scholarly journals In-Between Layers Modular Residual Neural Network for the Classification of Images

2021 ◽  
Vol 185 ◽  
pp. 223-230
Author(s):  
Iren Valova ◽  
Chris Harris ◽  
Natacha Gueorguieva ◽  
Tony Mai
Keyword(s):  
Neural Network ◽  
Classification Of Images

Method of determination of the text direction on the image with the use of convolutional neural network

2020 ◽  
pp. 96-99
Author(s):  
P.L. Nikolaev
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Deep Neural Network ◽  
Binary Classification ◽  
Synthetic Data ◽  
Real Data ◽  
Method Of Determination ◽  
Classification Of Images

This article deals with method of binary classification of images with small text on them Classification is based on the fact that the text can have 2 directions – it can be positioned horizontally and read from left to right or it can be turned 180 degrees so the image must be rotated to read the sign. This type of text can be found on the covers of a variety of books, so in case of recognizing the covers, it is necessary first to determine the direction of the text before we will directly recognize it. The article suggests the development of a deep neural network for determination of the text position in the context of book covers recognizing. The results of training and testing of a convolutional neural network on synthetic data as well as the examples of the network functioning on the real data are presented.

scholarly journals Classification of masked image data

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254181
Author(s):  
Kamila Lis ◽  
Mateusz Koryciński ◽  
Konrad A. Ciecierski
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Data ◽  
Original Data ◽  
Machine Learning Algorithms ◽  
General Data Protection Regulation ◽  
Additional Information ◽  
Classification Of Images ◽  
Applications Of Machine Learning

Data classification is one of the most commonly used applications of machine learning. The are many developed algorithms that can work in various environments and for different data distributions that perform this task with excellence. Classification algorithms, just like other machine learning algorithms have one thing in common: in order to operate on data, they must see the data. In the present world, where concerns about privacy, GDPR (General Data Protection Regulation), business confidentiality and security are growing bigger and bigger; this requirement to work directly on the original data might become, in some situations, a burden. In this paper, an approach to the classification of images that cannot be directly accessed during training has been made. It has been shown that one can train a deep neural network to create such a representation of the original data that i) without additional information, the original data cannot be restored, and ii) that this representation—called a masked form—can still be used for classification purposes. Moreover, it has been shown that classification of the masked data can be done using both classical and neural network-based classifiers.

scholarly journals Detection of Fake Doctored Images used to Circulate Fake News

2020 ◽  
Vol 9 (7) ◽  
pp. 1031-1036
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Machine Learning Techniques ◽  
Fake News ◽  
Neural Network Models ◽  
The Public ◽  
Learning Techniques ◽  
The World ◽  
Classification Of Images

Over the few years the world has seen a surge in fake news and some people are even calling it an epidemic. Misleading false articles are sold as news items over social media, whatsapp etc where no proper barrier is set to check the authenticity of posts. And not only articles but news items also contain images which are doctored to mislead the public or cause sabotage. Hence a proper barrier to check for authenticity of images related to news items is absolutely necessary. And hence classification of images(related to news items) on the basis of authenticity is imminent. This paper discusses the possibilities of identifying fake images using machine learning techniques. This is an introduction into fake news detection using the latest evolving neural network models

Criteria for comparing artificial intelligence systems

2021 ◽  
pp. 21-27
Author(s):  
В.А. Пятакович ◽  
В.Ф. Рычкова ◽  
Н.Г. Левченко
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Neural Model ◽  
Neural Network Models ◽  
Actual Problem ◽  
Neuro Fuzzy ◽  
Classification Of Images ◽  
Fuzzy Network ◽  
First Time

