scholarly journals Classification of Non-Conventional Ships Using a Neural Bag-Of-Words Mechanism

Sensors ◽  
2020 ◽  
Vol 20 (6) ◽  
pp. 1608 ◽  
Author(s):  
Dawid Polap ◽  
Marta Wlodarczyk-Sielicka
Keyword(s):  
Neural Network ◽  
Input Data ◽  
Automatic Identification ◽  
Bag Of Words ◽  
Specific Class ◽  
Classification Problems ◽  
Video Cameras ◽  
Video Frames ◽  
Alternative Approach

The existing methods for monitoring vessels are mainly based on radar and automatic identification systems. Additional sensors that are used include video cameras. Such systems feature cameras that capture images and software that analyzes the selected video frames. Methods for the classification of non-conventional vessels are not widely known. These methods, based on image samples, can be considered difficult. This paper is intended to show an alternative way to approach image classification problems; not by classifying the entire input data, but smaller parts. The described solution is based on splitting the image of a ship into smaller parts and classifying them into vectors that can be identified as features using a convolutional neural network (CNN). This idea is a representation of a bag-of-words mechanism, where created feature vectors might be called words, and by using them a solution can assign images a specific class. As part of the experiment, the authors performed two tests. In the first, two classes were analyzed and the results obtained show great potential for application. In the second, the authors used much larger sets of images belonging to five vessel types. The proposed method indeed improved the results of classic approaches by 5%. The paper shows an alternative approach for the classification of non-conventional vessels to increase accuracy.

scholarly journals Pattern Recognition by Hierarchical Feature Extraction

2003 ◽  
Vol 15 (3) ◽  
pp. 278-285
Author(s):  
Daigo Misaki ◽  
◽  
Shigeru Aomura ◽  
Noriyuki Aoyama
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Pattern Recognition ◽  
Feature Extraction ◽  
Input Data ◽  
Local Features ◽  
Detailed Classification

We discuss effective pattern recognition for contour images by hierarchical feature extraction. When pattern recognition is done for an unlimited object, it is effective to see the object in a perspective manner at the beginning and next to see in detail. General features are used for rough classification and local features are used for a more detailed classification. D-P matching is applied for classification of a typical contour image of individual class, which contains selected points called ""landmark""s, and rough classification is done. Features between these landmarks are analyzed and used as input data of neural networks for more detailed classification. We apply this to an illustrated referenced book of insects in which much information is classified hierarchically to verify the proposed method. By introducing landmarks, a neural network can be used effectively for pattern recognition of contour images.

scholarly journals Potential and Use of the Googlenet Ann for the Purposes of Inland Water Ships Classification

2020 ◽  
Vol 27 (4) ◽  
pp. 170-178
Author(s):  
Katarzyna Bobkowska ◽  
Izabela Bodus-Olkowska
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Video Recording ◽  
Inland Water ◽  
Video Cameras ◽  
Image Repository ◽  
Ship Recognition ◽  
Number Of Classes ◽  
Classification Quality

AbstractThis article presents an analysis of the possibilities of using the pre-degraded GoogLeNet artificial neural network to classify inland vessels. Inland water authorities monitor the intensity of the vessels via CCTV. Such classification seems to be an improvement in their statutory tasks. The automatic classification of the inland vessels from video recording is a one of the main objectives of the Automatic Ship Recognition and Identification (SHREC) project. The image repository for the training purposes consists about 6,000 images of different categories of the vessels. Some images were gathered from internet websites, and some were collected by the project’s video cameras. The GoogLeNet network was trained and tested using 11 variants. These variants assumed modifications of image sets representing (e.g., change in the number of classes, change of class types, initial reconstruction of images, removal of images of insufficient quality). The final result of the classification quality was 83.6%. The newly obtained neural network can be an extension and a component of a comprehensive geoinformatics system for vessel recognition.

