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Detecting cervical spondylosis using deep learning

This paper looks at the antibacterial and toxic effects of silver nanoparticles (AgNPs) on Escherichia coli bacteria and Drosophila melanogaster fruit flies. They modified the AgNPs size, concentration, and surface coating to determine the effects on each of the organisms. For both organisms, increased AgNP concentration demonstrated increased toxicity but particle size and surface coating had opposing effects.

The authors studied the ability of deep learning models to predict droughts in the midwestern United States.

The authors looked the ability of sound sensors to predict clogged pipes when the sound intensity data is run through a machine learning algorithm.

The authors re-examine the galactic kinematics of relic galaxy NGC 1277, recently identified as dark matter deficient, by reproducing its rotation curve with data from the George and Cynthia Mitchell Spectrograph.

The authors looked at non-natural factors that influenced the spread rate of fire ants in multiple cities in China.

The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.

The authors looked at the ability of different machine learning algorithms to predict the level of financial corruption in different countries.

This study explored how green spaces, climate, traffic, and air quality (GCTA) collectively influence asthma-related emergency department visits in the U.S using machine learning models and explainable AI.

The authors used machine learning to predict the presence of anxiety and burnout in students based on facial expressions and demographic information.