Abstract Gamma-ray bursts (GRBs) can be probes of the early Universe, but currently, only 26% GRBs observed by Neil Gehrels Swift Observatory have known redshifts ( z ) due to observational limitations. To address this, we estimated GRB redshift (distance) via a supervised statistical learning model that uses optical afterglow and ground-based telescopes. The inferred are strongly correlated (a Pearson coefficient 0.93) with redshifts, thus proving reliability this method. allow us estimate...

Abstract Gamma-ray bursts (GRBs), due to their high luminosities, are detected up a redshift of 10, and thus have the potential be vital cosmological probes early processes in Universe. Fulfilling this requires large sample GRBs with known redshifts, but observational limitations, only 11% redshifts ( z ). There been numerous attempts estimate via correlation studies, most which led inaccurate predictions. To overcome this, we estimated GRB an ensemble-supervised machine-learning (ML) model...

The current knowledge in cosmology deals with open problems whose solutions are still under investigation. main issue is the so-called Hubble constant ($H_0$) tension, namely, $4-6 \sigma$ discrepancy between local value of $H_0$ obtained Cepheids+Supernovae Ia (SNe Ia) and cosmological one estimated from observations Cosmic Microwave Background (CMB). For investigation this problem, probes that span all over redshift $z$ ranges needed. Cepheids objects, SNe reached up to $z=2.9$, CMB...

Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, must have good coverage light curves (LCs). However, gaps in LC hinder precise determination properties are often unavoidable. Therefore, extensive categorization LCs remains a hurdle. We address using 'stochastic...

Measuring the redshift of active galactic nuclei (AGNs) requires use time-consuming and expensive spectroscopic analysis. However, obtaining measurements AGNs is crucial as it can enable AGN population studies, provide insight into star formation rate, luminosity function, density rate evolution. Hence, there a requirement for alternative measurement techniques. In this project, we aim to Fermi gamma-ray space telescope's 4LAC Data Release (DR2) catalog train machine learning model capable...

Abstract Active galactic nuclei (AGNs) are very powerful galaxies characterized by extremely bright emissions coming from their central massive black holes. Knowing the redshifts of AGNs provides us with an opportunity to determine distance investigate important astrophysical problems, such as evolution early stars and formation, along structure galaxies. The redshift determination is challenging because it requires detailed follow-up multiwavelength observations, often involving various...

ABSTRACT The division of gamma-ray bursts (GRBs) into different classes, other than the ‘short’ and ‘long’, has been an active field research. We investigate whether GRBs can be classified based on a broader set parameters, including prompt plateau emission ones. Observational evidence suggests existence more GRB subclasses, but results so far are either conflicting or not statistically significant. novelty here is producing machine-learning-based classification using their observed X-rays...

Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use machine learning (ML) this purpose has been growing over the last few years, primarily due to availability large-scale surveys. However, observational errors, significant fraction these data sets often have missing entries,...

<p>The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence next change making popularly known as Industry 4.0. Indeed, experts predict that will be main force behind following significant virtual shift way we stay, converse, study, live, communicate, conduct business (AI). All facets our social connection are being transformed by this growing...

The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. industry of the future generation is evolving, and artificial intelligence following change making popularly known as Industry 4.0. Indeed, experts predict that intelligence(AI) will be main force behind significant virtual shift way we stay, converse, study, live, communicate conduct business. All facets our social connection are being transformed by this growing technology....

Gamma-Ray Bursts (GRBs), due to their high luminosities are detected up redshift 10, and thus have the potential be vital cosmological probes of early processes in universe. Fulfilling this requires a large sample GRBs with known redshifts, but observational limitations, only 11\% redshifts ($z$). There been numerous attempts estimate via correlation studies, most which led inaccurate predictions. To overcome this, we estimated GRB an ensemble supervised machine learning model that uses...

Gamma-ray bursts (GRBs) can be probes of the early universe, but currently, only 26% GRBs observed by Neil Gehrels Swift Observatory have known redshifts ($z$) due to observational limitations. To address this, we estimated GRB redshift (distance) via a supervised machine learning model that uses optical afterglow and ground-based telescopes. The inferred are strongly correlated (a Pearson coefficient 0.93) with redshifts, thus proving reliability this method. allow us estimate number...

