scholarly journals Predicting sow postures from video images: Comparison of convolutional neural networks and segmentation combined with support vector machines under various training and testing setups

2021 ◽  
Vol 212 ◽  
pp. 19-29
Author(s):  
Mathieu Bonneau ◽  
Bernard Benet ◽  
Yann Labrune ◽  
Jean Bailly ◽  
Edmond Ricard ◽  
...  
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Video Images ◽  
Vector Machines

scholarly journals Automatic Detection of Depression in Speech Using Ensemble Convolutional Neural Networks

Entropy ◽  
2020 ◽  
Vol 22 (6) ◽  
pp. 688
Author(s):  
Adrián Vázquez-Romero ◽  
Ascensión Gallardo-Antolín
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Final Decision ◽  
Experimental Protocol ◽  
Ensemble Averaging ◽  
One Dimensional ◽  
Vector Machines ◽  
Detection Of Depression

This paper proposes a speech-based method for automatic depression classification. The system is based on ensemble learning for Convolutional Neural Networks (CNNs) and is evaluated using the data and the experimental protocol provided in the Depression Classification Sub-Challenge (DCC) at the 2016 Audio–Visual Emotion Challenge (AVEC-2016). In the pre-processing phase, speech files are represented as a sequence of log-spectrograms and randomly sampled to balance positive and negative samples. For the classification task itself, first, a more suitable architecture for this task, based on One-Dimensional Convolutional Neural Networks, is built. Secondly, several of these CNN-based models are trained with different initializations and then the corresponding individual predictions are fused by using an Ensemble Averaging algorithm and combined per speaker to get an appropriate final decision. The proposed ensemble system achieves satisfactory results on the DCC at the AVEC-2016 in comparison with a reference system based on Support Vector Machines and hand-crafted features, with a CNN+LSTM-based system called DepAudionet, and with the case of a single CNN-based classifier.

Development of a hybrid system based on convolutional neural networks and support vector machines for recognition and tracking color changes in food during thermal processing

2021 ◽  
Vol 240 ◽  
pp. 116679
Author(s):  
Weskley da Silva Cotrim ◽  
Leonardo Bonato Felix ◽  
Valéria Paula Rodrigues Minim ◽  
Renata Cássia Campos ◽  
Luis Antônio Minim
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Hybrid System ◽  
Thermal Processing ◽  
Support Vector ◽  
Color Changes ◽  
Vector Machines
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