Модели нейронных и нейро-нечетких сетевых критериев сравнения в задачах диагностики и классификации образов. Предложен комплекс критериев для оценки свойств искусственных нейронных и нейро-нечетких сетей. Он включает в себя критерии разнообразия, подгонки, эластичности, равнозначности, устойчивости к шуму, аварийной ситуации, а также заданную монотонность для построения нейронной модели. Применение предложенных критериев на практике позволяет автоматизировать процесс построения, анализа и сравнения нейронных моделей для решения задач диагностики и классификации паттернов. Предложено решение задачи повышения эффективности параметрического синтеза нейросетевых моделей сложных систем для обоснованного принятия решений о классификации подводных целей. Научная новизна работы заключается в том, что впервые предложен комплекс моделей критериев, характеризующих такие свойства нейронных и нейро-нечетких сетей как разнообразие, переобученность, эластичность, эквифинальность, устойчивость к шуму, эмерджентность, что позволяет автоматизировать решение задачи анализа свойств и сравнения нейросетевых и нейро-нечетких моделей при решении задач диагностики и классификации образов. В работе решена актуальная задача автоматизации анализа свойств и сравнения нейросетевых моделей. Models of neural and neuro-fuzzy network comparison criterions in the tasks of diagnostics and pattern classification. The complex of criterions for an estimation of properties artificial neural and neuro-fuzzy networks is proposed. It includes criterions of variety, overfitting, elasticity, equifinality, stability to a noise, emergency, and also set monotonicity for a neural model construction. The application of offered criterions in practice allows to automatize the process of a construction, analysis and comparison of neural models for problem solving of diagnostics and patternt classification. The solution of the problem of increasing the efficiency of parametric synthesis of neural network models of complex systems for informed decision-making on the classification of underwater targets is proposed. The scientific novelty of the work lies in the fact that for the first time a set of models of criteria characterizing such properties of neural and neuro-fuzzy networks as diversity, retraining, elasticity, equifinality, noise resistance, emergence is proposed, which allows automating the solution of the problem of analyzing the properties and comparing neural network and neuro-fuzzy models when solving problems of diagnostics and classification of images. The paper solves the actual problem of automating the analysis of properties and comparison of neural network models.

Understanding Image Classification Using TensorFlow Deep Learning - Convolution Neural Network

2019 ◽  
Vol 3 (2) ◽  
pp. 19-37
Author(s):  
Vinit Kumar Gunjan ◽  
Rashmi Pathak ◽  
Omveer Singh
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolution Neural Network ◽  
Fine Tuning ◽  
Learning Technology ◽  
The Neural Network ◽  
Learning Technique ◽  
Classification Of Images ◽  
Initial Classification

This article describes how to establish the neural network technique for various image groupings in a convolution neural network (CNN) training. In addition, it also suggests initial classification results using CNN learning characteristics and classification of images from different categories. To determine the correct architecture, we explore a transfer learning technique, called Fine-Tuning of Deep Learning Technology, a dataset used to provide solutions for individually classified image-classes.

NEURAL NETWORK CLASSIFICATION OF SYMMETRICAL AND NONSYMMETRICAL IMAGES USING NEW MOMENTS WITH HIGH NOISE TOLERANCE

1999 ◽  
Vol 13 (08) ◽  
pp. 1233-1250
Author(s):  
R. PALANIAPPAN ◽  
P. RAVEENDRAN ◽  
SIGERU OMATU
Keyword(s):  
Neural Network ◽  
Random Noise ◽  
Noise Tolerance ◽  
Neural Network Classification ◽  
Reference Centre ◽  
Different Types ◽  
Moment Functions ◽  
Classification Of Images ◽  
Invariant Properties

The classification of images using regular or geometric moment functions suffers from two major problems. First, odd orders of central moments give zero value for images with symmetry in the x and/or y directions and symmetry at centroid. Secondly, these moments are very sensitive to noise especially for higher order moments. In this paper, a single solution is proposed to solve both these problems. The solution involves the computation of the moments from a reference point other than the image centroid. The new reference centre is selected such that the invariant properties like translation, scaling and rotation are still maintained. In this paper, it is shown that the new proposed moments can solve the symmetrical problem. Next, we show that the new proposed moments are less sensitive to Gaussian and random noise as compared to two different types of regular moments derived by Hu.6 Extensive experimental study using a neural network classification scheme with these moments as inputs are conducted to verify the proposed method.

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