Кафедра комп’ютерних наук
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Перегляд Кафедра комп’ютерних наук за Автор "Klimenko, V."
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Документ Datalogic Model for Determining the Clients Compatibility Based on Questionnaires Data Analysis by Artificial Intelligence Means(2024) Zhuk, D.; Mazurets, O.; Sobko, O.; Klimenko, V.; Мазурець, Олександр ВікторовичTherefore, the Datalogic Model for Determining the Clients Compatibility Based on Questionnaires Data Analysis by Artificial Intelligence Means was designed. To determine compatibility between users, the system uses a test in which points will be added for each matching criterion. After comparing the questionnaires according to these criteria, the system displays the percentage of compatibility of the questionnaires, which is calculated based on the matches of the given criteria. The more criteria match, the higher the match percentage. The need to create a database for the information system, which is intended for many users, was also described in detail. Using a database will allow efficient and convenient storage and processing of data about users and their interestsДокумент Datalogic Model for Image Recognition by Convolutional Neural Network Using Cloud Services(2024) Mazurets, O.; Molchanova, M.; Klimenko, V.; Klopotivskyi, D.; Мазурець, Олександр ВікторовичThe developed database and information system from the research for recognizing potato fruit diseases by a convolutional neural network using cloud services allows for the rapid detection of diseases, which helps farmers make effective decisions on the application of control measures and plant protection. In addition, given its high accuracy (0.96 per 100 training epochs), the software can be used as a tool for research and monitoring plant health in agriculture and for developing new methods and technologies to increase yields and reduce crop losses.Документ Datalogic structure for intelligent system for areas localization in photos with the remains of buildings using neural network(2025) Dydo, R.; Sobko, O.; Klimenko, V.; Mazurets, O.; Мазурець, Олександр ВікторовичThis paper presents the development of a datalogic structure for an intelligent system designed to detect and localize areas in photographic images that contain remains of destroyed buildings using neural networks. The system integrates pre-processing, object detection via the YOLO model, and multiclass classification of building materials. A relational database was designed to store and manage information about images, segments, detected materials, experiments, and classification metrics. This structured approach ensures efficient data handling, supports analytical reporting, and enables retraining of models based on historical data, contributing to more accurate and scalable damage assessment solutions in post-conflict or disaster-struck areas.Документ Intelligent System for Automated Assessment of Test Tasks Sets Conformity to Semantic Structure of Educational Materials(2024) Hardysh, D.; Klimenko, V.; Mazurets, O.; Мазурець, Олександр ВікторовичThe developed intelligent system for automated assessment of test tasks set conformity to semantic structure of educational materials can have great potential during its use in educational institutions and organizations where it is important to assess the conformity of test tasks to educational materials. It will allow to automate the evaluation process and ensure the objectivity of the results.Документ Intelligent System for Neural Network Detection of Fake Document Images for Automated Personality Identification(2024) Zharnovskyi, O.; Sobko, O.; Klimenko, V.The outcome of the work is the development of a method for the neural network detection of fake document images and an information system for personal identification. The system utilizes the developed method and allows for the automated assessment of the authenticity level of a photographed identity document image using the developed approach. The research has demonstrated that the method achieves an accuracy of 95.16%. The developed information system for detecting fake document images can effectively prevent fraud in personal identification systems. It can aid in identifying forged document images that may be used for identity theft, credit card fraud, or other criminal activities.Документ Object-oriented model for neural network damage detection of mail packages(2024) Molchanova, M.; Mazurets, O.; Klimenko, V.; Kuflevsky, Ev.; Мазурець, Олександр ВікторовичThe software structure of the object-oriented information system was designed, and the functional purpose of the software components of the mail package damage detection system, which consists of three main classes "DamageDetectionApp", "ModelTrainer", and "ImageGalery" were described. The components of the object-oriented information system for detecting damage to postal packages, consisting of three classes responsible for subsystem implementation were implemented.Документ Practice Implementation of Neural Network Model BART-Large-CNN for Text Annotation(2024) Mazurets, O.; Molchanova, M.; Klimenko, V.; Prosvitliuk, M; Мазурець, Олександр ВікторовичThe scheme of the method of annotating works of art was described, which works by converting input data in the form of text for annotating a work of art, a trained machine learning model, and desired annotation parameters into output data in the form of an annotation and a numerical evaluation of the quality of the annotation and is intended for automated annotation creation. We also present the neural network architecture of the machine learning model, which is the input to the proposed method of annotating works of art. This neural network model belongs to the «transformers» type and is currently one of the most powerful text generation models. A practical implementation of the method of annotating works of art has been created and the main purposes of the software components of the intelligent system for annotating works of art have been described.Документ Relation Datalogic Model for Determining the Diagnosis Based on Intellectual NLP-analysis of Symptom Description(2024) Mazurets, O.; Sobko, O.; Klimenko, V.; Kozenko, Y.; Мазурець, Олександр ВікторовичA relational data model for a database was designed for the automated determination of a diagnosis based on textual descriptions of symptoms using NLP tools, along with corresponding software in the form of a desktop application. By utilizing an SQLite database, reliable storage and organization of medical information can be ensured. This enables high-speed data access and efficient processing of user queries. All of this together forms a robust foundation for creating a useful application that can significantly streamline the diagnosis determination process in medical practice and enhance the quality of healthcare services. Additionally, a software application was implemented to determine a diagnosis based on textual descriptions of symptoms using NLP tools, utilizing the developed relational data model for the database. The created software product includes functions for determining the probable diagnosis and text description, entering new information, and working with the dataset. The functional structure of the information system for diagnosing diseases based on textual descriptions was described, consisting of three subsystems: working with the database, diagnosis, preprocessing of text data, and the corresponding database.Документ Research on the effectiveness of classifying the remains of destroyed buildings using MobileNetV3 neural network architecture(2025) Hladun, O.; Zalutska, O.; Klimenko, V.; Mazurets, O.; Мазурець, Олександр ВікторовичThe study investigates the effectiveness of the MobileNetV3 neural network architecture in classifying the remains of destroyed buildings, a task of increasing relevance due to the widespread destruction of infrastructure caused by military actions, natural disasters, and industrial accidents. A software application with a graphical interface was developed to enable interactive analysis of photo data using pre-trained models. The system allows users to classify construction debris into multiple categories with high accuracy and reliability. Experimental results demonstrated strong performance metrics, including an overall accuracy of 95% and high values for precision, recall, and F1-score across ten classes of construction materials. The model showed excellent discriminative capability, as evidenced by ROC curves with AUC values close to 1.00. The solution holds promise for practical applications such as automated sorting of construction waste and monitoring of damage zones, contributing to more efficient disaster response and recovery operations.