Fire Hazard Research of Forest Areas based on the use of Convolutional and Capsule Neural Networks

Yaloveha, Vladyslav; Hlavcheva, Daria; Podorozhniak, Andrii; Kuchuk, Heorhii

doi:10.1109/ukrcon.2019.8879867

Cited by 15 publications

(13 citation statements)

References 8 publications

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“…На фоне огромного количества различных исходных данных и архивных наборов верифицированных данных о пожарах появилась возможность использовать нейронные сети. Их применение позволяет обучать вычислительные алгоритмы, на основе которых выявляются неочевидные закономерности, свойства изображений и их комбинации для точного прогноза природных возгораний [33]. Интерпретация процессов возгорания.…”

Section: географический вестник 1(56)unclassified

Development of Remote Sensing Methods for Natural Fire Prevention

Gizatullin¹

2021

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The study deals with remote sensing methods for natural fire prevention, provides analysis and systematization on the subject. It traces the historical development and demonstrates the diversity of the methods. The main development stages and their characteristics were identified taking into account the increasing number of the sources and types of remote sensing and deepening knowledge of the subject. Fire interpretation includes fundamentally different processes of ignition and fire spread. The concepts of fire danger and its factors were introduced, the ways for their selection and application in the methods were analyzed. The source data for the methods were defined: satellite imagery of various resolutions (Landsat, Sentinel, MODIS/Terra-Aqua, AVHRR/NOAA, etc.), UAV images, lidar data, as well as technologies to process those. The study demonstrates that the most commonly used are traditional methods of geoinformation analysis, simulation modelling and neural networks. The methods were described, features of their implementation were identified. The description includes specific examples of fire danger assessment methods based on GIS, simulation models of fire spread, fire prevention methods based on neural networks and their application for territories of different spatial levels – global, regional and local.

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Section: географический вестник 1(56)unclassified

Development of Remote Sensing Methods for Natural Fire Prevention

Gizatullin¹

2021

Геогр. вест

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“…CNNs became one of the most popular neural networks last years. They are widely used for image analyzing [3] and classification issues [19,20].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

A Comparison of Classifiers Applied to the Problem of Biopsy Images Analysis

Hlavcheva

Yaloveha

Podorozhniak

et al. 2020

A.I.S.

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Ab s t r a c t. 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 process of 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 methods are used: the theory of deep learning, mathematical statistics, parameters optimization.

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“…The term "dropout" refers to dropping out units (hidden and visible) in a neural network [24]. In this layer with probability of the neuron is excluded from the network at the time of current iteration [25].…”

Section: Convolutional Neural Networkmentioning

confidence: 99%

Application of Convolutional Neural Network for Histopathological Analysis

Hlavcheva

Yaloveha

Podorozhniak

2019

A.I.S.

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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 tasks of 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 novelty of 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 result it is obtained that the classification accuracy for a convolutional neural network on the test data is 0.935, ConvNet was effectively used for heatmap building. K e ywor d s : deep learning; convolutional neural networks; breast cancer; histopathological analysis; biopsy images; BreCaHAD.

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Fire Hazard Research of Forest Areas based on the use of Convolutional and Capsule Neural Networks

Cited by 15 publications

References 8 publications

Development of Remote Sensing Methods for Natural Fire Prevention

Development of Remote Sensing Methods for Natural Fire Prevention

A Comparison of Classifiers Applied to the Problem of Biopsy Images Analysis

Application of Convolutional Neural Network for Histopathological Analysis

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