Integration of heterogeneous field data and remote sensing information is a key and necessary step in modern geological exploration. This article proposes a method based on the creation of a regular spatial grid, which enables the efficient interpolation and integration of point, linear, and polygonal data represented in both vector and raster formats. The primary objective is to generate a structured and enriched dataset suitable for training predictive models, including neural networks. The proposed approach involves transforming geospatial data to ensure their accuracy and consistency within GIS environments. This method provides a reliable foundation for identifying prospective areas with high mineral potential and highlights the importance of rigorous data preparation in spatial modeling and analysis processes.

Keywords: reservoir exploration, data integration, interpolation, spatial grid, geochemistry, spatial modeling process, remote sensing, GIS

This study is devoted to the analysis of decision-making models in ensuring the protection of public order. The results obtained will allow us to formulate a new mathematical model of decision-making, which will allow us to obtain objective management decisions to ensure the protection of public order in the territory of the Republic of Tajikistan with the possibility of simulation. The object of the study is the process of ensuring the protection of public order. In the scientific literature and in open sources of information, there is a large number of works describing models and algorithms developed on the basis of various mathematical tools. The analysis of a number of papers on this topic will allow us to formulate a new mathematical model of decision-making, which will optimize and improve the quality of prepared decision-making projects while ensuring the protection of public order. The study revealed that the basis for improving the effectiveness of ensuring the safety of citizens during mass events is an effective management decision. 1) Based on this, an analysis of decision-making models is presented, the purpose of which is to determine the need to create a decision-making model while ensuring the protection of public order in the Republic of Tajikistan. 2) A model of decision-making in ensuring the protection of public order in the Republic of Tajikistan is proposed. The model is implemented based on the synthesis of mathematical modeling methods, including cluster analysis, pairwise comparison method and Petri nets. The model allows you to divide committed events, i.e. crimes into clusters according to previously defined criteria. At the final stage, the model allows you to simulate each event, thereby predicting the possible development of the event under study. The presented results of the analysis of decision-making models made it possible to formulate a new mathematical model of decision-making in ensuring the protection of public order in the interests of the Republic of Tajikistan.

Keywords: public order protection, mathematical model, cluster analysis, pairwise comparison method, expert assessments, Petri nets

This article presents an analysis of corporate network traffic over the SMTP protocol to identify malicious traffic. The relevance of the study is driven by the increasing number of email-based attacks, such as the distribution of viruses, spam, and phishing messages. The objective of the work is to develop an algorithm for detecting malicious traffic that combines traditional analysis methods with modern machine learning approaches. The article describes the research stages: data collection, preprocessing, model training, algorithm testing, and effectiveness analysis. The data used were collected with the Wireshark tool and include SMTP logs, message headers, and attachments. The experimental results demonstrated high accuracy in detecting malicious traffic, confirming the potential of the proposed approach.

Keywords: SMTP, malicious traffic, network traffic analysis, email, machine learning, Wireshark, spam, phishing, classification algorithms

Optimization of automated management systems for facility protection complexes remains relevant today. This research paper provides an overview of the tools for implementing separate monitoring processes: device polling, processing of the received data, and transferring data to the graphic user interface. Based on the analysis of the reviewed information, a basis of solutions for developing management system of the technical means complex is planned to be formed. During the research, it was found that the combination of multi-threading architecture and adaptive polling algorithm allows to implement a large-scale polling; the clustering algorithm and special settings of frameworks for processing large-scale datasets can enhance job performance; WebSocket protocol has proved its efficiency for transferring the real-time data. The result of the evaluation of solutions was a set of tools for implementation of a hardware-software complex.

Keywords: sensor, management system, monitoring, SNMP manager, clustering, Hadoop, MapReduce, Spark, Apache Kafka, WebSocket

This article presents a conceptual framework for assessing the maturity of construction control and supervision systems at construction sites. A multi-level assessment model has been developed, integrating the Analytic Hierarchy Process (AHP) and fuzzy comprehensive evaluation method. A five-level taxonomic system for grading the maturity of regulatory mechanisms in the construction industry is proposed. The procedures for forming a hierarchical structure of assessment indicators, constructing judgment matrices, determining weight coefficients, and applying the fuzzy comprehensive evaluation method to quantify the maturity level of supervision systems are described in detail. The developed methodology represents a universal tool for conducting comparative analysis of construction control and state supervision systems in various national and regional jurisdictions based on objective quantified criteria.

