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Публікація Публікація Публікація Transforming image descriptions as a set of descriptors to construct classification features(Indonesian Journal of Electrical Engineering and Computer Science, 2024) Gorokhovatskyi, V.; Tvoroshenko, I.; Yakovleva OlenaThe article develops methods to solve a fundamental problem in computer vision: image recognition of visual objects. The results of the research on the construction of modifications for the space of classification features based on the application of the transformation of the structural description through the decomposition in the orthogonal basis and the implementation of the distance matrix model between the components of the description are presented. The application of the system of orthogonal functions as an apparatus for the transformation of the description showed the possibility of a significant gain in the speed of processing while maintaining high indicators of classification accuracy and interference resistance. The synthesized feature systems’ effectiveness has been confirmed in terms of a significant increase in the rate of codes and a sufficient level of efficiency. An experimental example showed that the time spent calculating the relevance of descriptions according to their modified presentation is more than ten times shorter than for traditional metric approaches. The developed classification features can be used in applied tasks where the time of visual objects’ identification is critical.Публікація Використання розподілів даних для дескрипторів зображень у задачі класифікації(2024) Оченашко, М.Публікація Accelerating Image Classification based on a Model for Estimating Descriptor-to-Class Distance(2023) Gorokhovatskyi, V.; Gadetska, S.; Stiahlyk, N.The article describes a method of image classification based on the estimation of the distance to the etalon class. The implementation of estimates gives a significant gain in classification speed compared to linear search while maintaining a decent level of accuracy. The methodology is based on the use of the triangle inequality for images given by a set of binary vectors as descriptors of the image key points. The evaluation is applied to the "object descriptor – etalon" classification method, which is based on the descriptor voting procedure. An analysis of evaluation options is carried out using the parameters of the etalon sets in the form of a medoid and the closest or farthest points from it. The gain in classification time compared to the traditional method proportionally depends on the number of descriptors in the etalon description. Software simulation of classifiers with the implementation of evaluation shows a gain in speed of 350-450 times for the description of 500 descriptors while maintaining one hundred percent classification accuracy on the training set of similar NFT images. A control sample experiment shows that the classifier with estimation can respond better to image details compared to the traditional method.Публікація Порівняльний аналіз популярних JavaScript - фреймворків та бібліотек для front-end розробки(ХНУРЕ, 2018) Танянський, О.; Руденко, Д.У даній роботі проводиться порівняльний аналіз сучасних JavaScript фреймворків, таких як Angular, React, Vue, Backbone, Ember, Knockout. Розглядаються можливості та характеристики, властиві даним фреймворкам, та проводиться їх порівняльний аналіз. This article describes a comparative analysis of tool of modern JavaScript frameworks like Angular, React and Vue.js, Backbone, Ember, Knockout. There are described all possibilities and characteristics of frameworks.Публікація Використання Фреймворку Angular для Розробки Веб-застосунків(ХНУРЕ, 2018) Кочкін, А.; Руденко, Д.Angular - написаний на TypeScript front-end фреймворк з відкритим кодом, який розробляється під керівництвом Angular Team у компанії Google, а також спільнотою приватних розробників та корпорацій. Angular — це AngularJS, який переосмислили та який був повністю переписаний тією ж командою розробників. Angular - is a TypeScript-based open-source frontend web application platform led by the Angular Team at Google and by a community of individuals and corporations. Angular is a complete rewrite from the same team that built AngularJS.Публікація Дослідження та реалізація методу для розпізнання обличчя(2023) Волков, Д.Публікація Публікація Statistical data analysis models for determining the relevance of structural image descriptions(IEEE Access, 2023) Daradkeh Yousef Ibrahim; Gorokhovatskyi, V.; Tvoroshenko, I.; Gadetska, S.; Al-Dhaifallah MujahedThe aim of the research is to improve the effectiveness of image recognition methods according to the description in the form of a set of keypoint descriptors. The focus is on increasing the speed of analysis and processing of description data while maintaining the required level of classification efficiency. The class of the image is provided as a description of the etalon. It is proposed to transform the description by implementing a statistical system of features for non-intersecting data fragments. The developed method is based on the aggregation of data distribution values within the description, the basis of which is the bit representation of the descriptors. Statistical features are calculated as the frequency of occurrence of the fixed value of a fragment on a set of description data and thus reflect the individual properties of images. Three main classifier models are analyzed: calculating the measure of data relevance in the form of distributions; assigning each of the descriptors to defined classes (voting); using the apparatus of statistical data analysis to decide on the significance of the difference between the distributions of the object and etalons. The results of software modeling of methods and calculations of statistical significance of differences based on distributions for training sets of images are represented. Using distributions instead of a set of descriptors increases the processing speed by hundreds of times, while the classification accuracy is maintained at a sufficient level and does not deteriorate compared to traditional voting.Публікація Application of video data classification models using convolutional neural networks(International Journal of Academic and Applied Research, 2023) Tvoroshenko, I.; Pomazan, V.; Gorokhovatskyi, V.; Kobylin, O.This paper explores the classification of video data with convolutional neural networks. It discusses how convolutional neural network architecture can do this task with fastness and maximum efficiency. By analyzing the different convolutional neural network models model was proposed that showed great results in this video classification task. The strengths and weaknesses of the neural network model for video classification have been identified, and the prospects for further work have also been outlined.Публікація Дослідження методів видалення шуму з відеоданих(2023) Миколенко, Е.Публікація Аналіз JavaScript-фреймворків для розроблення вебзастосунків(2023) Ткачов, В.Публікація Основні елементи розроблення вебсайту для моніторингу ICO(2023) Столяренко, Н.Публікація Публікація Особливості методів онлайн достовірної кластеризації даних(2023) Захаров, Є.Публікація Публікація Розробка сканера виявлення вразливостей вебсайту на основі методів захисту від різних типів атак(2023) Лопатінський, А.