machine learning

Computer Modelling of Logistic Regression for Binary Classification

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.

Information System for Adapting Road Lane Segmentation Methods in Navigation Systems in Order to Increase the Accuracy of Road Signs Detection

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.

Intelligent System for Complex Military Information Analysis Based on Machine Learning and NLP to Assist Tactical Links Commanders

 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.

UNDERSTANDING LARGE LANGUAGE MODELS: THE FUTURE OF ARTIFICIAL INTELLIGENCE

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.

FRACTAL MARKET HYPOTHESIS FOR TRADING AND MARKET PRICE FORECAST

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.

Application of machine learning algorithms to enhance blockchain network security

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

Predicting students' academic performance and modeling using data mining techniques

In educational institutions and universities, the issue of study interruptions can be addressed by using e-learning.  As a result, this field has recently attracted a lot of attention.  In this study, we applied four machine-learning methods to predict students' academic progress: logistic regression, decision trees, random forests, and Naive Bayes.  The Open University Learning Analytics Dataset (OULAD), which contains a subset of the OU student data, was the source of the student data for all of these techniques.  There is information regarding the students' VLE inter

Mathematical Model of Logistic Regression for Binary Classification. Part 2. Data Preparation, Learning and Testing Processes

This article reviews the theoretical aspects of logistic regression for binary data classification, including data preparation processes, training, testing, and model evaluation metrics.

Requirements for input data sets are formulated, methods of coding categorical data are described, methods of scaling input features are defined and substantiated.

Mathematical Model of Logistic Regression for Binary Classification. Part 1. Regression Models of Data Generalization

In this article, the mathematical justification of logistic regression as an effective and simple to implement method of machine learning is performed.

A review of literary sources was conducted in the direction of statistical processing, analysis and classification of data using the logistic regression method, which confirmed the popularity of this method in various subject areas.