Keywords: construction control, state supervision, maturity model, Analytic Hierarchy Process, fuzzy comprehensive evaluation, quantification, assessment indicators

The article presents the application of fuzzy modeling to solve the systemic task of qualitative assessment of the properties of land plots by an expert method using fuzzy modeling. The set of factors by which the suitability of the plot is estimated depends on the goals of the development project. The technique includes the decomposition of the model into additive models of internal and external factors and a combining multiplicative model, which reduces the dimensionality of the task of assessing the properties of the plot. At the second stage, a fuzzy model of expert assessment of the properties of land plots is formed. It includes a fuzzyification block using linear membership functions, max-min fuzzy inference technology, and defuzzification using the height method, which most adequately translates fuzzy expert assessments into clear numerical (point) values. At the third stage, the contribution coefficients of each factor in the assessment of the properties of plots are determined using the hierarchy analysis method and the fuzzy pairwise comparison scale.

Keywords: land plot, individual assessment, expert method, fuzzy modeling

The paper examines the current state of the industrial Internet of Things market in Russia and around the world, the main areas of its application, as well as the prospects and challenges that businesses and industrial enterprises will face in implementing this technology. Special attention is paid to the advantages of implementing IIoT, such as increased productivity, reduced costs, improved security and transparency of processes. The barriers specific to the Russian market are discussed, including cybersecurity, hardware compatibility, and significant initial costs. Examples of successful implementations of IIoT technologies in various industries such as the oil and gas industry, logistics and chemical production are given. The emphasis is placed on the need for government support and adaptation of the regulatory framework to accelerate implementation. The article highlights the importance of an integrated approach to IIoT implementation, including using international experience and consolidating efforts to develop the digital economy in the face of global and local challenges.

Keywords: industrial Internet of Things, IIoT, industry 4.0, 5G, production automation, digital transformation

The widespread use of social media platforms has led to the accumulation of vast amounts of stored data, enabling the prediction of rare events based on user interaction analysis. This study presents a method for predicting rare events using graph theory, particularly graphlets. The social network VKontakte, with over 90 million users, serves as the data source. The ORCA algorithm is utilized to identify characteristic graph structures within the data. Throughout the study, user interactions were analyzed to identify precursors of rare events and assess prediction accuracy. The results demonstrate the effectiveness of the proposed method, its potential for threat monitoring, and the possibilities for further refinement of graphlet-based prediction models.

Keywords: social media, security event, event prediction, graph theory, graphlet, interaction analysis, time series analysis, correlation analysis, data processing, anomalous activity

In modern conditions of digital transformation, companies are actively implementing customer Relationship Management systems (CRM systems) to manage customer relationships. However, the issues of data protection, confidentiality and transparency of interaction remain critically important. This article explores the possibilities of using blockchain technology to enhance the security of CRM systems and improve trust between businesses and customers. The purpose of the work is to analyze the potential of using blockchain in data protection of CRM systems, as well as to assess its impact on the transparency of customer transactions. The paper examines the main threats to data security in CRM, the principles of blockchain technology and its key advantages in this context, including decentralization, immutability of records and protection from unauthorized access. Based on the analysis, promising areas of blockchain integration into CRM systems have been identified, practical recommendations for its application have been proposed, and the potential effectiveness of this technology has been assessed. The results of the study may be useful to companies interested in strengthening the protection of customer data and increasing the transparency of user interaction processes.

Keywords: blockchain, CRM-system, security, data protection, transparency, customer interaction

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

The article considers issues related to making management decisions when ensuring safety in emergency situations. It reflects the features of making management decisions in emergency situations, when achieving a guaranteed level of safety is not always possible. The control loop is presented and the connections between the elements of the first and second stages are analyzed. It is shown that uncertainty in making management decisions arises due to a lack of information about the control object or is caused by unprofessional actions of the decision maker. It is proposed to create and use in practice a digital twin of safety in an emergency situation to eliminate uncertainties in making management decisions. Decomposition of the task into subtasks allows for the process of collecting and analyzing aggregate information about the control object to eliminate uncertainties and minimize risks in the development, adoption and implementation of management decisions in an emergency situation when ensuring safety.

