Tornado prediction methods are analyzed. A model, based on the superposition principle applied for different methods of data analysis has been built. For efficiency evaluation, the constructed model has been tested on real data. It is shown that the constructed tornado prediction model is more efficient than previous models.

Tornado prediction variables are analyzed using machine learning and decision analysis techniques. A model based on several choice procedures and the superposition principle is applied for different methods of data analysis. The constructed model has been tested on a database of tornadic events. It is shown that the tornado prediction model developed herein is more efficient than a previous set of machine learning models, opening the way to more accurate decisions.

Since 9/11, terrorism has become a global issue of the twenty-first century. Terrorist organizations become important actors of world politics as they gain influence on political process and decision-making. Some organizations compete with each other in order to gain more power and influence. We study the distribution of power among terrorist groups using network approach and applying classic and new centrality indices (Short-Range (SRIC) and Long-Range interactions indices (LRIC)). These indices allow to identify terrorist groups with direct and indirect influence on the terrorist network.

The problem of quick detection of central nodes in large networks is studied. There are many measures that allow to evaluate a topological importance of nodes of the network. Unfortunately, most of them cannot be applied to large networks due to their high computational complexity. However, if we narrow the initial network and apply these centrality measures to the sparse network, it is possible that the obtained set of central nodes will be similar to the set of central nodes in large networks. If these sets are similar, the centrality measures with a high computational complexity can be used for central nodes detection in large networks. To check the idea, several random networks were generated and different techniques of network reduction were considered. We also adapted some rules from social choice theory for the key nodes detection. As a result, we show how the initial network should be narrowed in order to apply centrality measures with a high computational complexity and maintain the set of key nodes of a large network.

The paper examines a choice problem in case of large number of alternatives characterized by a set of criteria. Very often existing choice procedures cannot be used due to their computational complexity. In contrast, the approach proposed in this paper utilizes a set of easy to use techniques having complexity less than the quadratic. We apply super-threshold procedures sequentially in order to find the best alternatives. The application to learning to rank problem is also considered. Finally, two methods of defining thresholds and order of criteria in super-threshold procedures are studied. While the first method is based on the etalon criterion, the second method uses the distribution function. Both methods developed are mainly oriented on the choice of search results in search engines and capable of processing large amount of data in networks, but can also be applied to any system in which the alternatives are represented by a set of criteria.