scholarly journals Reconstruction of undersampled 3D non‐Cartesian image‐based navigators for coronary MRA using an unrolled deep learning model

2020 ◽  
Vol 84 (2) ◽  
pp. 800-812 ◽  
Author(s):  
Mario O. Malavé ◽  
Corey A. Baron ◽  
Srivathsan P. Koundinyan ◽  
Christopher M. Sandino ◽  
Frank Ong ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Coronary Mra ◽  
Deep Learning Model

Deep Learning Model Selection of Suboptimal Complexity

2018 ◽  
pp. 129-147
Author(s):  
Oleg Bakhteev ◽  
◽  
Vadim Strijov ◽  
Keyword(s):  
Deep Learning ◽  
Model Selection ◽  
Learning Model ◽  
Deep Learning Model ◽  
Selection Of

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

Gene Expression Data Based Deep Learning Model for Accurate Prediction of Drug-Induced Liver Injury in Advance

2019 ◽  
Vol 59 (7) ◽  
pp. 3240-3250 ◽  
Author(s):  
Chunlai Feng ◽  
Hengwei Chen ◽  
Xianqin Yuan ◽  
Mengqiu Sun ◽  
Kexin Chu ◽  
...  
Keyword(s):  
Gene Expression ◽  
Deep Learning ◽  
Liver Injury ◽  
Gene Expression Data ◽  
Learning Model ◽  
Accurate Prediction ◽  
Expression Data ◽  
Drug Induced ◽  
Drug Induced Liver Injury ◽  
Deep Learning Model

Detection of peripherally inserted central catheter (PICC) in chest X-ray images: A multi-task deep learning model

2020 ◽  
Vol 197 ◽  
pp. 105674
Author(s):  
Dingding Yu ◽  
Kaijie Zhang ◽  
Lingyan Huang ◽  
Bonan Zhao ◽  
Xiaoshan Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
X Ray ◽  
Chest X Ray ◽  
Deep Learning Model

Very short-term forecasting of wind power generation using hybrid deep learning model

2021 ◽  
Vol 296 ◽  
pp. 126564
Author(s):  
Md Alamgir Hossain ◽  
Ripon K. Chakrabortty ◽  
Sondoss Elsawah ◽  
Michael J. Ryan
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Power ◽  
Learning Model ◽  
Wind Power Generation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Deep Learning Model

Advanced deep learning model-based impact characterization method for composite laminates

2021 ◽  
Vol 207 ◽  
pp. 108713
Author(s):  
Kyung-Chae Jung ◽  
Seung-Hwan Chang
Keyword(s):  
Deep Learning ◽  
Composite Laminates ◽  
Learning Model ◽  
Model Based ◽  
Deep Learning Model

An efficiency-enhanced deep learning model for citywide crowd flows prediction

2021 ◽  
Author(s):  
Zhongyi Zhai ◽  
Peipei Liu ◽  
Lingzhong Zhao ◽  
Junyan Qian ◽  
Bo Cheng
Keyword(s):  
Deep Learning ◽  
Learning Model ◽  
Deep Learning Model
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