Keywords: control model, control loop, uncertainty, risk, digital twin, decomposition, emergency safety

The article explores the application of a systems approach and machine learning methods to forecast psychoemotional states based on digital activity in social networks. The study addresses the urgent need to assess the psychological impact of increasing user engagement with digital platforms by using quantitative and algorithmic tools instead of subjective expert assessment.
The main objective of the research is to identify patterns in the relationship between time spent on social networks and self-reported indicators of mental well-being, including symptoms related to ADHD, anxiety, self-esteem, and depression.
Data was collected through an anonymous survey administered via the LMS platform of SUAI. The sample included 473 participants, with 75% under the age of 35. Preprocessing steps involved cleaning outliers, imputing missing values, and formatting the data for analysis. Correlation matrices and heatmaps were created, followed by clustering using the k-means method. A stacked meta-model based on logistic regression and Gaussian Naive Bayes with a random forest as the final estimator was used for classification.
The study revealed distinct user groups with varying levels of vulnerability to the influence of social media. The results can be used to develop intelligent systems for monitoring mental health risks and delivering personalized digital recommendations.
The article is relevant to researchers in system analysis and applied machine learning.

Keywords: system analysis, digital activity, social networks, machine learning, clustering, correlation analysis, digital addiction, psycho-emotional state, information mining

The article is devoted to the creation of a highly specialized automated information system for automated processing of orders for the production of abrasive tools. The development of such software products will improve production efficiency through the transition from order-based production to batch production.

Keywords: automated information system, production order processing system, Rammler-Breich diagram, role-based data access system

The article is the result of an analytical study on the topic of risk management in the creation and modernization of business processes. The article proposes risk management methods using the organization's human resources and methods for training personnel taking into account trends in the labor market. The effect of implementing risk management measures and the method for assessing the effectiveness of the implemented training are separately noted.

Keywords: risk management, human resources, employee training, experts, SWOT analysis

 The assessment of the properties of urbanized territories or plots is necessary to determine the most effective use of them and to determine the cadastral or market price. A comprehensive model for assessing the properties of urbanized territories is presented, which is a multiplicative model consisting of two models: an additive model for assessing the properties of the plot under consideration and an additive model for assessing the influence of external factors determined by the adjacent territory. This multiplicative combination of additive models allows for the differentiated determination of the best alternative for different types of plot use based on the influence of internal and external factors when comparing multiple plots at different stages of a development project. To do this, the preference coefficients are calculated using the ratio of the integral estimates of the compared areas. If there are several areas, they can be selected using pairwise comparisons and the analysis hierarchy method.

Keywords: urbanized territory, property valuation, internal and external factors, additive and multiplicative models, development project

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 explores the opportunities and challenges of integrating cloud, fog, and edge computing in the context of digital transformation. The analysis reveals that the synergy of these technologies enables optimization of big data processing, enhances system adaptability, and ensures information security. Special attention is given to hybrid architectures that combine the advantages of centralized and decentralized approaches. Practical aspects are addressed, such as the use of the ENIGMA simulator for modeling scalable infrastructures and the EC-CC architecture for smart grids and IoT systems. The role of specialized frameworks in optimizing routing and improving infrastructure reliability is also highlighted. The integration of these technologies drives advancements in key industries, including energy, healthcare, and the Internet of Things, despite challenges related to data security.

Keywords: cloud computing, fog computing, edge computing, hybrid architectures, Internet of Things, digital transformation, big data, decentralized systems, computing integration, distributed computing, data security, resource optimization, data transfer speed

The article is devoted to the topic of improving the environmental characteristics of construction sites through the introduction of the principles of "green" construction through a comprehensive assessment of various criteria. Compliance with environmental standards contributes to the creation of a favorable urban environment and ensures comfortable living conditions for residents. The introduction of such approaches is becoming extremely important for sustainable development and the preservation of the natural balance.

Keywords: Green construction, ecological construction, life cycle, construction, multi-criteria decision-making.

The paper analyzes existing effective technologies of waste recycling and utilization. The authors consider various approaches in the international practice of recycling production and consumption waste. An assessment is given of the possibilities of using effective technologies for waste recycling and disposal and the necessary costs for their implementation in relation to the conditions of an industrial enterprise. The types and volumes of waste that can be recycled and disposed of irrevocably are considered, for which the carbon footprint parameters are calculated using the materials management model. A statistical regression analysis of data on the production, processing, disposal and incineration of polyethylene waste, solid municipal waste and paper was carried out. The principles of building a system for reducing technogenic risks and managing production and consumption waste were determined.

