Browsing by Author "Chumachenko, O. I."

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Determination of Characteristics of Infectious Endocarditis Based on Intelligent Processing of Ultrasonic Images
(National Aviation University, 2022-12-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Chumachenko, O. I.; Чумаченко, Олена Іллівна; Kolomoiets, S. O.; Коломоєць, Сергій Олексійович
The paper presents the pathogenetic factors in the development of infective endocarditis and identifies its predictors. The need for an echographic study associated with the search for the anatomical characteristics of infective endocarditis is shown: vegetation, destructive lesions (valve aneurysms, perforation or prolapse, etc.), the presence of abscesses, in the case of a prosthesis, a new divergence of the valve prosthesis may be a characteristic feature. A classification of research methods is presented that includes classical approaches of echocardiography (transthoracic, transesophageal) and new multidetector computed tomographic angiography and positron emission tomography with 18F-fluorodeoxyglucose and the need for their use in different cases is determined. A block diagram of an intelligent diagnostic system for infective endocarditis has been developed. To process the obtained images in order to diagnose and determine the geometric dimensions, shapes, quantity, location, characteristics of infective endocarditis, it is proposed to use convolutional neural networks that allow solving the problem of image segmentation.
Modification of Semi-supervised Algorithm Based on Gaussian Random Fields and Harmonic Functions
(National Aviation University, 2023-06-27) Sineglazov, V. M.; Синєглазов, Віктор Михайлович; Chumachenko, O. I.; Чумаченко, Олена Іллівна; Lesohorskyi, K. S.; Лесогорський, Кирило Сергійович
In this paper we propose an improvement for a semi-supervised learning algorithm based on Gaussian random fields and harmonic functions. Semi-supervised learning based on Gaussian random fields and harmonic functions is a graph-based semi-supervised learning method that uses data point similarity to connect unlabeled data points with labeled data points, thus achieving label propagation. The proposed improvement concerns the way of determining similarity between two points by using a hybrid RBF-kNN kernel. This improvement makes the algorithm more resilient to noise and makes label propagation more locality-aware. The proposed improvement was tested on five synthetic datasets. Results indicate that there is no improvement for datasets with big margin between classes, however in datasets with low margin proposed approach with hybrid kernel outperforms existing algorithms with a simple kernel.
STRUCTURAL SYNTHESIS OF HYBRID NEURAL NETWORKS ENSEMBLES
(Київ «Освіта України», 2018-09) Sineglazov, V. M.; Chumachenko, O. I.; Bedukha, O. R.
It is considered the structural synthesis of hybrid neural networks ensembles. It is chosen the ensemble topology as parallel structure with united layer. It is developed a hybrid algorithm for the problem solution which includes some algorithms preliminary choice of classifiers(modules of neural networks-hybrid neural networks, which consist of Kohonen, basic neural networks and bi-directional associative memory), creation the bootstrap training samples for every classifier, training these classifiers, optimal choice of necessity ones, determination of layer union weight coefficients, ensemble pruning. For the solution of optimal choice classifiers it is used two criteria: accuracy and variety.