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the new approach to the medical, in particular, the toxicological data analysis is considered. For the data processing multilevel system realization, the three-stage technique for data analysis with data mining usage is offered. The results of the research are discussed.

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Situation-oriented databases provide processing of documents from heterogeneous data sources under the control of a hierarchical situational model. This article discusses the problem of processing database documents in JSON format, along with XML. Two implementation approaches are discussed: (1) on the fly JSON to XML document conversion and using Document Object Model for processing XML, and (2) loading the JSON document into an associative/indexed array followed by applying the template engine. The database interpreter works with external heterogeneous data extracted from files, databases, archives, web services, data is processed using virtual documents. Examples of processing JSON documents received from a web service are analyzed. Data from the San Francisco Open Data web server is used as the JSON test source. Query in Socrata Query Language used for JSON data extraction is presented. The implementation of approaches in the research situation-oriented database prototype based on Hypertext Preprocessor is considered.

The article is devoted to the problem of the reliability of applications based on artificial intelligence. The authors made an attempt to evaluate the impact of the graphic data distortion at the input of a convolutional neural network on the result of image classification. The experiment is based on the fault injection method. A series of independent tests were carried out for such distortions as Gaussian noise, salt and pepper, speckle and Poisson noise, as well as median blur, motion blur, scene brightness changing, rotation, rain, and snow. The results showed that Gaussian noise was the least critical distortion; environmental conditions (rain, snow, brightness) and image rotation up to 20 degrees are less critical than focus losing and motion blur, while the most critical distortion is speckle noise. It was verified that preprocessing the input data of the neural network improves the accuracy of image recognition.