GENERALIZATION OF FEATURES IN THE ASSEMBLY NEURAL NETWORKS

2004 ◽  
Vol 14 (01) ◽  
pp. 39-56 ◽  
Author(s):  
ALEXANDER GOLTSEV ◽  
DONALD C. WUNSCH
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Experimental Study ◽  
Character Recognition ◽  
Input Data ◽  
Specific Class ◽  
Neural Assemblies ◽  
Column Structure ◽  
Number Of Classes

The purpose of the paper is an experimental study of the formation of class descriptions, taking place during learning, in assembly neural networks. The assembly neural network is artificially partitioned into several sub-networks according to the number of classes that the network has to recognize. The features extracted from input data are represented in neural column structures of the sub-networks. Hebbian neural assemblies are formed in the column structure of the sub-networks by weight adaptation. A specific class description is formed in each sub-network of the assembly neural network due to intersections between the neural assemblies. The process of formation of class descriptions in the sub-networks is interpreted as feature generalization. A set of special experiments is performed to study this process, on a task of character recognition using the MNIST database.

scholarly journals Data adjustment for the purposes of self-teaching of the neural network, and its application for the model-reduction of classification of patients suffering from the ischemic heart disease

2013 ◽  
Vol 61 (2) ◽  
pp. 377-384 ◽  
Author(s):  
Vladimír Konečný ◽  
Oldřich Trenz ◽  
Milan Sepši
Keyword(s):  
Neural Network ◽  
Visual Information ◽  
Input Data ◽  
Data Use ◽  
The Neural Network ◽  
Output Neurons ◽  
Practical Instrument ◽  
Network Type ◽  
Data Adjustment

Neural networks present a modern, very effective and practical instrument designated for decision-making support. To make use of them, we not only need to select the neural network type and structure, but also a corresponding data adjustment. One consequence of unsuitable data use can be an inexact or absolutely mistaken function of the model. The need for a certain adjustment of input data comes from the features of the chosen neural network type, from the use of various metrics systems of object attributes, but also from the weight, i.e., the importance of individual attributes, but also from establishing representatives of classifying sets and learning about their characteristics. For the purposes of the classification itself, we can suffice with a model in which the number of output neurons equals the number of classifying sets. Nonetheless, the model with a greater number of neurons assembled into a matrix can testify more about the problem, and provides clearer visual information.

scholarly journals Automatic detection and classification of cardiac arrhythmia using neural network

2018 ◽  
Vol 7 (3) ◽  
pp. 1482
Author(s):  
N N. S. V Rama Raju ◽  
V Malleswara Rao ◽  
I Srinivasa Rao
Keyword(s):  
Neural Network ◽  
Wavelet Transform ◽  
Normal Sinus Rhythm ◽  
Automatic Identification ◽  
Supraventricular Arrhythmia ◽  
Qrs Complex ◽  
Complex Wavelet Transform ◽  
Normal Sinus ◽  
Ecg Database

This paper proposes a Neural Network classifier model for the automatic identification of the ventricular and supraventricular arrhythmias cardiac arrhythmias. The wavelet transform (DWT) and dual tree complex wavelet transform (DTCWT) is applied for QRS complex detec-tion. After segmentation both feature of DWT and DTCWT is combined for feature extraction, statistical feature has been calculated to re-duce the overhead of classifier. An adaptive filtering with the soft computed wavelet thersholding to the signals before the extraction is done in pre-processing. Different ECG database is considered to evaluate the propose work with MIT-BIH database Normal Sinus Rhythm Da-tabase (NSRD) , and MIT-BIH Supraventricular Arrhythmia Database (svdb) .The evaluated outcomes of ECG classification claims 98 -99 % of accuracy under different training and testing situation.  

scholarly journals Classification of rare traffic signs

2020 ◽  
Vol 44 (2) ◽  
pp. 236-243 ◽  
Author(s):  
B.V. Faizov ◽  
V.I. Shakhuro ◽  
V.V. Sanzharov ◽  
A.S. Konushin
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Real Data ◽  
Significant Loss ◽  
Classification Problems ◽  
Training Set ◽  
Traffic Signs ◽  
Different Types ◽  
Classification Quality

