The work is devoted to the development and analysis of computer vision algorithms designed to recognize objects in conditions of limited visibility, such as fog, rain or poor lighting. In the context of modern requirements for safety and automation, the task of identifying objects becomes especially relevant. The theoretical foundations of computer vision methods and their application in difficult conditions are considered. An analysis of image processing algorithms is carried out, including machine learning and deep learning methods that are adapted to work in conditions of poor visibility. The results of experiments demonstrating the effectiveness of the proposed approaches are presented, as well as a comparison with existing recognition systems. The results of the study can be useful in the development of autonomous vehicles and video surveillance systems.

Keywords: computer vision, mathematical modeling, software package, machine learning methods, autonomous transport systems

The work analyzes existing approaches to forecasting contract execution, including traditional statistical models and modern methods based on machine learning. A comparative analysis of various machine learning algorithms, such as logistic regression, decision trees, random forest and neural networks, was carried out to identify the most effective forecasting models.An extensive database of information on government contracts was used as initial data, including information about contractors, contract terms, deadlines and other significant factors. A prototype of an intelligent forecasting system was developed, testing was carried out on real data, as well as an assessment of the accuracy and reliability of the resulting forecasts. The results of the study show that the use of machine learning methods can significantly improve the quality of forecasting the execution of government contracts compared to traditional approaches

Keywords: intelligent system, mathematical modeling, government procurement, government contracts, software package, forecasting, machine learning

The article proposes the use of intelligent methods for predicting the reliability of contract execution as a key element of the system for ensuring information security of the critical infrastructure of financial sector organizations. Based on the analysis of historical data and the use of machine learning methods, a comprehensive model for assessing and predicting the risks of failure or poor performance of contracts by suppliers has been developed. It is shown how the use of predictive analytics can improve the efficiency of information security risk management, optimize planning and resource allocation, and make informed decisions when interacting with suppliers of critical services and equipment.

Keywords: intelligent system, predictive analytics, information security, critical infrastructure, financial sector, contract execution, machine learning

The constant growth of cyber attacks on the financial sector requires the construction of a modern protection system based on the use of artificial intelligence or machine learning. The paper provides an analysis of specific products and solutions of the global market based on artificial intelligence technologies that can be used to protect critical information infrastructure.

Keywords: cyber attacks, critical infrastructure, artificial intelligence, information security, machine learning

In order to provide information support for decision-making on the issuance of bank guarantees for the execution of a contract in the field of public procurement, it is important for banks to obtain historically accumulated information on the execution of government contracts. This is necessary to assess the possibility of the supplier's performance of his future contract. This can be done by collecting and aggregating information about contracts from the Unified Information System in the field of procurement. The paper proposes to use IT technologies and data analysis to predict the performance of the contract and identify bona fide suppliers. In the work, a selection of primary data on contracts was formed for modeling using the parsing of the FTP server of the Unified Information System in the field of procurement, and the parsed data was preprocessed for use in machine learning models.

Keywords: information system, data analysis, government contract, data parsing, machine learning