Due to the popularity of the easiest way to obtain personal information among attackers, phishing detection is becoming a popular area for research aimed at countering the implementation of such attacks. Malicious website detection is essential to prevent the spread of malware and protect end users from victims. Unfortunately, malicious URL detection still needs to be better understood due to a lack of features and inaccurate classification. Possible sources were examined in order to investigate the subject. Based on the collected information from previous studies, this study is devoted to solving the problem of detecting phishing websites using Ensemble Learning. The aim of the work is to choose the most optimal algorithm for classifying phishing websites using gradient boosting algorithms. AdaBoost, CatBoost, and Gradient Boosting Classifier were chosen as Ensemble Learning algorithms and were used to improve the efficiency of classifiers. Practical studies of the parameters of each algorithm for finding the optimal classification model are given. Research and experiments were carried out on a dataset containing information extracted from the contents of a URL: main URL, domain, directory, and file. A thorough Exploratory Data Analysis (EDA) was carried out, as a result of which the main dependencies and patterns of determining phishing resources were identified using correlation analysis. ROC AUC Score was chosen as an evaluation metric for the algorithms. The best result for predicting phishing websites was demonstrated by the AdaBoost Classifier algorithm, with an average ROC AUC score of 99%. The results of the experiments were illustrated in the form of graphs and tables.

Grading students' written work such as papers, essay questions on exams, and laboratory reports can be a significantly time-consuming task, although becoming a common practice. One of the common problems that arise when evaluating text documents can be the low quality of the source data that does not comply with the formatting rules. This study uses a Machine Learning approach to evaluate the quality of student paper text documents. During the experiment, data were taken from several works for testing. The quality assessment indicators used in this study are the compliance of the explanatory note with the structural elements of the document. Several Machine Learning algorithms and methods are tested, such as k-nearest neighbors (kNN), Support Vector Regressor (SVR), and Random Forest, to find the best model suitable for estimation. The result of the comparison reveals that kNN shows the highest prediction accuracy while having advantages in the learning process. The learning results of all algorithms are clearly presented in tables and graphical illustrations.

The Mining Industry has changed significantly in recent decades with the introduction of advanced technologies such as Artificial Intelligence (AI) and Machine Learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the Mining Industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the Mining Industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

This study addresses the issue of accurate and rapid aboveground biomass estimation in rangeland ecosystems, as traditional grazing methods are labor-intensive, while modern remote sensing techniques often require expensive equipment and controlled conditions. The goal of this work is to develop an efficient and accessible approach for biomass estimation of natural pastures based on ground-level RGB images captured with smartphones. For this purpose, a dataset consisting of 1196 field images and corresponding biomass values collected from 40 districts in southern Kazakhstan was used, and a wide + tiles architecture based on the DINOv3 model of Vision Transformer was proposed. The model utilized attention pooling and feature fusion mechanisms to integrate both global and local features, and various preprocessing and augmentation strategies were comparatively examined. Experimental results demonstrated that the proposed method exhibits high accuracy (with the best result being R² = 0.733, MAE ≈ 0.779 c/ha), where the DINOv3 model showed clear advantages over ConvNeXtV2. Furthermore, the impact of preprocessing strategies was minimal, and the importance of high-resolution images was clearly established. The obtained results show that the proposed method performs consistently under heterogeneous field conditions and allows for reliable biomass estimation without the need for specialized equipment. This makes it a practical tool for monitoring pastures, planning forage supply, and supporting agronomic decision-making.

The mining industry has changed significantly in recent decades with the introduction of advanced technologies such as artificial intelligence (AI) and machine learning (ML). These innovations contribute to the creation of expert systems that help in optimizing processes, increasing the safety and sustainability of operations. This article is a literature review of modern AI and ML methods and technologies used in the mining industry. Discusses various intelligent and expert systems used to improve productivity, reduce operating costs, improve occupational safety, environmental sustainability, machine automation, predictive analytics, quality monitoring and control, and inventory and logistics management. The advantages and disadvantages of different approaches are analyzed, as well as their potential impact on the future of the mining industry. The review highlights the importance of integrating AI and ML into mining processes to achieve more efficient and safer solutions.

This research is devoted to the development of software to increase the efficiency of autonomous wind-generating substations using panel structures, which will allow the use of wind energy to generate electricity with minimal losses and for the life support of buildings and structures. In the course of the work, a software and hardware system with a functional diagram for experimental measurements was developed. The paper also describes the process of modeling wind generation, collecting and transmitting real-time data to a web server via the HTTPS protocol. Due to the intensive development of wind energy in Kazakhstan, there is a need to apply methods to improve the energy generation process. In particular, the use of hardware and software to monitor and make decisions on optimizing the power generation process will help solve the problem of limited economic and labor resources. The results of the experiments revealed that the automatic control of the shield structures allows specialists to increase the effectiveness of the energy generation process by 25 % and, thus, a non-linear relationship between the power of the generated energy, the speed and direction of wind has been revealed. It should also be noted that the results obtained in the course of this research make it possible to solve the problem of saving electricity in the cities of Kazakhstan, since so far there are only large-scale wind farms, which is not always available in simple urban conditions. Moreover, the software developed during the study will allow autonomous control and analysis of the behavior of the wind farm, taking into account various weather conditions. In the future, the methods of data analysis will be applied to the data obtained via the process of modeling. A script for receiving and transmitting real-time data with wind speed and direction sensors has been developed