Кафедра "Комп'ютерна інженерія та програмування"

Постійне посилання колекціїhttps://repository.kpi.kharkov.ua/handle/KhPI-Press/1095

Офіційний сайт кафедри https://web.kpi.kharkov.ua/cep

Від 26 листопада 2021 року кафедра має назву – "Комп’ютерна інженерія та програмування"; попередні назви – “Обчислювальна техніка та програмування”, “Електронні обчислювальні машини”, первісна назва – кафедра “Математичні та лічильно-вирішальні прилади та пристрої”.

Кафедра “Математичні та лічильно-вирішальні прилади та пристрої” заснована 1 вересня 1961 року. Організатором та її першим завідувачем був професор Віктор Георгійович Васильєв.

Кафедра входить до складу Навчально-наукового інституту комп'ютерних наук та інформаційних технологій Національного технічного університету "Харківський політехнічний інститут". Перший випуск – 24 інженери, підготовлених кафедрою, відбувся в 1964 році. З тих пір кафедрою підготовлено понад 4 тисячі фахівців, зокрема близько 500 для 50 країн світу.

У складі науково-педагогічного колективу кафедри працюють: 11 докторів технічних наук, 21 кандидат технічних наук, 1 – економічних, 1 – фізико-математичних, 1 – педагогічних, 1 доктор філософії; 9 співробітників мають звання професора, 14 – доцента, 2 – старшого наукового співробітника.

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Usage of Mask R-CNN for automatic license plate recognition
(Національний технічний університет "Харківський політехнічний інститут", 2023) Podorozhniak, A. O.; Liubchenko, N. Yu.; Sobol, Maksym; Onishchenko, D. P.
The subject of study is the creation process of an artificial intelligence system for automatic license plate detection. The goal is to achieve high license plate recognition accuracy on large camera angles with character extraction. The tasks are to study existing license plate recognition technics and to create an artificial intelligence system that works on big shooting camera angles with the help of modern machine learning solution – deep learning. As part of the research, both hardware and software-based solutions were studied and developed. For testing purposes, different datasets and competing systems were used. Main research methods are experiment, literature analysis and case study for hardware systems. As a result of analysis of modern methods, Mask R-CNN algorithm was chosen due to high accuracy. Conclusions. Problem statement was declared; solution methods were listed and characterized; main algorithm was chosen and mathematical background was presented. As part of the development procedure, accurate automatic license plate system was presented and implemented in different hardware environments. Comparison of the network with existing competitive systems was made. Different object detection characteristics, such as Recall, Precision and F1-Score, were calculated. The acquired results show that developed system on Mask R-CNN algorithm process images with high accuracy on large camera shooting angles.
Forestry application based on deep learning technology
(ФОП Петров В. В., 2022) Liubchenko, N.; Onishchenko, D.; Podorozhniak, A.
A comparison of classifiers applied to the problem of biopsy images analysis
(Національний технічний університет "Харківський політехнічний інститут", 2020) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, Andrii; Lukova-Chuiko, Nataliia
The purpose of the research is to compare classification algorithms for the histopathological images analyzing issue and to optimize the parameters for obtaining better classification accuracy. The following tasks are solved in the article: preprocessing of BreCaHAD dataset images, implementation and training of CNN, applying K-nearest neighbours, SVM, Random Forest,XGBoost, and perceptron algorithms for classifying features that were extracted by CNN, and results comparison. The object of the research is the process of classifying tumor cells in the microscopic biopsy images. The subject of the research is the processof using ML algorithms for classification of the features extracted by CNN from input biopsy image. The scientific novelty of the research is a comparative analysis of classifiers on the task of “tumor” and “healthy” cells images classification from processed BreCaHAD dataset. As a result it was obtained that from chosen classifiers SVM reached the highest accuracy on test data –0.972. This is the only algorithm that shows better accuracy than perceptron. Perceptron gets 0.966 classification accuracy. K-nearest neighbours, Random Forest, and XGBoost algorithms reached lower results. The algorithms' hyperparameters optimization was carried out. The results have been compared with related works. The following research methodsare used: the theory of deep learning, mathematical statistics, parameters optimization.
Application of convolutional neural network for histopathological analysis
(Національний технічний університет "Харківський політехнічний інститут", 2019) Hlavcheva, Daria; Yaloveha, Vladyslav; Podorozhniak, Andrii
Among all types of cancer, breast cancer is the most common. In 2017 breast cancer was the fourth rate for death reasons in Ukraine. The paper is devoted to the automatization of histopathological analysis, which can improve the process of cancer stage diagnosis. The purpose of the paper is to research the ability to use convolutional neural networks for classifying biopsy images for cancer diagnosis. The tasksof research are: analyzing cancer statistics in Europe and Ukraine; analyzing usage of Machine Learning in cancer prognosis and diagnosis tasks; preprocessing of BreCaHAD dataset images; developing a convolutional neural network and analyzing results; the building of heatmap. The object of the research is the process of detecting tumors in microscopic biopsy images using Convolutional Neural Network. The subject of the research is the process of classifying healthy and cancerous cells using deep learning neural networks. The scientific noveltyof the research is using ConvNet trained on the BreCaHAD dataset for histopathological analysis. The theory of deep learning neural networks and mathematical statistics methods are used. In resultit is obtained that the classification accuracy for a convolutional neural networkon the test data is 0.935, ConvNet waseffectively used for heatmap building.
Usage of convolutional neural network for multispectral image processing applied to the problem of detecting fire hazardous forest areas
(Національний технічний університет "Харківський політехнічний інститут", 2019) Yaloveha, V.; Hlavcheva, D.; Podorozhniak, A.
Neural networks are intensively developed and used in all spheres of human activity in the modern world. Their use to determine the fire hazardous forest areas can begin to solve the problem of preventing wildfires. In recent years, wildfires have acquired enormous proportions. Wildfires are difficult to control and, if they occur, require alarge amount of resources to eliminate them. The paper is devoted to solve the problem of identifying fire hazardous forest areas. The Camp Fire (California, USA) areas are considered. The purpose of the paper is to research the possibility of using convolutional neural networks for the detection fire hazardous forest areas using multispectral images obtained from Landsat 8. The tasks of research are finding the territories where the largest fires occurred in recent time; analyzing economic and ecologic losses from wildfires; receiving and processing multispectral images of wildfire areas from satellite Landsat 8; calculation of spectral indices (NDVI, NDWI, PSRI); developing convolutional neural network and analyzing results. The object of the research is the process of detecting fire hazardous forest areas using convolutional neural network. The subject of the research is the process of recognition multispectral images using deep learning neural network. The scientific novelty of the research is the recognition method of multispectral images by using convolutional neural networkhas been improved. The theory of deep learning neural networks, the theory of recognition multispectral images and mathematical statistics methodsare used. The spectral indices for allocating the object under research (green vegetation, humidity, dry carbon) were calculated. It is obtained that the classification accuracy for a convolutional neural network on the test data is 94.27%.