In this paper, our aim is to improve the accuracy and effectiveness of energy demand forecasting, particularly within modern electricity transmission systems and smart grid technology. To achieve this, we developed a hybrid approach that combines machine learning, representation learning, and other deep learning techniques. This approach is based on extracting essential features, including time-based attributes, identifiable trends, and optimal lags. The outcome of our investigation is the observation that triplet losses demonstrate remarkable accuracy, particularly when employed with a larger margin size and for longer prediction lengths. This finding signifies a substantial improvement in the precision and reliability of energy demand forecasting within modern electricity transmission systems. Our research not only improves predictive modeling in the power grid but also demonstrates the practical use of advanced analytics in addressing renewable energy integration challenges, refining energy demand forecasting for efficient management, system operation, and market analysis.

In the current digital era, the advancement of network-based technologies has brought a surge in security vulnerabilities, necessitating complex and dynamic defense mechanisms. This paper explores the integration of machine learning techniques within intrusion detection systems (IDS) to tackle the intricacies of modern network threats. A detailed comparative analysis of various algorithms, including k-nearest neighbors (KNN), logistic regression, and perceptron neural networks, is conducted to evaluate their efficiency in detecting and classifying different types of network intrusions such as denial of service (DoS), probe, user to root (U2R), and remote to local (R2L). Utilizing the national software laboratory knowledge discovery and data mining (NSL-KDD) dataset, a standard in the field, the study examines the algorithms’ ability to identify complex patterns and anomalies indicative of security breaches. Principal component analysis is utilized to streamline the dataset into 20 principal components for data processing efficiency. Results indicate that the neural network model is particularly effective, demonstrating exceptional performance metrics across accuracy, precision, and recall in both training and testing phases, affirming its reliability and utility in IDS. The potential for hybrid models combining different machine learning (ML) strategies is also discussed, highlighting a path towards more robust and adaptable IDS solutions.

This paper explores the essential dynamics of social networks, specifically examining the phenomenon of information confrontation among users. The goal of the research is the development of a novel simulation methodology that integrates game-theoretic principles with probabilistic techniques to provide a robust model for these interactions. The theoretical framework of the study is founded on the conceptualization of user conflicts as a strategic game between two players. The primary objective for each player in this game is to exert influence and control over as many nodes within the network as possible. To capture the essence of these strategic interactions, we have introduced an innovative algorithm that facilitates dynamic strategy adaptation. This algorithm is pivotal in allowing players to modify their decision-making processes in real-time, based on the continually changing conditions of the network. For practical implementation and validation of the methodology, we used the Facebook Researcher open dataset, with a particular focus on its Kazakhstani segment. This dataset provides a rich source of empirical data, reflecting diverse user interactions and network configurations, which are essential for testing the model. This approach stands out by offering significant improvements in computational efficiency and resource management. By dynamically tracking and updating the network's status, the proposed method reduces the computational resources required, thereby enhancing the scalability of the simulation. In comparing our methodology with other existing models in the field, it becomes evident that it not only matches but in several respects surpasses these methodologies in terms of flexibility. This study makes substantial contributions to the field of social network analysis by providing a sophisticated tool that can be effectively employed to navigate and analyze the complexities of information confrontation in digital social spaces.

One of the new areas of cryptography considered-homomorphic cryptography. The article presents the main areas of application of homomorphic encryption. An analysis of existing developments in the field of homomorphic encryption carried out. The analysis showed that existing library implementations only allow processing bits or arrays of bits and do not support division and subtraction operations. However, to solve applied problems, support for performing integer operations are necessary. Because of the analysis, the need to implement the homomorphic division and subtraction operations identified, as well as the relevance of developing our own implementation of a homomorphic encryption library over integers. The ability to perform four operations (addition, difference, multiplication and division) on encrypted data will expand the areas of application of homomorphic encryption. A homomorphic division and subtraction methods proposed that allows the division operation performed on homomorphically encrypted data. An architecture for a library of fully homomorphic operations on integers is proposed. The library supports basic homomorphic operations on integers, as well as homomorphic division method. The article also provides measurements of the time required to perform certain operations on encrypted data and analyzes the efficiency of the developed implementation of the library.

High-frequency induction logging is a crucial technique in subsurface exploration, particularly in the oil and gas industry. It involves transmitting electromagnetic signals into the ground and analyzing their responses to determine the geological structure, lithology, and fluid content of the formation. However, one of the major technical challenges in processing digital sounding signals is the complexity of wave propagation paths, which can be influenced by multiple factors such as subsurface heterogeneity, noise interference, and signal attenuation. Traditional signal processing techniques often struggle to accurately interpret low-amplitude, high-frequency signals that contain valuable subsurface information. These signals can be obscured by noise or exhibit complex, non-stationary behavior, making them difficult to detect and analyze using conventional Fourier-based methods. Moreover, standard regression-based approaches for interpreting induction logging data may fail to capture intricate signal variations, limiting their effectiveness in real-time decision-making. To address these challenges, this research explores the application of machine learning and wavelet transform techniques for improving the accuracy and efficiency of digital signal processing in high-frequency induction logging. The wavelet transform, particularly the Daubechies, Morlet, and Galerkin wavelets, is employed to decompose signals into different frequency components while preserving time-domain information. This enables better identification of subtle variations and anomalies in the recorded data. Furthermore, machine learning models, including standard regression algorithms and neural networks, are leveraged to enhance signal interpretation and predictive capabilities. A key innovation of this study is the development of an algorithm that processes low-amplitude high-frequency signals, which are often difficult to detect with conventional methods. Additionally, a neural network-based forecasting algorithm utilizing the Daubechies wavelet transform is introduced to improve the predictive accuracy of subsurface characteristics. By integrating wavelet-based feature extraction with machine learning-driven analysis, this approach enhances the ability to detect complex wave propagation patterns, leading to more precise subsurface modeling. This advancement supports the implementation of smart field technology in Kazakhstan’s oil and gas industry, ultimately improving decision-making speed, operational efficiency, and resource optimization The results were based on a set of three drilled oil wells, namely, West Kazakhstan region, Mangystau, publicly available well log dataset. The evaluation performed using R-square and root mean square error to validate the proposed approach revealed values of 0.887 and 0.0091. This implementation demonstrated the effectiveness of the proposed model, significantly outperforming traditional methods.

Use of the latest achievements in the field of microcontroller programming, such as the Arduino platform, allows to qualitatively change the educational process, makes it more intense, increases student motivation, and makes it possible to implement an individual approach, which is important. And this, in turn, improves the efficiency and quality of microcontroller programming. The purpose of this study is to propose an effective methodology for using Arduino Atmega 328 microcontrollers for teaching students and evaluate the effectiveness of teaching programming based on the use of Arduino Atmega 328 microcontrollers based on the Kirkpatrick model. The paper presents a broad review of works that consider the interaction of a person and microcontrollers. In addition, the impact of this approach on the process of learning and teaching is being evaluated. More than 95 students took part in this experiment. First, during the semester, students were taught programming using Arduino Atmega 328 microcontrollers, after which they evaluated this learning. The evaluation was carried out at three levels of the Kirkpatrick model [1], and as a result, the second and third levels showed almost the same results with an error of 3 percent. This study concluded that such teaching methodology is very important in the process of student learning. Interaction and collaboration in the field of microcontroller programming has also been used to introduce non-traditional curricula, including courses in robotics as a tool for addressing the social aspects of robotics and artificial intelligence.