Detecting cervical spondylosis using deep learning
Read More...The effects of image manipulation on classification of cervical spondylosis X-ray images using deep learning
Evaluating the performance of Q-learning-based AI in auctions
Advertising platforms like Google Ads use AI to drive the algorithms used to maximize advertisers benefits. This study shows that AI does not adjust it strategy based on auction type and highlights the limitations of AI running without explicit guidance.
Read More...Examination of the rotation curve for the dark matter deficient relic galaxy NGC 1277
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.
Read More...Study of neural network parameters in detecting heart disease
The authors looked at the ability to detect heart disease before the onset of severe clinical symptoms.
Read More...Stock price prediction: Long short-term memory vs. Autoformer and time series foundation model
The authors looked the ability to predict future stock prices using various machine learning models.
Read More...Calculating the dynamic viscosity of a fluid using image processing of a falling ball
The authors measure changes in the viscosity of glycerol with increasing temperature using the falling ball approach.
Read More...Automated classification of nebulae using deep learning & machine learning for enhanced discovery
There are believed to be ~20,000 nebulae in the Milky Way Galaxy. However, humans have only cataloged ~1,800 of them even though we have gathered 1.3 million nebula images. Classification of nebulae is important as it helps scientists understand the chemical composition of a nebula which in turn helps them understand the material of the original star. Our research on nebulae classification aims to make the process of classifying new nebulae faster and more accurate using a hybrid of deep learning and machine learning techniques.
Read More...Analyzing breath sounds by using deep learning in diagnosing bronchial blockages with artificial lung
Many common respiratory illnesses like bronchitis, asthma, and chronic obstructive pulmonary disease (COPD) lead to bronchial inflammation and, subsequently, a blockage. However, there are many difficulties in measuring the severity of the blockage. A numeric metric to determine the degree of the blockage severity is necessary. To tackle this demand, we aimed to develop a novel human respiratory model and design a deep-learning program that can constantly monitor and report bronchial blockage by recording breath sounds in a non-intrusive way.
Read More...Building deep neural networks to detect candy from photos and estimate nutrient portfolio
The authors use pictures of candy wrappers and neural networks to improve nutritional accuracy of diet-tracking apps.
Read More...Comparing the performance of lateral control algorithms on long rigid vehicles in urban environments
Here, seeking to better understand the control algorithms used in autonomous vehicles, the authors compared the Stanley and pure pursuit control algorithms along with a new version of each. Unexpectedly, they found that no control algorithm offered optimal performance, but rather resulted in tradeoffs between the various ideal results.
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