scholarly journals Multiwavelength Spectral Analysis and Neural Network Classification of Counterparts to 4FGL Unassociated Sources

2021 ◽  
Vol 923 (1) ◽  
pp. 75
Author(s):  
Stephen Kerby ◽  
Amanpreet Kaur ◽  
Abraham D. Falcone ◽  
Ryan Eskenasy ◽  
Fredric Hancock ◽  
...  
Keyword(s):  
Neural Network ◽  
Gamma Ray ◽  
Neural Network Classifier ◽  
Optical Counterpart ◽  
Spectral Parameters ◽  
X Ray ◽  
Spectral Fitting ◽  
Systematic Expansion ◽  
Primary Sample

Abstract The Fermi-LAT unassociated sources represent some of the most enigmatic gamma-ray sources in the sky. Observations with the Swift-XRT and -UVOT telescopes have identified hundreds of likely X-ray and UV/optical counterparts in the uncertainty ellipses of the unassociated sources. In this work we present spectral fitting results for 205 possible X-ray/UV/optical counterparts to 4FGL unassociated targets. Assuming that the unassociated sources contain mostly pulsars and blazars, we develop a neural network classifier approach that applies gamma-ray, X-ray, and UV/optical spectral parameters to yield a descriptive classification of unassociated spectra into pulsars and blazars. From our primary sample of 174 Fermi sources with a single X-ray/UV/optical counterpart, we present 132 P bzr > 0.99 likely blazars and 14 P bzr < 0.01 likely pulsars, with 28 remaining ambiguous. These subsets of the unassociated sources suggest a systematic expansion to catalogs of gamma-ray pulsars and blazars. Compared to previous classification approaches our neural network classifier achieves significantly higher validation accuracy and returns more bifurcated P bzr values, suggesting that multiwavelength analysis is a valuable tool for confident classification of Fermi unassociated sources.

Neural network classification of TeV gamma-ray images

1995 ◽  
Vol 3 (2) ◽  
pp. 137-150 ◽  
Author(s):  
P.T. Reynolds ◽  
D.J. Fegan
Keyword(s):  
Neural Network ◽  
Gamma Ray ◽  
Neural Network Classification

Classification of grazing-incidence small-angle X-ray scattering patterns by convolutional neural network

2020 ◽  
Vol 27 (4) ◽  
pp. 1069-1073
Author(s):  
Hiroyuki Ikemoto ◽  
Kazushi Yamamoto ◽  
Hideaki Touyama ◽  
Daisuke Yamashita ◽  
Masataka Nakamura ◽  
...  
Keyword(s):  
Neural Network ◽  
Convolutional Neural Network ◽  
Small Angle ◽  
Pair Correlation ◽  
Grazing Incidence ◽  
X Ray ◽  
X Ray Scattering ◽  
Nanoscale Structure ◽  
Ray Scattering

Grazing-incidence small-angle X-ray scattering (GISAXS) patterns have multiple superimposed contributions from the shape of the nanoscale structure, the coupling between the particles, the partial pair correlation, and the layer geometry. Therefore, it is not easy to identify the model manually from the huge amounts of combinations. The convolutional neural network (CNN), which is one of the artificial neural networks, can find regularities to classify patterns from large amounts of combinations. CNN was applied to classify GISAXS patterns, focusing on the shape of the nanoparticles. The network found regularities from the GISAXS patterns and showed a success rate of about 90% for the classification. This method can efficiently classify a large amount of experimental GISAXS patterns according to a set of model shapes and their combinations.

scholarly journals Lung Cancer Classification Employing Proposed Real Coded Genetic Algorithm Based Radial Basis Function Neural Network Classifier

2016 ◽  
Vol 2016 ◽  
pp. 1-15 ◽  
Author(s):  
I. Jasmine Selvakumari Jeya ◽  
S. N. Deepa
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Radial Basis Function ◽  
Basis Function ◽  
Minimum Mean Square Error ◽  
Gaussian Kernel ◽  
Neural Network Classifier ◽  
Real Coded Genetic Algorithm ◽  
Radial Basis

A proposed real coded genetic algorithm based radial basis function neural network classifier is employed to perform effective classification of healthy and cancer affected lung images. Real Coded Genetic Algorithm (RCGA) is proposed to overcome the Hamming Cliff problem encountered with the Binary Coded Genetic Algorithm (BCGA). Radial Basis Function Neural Network (RBFNN) classifier is chosen as a classifier model because of its Gaussian Kernel function and its effective learning process to avoid local and global minima problem and enable faster convergence. This paper specifically focused on tuning the weights and bias of RBFNN classifier employing the proposed RCGA. The operators used in RCGA enable the algorithm flow to compute weights and bias value so that minimum Mean Square Error (MSE) is obtained. With both the lung healthy and cancer images from Lung Image Database Consortium (LIDC) database and Real time database, it is noted that the proposed RCGA based RBFNN classifier has performed effective classification of the healthy lung tissues and that of the cancer affected lung nodules. The classification accuracy computed using the proposed approach is noted to be higher in comparison with that of the classifiers proposed earlier in the literatures.

scholarly journals Classification of coronary artery disease data sets by using a deep neural network

2017 ◽  
Vol 1 (4) ◽  
pp. 271-277 ◽  
Author(s):  
Abdullah Caliskan ◽  
Mehmet Emin Yuksel
Keyword(s):  
Neural Network ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Deep Neural Network ◽  
Cost Effective ◽  
Classification Performance ◽  
Data Sets ◽  
Neural Network Classifier ◽  
Artery Disease

Abstract In this study, a deep neural network classifier is proposed for the classification of coronary artery disease medical data sets. The proposed classifier is tested on reference CAD data sets from the literature and also compared with popular representative classification methods regarding its classification performance. Experimental results show that the deep neural network classifier offers much better accuracy, sensitivity and specificity rates when compared with other methods. The proposed method presents itself as an easily accessible and cost-effective alternative to currently existing methods used for the diagnosis of CAD and it can be applied for easily checking whether a given subject under examination has at least one occluded coronary artery or not.

scholarly journals Classification of Pneumonia images on mobile devices with Quantized Neural Network

2020 ◽  
Vol 9 (10) ◽  
pp. e889108382
Author(s):  
Jose Vigno Moura Sousa ◽  
Vilson Rosa de Almeida ◽  
Aratã Andrade Saraiva ◽  
Domingos Bruno Sousa Santos ◽  
Pedro Mateus Cunha Pimentel ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
User Interface ◽  
Computational Cost ◽  
Mobile App ◽  
X Ray ◽  
Chest X Ray ◽  
Intuitive User Interface ◽  
Processing Requirement

This paper presents an approach for the classification of child chest X-ray images into two classes: pneumonia and normal. We employ Convolutional Neural Networks, from pre-trained networks together with a quantization process, using the platform TensorFlow Lite method. This reduces the processing requirement and computational cost. Results have shown accuracy up to 95.4% and 94.2% for MobileNetV1 and MobileNetV2, respectively. The resulting mobile app also presents a simple and intuitive user interface.

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