI

364

159

View full text Add to dashboard Cite

show abstract

“…LSTM is a sequence prediction model which predicts output through information embedded in a series of time steps. In recent years, researchers have applied LSTM to some time series problems such as inventory, weather forecasts, and machine translation [32]- [34]. In these tasks, LSTM is usually superior to traditional machine learning models.…”

Section: Ioc Fault Detection Based On Lstmmentioning

confidence: 99%

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Zuo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

Insulated conductors can improve the stability of power transmission and reduce the construction space compared with traditional bare conductors. Therefore, insulated conductors are used more and more in overhead power transmission. However, a major challenge of using insulated overhead conductors (IOC) is that the ordinary protection devices are not able to detect the phase-to-ground faults and something, such as tree branch, hitting conductor events. This may cause an accident such as power failure or electrical fire and result in serious damage. In this paper, a new approach, which is based on Discrete Wavelet Transform (DWT) and Long Short Term Memory network (LSTM) for detecting of IOC fault according to partial discharge, is presented. Firstly, the original signal is denoised by DWT. Secondly, the denoised signal is decomposed and extracted features on different layers by DWT. Finally, IOC fault is detected by LSTM. This method can improve the detection accuracy of IOC fault which is tested on the ENET public data set and compared with other classification methods. INDEX TERMS Insulated overhead conductors, partial discharge, discrete wavelet transform, long short term memory network, fault detection.

show abstract

“…Second, most existing schizophrenia classification approaches can be classified as shallow learning. Intuitionally, deep learning approaches [15]- [18] that are suitable for complex tasks should be used for highly complex schizophrenia classification tasks. Besides, recently proposed approaches using deep learning [19]- [21] involve concise operations and automatic feature extraction is realized by end-to-end learning.…”

Section: B Motivationmentioning

confidence: 99%

Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

et al. 2019

View full text Add to dashboard Cite

Automatic diagnosis and classification of schizophrenia based on functional magnetic resonance imaging (fMRI) data have attracted increasing attention in recent years. Most previous studies abstracted highly compressed functional features from the view of brain science and fed them into shallow classifiers for this purpose. However, their classification performance in practical applications is unstable and unsatisfactory. As an acute psychotic disorder, schizophrenia shows functional complexity in fMRI data. Therefore, additional features and deep classification methods are needed to improve classification performance. In this study, we propose a multiple feature image capsule network ensemble approach for schizophrenia classification. The proposed approach proceeds in three steps: 1) extracting multiple image features from the perspective of linear sparse representation, nonlinear multiple kernel representation, and function connection of brain areas respectively; 2) feeding these image features into three specially designed independent capsule networks for classification; 3) obtaining the final results by fusing the outputs of these three deep capsule network using a ensemble approach. To further improve the classification performance, we design a optimization model of maximizing the square of correlation coefficients and propose a weighted ensemble technology based on this model, which is mathematically proved to be solved as a eigenvalue decomposition problem in certain case. Finally, the proposed approach is implemented and evaluated on the schizophrenia fMRI dataset from COBRE, UCLA and WUSTL. From the experimental results, we conclude that the proposed method outperforms some current methods and further improves the accuracy of schizophrenia classification. INDEX TERMS Schizophrenia classification, multiple features extraction, deep capsule network, classifier ensemble.

show abstract

Machine translation using deep learning: An overview

Cited by 144 publications

References 3 publications

Deep Learning and Medical Diagnosis: A Review of Literature

Deep Learning and Medical Diagnosis: A Review of Literature

Fault Detection on Insulated Overhead Conductors Based on DWT-LSTM and Partial Discharge

Schizophrenia Classification Using fMRI Data Based on a Multiple Feature Image Capsule Network Ensemble

Contact Info

Product

Resources

About