G. Yu. Tereshchenko

The study examines automated forecasting and evaluation of digital marketing performance using statistical models from different methodological classes. The paper aims to experimentally compare deterministic and probabilistic models in three groups of digital marketing analytics tasks: advertising channel attribution, media-mix modelling, and customer retention forecasting. To achieve this aim, a unified experimental data-processing pipeline for a SaaS enterprise was developed, evaluation metrics were defined for each analytical task, rolling-origin cross-validation was implemented, and the results were interpreted from a managerial decision-making perspective. The study employs multi-touch attribution methods, including Last-Click, Shapley values, and Markov models; regression and Bayesian structural time-series models for estimating the impact of media spending on revenue; and discrete probabilistic retention models, particularly BdW, for forecasting subscriber activity and customer lifetime value. The results show that algorithmic attribution models reveal the hidden contribution of upper-funnel channels, while still confirming the limited predictive role of attribution models. In media-mix modelling, Bayesian structural time-series models produced lower forecasting error and more stable forecast bias than ordinary least squares regression. In retention modelling, the BdW model reduced the overestimation of the active customer base and the risk of inflated LTV estimates. The scientific value of the paper lies in the formalisation of an experimental comparison of different statistical model classes within a unified analytical pipeline. The practical significance lies in supporting the selection of appropriate models depending on the business scenario, data structure, and uncertainty level.

The study examines the processes of assessing performance and predicting the effectiveness of digital marketing using statistical modelling tools. The purpose of the work is to conduct a comprehensive analysis of statistical models for assessing marketing effectiveness, identify their functional strengths and weaknesses, and formulate recommendations for selecting the optimal method based on the business scenario. The article addresses the following tasks: systematising statistical modelling methods, assessing their applicability to digital marketing analytics, developing criteria for selecting relevant tools for various business scenarios, and determining the most effective statistical approach by solving a multi-criteria decision-making (MCDM) problem using a weighted additive utility model (weighted sum model). The following methods were used: analysis of the scientific literature to systematise approaches, expert assessment to rank methods, and a toolkit for utility theory to support multi-criteria decision-making. Conclusions: It is substantiated that high marketing analytics efficiency is achieved not through a universal algorithm, but through the adaptive selection of statistical tools in accordance with the business model and the specifics of the available data. The obtained experimental results allow us to assert the feasibility of using survival analysis tools for enterprises with a contract sales model. Scientific value: the work systematises existing approaches to statistical modelling, filling a gap in the domestic literature by providing a methodological comparison of alternatives to machine learning methods in marketing analytics.

This paper presents a decentralized electronic voting system built on the Ethereum blockchain. The proposed model integrates smart contracts, a commit–reveal mechanism, and soulbound voting rights tokens to ensure transparency, resistance to manipulation, and prevention of vote transfer. An off-chain server is used only for logging and analytics, without affecting vote counting. Experimental evaluation in the Sepolia test network confirmed the system’s correctness and suitability for organizational voting scenarios. The results highlight the potential of blockchain-based solutions to enhance trust, security, and transparency in modern digital voting processes

The article is dedicated to the study of the efficiency of various technologies and storage approaches for large volumes of visual data. Through the research, the modern data storage methods were analysed, along with the advantages and disadvantages of using relational and non-relational databases, available built-in technologies for working with larger files, such as MS SQL Server FILESTREAM, compared to storing this data in separate storage. Writing, reading and filtering benchmarks were performed with data of different sizes and volumes to determine the performance, as well as the efficiency of system resources usage. FILESTREAM was supposed to increase the performance of the system, but based on the research results, storing large-sized data increased write time by 110%, sequential read time by 118% and filtering time by 32%, compared to storing images in the database. Direct image storage in the database increased data fragmentation and reduced index efficiency, which ended up lowering system scalability and increasing experiment runtime under high load. Hybrid methods of data storage showed the best results. DBMS Microsoft SQL Server was 1.95 times on average faster than MongoDB, even though MongoDB showed less performance loss for data writing and filtering.

