Auto Recognition of Solar Radio Bursts Using the C-DCGAN Method

Solar radio bursts can be used to study the properties of solar activities and the underlying coronal conditions on the basis of the present understanding of their emission mechanisms. With the construction of observational instruments, around the world, a vast volume of solar radio observational data has been obtained. Manual classifications of these data require significant efforts and human labor in addition to necessary expertise in the field. Misclassifications are unavoidable due to subjective judgments of various types of radio bursts and strong radio interference in some events. It is therefore timely and demanding to develop techniques of auto-classification or recognition of solar radio bursts. The latest advances in deep learning technology provide an opportunity along this line of research. In this study, we develop a deep convolutional generative adversarial network model with conditional information (C-DCGAN) to auto-classify various types of solar radio bursts, using the solar radio spectral data from the Culgoora Observatory (1995, 2015) and the Learmonth Observatory (2001, 2019), in the metric decametric wavelengths. The technique generates pseudo images based on available data inputs, by modifying the layers of the generator and discriminator of the deep convolutional generative adversarial network. It is demonstrated that the C-DCGAN method can reach a high-level accuracy of auto-recognition of various types of solar radio bursts. And the issue caused by inadequate numbers of data samples and the consequent over-fitting issue has been partly resolved.

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Type II Solar Radio Bursts over Solar Cycle 21

International Astronomical Union Colloquium ◽

10.1017/s025292110002546x ◽

1994 ◽

Vol 144 ◽

pp. 283-284

Author(s):

G. Maris ◽

E. Tifrea

Keyword(s):

Solar Radio ◽

Interplanetary Space ◽

Impulsive Phase ◽

Radio Bursts ◽

Type Ii ◽

Solar Radio Bursts ◽

Strong Energy ◽

Mass Ejection ◽

Geophysical Effect ◽

Radio Event

The type II solar radio bursts produced by a shock wave passing through the solar corona are one of the most frequently studied solar activity phenomena. The scientific interest in this type of phenomenon is due to the fact that the presence of this radio event in a solar flare is an almost certain indicator of a future geophysical effect. The origin of the shock waves which produce these bursts is not at all simple; besides the shocks which are generated as a result of a strong energy release during the impulsive phase of a flare, there are also the shocks generated by a coronal mass ejection or the shocks which appear in the interplanetary space due to the supplementary acceleration of the solar particles.

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Role of General Adversarial Networks in Mammogram Analysis: A Review

Current Medical Imaging Formerly Current Medical Imaging Reviews ◽

10.2174/1573405614666191115102318 ◽

2020 ◽

Vol 16 (7) ◽

pp. 863-877

Author(s):

Annapoorani Gopal ◽

Lathaselvi Gandhimaruthian ◽

Javid Ali

Keyword(s):

Breast Tumor ◽

Deep Neural Networks ◽

Training Data ◽

Learning Technology ◽

Breast Cancers ◽

Generative Adversarial Network ◽

Adversarial Network ◽

Adversarial Networks ◽

Tumor Extraction

The Deep Neural Networks have gained prominence in the biomedical domain, becoming the most commonly used networks after machine learning technology. Mammograms can be used to detect breast cancers with high precision with the help of Convolutional Neural Network (CNN) which is deep learning technology. An exhaustive labeled data is required to train the CNN from scratch. This can be overcome by deploying Generative Adversarial Network (GAN) which comparatively needs lesser training data during a mammogram screening. In the proposed study, the application of GANs in estimating breast density, high-resolution mammogram synthesis for clustered microcalcification analysis, effective segmentation of breast tumor, analysis of the shape of breast tumor, extraction of features and augmentation of the image during mammogram classification have been extensively reviewed.

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