Supervised, semi-supervised, and unsupervised learning of the Domany-Kinzel model

The Domany Kinzel (DK) model encompasses several types of non-equilibrium phase transitions, depending on the selected parameters. We apply supervised, semi-supervised, and unsupervised learning methods to studying the phase transitions and critical behaviors of the (1 + 1)-dimensional DK model. The supervised and the semi-supervised learning methods permit the estimations of the critical points, the spatial and temporal correlation exponents, concerning labelled and unlabelled DK configurations, respectively. Furthermore, we also predict the critical points by employing principal component analysis (PCA) and autoencoder. The PCA and autoencoder can produce results in good agreement with simulated particle number density

Semi-Supervised Variational Autoencoder for Survival Prediction

In this paper we propose a semi-supervised variational autoencoder for classification of overall survival groups from tumor segmentation masks. The model can use the output of any tumor segmentation algorithm, removing all assumptions on the scanning platform and the specific type of pulse sequences used, thereby increasing its generalization properties. Due to its semi-supervised nature, the method can learn to classify survival time by using a relatively small number of labeled subjects. We validate our model on the publicly available dataset from the Multimodal Brain Tumor Segmentation Challenge (BraTS) 2019.Comment: Published in the pre-conference proceeding of "2019 International MICCAI BraTS Challenge

Semi-Supervised Spatial-Temporal Feature Learning on Anomaly-Based Network Intrusion Detection

Due to a rapid increase in network traffic, it is growing more imperative to have systems that detect attacks that are both known and unknown to networks. Anomaly-based detection methods utilize deep learning techniques, including semi-supervised learning, in order to effectively detect these attacks. Semi-supervision is advantageous as it doesn\u27t fully depend on the labelling of network traffic data points, which may be a daunting task especially considering the amount of traffic data collected. Even though deep learning models such as the convolutional neural network have been integrated into a number of proposed network intrusion detection systems in recent years, little work has been done on spatial-temporal feature extraction for network intrusion anomaly detection using semi-supervised learning. This paper introduces Anomaly-CNVAE, a variational autoencoder where the encoding and decoding layers perform convolution and transpose convolution, respectively, in order to account for spatial feature extraction. In addition, in order to account for time-based features in the dataset, the proposed model utilizes 1D-CNN for the convolution operations. The performance of the model in network intrusion detection is evaluated against an autoencoder and a vanilla variational autoencoder. Results show that Anomaly-CNVAE significantly outperforms the other semi-supervised learning models with a 5-10 percent increase in evaluation metrics