Related papers: Interpreting automatic AGN classifiers with salien…

One of the significant challenges of deep neural networks is that the complex nature of the network prevents human comprehension of the outcome of the network. Consequently, the applicability of complex machine learning models is limited in…

Interpretability is a critical factor in applying complex deep learning models to advance the understanding of brain disorders in neuroimaging studies. To interpret the decision process of a trained classifier, existing techniques typically…

A fundamental bottleneck in utilising complex machine learning systems for critical applications has been not knowing why they do and what they do, thus preventing the development of any crucial safety protocols. To date, no method exist…

Context: Finding Active Galactic Nuclei (AGN) behind the Magellanic Clouds (MCs) is difficult because of the high stellar density in these fields. Although the first AGN behind the Small Magellanic Cloud (SMC) were reported in the 1980s, it…

Generative Adversarial Networks (GANs) are Machine Learning (ML) algorithms that have the ability to address competitive resource allocation problems together with detection and mitigation of anomalous behavior. In this paper, we…

We introduce the Mass-Excitation (MEx) diagnostic to identify active galactic nuclei (AGN) in galaxies at intermediate redshift. In the absence of near-infrared spectroscopy, necessary to use traditional nebular line diagrams at z>0.4, we…

In order to find a fast and reliable method for selecting metal poor galaxies (MPGs), especially in large surveys and huge database, an Artificial Neural Network (ANN) method is applied to a sample of star-forming galaxies from the Sloan…

In drug discovery, aqueous solubility is an important pharmacokinetic property which affects absorption and assay availability of drug. Thus, in silico prediction of solubility has been studied for its utility in virtual screening and lead…

Context. Active galactic nuclei (AGNs) and star forming galaxies (SFGs) are the primary sources of extragalactic radio sky. But it is difficult to distinguish the radio emission produced by AGNs from that by SFGs, especially when the radio…

Active Galactic Nuclei (AGN) unification, which implies a large number of obscured AGN, can explain the optical, infrared and X-ray content of deep multiwavelength surveys. Here, we show that the same model also successfully explains the…

This study evaluates the efficacy of two machine learning (ML) techniques, namely artificial neural networks (ANN) and gene expression programming (GEP) that use data-driven modeling to predict wall pressure spectra (WPS) underneath…

Low luminosity active galactic nuclei (LLAGN) probe accretion physics in the low Eddington regime and can provide additional clues about galaxy evolution. AGN variability is ubiquitous and thus provides a reliable tool for finding AGN. We…

Improving the interpretability of geospatial artificial intelligence (GeoAI) models has become critically important to open the "black box" of complex AI models, such as deep learning. This paper compares popular saliency map generation…

In a series of papers based on the FIRST and Sloan Digital Sky Surveys (SDSS), we investigate the local population of star-forming galaxies and Active Galactic Nuclei (AGN) in order to clarify the link between these two types of activity,…

Deep neural networks, especially convolutional deep neural networks, are state-of-the-art methods to classify, segment or even generate images, movies, or sounds. However, these methods lack of a good semantic understanding of what happens…

We investigate the physical cause of the great range in the ionization level seen in the spectra of narrow lined active galactic nuclei (AGN). Mean field independent component analysis identifies examples of individual SDSS galaxies whose…

We describe an explainable AI saliency map method for use with deep convolutional neural networks (CNN) that is much more efficient than popular fine-resolution gradient methods. It is also quantitatively similar or better in accuracy. Our…

We present a new method of predicting the ages of galaxies using a machine learning (ML) algorithm with the goal of providing an alternative to traditional methods. We aim to match the ability of traditional models to predict the ages of…

We propose a novel attention gate (AG) model for medical image analysis that automatically learns to focus on target structures of varying shapes and sizes. Models trained with AGs implicitly learn to suppress irrelevant regions in an input…

We describe an Artificial Neural Network (ANN) approach to classification of galaxy images and spectra. ANNs can replicate the classification of galaxy images by a human expert to the same degree of agreement as that between two human…