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Researchers Develop AI Tool to Help Astronomers Identify Galaxy Clusters Quickly

The study was presented at the Royal Astronomical Society’s National Astronomy meeting at Lancaster University

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The decision is also intended to make image forensics understandable for everyone. Pixabay

Researchers have developed an Artificial Intelligence (AI)-powered tool that has been trained to “look” at colour images and identify galaxy clusters quickly.

The “Deep-CEE” – Deep Learning for Galaxy Cluster Extraction and Evaluation – model is based on neural networks, which are designed to mimic the way a human brain learns to recognise objects by activating specific neurons when visualising distinctive patterns and colours.

Matthew Chan, a PhD student at Lancaster University in Britain trained the AI by repeatedly showing it examples of known, labelled objects in images until the algorithm is able to learn to associate objects on its own.

Then the researchers ran a pilot study to test the algorithm’s ability to identify and classify galaxy clusters in images that contain many other astronomical objects.

“Data mining techniques such as deep learning will help us to analyse the enormous outputs of modern telescopes” said John Stott from Lancaster University.

“We expect our method to find thousands of clusters never seen before by science,” Stott said.

Galaxy clusters represent the most extreme environments that galaxies can live in and studying them can help us better understand dark matter and dark energy.

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“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

New state-of-the-art telescopes have enabled astronomers to observe wider and deeper than ever before, such as studying the large-scale structure of the universe and mapping its vast undiscovered content.

By automating the discovery process, scientists can quickly scan sets of images, and return precise predictions with minimal human interaction.

This will be essential for analysing data in future. The upcoming Large Synoptic Survey telescope (LSST) sky survey (due to come online in 2021) will image the skies of the entire southern hemisphere, generating an estimated 15 TB of data every night.

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“We have successfully applied Deep-CEE to the Sloan Digital Sky Survey,” said Chan.

“Ultimately, we will run our model on revolutionary surveys such as the LSST that will probe wider and deeper into regions of the Universe never before explored,” Chan added.

The study was presented at the Royal Astronomical Society’s National Astronomy meeting at Lancaster University. (IANS)

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Novel AI Tool to Detect Depression Via Sound of Your Voice

Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time

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Depression is a major issue affecting millions of people, especially the teenagers. Pixabay

India — the sixth most depressed country in the world — has an estimated 56 million people suffering from depression and 38 million from anxiety disorders, according to a recent report by the World Health Organisation (WHO).

To help identify depression early, scientists have now enhanced a technology that uses Artificial Intelligence (AI) to sift through sound of your voice to gauge whether you are depressed or not.

Computing science researchers from University of Alberta in Canada have improved technology for identifying depression through vocal cues.

The study, conducted by Mashrura Tasnim and Professor Eleni Stroulia, builds on past research that suggests that the timbre of our voice contains information about our mood.

Using standard benchmark data sets, Tasnim and Stroulia developed a methodology that combines several Machine Learning (ML) algorithms to recognize depression more accurately using acoustic cues.

A realistic scenario is to have people use an app that will collect voice samples as they speak naturally.

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“Artificial intelligence is now one of the fastest-growing areas in all of science and one of the most talked-about topics in society.” VOA

“The app, running on the user’s phone, will recognize and track indicators of mood, such as depression, over time. Much like you have a step counter on your phone, you could have a depression indicator based on your voice as you use the phone,” said Stroulia.

Depression is ranked by WHO as the single largest contributor to global disability. It is also the major contributor to suicide deaths.

The ultimate goal, said researchers, is to develop meaningful applications from this technology.

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Such a tool could prove useful to support work with care providers or to help individuals reflect on their own moods over time.

“This work, developing more accurate detection in standard benchmark data sets, is the first step,” added Stroulia while presenting the paper at the Canadian Conference on Artificial Intelligence recently. (IANS)