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Item Open Access PREDICTING COURSE GRADES OF STUDENTS’ ACADEMIC PERFORMANCE USING THE LIGHTGBM REGRESSOR.(СДУ хабаршысы - 2023, 2023) Bairamova D.Abstract. In the modern world, using all available opportunities and technologies, special attention should be paid to the development of the education system of students, since education serves as the basis for the development of the future generation. Nowadays, thanks to the use of available Artificial Intelligence methods, it is possible to predict various events, anomalies or other important things. With the help of machine learning, it is possible to predict at an early stage of a student's education whether he will finish the course successfully or not. In this study, it is proposed to predict the final score which student will receive at the end of the course using a number of predictors as an assessment for the first quiz and 3 types of tasks using the LightGBM regressor, which is a high-performance algorithm with gradient boosting. The results of using the LGBM regressor using GridSearchCV allowed to determine the best settings of hyperparameters from three selected tree-like boosting methods: 'dart', 'gbdt', 'goss'. The GOSS method was determined to be the best of the three methods listed with an estimate of R2 score in 0.81, which is 0.24 more than the R2 score of the Linear Regression forecast of — (0.57).Item Open Access The prediction of information security level in the enterprise(faculty of engineering and natural sciences, 2020) Khashimova D.This thesis presents the results of an analysis to identify groups of threats specific to the infrastructure and systems of an enterprise, which is one of the main stages in forecasting. The state of information security at enterprises is considered, the qualifications of security threats and classification methods based on attack methods and the impact of threats are analyzed. Threats for the safe use of the Internet and hacking sites, data theft, phishing attacks and social engineering are assessed; Identification of cloud computing security threats that are encountered in the enterprise's Internet networks. The advantages and disadvantages of Web Application Firewall, which are used to protect attacks, such as DDoS attacks, SQL injections, cross-site scripting, and others, are studied. Works for providing protection using artificial intelligence and machine learning are presented.Item Open Access Applying machine learning models for predicting forest fires in Australia and the influence of weather on the spread of fires based on satellite and weather forecast data(2020 International Young Scholars Workshop, 2020) Meraliyev B.; Kongratbayev K.Abstract What shall we expect from the year 2020? The coronavirus pandemic is not the worst thing that humanity can face in the near future. According to the observations of the scientists, in March, 2020, the planet temperature warmed up to the record-high level. Also, the temperature of the world’s oceans exceeded its average temperatures by 80%, and prognosis of the meteorological observations is not good. The warming seas had already led to catastrophic disaster. The average temperature increase can also lead to hurricanes, drought, invasion of locusts and, the worst, to forest fires. Natural disasters lead to loss of life, destruction of properties and infrastructure, loss of animal natural habitats, displacement of humans. And the results of these all lead to humanitarian catastrophes, including social and economic. The situations related to the nature are always very serious, as the whole world is involved. This is like butterfly effect, i.e., the natural disaster in Australia affect the economic and ecologic situation in USA and England. Taking the Australia, they faced problem that cannot be avoided. Nevertheless, the world can be prepared and prevent from the huge disasters. The forecasting of forest fires can really be helpful, as well as the inquiry of the weather impact on fires. The current paper is focused on the study of fire forecasting and weather influence on fire. The relevance of the study is important, as the global warming and human caused fires are increasing and there is a trend that Australia’s fires became more dangerous and longer lasting. The artificial intelligence, particularly machine learning algorithms, can help to make appropriate calculations and predictions to safe the ecosystem and human lives. According to the preliminary research results we acquire; in-depth multidimensional analysis confirms almost 100 percent dependence of bushfires on the weather conditions. Using the machine learning algorithms, it would be possible to predict the time and positioning of inflammation source.Item Open Access DATA COLLECTION TO IDENTIFY STUDENTS AT RISK OF NOT COMPLETING A COURSE USING MACHINE LEARNING(СДУ хабаршысы - 2023, 2023) Bairamova D.Abstract. One of the most important methods in the study of various subjects is the understanding at an early stage of the learning process on the part of both the teacher and the student that the student is in a risk group that will not complete the course successfully. Identifying this group of students at an early stage of learning increases the level of motivation of students to start studying well in time and can help the teacher individually determine which student needs help. Before identifying a group of students at risk of not completing the course successfully, an important part is to collect and prepare the necessary data (predictors) for teaching machine learning algorithms. Currently, this is necessary for both online and offline education. In the presented method of determining a group of students, various types of algorithms were used, where one of the best results of determining a group of students with risk and without risk was shown by Logistic Regression with a high AUC =0.8003. The SMOTE method was used in the work, which coped well with the problem of data imbalance of the "Pass" and "Not Pass" classes, while increasing the accuracy of the forecast for the minority class "Not Pass" by 11%. Using certain predictors of student performance, it is possible to derive additional information such as the level of interest in the lesson. the determination of the final score for the lesson, a certain category (A, B, C, D) of students with different characteristics and other indicators that contribute to the involvement of students in the lesson at the earliest stage of learning.