With the rapid growth of image data in recent years, efficient management and retrieval of image data have become increasingly important. In this paper, we propose an image warehouse architecture in the era of big data that combines data preprocessing, compression and deduplication, distributed processing and parallel computing, machine learning, and deep learning, and security and privacy techniques to improve the efficiency and scalability of image warehouses. We conducted experiments on a large-scale image dataset, and the results show that our approach significantly outperforms existing methods in terms of retrieval accuracy and efficiency. The proposed architecture provides a promising solution for managing and retrieving large-scale image data in the era of big data

There different types of blockchains and their possible use for creating an image repository are investigated. The purpose of the study was to evaluate the advantages and limitations of different types of blockchains in terms of image storage. Data processing methods were applied to analyze the technical characteristics of various types of blockchains and comparative analysis of efficiency and reliability parameters. The results were obtained, which made it possible to formulate the principles of choosing the type of blockchain for creating image storage and to identify the advantages and limitations of each type from the point of view of image storage, depending on the priorities of the software product. The conclusion is that the use of blockchain provides a high level of security and integrity of images, some types of blockchains exhibit high speed and scalability. However, it is important to understand that the preservation process may remain centralized, so more research is needed to optimally use and develop these technologies. Future research may include an analysis of the possibilities of ensuring the privacy of participants and the development of standards for the sharing of multimedia content via blockchain. It is important to consider that the use of blockchain can contribute to increasing transparency and trust in the process of storing and sharing multimedia content, which is important for the development of the digital economy. However, in order to achieve the full potential of blockchain in the field of multimedia, it is necessary to develop effective strategies to solve the problems of privacy, scalability and centralization that arise when implementing these technologies. Such a comprehensive approach will provide a stable and effective infrastructure for managing multimedia content in the digital environment.

The methods of image compression for storage in decentralized blockchain repositories, specifically in the IPFS system, are examined. A methodology for evaluating the efficiency of different image compression algorithms is proposed, incorporating both lossless compression (PNG, TIFF, GIF) and lossy compression (JPEG, WEBP). The comparison includes metrics such as file size, image quality (measured by PSNR), upload time to IPFS, and bandwidth for each compression method. Each image is hashed, and the hash is stored in a smart contract within a local blockchain network deployed using Ganache. Such methods enable the optimization of image storage and transfer within blockchain repositories, ensuring a balance between image quality, file size, and access speed.

The subject of this article is consideration of modern blockchain solutions and their potential use in the context of digital asset protection. Various aspects of blockchain technology are explored, including consensus mechanisms, security levels, and functionality. The goal of the work is a systematic analysis and justification of the application of various blockchain solutions for the protection of digital assets. The article is aimed at determining the effectiveness and feasibility of using specific blockchain protocols and their functional elements to ensure the safety, reliability, and integrity of digital assets. The following tasks were solved in the article: consideration of modern blockchain technologies and consideration of their role in ensuring the security of digital assets. Conducting a detailed analysis of popular blockchain protocols, including Bitcoin, Ethereum, and Hyperledger Fabric, with a focus on their security against various types of cyber threats and attacks. The following methods are used: analysis of blockchain protocols, expert evaluations of the effectiveness of protection of digital assets, and study of the technical features of each solution. The following results were obtained: clearly defined advantages and disadvantages of each protocol were obtained, taking into account their applicability in the field of digital assets in various fields of application, such as semantic analysis of texts, E-Learning, Big Data, DDP-systems, finance, etc. In addition, the issue of network privacy in the context of information protection was investigated and justifies the choice of the optimal blockchain solution for a specific use. Conclusions: The article provides readers with an overview of how to effectively use blockchain to ensure the reliability and security of digital assets in a variety of usage scenarios. In today's digital world, where the value of digital assets is growing exponentially, protecting them from cyber threats becomes a critical task. Blockchain technologies, originally developed for cryptocurrencies, have gained recognition as an effective tool in the field of cyber security. The importance of standardization and regulation in the field of blockchain technologies to ensure their effective integration and compliance with the requirements of the law is put forward.