We address a task of local trajectory planning for the mobile robot in presence static and dynamic obstacles. Local is obtained as numerical solution Model Predictive Control (MPC) problem. Collision avoidance may be provided by adding repulsive potential obstacles to cost function MPC. develop an approach, where estimated neural model. propose explore three possible strategies handling First, environment with considered sequence environments. Second, model predict at once. Third, future...

Context. Gamma-ray bursts (GRBs), observed at redshifts as high 9.4, could serve valuable probes for investigating the distant Universe. However, this necessitates an increase in number of GRBs with determined redshifts, currently, only 12% have known due to observational biases. Aims. We aim address shortage measured enabling us fully realize their potential cosmological Methods. Following Dainotti et al. (2024c), we taken a second step overcome issue by adding 30 more our ensemble...

<p>The world around us has undergone a radical transformation due to rapid technological advancement in recent decades. The industry of the future generation is evolving, and artificial intelligence next change making popularly known as Industry 4.0. Indeed, experts predict that will be main force behind following significant virtual shift way we stay, converse, study, live, communicate, conduct business (AI). All facets our social connection are being transformed by this growing...

<p>The widespread usage of cars and other large, heavy vehicles necessitates the development an effective parking infrastructure. Additionally, algorithms for detection recognition number plates are often used to identify automobiles all around world where standardized plate sizes fonts enforced, making a effortless task. As result, both kinds data can be combined develop intelligent system centered on ANPR technology. Extraction license characters from photo is primary objective ANPR....

Gamma-Ray Bursts (GRBs), being observed at high redshift (z = 9.4), vital to cosmological studies and investigating Population III stars. To tackle these studies, we need correlations among relevant GRB variables with the requirement of small uncertainties on their variables. Thus, must have good coverage light curves (LCs). However, gaps in LC hinder precise determination properties are often unavoidable. Therefore, extensive categorization LCs remains a hurdle. We address using 'stochastic...

ABSTRACT Classifying active galactic nuclei (AGNs) is a challenge, especially for BL Lacertae objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from fourth Fermi Catalog, Data Release 3. Missing hinder machine learning to classify AGNs. A previous paper found that Multivariate Imputation Chain Equations (MICE) imputation useful estimating missing values. Since many AGNs have redshift and highest energy, with MICE...

Classifying Active Galactic Nuclei (AGN) is a challenge, especially for BL Lac Objects (BLLs), which are identified by their weak emission line spectra. To address the problem of classification, we use data from 4th Fermi Catalog, Data Release 3. Missing hinders machine learning to classify AGN. A previous paper found that Multiple Imputation Chain Equations (MICE) imputation useful estimating missing values. Since many AGN have redshift and highest energy, with MICE K-nearest neighbor (kNN)...

The division of Gamma-ray bursts (GRBs) into different classes, other than the "short" and "long", has been an active field research. We investigate whether GRBs can be classified based on a broader set parameters, including prompt plateau emission ones. Observational evidence suggests existence more GRB sub-classes, but results so far are either conflicting or not statistically significant. novelty here is producing machine-learning-based classification using their observed X-rays optical...

The widespread usage of cars and other large, heavy vehicles necessitates the development an effective parking infrastructure. Additionally, algorithms for detection recognition number plates are often used to identify automobiles all around world where standardized plate sizes fonts enforced, making effortless task. As a result, both kinds data can be combined develop intelligent system focuses on technology Automatic Number Plate Recognition (ANPR). Retrieving characters from inputted...

Redshift measurement of active galactic nuclei (AGNs) remains a time-consuming and challenging task, as it requires follow up spectroscopic observations detailed analysis. Hence, there exists an urgent requirement for alternative redshift estimation techniques. The use machine learning (ML) this purpose has been growing over the last few years, primarily due to availability large-scale surveys. However, observational errors, significant fraction these data sets often have missing entries,...