Keywords: waste processing; waste disposal; carbon footprint; carbon footprint calculation methods; man-made risk management system; hazardous impact factors; industrial waste management

This article will present the mlreflect package, written in Python, which is an optimized data pipeline for automated analysis of reflectometry data using machine learning. This package combines several methods of training and data processing. The predictions made by the neural network are accurate and reliable enough to serve as good starting parameters for subsequent data fitting using the least-mean-squares (LSC) method. For a large dataset consisting of 250 reflectivity curves of various thin films on silicon substrates, it was demonstrated that the analytical data pipeline with high accuracy finds the minimum of the film, which is very close to the set by the researcher using physical knowledge and carefully selected boundary conditions.

Keywords: neural network, radiography, thin films, data pipeline, machine learning

This paper is devoted to the application of the Winograd method to perform the wavelet transform in the problem of image compression. The application of this method reduces the computational complexity and also increases the speed of computation due to group processing of pixels. In this paper, the minimum number of bits at which high quality of processed images is achieved as a result of performing discrete wavelet transform in fixed-point computation format is determined. The experimental results showed that for processing fragments of 2 and 3 pixels without loss of accuracy using the Winograd method it is enough to use 2 binary decimal places for calculations. To obtain a high-quality image when processing groups of 4 and 5 pixels, it is sufficient to use 4 and 7 binary decimal places, respectively. Development of hardware accelerators of the proposed method of image compression is a promising direction for further research.

Keywords: wavelet transform, Winograd method, image processing, digital filtering, convolution with step

The article discusses the main approaches to solving computer vision problems using neural networks, focusing on their application to a wide range of tasks. It describes the types of problems addressed by computer vision, such as image classification, object detection, segmentation, and activity recognition. The functioning mechanisms of convolutional neural networks (CNNs) are explained in detail, highlighting key features like convolutional layers, pooling operations, and activation functions. The problem of selecting object detection models, which generalize the more studied problem of object classification, is examined in depth, along with an evaluation of the efficiency of various algorithms using metrics like mAP (mean Average Precision) and IoU (Intersection over Union). Modern approaches to training neural networks are discussed, including the use of pre-trained models, transfer learning methods, and fine-tuning techniques for domain-specific applications. The article describes the advantages and limitations of prominent CNN architectures such as ResNet, VGG, and EfficientNet, offering insights into their suitability for different tasks. Data augmentation methods, aimed at improving the generalization ability of models, are also considered, emphasizing their importance for addressing data scarcity challenges. Practical examples of computer vision applications in areas like facial recognition, autonomous driving, and medical diagnostics are provided to illustrate the real-world relevance of these methods. Additionally, the integration of computer vision algorithms into complex systems and workflows is analyzed, highlighting its transformative potential across industries. Finally, the article discusses the future directions for research in this domain, including advancements in unsupervised learning, real-time processing, and explainable AI in computer vision.

Keywords: computer vision, architecture, convolutional neural networks, digital image, object classification

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

The dynamic time warping algorithm (DTW) is designed to compare two time series by measuring the distance between them. DTW is widely used in medicine, speech recognition, financial market and gaze trajectories analysis. Considering the classic version of DTW, as well as its various modifications, it was found that in the tasks of analyzing the distance between gaze trajectories, they are not able to correctly take into account the duration of its fixations on visual stimuli. The problem has not attracted much attention so far, although its solution will improve the accuracy and interpretation of the results of many experimental studies, since assessing the time of visual focus on objects is an important factor in visual analysis. Hence the need to adapt DTW for such tasks. The goal of this work is to adapt the classic DTW to the problem of finding the distance between two time series with periods of low variability of values. During the demonstration of the developed algorithm, it was proven that the effect of a given minimum threshold of fixation duration on the result is significant. The proposed adaptation of DTW will improve the quality of visual data analysis and can be applied to understanding the mechanisms of human perception and decision-making in various fields of activity, such as psychology and marketing, as well as to developing effective methods for testing interfaces.

Keywords: dynamic time warping algorithm, eye tracking, time series, gaze trajectory, gaze fixation duration

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