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Melanoma is one of the most aggressive forms of cancer, which can be treated only with early detection of the disease. The article discusses the existing algorithms and methods of visual diagnosis of melanoma. The systems of automatic diagnosis of dermatoscopic images and the methods used by them are also considered. The article considers the limitations hindering the development of automatic diagnosis systems: the lack of relevant domestic data sets that allow training artificial intelligence models, insufficient level of patient metadata accounting, low coverage of the population for the presence of melanoma during routine examinations. A variant of building a decision support system by general practitioners in the analysis of dermatoscopic images of the skin is proposed.

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Recently, the number of confidential data leaks caused by internal violators has increased. Since modern DLP-systems cannot detect and prevent information leakage channels in encrypted or compressed form, an algorithm was proposed to classify pseudo-random sequences formed by data encryption and compression algorithms. Algorithm for constructing a random forest was used. An array of the frequency of occurrence of binary subsequences of 9-bit length and statistical characteristics of the byte distribution of sequences was chosen as the feature space. The presented algorithm showed the accuracy of 0,99 for classification of pseudorandom sequences. The proposed algorithm will improve the existing DLP-systems by increasing the accuracy of classification of encrypted and compressed data.