![Determining the best convolutional neural network for identifying tuberculosis and pneumonia in chest x-rays](/rails/active_storage/representations/proxy/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaHBBbUFLIiwiZXhwIjpudWxsLCJwdXIiOiJibG9iX2lkIn19--82a9bf00f146ac0ff43bda4e56ecaab0f4605cca/eyJfcmFpbHMiOnsibWVzc2FnZSI6IkJBaDdCem9MWm05eWJXRjBTU0lJY0c1bkJqb0dSVlE2QzNKbGMybDZaVWtpRFRZd01IZzJNREErQmpzR1ZBPT0iLCJleHAiOm51bGwsInB1ciI6InZhcmlhdGlvbiJ9fQ==--33b2b080106a274a4ca568f8742d366d42f20c14/Figure%201%20(2).png)
To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.
Read More...Determining the best convolutional neural network for identifying tuberculosis and pneumonia in chest x-rays
To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.
Read More...Augmented Reality Chess Analyzer (ARChessAnalyzer): In-Device Inference of Physical Chess Game Positions through Board Segmentation and Piece Recognition using Convolutional Neural Networks
In this study the authors develop an app for faster chess game entry method to help chess learners improve their game. This culminated in the Augmented Reality Chess Analyzer (ARChessAnalyzer) which uses traditional image and vision techniques for chess board recognition and Convolutional Neural Networks (CNN) for chess piece recognition.
Read More...Entropy-based subset selection principal component analysis for diabetes risk factor identification
In this article, the authors looked at developing a strategy that would allow for earlier diagnosis of Diabetes as that improves long-term outcomes. They were able to find that BMI, tricep skin fold thickness, and blood pressure are the risk factors with the highest accuracy in predicting diabetes risk.
Read More...Pediatric probiotic culture survival study in acidic pH using an in vitro model
In this study, the authors investigate the effects of acidity on the survival of commercial probiotic Lovebug bacterial strains.
Read More...Battling cultural bias within hate speech detection: An experimental correlation analysis
The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.
Read More...A spatiotemporal analysis of OECD member countries on sugar consumption and labor force participation
In this article the authors look at sugar consumption and the relationship to productivity in the work/labor force.
Read More...Identifying Neural Networks that Implement a Simple Spatial Concept
Modern artificial neural networks have been remarkably successful in various applications, from speech recognition to computer vision. However, it remains less clear whether they can implement abstract concepts, which are essential to generalization and understanding. To address this problem, the authors investigated the above vs. below task, a simple concept-based task that honeybees can solve, using a conventional neural network. They found that networks achieved 100% test accuracy when a visual target was presented below a black bar, however only 50% test accuracy when a visual target was presented below a reference shape.
Read More...Association of depression and suicidal ideation among adults with the use of H2 antagonists
In this study, the authors investigate associations between use of histamine H2 receptor antagonists and suicidal ideation in different age groups.
Read More...Effects of Withania Somnifera on Charcot-Marie-Tooth Disease Type 1A in the model organism Eisenia Fetida
In this study, the authors investigate whether Eisenia Fetida nerve signal speed correlates with Withania somnifera ingestion, a possible way to protect against demyelination.
Read More...The Effects of Atmospheric Attenuation on Cosmic Ray Muons: How is Surface Level Cosmic Ray Muon Flux Affected by Atmospheric Attenuation?
Cosmic rays are high-energy astronomical particles originating from various sources across the universe. Here, The authors sought to understand how surface-level cosmic-ray muon flux is affected by atmospheric attenuation by measuring the variation in relative muon-flux rate relative to zenith angle, testing the hypothesis that muons follow an exponential attenuation model. The attenuation model predicts an attenuation length of 6.3 km. This result implies that only a maximum of 24% of muons can reach the Earth’s surface, due to both decay and atmospheric interactions.
Read More...Search articles by title, author name, or tags