Robust classification approach for segmentation of blood defects in cod fillets based on deep convolutional neural networks and support vector machines and calculation of gripper vectors for robotic processing

2017 ◽  
Vol 139 ◽  
pp. 138-152 ◽  
Author(s):  
Ekrem Misimi ◽  
Elling Ruud Øye ◽  
Øystein Sture ◽  
John Reidar Mathiassen
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Classification Approach ◽  
Robust Classification ◽  
Vector Machines

scholarly journals Human action recognition using support vector machines and 3D convolutional neural networks

2017 ◽  
Vol 3 (1) ◽  
pp. 47 ◽  
Author(s):  
Majd Latah
Keyword(s):  
Neural Networks ◽  
Support Vector Machines ◽  
Convolutional Neural Networks ◽  
Action Recognition ◽  
Recognition Task ◽  
Human Action Recognition ◽  
Human Action ◽  
Support Vector ◽  
Deep Convolutional Neural Networks ◽  
Vector Machines

Recently, deep learning approach has been used widely in order to enhance the recognition accuracy with different application areas. In this paper, both of deep convolutional neural networks (CNN) and support vector machines approach were employed in human action recognition task. Firstly, 3D CNN approach was used to extract spatial and temporal features from adjacent video frames. Then, support vector machines approach was used in order to classify each instance based on previously extracted features. Both of the number of CNN layers and the resolution of the input frames were reduced to meet the limited memory constraints. The proposed architecture was trained and evaluated on KTH action recognition dataset and achieved a good performance.

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.

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