Nowadays, in the insurance industry the use of predictive modeling by means of regression and classification techniques is becoming increasingly important and popular. The success of an insurance company largely depends on the ability to perform such tasks as credibility estimation, determination of insurance premiums, estimation of probability of claim, detecting insurance fraud, managing insurance risk. This paper discusses regression and classification modeling for such types of prediction problems using the method of Adaptive Basis Function Construction
The paper describes the implementation of organic benchmarks for Java EE and ASP.NET Core, which are used to compare the performance characteristics of the language runtimes. The benchmarks are created as REST services, which process data in the JSON format. The ASP.NET Core implementation utilises the Kestrel web server, while the Java EE implementation uses Apache TomEE, which is based on Apache Tomcat. A separate service is created for invoking the benchmarks and collecting their results. It uses Express with ES6 (for its async features), Redis and MySQL. A web-based interface for utilising this service and displaying the results is also created, using Angular 5.
With the aim to compare methods for counting the number of lines of a raster matrix, intersecting a round mark image, and a number of pixels belonging to this image for measuring its radius, a numerical simulation is carried out in the present article. It is proved that the application of the method for counting the number of pixels belonging to the image of the round mark allows obtaining more than 30 times gain in the accuracy of this image radius measurement using the same equipment. The formulas proposed in the article are used for software implementation of non-contact vibration measurement systems.