scholarly journals Blending Colored and Depth CNN Pipelines in an Ensemble Learning Classification Approach for Warehouse Application Using Synthetic and Real Data

Machines ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 28
Author(s):  
Paulo Henrique Martinez Piratelo ◽  
Rodrigo Negri de Azeredo ◽  
Eduardo Massashi Yamao ◽  
Jose Francisco Bianchi Filho ◽  
Gabriel Maidl ◽  
...  
Keyword(s):  
Neural Networks ◽  
Real Data ◽  
Engineering Problem ◽  
Fine Tuning ◽  
Accuracy Measurement ◽  
Ensemble Approach ◽  
Depth Images ◽  
The Neural Networks ◽  
Improved Accuracy

Electric companies face flow control and inventory obstacles such as reliability, outlays, and time-consuming tasks. Convolutional Neural Networks (CNNs) combined with computational vision approaches can process image classification in warehouse management applications to tackle this problem. This study uses synthetic and real images applied to CNNs to deal with classification of inventory items. The results are compared to seek the neural networks that better suit this application. The methodology consists of fine-tuning several CNNs on Red–Green–Blue (RBG) and Red–Green–Blue-Depth (RGB-D) synthetic and real datasets, using the best architecture of each domain in a blended ensemble approach. The proposed blended ensemble approach was not yet explored in such an application, using RGB and RGB-D data, from synthetic and real domains. The use of a synthetic dataset improved accuracy, precision, recall and f1-score in comparison with models trained only on the real domain. Moreover, the use of a blend of DenseNet and Resnet pipelines for colored and depth images proved to outperform accuracy, precision and f1-score performance indicators over single CNNs, achieving an accuracy measurement of 95.23%. The classification task is a real logistics engineering problem handled by computer vision and artificial intelligence, making full use of RGB and RGB-D images of synthetic and real domains, applied in an approach of blended CNN pipelines.

Automate Labeling Of Bugs and Tickets Using Attention-Based Mechanism in Recurrent Neral Networks

2021 ◽  
Vol 9 (11) ◽  
pp. 1411-1418
Author(s):  
Ravi Kauthale
Keyword(s):  
Neural Networks ◽  
Text Classification ◽  
Recurrent Neural Networks ◽  
Support Systems ◽  
The Neural Networks

Abstract: The aim here is to explore the methods to automate the labelling of the information that is present in bug trackers and client support systems. This is majorly based on the classification of the content depending on some criteria e.g., priority or product area. Labelling of the tickets is important as it helps in effective and efficient handling of the ticket and help is quicker and comprehensive resolution of the tickets. The main goal of the project is to analyze the existing methodologies used for automated labelling and then use a newer approach and compare the results. The existing methodologies are the ones which are based of the neural networks and without neural networks. In this project, a newer approach based on the recurrent neural networks which are based on the hierarchical attention paradigm will be used. Keywords: Automate Labeling, Recurrent Neural Networks, Hierarchical Attention, Multi-class Text Classification, GRU

scholarly journals Performance of Fine-Tuning Convolutional Neural Networks for HEp-2 Image Classification

2020 ◽  
Vol 10 (19) ◽  
pp. 6940 ◽  
Author(s):  
Vincenzo Taormina ◽  
Donato Cascio ◽  
Leonardo Abbene ◽  
Giuseppe Raso
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Fluorescence Intensity ◽  
Characteristic Curve ◽  
Performance Comparison ◽  
Fine Tuning ◽  
Tuning Method ◽  
Effective Performance ◽  
Training Modalities

The search for anti-nucleus antibodies (ANA) represents a fundamental step in the diagnosis of autoimmune diseases. The test considered the gold standard for ANA research is indirect immunofluorescence (IIF). The best substrate for ANA detection is provided by Human Epithelial type 2 (HEp-2) cells. The first phase of HEp-2 type image analysis involves the classification of fluorescence intensity in the positive/negative classes. However, the analysis of IIF images is difficult to perform and particularly dependent on the experience of the immunologist. For this reason, the interest of the scientific community in finding relevant technological solutions to the problem has been high. Deep learning, and in particular the Convolutional Neural Networks (CNNs), have demonstrated their effectiveness in the classification of biomedical images. In this work the efficacy of the CNN fine-tuning method applied to the problem of classification of fluorescence intensity in HEp-2 images was investigated. For this purpose, four of the best known pre-trained networks were analyzed (AlexNet, SqueezeNet, ResNet18, GoogLeNet). The classifying power of CNN was investigated with different training modalities; three levels of freezing weights and scratch. Performance analysis was conducted, in terms of area under the ROC (Receiver Operating Characteristic) curve (AUC) and accuracy, using a public database. The best result achieved an AUC equal to 98.6% and an accuracy of 93.9%, demonstrating an excellent ability to discriminate between the positive/negative fluorescence classes. For an effective performance comparison, the fine-tuning mode was compared to those in which CNNs are used as feature extractors, and the best configuration found was compared with other state-of-the-art works.

scholarly journals Realtime Localization and Estimation of Loads on Aircraft Wings from Depth Images

Sensors ◽  
2020 ◽  
Vol 20 (12) ◽  
pp. 3405
Author(s):  
Diyar Khalis Bilal ◽  
Mustafa Unel ◽  
Mehmet Yildiz ◽  
Bahattin Koc
Keyword(s):  
Neural Networks ◽  
Classification Problem ◽  
Regression Problem ◽  
Health Monitoring System ◽  
Aircraft Wings ◽  
Depth Images ◽  
Aerial Vehicle ◽  
The Neural Networks ◽  
Critical Components ◽  
Castigliano’S Theorem

