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The purpose of our work was to study the effectiveness of using an intelligent informationanalytical system to assess the health status of students on the basis of one of the universities in Kazakhstan, the Kazakh National University named after Al-Farabi. For this purpose, a simulation was performed using individual health data 4456 university students. The study involved 47.4% of female students and 52.6% of males; the predominant age ranged from 18 to 21 years (66.9%), and the distribution of students by years of study was almost uniform. For classification were used such as Vector Machine, K -Nearest Neighbour, Random Forest and Naive Bayes supports. The performance metrics chosen to evaluate the use of various prediction algorithms were Specifity, Sensitivity, Accuracy, and Accessibility. It has been established that when using the classifier Support Vector Machine 's Specifity, Sensitivity, Accuracy, and Accessibility scores are at their highest, reaching 97%. The overall performance of the developed intelligent information and analytical system was evaluated using the Reliability parameter. In comparison with other wellknown systems for monitoring the health of patients (AmbIGEM and AAL ), the system developed by us showed higher reliability (90-95%). In the future, the developed model can be used to expand health monitoring by including external parameters that can also affect the health of students. In addition, it is planned to introduce Deep Learning to monitor the health of students in other educational institutions in Kazakhstan and the world.

The article considers the possibility of modifying the genetic algorithm (GA) to address the issues of selection, optimization and dynamic configuration management of information security tools (IMS) for the security circuit of information and communication systems (ICS). The scientific novelty of the work is that it is recommended to use the total cost of information loss risks, as well as cost indicators for each class of information security systems as a criterion for optimizing the composition of the information security system in GA. The genetic algorithm is considered as a variation of the problem associated with multi-choice in optimizing the choice of information security information and the dynamic management of cybersecurity resources. In this concept, the optimization of the placement of the information security system along the protective circuit of the ACS is considered as a modification of the problem of the combination backpack. The proposed approach allows not only to quickly calculate the different versions of hardware and software information systems and their combinations for CMS, but also to dynamically manage the proposed algorithm with existing models and algorithms for optimizing the composition of CSR cybersecurity chains and cybersecurity resources of various information objects. It is possible that such a combination of models and algorithms will allow to quickly restore the CSR protection by adjusting the profiles according to the new threats and classes of cyber attacks.

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