scholarly journals Supporting Emotion Automatic Detection and Analysis over Real-Life Text Corpora via Deep Learning: Model, Methodology and Framework

2021 ◽  
Vol 2021 (2) ◽  
pp. 1-6
Author(s):  
Alfredo Cuzzocrea ◽  
Alfredo Cuzzocrea
Keyword(s):  
Deep Learning ◽  
Real Life ◽  
Automatic Detection ◽  
Learning Model ◽  
Text Corpora ◽  
Deep Learning Model

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 Deep Learning Based Multi-modal Addressee Recognition in Visual Scenes with Utterances

2018 ◽  
Author(s):  
Thao Le Minh ◽  
Nobuyuki Shimizu ◽  
Takashi Miyazaki ◽  
Koichi Shinoda
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Intelligent Systems ◽  
Real Life ◽  
Smart Homes ◽  
Learning Model ◽  
Social Situations ◽  
Human Intention ◽  
Visual Scenes ◽  
Deep Learning Model

With the widespread use of intelligent systems, such as smart speakers, addressee recognition has become a concern in human-computer interaction, as more and more people expect such systems to understand complicated social scenes, including those outdoors, in cafeterias, and hospitals. Because previous studies typically focused only on pre-specified tasks with limited conversational situations such as controlling smart homes, we created a mock dataset called Addressee Recognition in Visual Scenes with Utterances (ARVSU) that contains a vast body of image variations in visual scenes with an annotated utterance and a corresponding addressee for each scenario. We also propose a multi-modal deep-learning-based model that takes different human cues, specifically eye gazes and transcripts of an utterance corpus, into account to predict the conversational addressee from a specific speaker's view in various real-life conversational scenarios. To the best of our knowledge, we are the first to introduce an end-to-end deep learning model that combines vision and transcripts of utterance for addressee recognition. As a result, our study suggests that future addressee recognition can reach the ability to understand human intention in many social situations previously unexplored, and our modality dataset is a first step in promoting research in this field.

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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