A computer model for analyzing, evaluating, and forecasting bank credit risks has been developed. Utilizing a Bayesian network (BN) and established parameter estimation methods, this model was implemented in the Python programming language. It predicts the probability that a borrower may fail to meet financial obligations, such as repaying a loan.
Summary. The article describes a study of identification of fake news based on natural language processing, big data analysis and deep learning technology. The developed system automatically checks the news for signs of fake news, such as the use of manipulative language, unverified sources and unreliable information. Data visualization is implemented on the basis of a friendly user interface that displays the results of news analysis in a convenient and understandable format.
The peculiarities of the use of the harmonized system of description and coding of goods, the harmonized tariff system of codes in modern customs classification systems are analyzed. Special attention is paid to the challenges that arise when applying these codes, in particular due to the complexity of the product nomenclature, as well as the variety of product descriptions. In addition, the possibilities of integrating IT technologies, machine learning and artificial intelligence methods to automate and optimize customs classification procedures are being explored.
The study substantiates the feasibility of using machine learning technology to improve the iteration planning process in IT projects implemented using the Scrum methodology. The problem of productivity planning in teams is set. The subject and object of the research are formulated. The expected scientific novelty and practical significance of the research results are described. A range of potential issues related to task planning in IT projects, particularly the accuracy of team productivity forecasting, is considered.
This article discusses the practical aspects of applying logistic regression for binary data classification. Logistic regression determines the probability of an object belonging to one of two classes. This probability is calculated with the help of a sigmoid function, the argument of which is a linear convolution of the feature vector of the object with the weighting coefficients obtained during the minimization of the logarithmic loss function. Predicted class labels are determined by comparing the calculated probability with a given threshold value.
In today’s world, where the speed of technological change is extremely impressive, the traffic industry is not left behind. The use of lane segmentation on the road is becoming a key element not only for safety, but also for improving navigation and traffic sign detection systems. This approach opens the door to a new level of efficiency and accuracy in traffic management, helping to improve the quality and safety of our movement. Let’s dive into the details of this exciting and promising area of road transport technology development.
Summary. The article describes the results of research into the processes of complex analysis of military information based on machine learning and natural language processing to help commanders of tactical units. The system should allow users to have the following capabilities: combining the dictionary and information material, adding terms and abbreviations to the dictionary, classifying objects for radio technical intelligence, visualizing aerial objects, classifying aerial objects, using information materials, organizing information materials.
The article examines the newest direction in artificial intelligence - Large Language Models, which open a new era in natural language processing, providing the opportunity to create more flexible and adaptive systems. With their help, a high level of understanding of the context is achieved, which enriches the user experience and expands the fields of application of artificial intelligence. Large language models have enormous potential to redefine human interaction with technology and change the way we think about machine learning.
The article explores the core principles of FMH and its application in trading and market price forecasting. FMH offers a new perspective for understanding market dynamics, allowing for the detection of patterns that traditional analysis methods often overlook. Special emphasis is placed on the scaling properties of market data, which enables the use of forecasting models across different time intervals, from short-term to long-term predictions.
This paper embarks on a detailed examination of the inherent security challenges faced by blockchain networks, including fraudulent transactions, double-spending, and 51% attacks, among others. Using recent advancements in ML, it presents a novel methodology for real-time anomaly detection, predictive threat modeling, and adaptive security protocols that leverage data-driven insights to fortify the blockchain against both known and emerging threats. By analyzing case studies and empirical data, this study illustrates the effectiveness of ML techniques in enhancing the