Abstract An open problem in data science is that of anomaly detection. Anomalies are instances do not maintain a certain property present the remaining observations dataset. Several detection algorithms exist, since process itself ill-posed mainly because criteria separates common or expected vectors from anomalies unique. In most extreme case, labelled and algorithm has to identify anomalous, ...

In recent years, neural network-based anomaly detection methods have attracted considerable attention in the hyperspectral remote sensing domain due to their powerful reconstruction ability compared with traditional methods. However, actual probability distribution statistics hidden latent space are not discovered by exploiting error because of anomalies is explicitly modeled. To address issue,...

This paper presents a model that describes spatial variations of the intensity of events that occur at random geographical locations. An inhomogeneous Poisson process is used to model the intensity over a spatial region with multiplicative spatial and temporal covariate effects. Dynamic temporal effects are incorporated into the model allowing changes in the intensity structure over time. Addit...

Automatic processes on seismic data using pattern recognition is one of the interesting fields in geophysical data interpretation. One part is the seismic object detection using different supervised classification methods that finally has an output as a probability cube. Object detection process starts with generating a pickset of two classes labeled as object and non-object and then selecting ...