This paper explores the content-based filtering approach in modern recommender systems, focusing on its key principles, implementation methods, and evaluation metrics. The study highlights the advantages of content-based systems in scenarios that require deep object analysis and user preference modeling, especially when there is a lack of data for collaborative filtering.

Keywords: сontent - oriented filtering, recommendation systems, feature extraction, similarity metrics, personalization

Introduction: Mobile Gaming Addiction (MGA) has emerged as a significant public health concern, with the World Health Organization recognizing it as a gaming disorder. Russia, with its growing mobile gaming market, is no exception. Aims and Objectives: This study aims to explore the feasibility of using neural networks for early MGA detection and intervention, with a focus on the Russian context. The primary objective is to develop and evaluate a neural network-based model for identifying behavioral patterns associated with MGA. Methods: A proof of concept study was conducted, employing a simplified neural network architecture and a dataset of 101 observations. The model's performance was evaluated using standard metrics, including accuracy, precision, recall, F1-score, and AUC-ROC score. Results: The study demonstrated the potential of neural networks in detecting MGA, achieving an F1-score of 0.75. However, the relatively low AUC-ROC score (0.58) highlights the need for addressing dataset limitations. Conclusion: This study contributes to the growing body of literature on MGA, emphasizing the importance of considering regional nuances and addressing dataset limitations. The findings suggest promising avenues for future research, including dataset expansion, advanced neural architectures, and region-specific mobile applications.

Keywords: neural networks, neural network architectures, autoencoder, digital addiction, gaming addiction, digital technologies, machine learning, artificial intelligence, mobile game addiction, gaming disorder

The article focuses on the application of machine learning methods for predicting failures in industrial equipment. A review of modern approaches such as Random Forest, SVM, and XGBoost is presented, with emphasis on their accuracy, robustness, and suitability for engineering tasks. Based on the analysis of real-world data (temperature, pressure, vibration, humidity), models were trained and compared, with XGBoost demonstrating the best performance. Key parameters influencing failures were identified, and a recommendation system was proposed, combining statistical analysis and predictive modeling. The developed solution enables timely detection of failure risks and optimization of maintenance processes.

Keywords: machine learning, predictive modeling, equipment management, failure prediction, data analysis

The article provides a comparative analysis of the approaches to forecasting rutting used in Russia and the USA. Mechanistic–Empirical Pavement Design Guide (MEPDG) and domestic regulatory documents are reviewed, and their key differences in forecast accuracy, applicability, and calculation complexity are identified.

Keywords: rutting, forecasting of road structures, MEPDG, monitoring of road conditions, regulatory methodologies

In the process of civil engineering, the role of the technical client is extremely important, since it is he who ensures control and coordination of all stages of construction, from the development of project documentation to commissioning of the facility. However, despite the importance of this role, technical client activities often face problems associated with ineffective management, high costs, schedule delays and quality deficiencies. Optimizing its activities can significantly increase the efficiency of the project and reduce risks. This article provides an analysis of possible ways to optimize the work of a technical client. Considered methods using modern software, training and improving the abilities of personnel, Total Quality Management and Lean Construction.

Keywords: technical client, project efficiency, civil engineering process management, lean construction

In this article, we examined the permeability of concrete and the effect of corrosion processes on the durability and reliability of reinforced concrete structures. Attention is paid not only to the causes and mechanisms of corrosion, but also modern methods and strategies for protecting concrete and reinforced concrete structures from it are provided, aimed at extending their service life and ensuring operational safety. This knowledge will allow engineers and builders to plan and implement projects more efficiently, reducing the risks and economic losses associated with corrosion processes.

Keywords: corrosion of concrete, corrosion of steel reinforcement, permeability, reinforced concrete, durability, strength, reliability

The article discusses the use of a recurrent neural network in the task of predicting pollutants in the air based on simulated data in the form of a time series. Neural recurrent network models with long Short-Term Memory (LSTM) are used to build the forecast. Unidirectional LSTM (hereinafter simply LSTM), as well as bidirectional LSTM (Bidirectional LSTM, hereinafter Bi-LSTM). Both algorithms were applied for temperature, humidity, pollutant concentration, and other parameters, taking into account both seasonal and short-term changes. The Bi-LSTM network showed the best performance and the least errors.

Keywords: environmental monitoring, data analysis, forecasting, recurrent neural networks, long-term short-term memory, unidirectional, bidirectional

Steel hoisting ropes play an important role in metallurgical equipment, ensuring reliability and efficiency of lifting operations. One of the key features of their operation is the high level of contamination typical of metallurgical operations. Metallurgical processes are often accompanied by dust, metal chips and other abrasive particles that can significantly degrade ropes, causing wear and corrosion. To maintain the efficient operation of equipment it is necessary to monitor the condition of hoisting ropes in real time, which makes the task of improving automatic systems for monitoring the condition of ropes urgent. The paper reviews the methods of optical control of defects in hoisting steel ropes, the advantages and limitations of different approaches are considered. The aim of the work is to justify the effectiveness of the authors' developed method of analyzing rope defect images using neural networks in relation to the method based on the discrete Fourier transform. It is revealed that one of the most promising in terms of technical and economic efficiency of inspection methods is the application of vision system with image processing based on convolutional neural network technology, which allows to effectively detect defects in complex and changing operating conditions, such as metallurgical and mining production, where the background of the image may be non-uniform, and the distance between the camera and the rope varies.