This paper deals with the development of a realtime structural health monitoring system for airframe structures to localize and estimate the magnitude of the loads causing deflections to the critical components, such as wings. To this end, a framework that is based on artificial neural networks is developed where features that are extracted from a depth camera are utilized. The localization of the load is treated as a multinomial logistic classification problem and the load magnitude estimation as a logistic regression problem. The neural networks trained for classification and regression are preceded with an autoencoder, through which maximum informative data at a much smaller scale are extracted from the depth features. The effectiveness of the proposed method is validated by an experimental study performed on a composite unmanned aerial vehicle (UAV) wing subject to concentrated and distributed loads, and the results obtained by the proposed method are superior when compared with a method based on Castigliano’s theorem.

scholarly journals Modified Deep Neural Networks for Dog Breeds Identification

2018 ◽  
Author(s):  
Aydin Ayanzadeh ◽  
Sahand Vahidnia
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
State Of The Art ◽  
The State ◽  
Fine Tuning ◽  
Test Accuracy ◽  
Data Sets ◽  
Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

Neural networks application for phasechronometric measurement information processing

2020 ◽  
pp. 31-35
Author(s):  
D. D. Boldasov ◽  
J. V. Drozdova ◽  
A. S. Komshin ◽  
A. B. Syritskii
Keyword(s):  
Neural Networks ◽  
Information Processing ◽  
Binary Classification ◽  
Processing Technique ◽  
Measurement Information ◽  
Perceptron Algorithm ◽  
Classification Feature ◽  
Operation Modes ◽  
The Neural Networks

This article describes the processing technique of measuring phasechronometric information based on the neural networks use. The novelty of the proposed approach lies in the choice of a classification feature and the perceptron algorithm use as an algorithm for binary classification performing. In this article, to assess the concept operability, the simplest binary classification of the lathe operation modes is made: idle or cutting.

scholarly journals Neural Networks and the Classification of Active Galactic Nucleus Spectra

1996 ◽  
Vol 13 (3) ◽  
pp. 207-211 ◽  
Author(s):  
Daya M. Rawson ◽  
Jeremy Bailey ◽  
Paul J. Francis
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Active Galactic Nucleus ◽  
Real Data ◽  
Neural Nets ◽  
Fourier Domain ◽  
Sample Sizes ◽  
Signal To Noise ◽  
Artificial Neural

AbstractThe use of artificial neural networks (ANNs) as a classifier of digital spectra is investigated. Using both simulated and real data, it is shown that neural networks can be trained to discriminate between the spectra of different classes of active galactic nucleus (AGN) with realistic sample sizes and signal-to-noise ratios. By working in the Fourier domain, neural nets can classify objects without knowledge of their redshifts.

Lettuce Images Features Extraction and Intelligent Classification of Growth Period

2013 ◽  
Vol 846-847 ◽  
pp. 1351-1354
Author(s):  
Yan Wang ◽  
Jun Sun ◽  
Hong Li Wang ◽  
Ze Gao Dai ◽  
Wen Xia Lv
Keyword(s):  
Neural Networks ◽  
Cluster Analysis ◽  
Recognition Accuracy ◽  
Growth Period ◽  
Modeling And Analysis ◽  
Good Image ◽  
Visible Images ◽  
The Neural Networks ◽  
Lettuce Growth

Classification of lettuce growth peroid is the premise of records of lettuce growth information. In this study, lettuce images in every growth period are collected. And visible images are preprocessed to extract features to establish initial feature library of lettuce images. Through R cluster analysis on many features, good image eigenvector are obtained. Classification of the lettuce samples are obtained by modeling and analysis of the neural networks. The experimental classification results compared with practical classification results, the recognition accuracy is up to 88.4%.

scholarly journals Deep Convolutional Neural Networks Capabilities for Binary Classification of Polar Mesocyclones in Satellite Mosaics

2018 ◽  
Author(s):  
Mikhail Krinitskiy ◽  
Polina Verezemskaya ◽  
Kirill Grashchenkov ◽  
Natalia Tilinina ◽  
Sergey Gulev ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
Satellite Imagery ◽  
Binary Classification ◽  
Fine Tuning ◽  
Reference Database ◽  
Accuracy Score ◽  
Deep Convolutional Neural Networks ◽  
Deep Ocean Water

Polar mesocyclones (MCs) are small marine atmospheric vortices. The class of intense MCs, called polar lows, are accompanied by extremely strong surface winds and heat fluxes and thus largely influencing deep ocean water formation in the polar regions. Accurate detection of polar mesocyclones in high-resolution satellite data, while challenging, is a time-consuming task, when performed manually. Existing algorithms for the automatic detection of polar mesocyclones are based on the conventional analysis of patterns of cloudiness and involve different empirically defined thresholds of geophysical variables. As a result, various detection methods typically reveal very different results when applied to a single dataset. We develop a conceptually novel approach for the detection of MCs based on the use of deep convolutional neural networks (DCNNs). As a first step, we demonstrate that DCNN model is capable of performing binary classification of 500x500km patches of satellite images regarding MC patterns presence in it. The training dataset is based on the reference database of MCs manually tracked in the Southern Hemisphere from satellite mosaics. We use a subset of this database with MC diameters falling in the range of 200-400 km. This dataset is further used for testing several different DCNN setups, specifically, DCNN built “from scratch”, DCNN based on VGG16 pre-trained weights also engaging the Transfer Learning technique, and DCNN based on VGG16 with Fine Tuning technique. Each of these networks is further applied to both infrared (IR) and a combination of infrared and water vapor (IR+WV) satellite imagery. The best skills (97% in terms of the binary classification accuracy score) is achieved with the model that averages the estimates of the ensemble of different DCNNs. The algorithm can be further extended to the automatic identification and tracking numerical scheme and applied to other atmospheric phenomena characterized by a distinct signature in satellite imagery.

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.

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