A deep neural network based multi-task learning approach to hate speech detection

2020 ◽  
Vol 210 ◽  
pp. 106458
Author(s):  
Prashant Kapil ◽  
Asif Ekbal
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Hate Speech ◽  
Learning Approach ◽  
Speech Detection ◽  
Task Learning

scholarly journals A Multi-task Learning Approach to Hate Speech Detection Leveraging Sentiment Analysis

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Flor Miriam Plaza-Del-Arco ◽  
M. Dolores Molina-Gonzalez ◽  
L. Alfonso Urena-Lopez ◽  
Maria Teresa Martin-Valdivia
Keyword(s):  
Sentiment Analysis ◽  
Hate Speech ◽  
Learning Approach ◽  
Speech Detection ◽  
Task Learning

scholarly journals Biomedical semantic indexing by deep neural network with multi-task learning

2018 ◽  
Vol 19 (S20) ◽  
Author(s):  
Yongping Du ◽  
Yunpeng Pan ◽  
Chencheng Wang ◽  
Junzhong Ji
Keyword(s):  
Neural Network ◽  
Deep Neural Network ◽  
Semantic Indexing ◽  
Task Learning

scholarly journals A Deep Learning Approach to Estimate the Respiratory Rate from Photoplethysmogram

Ingenius ◽  
2021 ◽  
Author(s):  
Lucas C. Lampier ◽  
Yves L. Coelho ◽  
Eliete M. O. Caldeira ◽  
Teodiano Bastos-Filho
Keyword(s):  
Neural Network ◽  
Heart Rate ◽  
Deep Learning ◽  
Open Access ◽  
Respiratory Rate ◽  
Analytical Methods ◽  
Deep Neural Network ◽  
Learning Approach ◽  
Genetic Optimization ◽  
Median Error

This article describes the methodology used to train and test a Deep Neural Network (DNN) with Photoplethysmography (PPG) data performing a regression task to estimate the Respiratory Rate (RR). The DNN architecture is based on a model used to infer the heart rate (HR) from noisy PPG signals, which is optimized to the RR problem using genetic optimization. Two open-access datasets were used in the tests, the BIDMC and the CapnoBase. With the CapnoBase dataset, the DNN achieved a median error of 1.16 breaths/min, which is comparable with analytical methods in the literature, in which the best error found is 1.1 breaths/min (excluding the 8 % noisiest data). The BIDMC dataset seems to be more challenging, as the minimum median error of the literature’s methods is 2.3 breaths/min (excluding 6 % of the noisiest data), and the DNN based approach achieved a median error of 1.52 breaths/min with the whole dataset.

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