The usefulness of unsupervised classification methods for landscape typification: The case of Slovenia

Supervised and unsupervised classification methods can be a useful tool in determining various geographical spatial divisions, especially regionalizations and typifications. Because Slovenia is geographically very diverse, its divisions are a particularly significant and interesting research challenge. The main objective of this article is to determine the effectiveness of unsupervised classification methods, and therefore we compare the well-established landscape typology of Slovenia from 1996 with landscape typologies that were modeled using various unsupervised classification methods. Our results show that landscape typologies modeled using unsupervised classification methods deviate more from the original landscape typology of Slovenia than landscape typologies modeled using random and expert-supervised classification methods

User-profile-based analytics for detecting cloud security breaches

While the growth of cloud-based technologies has benefited the society tremendously, it has also increased the surface area for cyber attacks. Given that cloud services are prevalent today, it is critical to devise systems that detect intrusions. One form of security breach in the cloud is when cyber-criminals compromise Virtual Machines (VMs) of unwitting users and, then, utilize user resources to run time-consuming, malicious, or illegal applications for their own benefit. This work proposes a method to detect unusual resource usage trends and alert the user and the administrator in real time. We experiment with three categories of methods: simple statistical techniques, unsupervised classification, and regression. So far, our approach successfully detects anomalous resource usage when experimenting with typical trends synthesized from published real-world web server logs and cluster traces. We observe the best results with unsupervised classification, which gives an average F1-score of 0.83 for web server logs and 0.95 for the cluster traces

Present/future California Department of Forestry remote sensing activities

The utilization of LANDSAT data in forestry inventories in California is summarized. Problems in selecting meaningful classification systems, utilization of supervised or unsupervised classification systems, and data smoothing are discussed