The sustainable functioning of the transport system requires solving the problems of identifying and classifying road users in order to predict the likelihood of accidents and prevent abnormal or emergency situations. The emergence of unmanned vehicles on urban highways significantly increases the risks of such events. To improve road safety, intelligent transport systems, embedded computer vision systems, video surveillance systems, and photo radar systems are used. The main problem is the recognition and classification of objects and critical events in difficult weather conditions. For example, water drops, snow, dust, and dirt on camera lenses make images less accurate in object identification, license plate recognition, vehicle trajectory detection, etc. Part of the image is overlapped, distorted, or blurred. The article proposes a way to improve the accuracy of object identification by using the Canny operator to exclude the damaged areas of the image from consideration by capturing the clear parts of objects and ignoring the blurry ones. Only those parts of the image where this operator has detected the boundaries of the objects are subjected to further processing. To classify images by the remaining whole parts, we propose using a combined approach that includes the histogram-oriented gradient (HOG) method, a bag-of-visual-words (BoVW), and a back propagation neural network (BPNN). For the binary classification of the images of the damaged objects, this method showed a significant advantage over the classical method of convolutional neural networks (CNNs) (79 and 65% accuracies, respectively). The article also presents the results of a multiclass classification of the recognition objects on the basis of the damaged images, with an accuracy spread of 71 to 86%.

With the exponential growth of cyberbullying cases on social media, there is a growing need to develop effective mechanisms for its detection and prediction, which can create a safer and more comfortable digital environment. One of the areas with such potential is the application of natural language processing (NLP) and artificial intelligence (AI). This study applies a novel hybrid-structure Hybrid Transformer–Enriched Attention with Multi-Domain Dynamic Attention Network (Hyb-KAN), which combines a transformer-based architecture, an attention mechanism, and BiLSTM recurrent neural networks. In this study, a multi-class classification method is used to identify comments containing cyberbullying features. For better verification, we compared the proposed method with baseline methods. The Hyb-KAN model demonstrated high results on the multi-class classification dataset, achieving an accuracy of 95.25%. The synergy of BiLSTM, Transformer, MD-DAN, and KAN components provides flexibility and accuracy of text analysis. The study used explainable visualization techniques, including SHAP and LIME, to analyze the interpretability of the Hyb-KAN model, providing a deeper understanding of the decision-making mechanisms. In the final stage of the study, the results were compared with current research data to confirm their relevance to current trends.

The purpose of the article is to create an effective method to monitor the state of the drill string and the bit without interfering with the drilling process itself in low-time delay mode. For continuous monitoring of the well drilling process, an experimental setup was developed that operates on the basis of the use of the phase-metric method of control. Any movement of the bit causes a change in the electrical characteristics of the probing signal. To obtain a stable signal from a bit immersion depth of up to 250 m, a frequency of probing electrical signals of 166 Hz and an amplitude of up to 500 V were used; sampling rate (analog-to-digital converter) ADC - 10101 Hz. To identify the state of the drill string and the bit according to the graphs of dependences of changes in the electrical characteristics of the probing signal on time, the authors of the article investigated a number of deep learning methods, based on the results of the research, a line of capsule neural network (CapsNet) methods was selected. The authors have developed two modifications of 1D-CapsNet and Windowed Fourier Transform (WFT) - 2D-CapsNet. To identify the transition between two rock layers with different properties, WFT-2D-CapsNet showed an accuracy of 99%, which is 2-3% higher than the results of modern rock studies based on measurement-while-drilling (MWD) and logging-while-drilling (LWD) methods. The WFT-2D-CapsNet method unambiguously detects self-oscillations in the drill string and detects the good condition of the bit with an accuracy of 99%.

In recent years, various methods and directions for solving a system of Boolean algebraic equations have been invented, and now they are being very actively investigated. One of these directions is the method of transforming a system of Boolean algebraic equations, given over a ring of Boolean polynomials, into systems of equations over a field of real numbers, and various optimization methods can be applied to these systems. In this paper, we propose a new transformation method for Solving Systems of Boolean Algebraic Equations (SBAE). The essence of the proposed method is that firstly, SBAE written with logical operations are transformed (approximated) in a system of harmonic-polynomial equations in the unit n-dimensional cube $K_n$ with the usual operations of addition and multiplication of numbers. Secondly, a transformed (approximated) system in $K_n$ is solved by using the optimization method. We substantiated the correctness and the right to exist of the proposed method with reliable evidence. Based on this work, plans for further research to improve the proposed method are outlined.