Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially the softwarization era where implementation of this concept can be done easily with function virtualization. Currently, many approaches have been proposed improve performance classification. However, as new types emerge every day (and they are generally not labeled), opens ...

Recently, Stacked Auto-Encoders (SAE) have been successfully used for learning imbalanced datasets. In this paper, for the first time, we propose to use a Neural Network classifier furnished by an SAE structure for detecting the errors made by a strong Automatic Speech Recognition (ASR) system. Error detection on an automatic transcription provided by a ”strong” ASR system, i.e. exhibiting a sm...

Speech feature learning is the key to speech mental health recognition. Deep can automatically extract features but suffers from small sample problem. The traditional method effective, cannot find inter-feature structure generate new high-quality features. This paper proposes an embedded hybrid deep sparse stacked autoencoder ensemble solve this Firstly, are extracted based on prior knowledge a...