Related papers: Artificial Neural Network based gamma-hadron segre…

The ground-based technique for imaging atmospheric Cherenkov telescopes became a rapidly developing and powerful branch of science. Thanks to this technique, over 250 very high-energy gamma-ray sources of galactic and extragalactic origin…

The Crab nebula has proved to be the nearest to a standard candle in VHE $\gamma-$ ray astronomy. Results on the gamma ray emission from the nebula at various energies have come in the last decade mostly from imaging telescopes. The aim of…

The neutrons emitted in heavy-ion fusion-evaporation (HIFE) reactions together with the gamma-rays cause unwanted backgrounds in gamma-ray spectra. Especially in the nuclear reactions, where relativistic ion beams (RIBs) are used, these…

We present TeV gamma-ray observations of the Crab Nebula, the standard reference source in ground-based gamma-ray astronomy, using data from the High Altitude Water Cherenkov (HAWC) Gamma-Ray Observatory. In this analysis we use two…

In this paper, we propose a spectral-spatial feature extraction and classification framework based on artificial neuron network (ANN) in the context of hyperspectral imagery. With limited labeled samples, only spectral information is…

We present a novel method for cell segmentation in microscopy images which is inspired by the Generative Adversarial Neural Network (GAN) approach. Our framework is built on a pair of two competitive artificial neural networks, with a…

We present a machine learning model to classify Active Galactic Nuclei (AGN) and galaxies (AGN-galaxy classifier) and a model to identify type 1 (optically unabsorbed) and type 2 (optically absorbed) AGN (type 1/2 classifier). We test…

With the advancement of technology, machine learning-based analytical methods have pervaded nearly every discipline in modern studies. Particularly, a number of methods have been employed to estimate the redshift of gamma-ray loud active…

As the convolutional neural network (CNN) gets deeper and wider in recent years, the requirements for the amount of data and hardware resources have gradually increased. Meanwhile, CNN also reveals salient redundancy in several tasks. The…

In this article we discuss the possibility of using the observations by GLAST of standard gamma sources, as the Crab Nebula, to calibrate Imaging Air Cherenkov detectors, MAGIC in particular, and optimise their energy resolution. We show…

We propose improvements to the Artificial Neural Network (ANN) method of determining electron scattering cross-sections from swarm data proposed by coauthors. A limitation inherent to this problem, known as the inverse swarm problem, is the…

Imaging atmospheric Cherenkov telescopes record an enormous number of cosmic-ray background events. Suppressing these background events while retaining $\gamma$-rays is key to achieving good sensitivity to faint $\gamma$-ray sources. The…

In this work, we present a new, high performance algorithm for background rejection in imaging atmospheric Cherenkov telescopes. We build on the already popular machine-learning techniques used in gamma-ray astronomy by the application of…

Ground based gamma-ray observations with Imaging Atmospheric Cherenkov Telescopes (IACTs) play a significant role in the discovery of very high energy (E > 100 GeV) gamma-ray emitters. The analysis of IACT data demands a highly efficient…

The Tunka Advanced Instrument for Gamma- and cosmic-ray Astronomy (TAIGA) is a multicomponent experiment for the measurement of TeV to PeV gamma- and cosmic rays. Our goal is to establish a novel hybrid direct air shower technique,…

The TACTIC $\gamma$-ray telescope, equipped with a light collector of area $\sim$9.5m$^2$ and a medium resolution imaging camera of 349-pixels, has been in operation at Mt.Abu, India since 2001. This paper describes the main features of its…

This paper presents a methodology for image classification using Graph Neural Network (GNN) models. We transform the input images into region adjacency graphs (RAGs), in which regions are superpixels and edges connect neighboring…

We apply and compare various Artificial Neural Network (ANN) and other algorithms for automatic morphological classification of galaxies. The ANNs are presented here mathematically, as non-linear extensions of conventional statistical…

In order to improve the detection and classification of malignant melanoma, this paper describes an image-based method that can achieve AUROC values of up to 0.78 without additional clinical information. Furthermore, the importance of the…

In this paper we present an efficient computer aided mass classification method in digitized mammograms using Artificial Neural Network (ANN), which performs benign-malignant classification on region of interest (ROI) that contains mass.…