In the directions of modern medicine, a new area of processing and analysis of visual data is actively developing - a radio municipality - a computer technology that allows you to deeply analyze medical images, such as computed tomography (CT), magnetic resonance imaging (MRI), chest radiography (CXR), electrocardiography and electrocardiography. This approach allows us to extract quantitative texture signs from signals and distinguish informative features to describe the heart's pathology, providing a personified approach to diagnosis and treatment. Cardiovascular diseases (SVD) are one of the main causes of death in the world, and early detection is crucial for timely intervention and improvement of results. This experiment aims to increase the accuracy of deep learning algorithms to determine cardiovascular diseases. To achieve the goal, the methods of deep learning were considered used to analyze cardiograms. To solve the tasks set in the work, 50 patients were used who are classified by three indicators, 13 anomalous, 24 nonbeat, and 1 healthy parameter, which is taken from the MIT-BIH Arrhythmia database.

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.

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.

Ensuring the stability of surrounding rock in underground excavations is a critical prerequisite for safe mining operations. This study examines the mechanisms of wedge failure formation and determines the performance of a combined support system (rock bolts + shotcrete) through probabilistic analysis. Field investigations in the Zhylandy ore field (Kazakhstan) included fracture mapping, rock mass quality assessment (RQD), fracture frequency (FF), and in situ stress measurements, which confirmed a thrust-faulting regime. Numerical modeling with Dips ver.8 and UnWedge ver.6 software (Rocscience) identified critical excavation orientations of 120° and 141° associated with maximum-volume wedge formation, as well as a “safe orientation window” of 70° ± 10°. The probabilistic analysis showed that rock bolts alone yield a factor of safety (FS) < 1.2, whereas the combined support system increases FS to 2.4–3.5, significantly reducing the likelihood of wedge failures. An adaptive framework integrating numerical modeling with intelligent monitoring (“monitor → update model → adjust support”) is proposed, allowing real-time adjustment of support parameters and optimization of material consumption. The practical significance of this work lies in providing design-ready recommendations for support selection and excavation orientation, contributing to accident prevention and sustainable mining operations.

This study summarizes the experience of conducting and maintaining mining operations in unstable zones in mines in Kazakhstan, assessing the mining and geological technical conditions of their operation under difficult mining and geological conditions. A field study and analytical modeling with an assessment of the stability of the boundary mass using the Barton mining method enabled the development of a technology for sinking mining operations with combined fasteners and friction anchors in ore mines.

This study presents an expert system for assessing the stability of underground mine workings and automatically selecting rock bolt support schemes. The system integrates physically based calculations of roof compressive strength (Rc) and expected maximum displacement (Um) with rule-based decision logic grounded in engineering practice. Unlike empirical classifications and black-box AI models, the proposed approach ensures interpretable, reproducible, and context-aware engineering decisions. The architecture includes a numerical solver that computes Rc and Um based on excavation geometry, geomechanical properties, and mining conditions. The support scheme is selected using a knowledge base of formalized rules, while the specific support parameters are calculated within the solver. The system was validated across 51 underground excavations, with approximately 85% of its recommendations matching field-proven support solutions, and 12% suggesting reinforced schemes that could have prevented failures. The expert system is suitable for integration into digital mine management platforms and offers a foundation for developing digital twin solutions in geomechanically variable environments.