The paper studies the possibility of using neural networks for the classification of objects that are few or absent at all in the training set. The task is illustrated by the example of classification of rare traffic signs. We consider neural networks trained using a contrastive loss function and its modifications, also we use different methods for generating synthetic samples for classification problems. As a basic method, the indexing of classes using neural network features is used. A comparison is made of classifiers trained with three different types of synthetic samples and their mixtures with real data. We propose a method of classification of rare traffic signs using a neural network discriminator of rare and frequent signs. The experimental evaluation shows that the proposed method allows rare traffic signs to be classified without significant loss of frequent sign classification quality.

RECOGNITION OF THE TEXT BY MEANS OF DEEP LEARNING

2020 ◽  
Vol 69 (1) ◽  
pp. 378-383
Author(s):  
T.A. Nurmukhanov ◽  
◽  
B.S. Daribayev ◽  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Neural Network Model ◽  
Input Data ◽  
Optimization Methods ◽  
Text Recognition ◽  
Activation Functions

Using neural networks, various variations of the classification of objects can be performed. Neural networks are used in many areas of recognition. A big area in this area is text recognition. The paper considers the optimal way to build a network for text recognition, the use of optimal methods for activation functions, and optimizers. Also, the article checked the correctness of text recognition with different optimization methods. This article is devoted to the analysis of convolutional neural networks. In the article, a convolutional neural network model will be trained with a teacher. Teaching with a teacher is a type of training for neural networks in which you provide the input data and the desired result, that is, the student looking at the input data will understand that you need to strive for the result that was provided to him.

scholarly journals A Convolutional Neural Network for Automatic Identification and Classification of Fall Army Worm Moth

2019 ◽  
Vol 10 (7) ◽  
Author(s):  
Francis Chulu ◽  
Jackson Phiri ◽  
Phillip O.Y. ◽  
Mayumbo Nyirenda ◽  
Monica M.Kabemba ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Automatic Identification ◽  
Army Worm

scholarly journals Convolutional Neural Network Demystified for a Comprehensive Learning with Industrial Application

2020 ◽  
Author(s):  
Anand Raju ◽  
Shanthi Thirunavukkarasu
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Industrial Applications ◽  
Input Image ◽  
Good Choice ◽  
Classification Problems ◽  
Entire Flow ◽  
Object Features

In the recent past of time, numerous investigators have driven on and subsidized novelties to image classification methods. In this chapter, an introduction to image classification scheme and their types is offered. Image classification discovers its application in a variety of fields, to name a few, judgment of diseases, finding and identification of faults, classification of nutrition goods based on superiority, valuation of usual capitals and conservation pollution, education of land use and land cover from remote sensing satellite images, character identification and detection in optical character reader, face recognition, object detection, and so on. Automatic image classification schemes found on actual algorithms deliver high accuracy and exactness in recognizing object/features. Convolution neural network is a superior genre of neural network that requires minimal preprocessing. The ability of the convolutional neural network (CNN) to understand the visual content of the input image makes its suitable for recognizing minute variation between the classes. This power of the CNN makes it a good choice to address image classification problems with multi-classes. So, in this chapter, the entire flow of CNN’s architecture with different industrial applications will be discussed.

scholarly journals Using deep learning neural network model to solve the problems of classification of unwanted content in social media

2021 ◽  
pp. 56-62
Author(s):  
А.С. Бобин
Keyword(s):  
Neural Network ◽  
Social Media ◽  
Deep Learning ◽  
Network Model ◽  
Neural Network Model ◽  
Classification Problems ◽  
Deep Learning Neural Network

При решении задач классификации с использование глубокого обучения сталкиваются с проблемой сходимости модели. Такая проблема возникает из за ограниченного объема данных в выборках. When solving classification problems using deep learning, they face the problem of model convergence. This problem occurs due to the limited amount of data in the samples.

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