Keywords: lifting ropes, vision systems, optical control methods, fast Fourier transform, hidden Markov models, convolutional neural networks

The article considers the issues of imitation modeling of fibrous material mixing processes using Markov processes. The correct combination and redistribution of components in a two-component mixture significantly affects their physical properties, and the developed model makes it possible to optimize this process. The authors propose an algorithm for modeling transitions between mixture states based on Markov processes.

Keywords: modeling, imitation, mixture, mixing, fibrous materials

This article discusses the basic concepts and practical aspects of programming using the actor model on the Akka platform. The actor model is a powerful tool for creating parallel and distributed systems, providing high performance, fault tolerance and scalability. The article describes in detail the basic principles of how actors work, their lifecycle, and messaging mechanisms, as well as provides examples of typical patterns such as Master/Worker and Proxy. Special attention is paid to clustering and remote interaction of actors, which makes the article useful for developers working on distributed systems.

Keywords: actor model, akka, parallel programming, distributed systems, messaging, clustering, fault tolerance, actor lifecycle, programming patterns, master worker, proxy actor, synchronization, asynchrony, scalability, error handling

This article is devoted to a comparative analysis of the resilience of ResNet18 and ResNet50 neural networks to adversarial attacks on training sets. The issue of the importance of ensuring the safety of learning sets is considered, taking into account the growing scope of artificial intelligence applications. The process of conducting an adversarial attack is described using the example of an animal recognition task. The results of two experiments are analyzed. The purpose of the first experiment was to identify the dependence of the number of epochs required for the successful execution of an adversarial attack on the training set on the neural network version of the ResNet architecture using the example of ResNet18 and ResNet50. The purpose of the second experiment was to get an answer to the question: how successful are attacks on one neural network using modified images of the second neural network. An analysis of the experimental results showed that ResNet50 is more resistant to competitive attacks, but further improvement is still necessary.

Keywords: artificial intelligence, computer vision, Reset, ResNet18, ResNet50, adversarial attacks, learning set, learning set security, neural networks, comparative analysis

The article presents an analysis of the application of the Socratic method for selecting machine learning models in corporate information systems. The study aims to explore the potential of utilizing the modular architecture of Socratic Models for integrating pretrained models without the need for additional training. The methodology relies on linguistic interactions between modules, enabling the combination of data from various domains, including text, images, and audio, to address multimodal tasks. The results demonstrate that the proposed approach holds significant potential for optimizing model selection, accelerating decision-making processes, and reducing the costs associated with implementing artificial intelligence in corporate environments.

Keywords: Socratic method, machine learning, corporate information systems, multimodal data, linguistic interaction, business process optimization, artificial intelligence

The article examines the modular structure of interactions between various models based on the Socratic dialogue. The research aims to explore the possibilities of synthesizing neural networks and system analysis using Socratic methods for managing corporate IT projects. The application of these methods enables the integration of knowledge stored in pre – trained models without additional training, facilitating the resolution of complex management tasks. The research methodology is based on analyzing the capabilities of multimodal models, their integration through linguistic interactions, and system analysis of key aspects of IT project management. The results include the development of a structured framework for selecting suitable models and generating recommendations, thereby improving the efficiency of project management in corporate environments. The scientific significance of the study lies in the integration of modern artificial intelligence approaches to implement system analysis using multi – agent solutions.

Keywords: neural networks, system analysis, Socratic method, corporate IT projects, multimodal models, project management

In this paper, an analysis of the calculation results is carried out, which makes it possible to assess the real impact of impact and explosive effects from UAVs on the strength of reinforced concrete structures. Load limits are set, depending on the four most common types of walls. The previously published classification made it possible to identify the main parameters necessary for a detailed load calculation.

Keywords: extreme loads, explosion, self-supporting walls, reinforced walls, non-reinforced walls, load-bearing walls, UAVs, strength testing, building structures, shock waves

The article explores the actor model as implemented in the Elixir programming language, which builds upon the principles of the Erlang language. The actor model is an approach to parallel programming where independent entities, called actors, communicate with each other through asynchronous messages. The article details the main concepts of Elixir, such as comparison with a sample, data immutability, types and collections, and mechanisms for working with the actors. Special attention is paid to the practical aspects of creating and managing actors, their interaction and maintenance. This article will be valuable for researchers and developers interested in parallel programming and functional programming languages.

Keywords: actor model, elixir, parallel programming, pattern matching, data immutability, processes, messages, mailbox, state, recursion, asynchrony, distributed systems, functional programming, fault tolerance, scalability