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.

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 A neural network framework for binary classification of radar detections

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Jabran Akhtar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
False Alarm ◽  
False Alarm Rate ◽  
High Probability ◽  
Binary Classification ◽  
Guard Cells ◽  
Probability Of Detection ◽  
Constant False Alarm Rate

AbstractA desired objective in radar target detection is to satisfy two very contradictory requirements: offer a high probability of detection with a low false alarm rate. In this paper, we propose the utilization of artificial neural networks for binary classification of targets detected by a depreciated detection process. It is shown that trained neural networks are capable of identifying false detections with considerable accuracy and can to this extent utilize information present in guard cells and Doppler profiles. This allows for a reduction in the false alarm rate with only moderate loss in the probability of detection. With an appropriately designed neural network, an overall improved system performance can be achieved when compared against traditional constant false alarm rate detectors for the specific trained scenarios.

Explainable Deep Learning Reproduces a ‘Professional Eye’ on the Diagnosis of Internal Disorders in Persimmon Fruit

2020 ◽  
Vol 61 (11) ◽  
pp. 1967-1973
Author(s):  
Takashi Akagi ◽  
Masanori Onishi ◽  
Kanae Masuda ◽  
Ryohei Kuroki ◽  
Kohei Baba ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Neural Network ◽  
Deep Neural Networks ◽  
Binary Classification ◽  
Network Models ◽  
Plant Biology ◽  
Neural Network Models ◽  
Persimmon Fruit

Abstract Recent rapid progress in deep neural network techniques has allowed recognition and classification of various objects, often exceeding the performance of the human eye. In plant biology and crop sciences, some deep neural network frameworks have been applied mainly for effective and rapid phenotyping. In this study, beyond simple optimizations of phenotyping, we propose an application of deep neural networks to make an image-based internal disorder diagnosis that is hard even for experts, and to visualize the reasons behind each diagnosis to provide biological interpretations. Here, we exemplified classification of calyx-end cracking in persimmon fruit by using five convolutional neural network models with various layer structures and examined potential analytical options involved in the diagnostic qualities. With 3,173 visible RGB images from the fruit apex side, the neural networks successfully made the binary classification of each degree of disorder, with up to 90% accuracy. Furthermore, feature visualizations, such as Grad-CAM and LRP, visualize the regions of the image that contribute to the diagnosis. They suggest that specific patterns of color unevenness, such as in the fruit peripheral area, can be indexes of calyx-end cracking. These results not only provided novel insights into indexes of fruit internal disorders but also proposed the potential applicability of deep neural networks in plant biology.

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%.

Detection of adverse events leading to inadvertent injury during laparoscopic cholecystectomy using convolutional neural networks

2021 ◽  
Vol 0 (0) ◽  
Author(s):  
Patrick Beyersdorffer ◽  
Wolfgang Kunert ◽  
Kai Jansen ◽  
Johanna Miller ◽  
Peter Wilhelm ◽  
...  
Keyword(s):  
Neural Networks ◽  
Adverse Events ◽  
Convolutional Neural Networks ◽  
Surgical Training ◽  
Binary Classification ◽  
Image Data ◽  
Training Image ◽  
Training Environment ◽  
Data Set

Abstract Uncontrolled movements of laparoscopic instruments can lead to inadvertent injury of adjacent structures. The risk becomes evident when the dissecting instrument is located outside the field of view of the laparoscopic camera. Technical solutions to ensure patient safety are appreciated. The present work evaluated the feasibility of an automated binary classification of laparoscopic image data using Convolutional Neural Networks (CNN) to determine whether the dissecting instrument is located within the laparoscopic image section. A unique record of images was generated from six laparoscopic cholecystectomies in a surgical training environment to configure and train the CNN. By using a temporary version of the neural network, the annotation of the training image files could be automated and accelerated. A combination of oversampling and selective data augmentation was used to enlarge the fully labeled image data set and prevent loss of accuracy due to imbalanced class volumes. Subsequently the same approach was applied to the comprehensive, fully annotated Cholec80 database. The described process led to the generation of extensive and balanced training image data sets. The performance of the CNN-based binary classifiers was evaluated on separate test records from both databases. On our recorded data, an accuracy of 0.88 with regard to the safety-relevant classification was achieved. The subsequent evaluation on the Cholec80 data set yielded an accuracy of 0.84. The presented results demonstrate the feasibility of a binary classification of laparoscopic image data for the detection of adverse events in a surgical training environment using a specifically configured CNN architecture.

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 10.5937/industrija42-5686 = The implementation of the neural networks to the problem of economic classification of countries

Industrija ◽  
2014 ◽  
Vol 42 (4) ◽  
pp. 25-42
Author(s):  
Sasa Obradovic ◽  
Miljan Lekovic ◽  
Milos Marinkovic
Keyword(s):  
Neural Networks ◽  
The Neural Networks
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