http://hdl.handle.net/123456789/175 Sat, 15 Aug 2026 09:05:35 GMT 2026-08-15T09:05:35Z http://hdl.handle.net/123456789/25984 Title: Аналіз сучасних підходів до розуміння візуальних сцен для реалізації мультимодального RAG-підходу Authors: Лазарович, Юлія Ігорівна; Козленко, Микола Іванович Abstract: В роботі представлено аналіз сучасних підходів до розуміння візуальних сцен для реалізації мультимодального RAG-підходу. Wed, 03 Dec 2025 00:00:00 GMT http://hdl.handle.net/123456789/25984 2025-12-03T00:00:00Z http://hdl.handle.net/123456789/25983 Title: Performance analysis of microservice architecture Authors: Berladyniuk, Yevhen; Kozlenko, Mykola Abstract: Microservices embody a paradigmatic shift toward modularity, scalability, and flexibility in software development. At the very core of microservice architecture lies the principle of decomposition, breaking down complex applications into a network of smaller, independent services. The main principles of microservice infrastructure are as follows: modularity, service independence, decentralized data management, automation of deployment processes and service lifecycle management, and the distributed nature of microservices. Overall, microservice architecture provides high scalability, flexibility, and reliability; however, it is accompanied by significant management complexity, increased infrastructure costs, and the need for a well-thoughtout approach to inter-service communication. Designing a microservice architecture involves the use of concepts and methodologies that ensure effective distribution of functionality among services and optimal interaction between them. The choice of architectural style is determined by the specifics of business logic, performance requirements, and operational characteristics. This report examines the key principles of microservice architecture, its advantages and disadvantages, as well as approaches to designing microservice-based systems. The application of microservice and monolitic architectures to energy efficiency monitoring systems, robotics, and remote learning systems is presented. Wed, 28 May 2025 00:00:00 GMT http://hdl.handle.net/123456789/25983 2025-05-28T00:00:00Z http://hdl.handle.net/123456789/25939 Title: Application of deep learning approaches for medieval historical documents transcription Authors: Voloshchuk, Maksym; Zarembovska, Bohdana; Kozlenko, Mykola Abstract: Handwritten text recognition and optical character recognition solutions show excellent results with processing data of modern era, but efficiency drops with Latin documents of medieval times. This paper presents a deep learning method to extract text information from handwritten Latin-language documents of the 9th to 11th centuries. The approach takes into account the properties inherent in medieval documents. The paper provides a brief introduction to the field of historical document transcription, a first-sight analysis of the raw data, and the related works and studies. The paper presents the steps of dataset development for further training of the models. The explanatory data analysis of the processed data is provided as well. The paper explains the pipeline of deep learning models to extract text information from the document images, from detecting objects to word recognition using classification models and embedding word images. The paper reports the following results: recall, precision, F1 score, intersection over union, confusion matrix, and mean string distance. The plots of the metrics are also included. The implementation is published on the GitHub repository. Tue, 16 Dec 2025 00:00:00 GMT http://hdl.handle.net/123456789/25939 2025-12-16T00:00:00Z http://hdl.handle.net/123456789/25924 Title: Demodulation of chaotic signals using convolutional neural network Authors: Kozlenko, Mykola; Demiral, Emrullah; Yudhana, Anton Abstract: Chaotic modulation is an effective communication technique that exploits deterministic chaos to produce pseudo-random signals. A widely adopted approach involves modulation of the chaotic bifurcation parameter. This paper introduces a deep learning-based demodulation method for keying of the bifurcation parameter. It describes the architecture of the convolutional neural network and evaluates performance metrics for signals generated using the chaotic logistic map. The study assesses the bit error rate for binary signals and reports a bit error rate of 0.0819 for a bifurcation parameter deviation of 1.34% under additive white Gaussian noise at a signal-to-noise ratio of -13 dB (corresponding to a normalized signal-to-noise ratio of +20 dB). The results demonstrate the capability to detect chaotic patterns even when the specific patterns were not included in the training dataset. Tue, 03 Mar 2026 00:00:00 GMT http://hdl.handle.net/123456789/25924 2026-03-03T00:00:00Z