EEG-based deep learning model for the automatic detection of clinical depression

2020 ◽  
Vol 43 (4) ◽  
pp. 1349-1360
Author(s):  
Pristy Paul Thoduparambil ◽  
Anna Dominic ◽  
Surekha Mariam Varghese
Keyword(s):  
Deep Learning ◽  
Automatic Detection ◽  
Learning Model ◽  
Clinical Depression ◽  
Deep Learning Model

scholarly journals Automatic detection of acute ischemic stroke using non-contrast computed tomography and two-stage deep learning model

2020 ◽  
Vol 196 ◽  
pp. 105711
Author(s):  
Mizuho Nishio ◽  
Sho Koyasu ◽  
Shunjiro Noguchi ◽  
Takao Kiguchi ◽  
Kanako Nakatsu ◽  
...  
Keyword(s):  
Computed Tomography ◽  
Ischemic Stroke ◽  
Deep Learning ◽  
Acute Ischemic Stroke ◽  
Automatic Detection ◽  
Learning Model ◽  
Two Stage ◽  
Deep Learning Model

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.

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