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Searching for extraterrestrial, transient signals in astronomical data sets is an active area of current research. However, machine learning techniques are lacking in the literature concerning single-pulse detection. This paper presents a…

In the context of observations of the rest-frame ultraviolet and optical emission from distant galaxies, we explore the emission-line properties of photoionization models of active and inactive galaxies. Our aim is to identify new…

One of the major challenges in astronomy involves accurately classifying galaxies, particularly distinguishing between different galaxy types. While many complex algorithms have shown strong performance in classification tasks, their…

Active learning is a label-efficient approach to train highly effective models while interactively selecting only small subsets of unlabelled data for labelling and training. In "open world" settings, the classes of interest can make up a…

While emission-line flux ratio diagnostics are the most common technique for identifying active galactic nuclei (AGNs) in optical spectra, applying this approach to single fiber spectra of galaxies can omit entire subpopulations of AGNs.…

Human Activity Recognition (HAR) describes the machines ability to recognize human actions. Nowadays, most people on earth are health conscious, so people are more interested in tracking their daily activities using Smartphones or Smart…

We present the results of a 5-8 micron spectral analysis performed on the largest sample of local ultraluminous infrared galaxies (ULIRGs) selected so far, consisting of 164 objects up to a redshift of ~0.35. The unprecedented sensitivity…

Context.- This paper is part of a series involving the AMIGA project (Analysis of the Interstellar Medium of Isolated GAlaxies), which identifies and studies a statistically-significant sample of the most isolated galaxies in the northern…

This article introduces DT4ECG, an innovative dual-task learning framework for Electrocardiogram (ECG)-based human identity recognition and activity detection. The framework employs a robust one-dimensional convolutional neural network…

We use the sample of emission-line nuclei derived from a recently completed optical spectroscopic survey of nearby galaxies to quantify the incidence of local (z = 0) nuclear activity. Consistent with previous studies, we find detectable…

The aim of this work is to propose a meta-algorithm for automatic classification in the presence of discrete binary classes. Classifier learning in the presence of overlapping class distributions is a challenging problem in machine…

(Abridged) We develop a novel technique to identify active galactic nuclei (AGNs) and study the nature of low-luminosity AGNs in the Sloan Digital Sky Survey. This is the first part of a series of papers and we develop a new, sensitive…

Differentiating between active galactic nuclei (AGN) activity and star formation in z ~ 2 galaxies is difficult because traditional methods, such as line ratio diagnostics, change with redshift while multi-wavelength methods (X-ray, radio,…

[Abridged] We present an analysis of optical spectroscopically-identified AGN to M*+1 in a sample of 6 self-similar SDSS galaxy clusters at z=0.07. These clusters are specifically selected to lack significant substructure at bright limits…

Accurate active galactic nucleus (AGN) identifications in large galaxy samples are crucial to assess the role of AGN and AGN feedback in the coevolution of galaxies and their central supermassive black holes. Emission line flux ratio…

We present a novel technique for ranking the relative importance of galaxy properties in the process of quenching star formation. Specifically, we develop an artificial neural network (ANN) approach for pattern recognition and apply it to a…

Identification of different neuronal cell types is critical for understanding their contribution to brain functions. Yet, automated and reliable classification of neurons remains a challenge, primarily because of their biological…

Active learning aims to reduce the labeling effort that is required to train algorithms by learning an acquisition function selecting the most relevant data for which a label should be requested from a large unlabeled data pool. Active…

Activity recognition has become a popular research branch in the field of pervasive computing in recent years. A large number of experiments can be obtained that activity sensor-based data's characteristic in activity recognition is…

Deep Neural Networks (or DNNs) must constantly cope with distribution changes in the input data when the task of interest or the data collection protocol changes. Retraining a network from scratch to combat this issue poses a significant…