DOI: 10.7256/2454-0668.2023.4.44013

Abstract: The subject of the research is assessing the risks of performing government contracts. The object of the study is the process of analysis and evaluation of the implementation of government contracts. The study is aimed at developing a methodology that determines the importance and significance of signs that influence the risk of non-fulfillment of government contracts. Research methods were used: data analysis to detect connections and dependencies between various characteristics and the risk of non-fulfillment of government contracts; statistical analysis to obtain an assessment of the impact of each characteristic on the risk of non-fulfillment of contracts and ranking them in order of importance; machine learning to predict the risk of non-fulfillment of government contracts; expert assessments to take into account contextual factors and features, their impact on the importance of features. The main conclusions of the study are the presented methods for assessing the importance of features when analyzing the implementation of government contracts, by using data from various sources, including the register of public procurement of the unified information system (UIS), the register of unscrupulous suppliers (RNP) of the EIS and the SPARK information system. The authors managed to achieve high prediction accuracy (more than 97%) and analyze the most important and significant features. The scientific novelty lies in the fact that the results obtained make it possible to identify and analyze factors from three information systems that influence the risks of non-fulfillment of government contracts. Thus, this study is valuable and important in its field, which contributes to the development of more effective risk management methods and increased efficiency in the implementation of government contracts. The results obtained allow us to identify the factors that have the greatest impact on the risks of non-fulfillment of contracts, which makes the study valuable and important in this area.

DOI: 10.7256/2454-0714.2023.4.44113

Abstract: The subject of the research is the development of a software package for intelligent forecasting of the execution of government contracts using machine learning methods and analysis of unstructured information. The object of the study is the process of control and decision-making in the field of public procurement, including the selection of contractors, the execution of contracts and the assessment of the timing and cost of their implementation. Special attention in the study is paid to the development and application of interpreted machine learning methods to solve the problems of assessing the risks of choosing an unscrupulous contractor, the risks of non-fulfillment of the contract on time and forecasting the likely timing and cost of contract implementation. The authors consider in detail such aspects as a unique set of data that was collected from various information systems. They have also developed automated data collection and update systems that can be installed on customers' servers. The methods of machine learning, analysis of unstructured information and interpreted methods were used in the work. Interpreted machine learning models were built to assess the risk of choosing an unscrupulous contractor, assess the risk of non-fulfillment of the contract on time, as well as assess the likely timing and cost of contract implementation. A unique set of data was collected in the work, including more than 83 thousand data on more than 190 features from various systems, such as the Unified Information System (UIS) Public Procurement Register, the Register of Unscrupulous Suppliers (RNP) EIS and SPARK Information System. Automated data collection and updating systems have been developed that can be deployed on customer servers. In the course of the study, software packages were developed for intelligent forecasting of the execution of government contracts, which provide an opportunity to conduct a more accurate risk analysis using unstructured information analysis methods, machine learning models and interpreted methods. This makes it possible to increase the effectiveness of monitoring the implementation of government contracts and reduce the likelihood of corruption and violations. The study demonstrates the importance and applicability of machine learning methods and models in the field of public contracts and provides new opportunities for improving control and decision-making processes in the field of public procurement.

DOI: 10.25136/2409-7543.2023.3.43543

Abstract: The research is aimed at solving the problem of the execution of government contracts, the importance of using unstructured information and possible methods of analysis to improve the control and management of this process. The execution of government contracts has a direct impact on the security of the country, its interests, economy and political stability. Proper execution of these contracts contributes to the protection of national interests and ensures the security of the country in every sense. The object of research is algorithms used to extract information from texts. These algorithms include machine learning technologies and natural language processing. They are able to automatically find and structure various entities and data from government contracts. The scientific novelty of this study is the accounting of unstructured information in the analysis of the execution of government contracts. The authors drew attention to the problem-oriented texts in the contract documentation and suggested analyzing them with numerical indicators to assess the current state of the contract. Thus, a contribution was made to the development of methods for analyzing government contracts by taking into account unstructured information. The proposed methods for analyzing problem-oriented texts using machine learning. This approach can significantly improve the evaluation and management of the execution of government contracts. The results of the interpretation of problem-oriented texts can be used to optimize the risk assessment model for the execution of a government contract, as well as to increase its accuracy and efficiency.

DOI: 10.7256/2454-0714.2022.3.38674

Abstract: The relevance of the study is dictated by the current state in the field of telephone fraud. According to research conducted by Kaspersky Lab, the share of users who encountered various unwanted spam calls in the spring of 2022 was at the level of 71%. The subject of the research is machine learning and deep learning technologies for determining emotions by the timbre of the voice. The authors consider in detail such aspects as: the creation of a marked-up dataset; the conversion of WAV audio format into a numerical form convenient for fast processing; machine learning methods for solving the problem of multiclass classification; the construction and optimization of neural network architecture to determine emotions in real time. A special contribution to the study of the topic is that the authors implemented a fast method of conversion sound formats into numerical coefficients, which significantly increased the speed of data processing, practically without sacrificing their informativeness. As a result, the models were trained by machine learning algorithms quickly and efficiently. It should be particularly noted that the architecture of a convolutional neural network was modeled, which allowed to obtain the quality of model training up to 98%. The model turned out to be lightweight and was taken as the basis for training the model to determine emotions in real time. The results of the real-time operation of the model were comparable with the results of the trained model. The developed algorithms can be implemented in the work of mobile operators or banks in the fight against telephone fraud. The article was prepared as part of the state assignment of the Government of the Russian Federation to the Financial University for 2022 on the topic "Models and methods of text recognition in anti-telephone fraud systems" (VTK-GZ-PI-30-2022).