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In this study, the authors developed and assessed the accuracy of a machine learning algorithm to identify skin cancers using images of biopsies.

This study shows the efficacy of leveraging transfer learning, specifically from residual networks, to detect CVDs and possible signs of CVDs. The findings indicate that leveraging transfer learning from residual networks alongside medical professionals is a highly promising approach for CVD detection and diagnosis, warranting further investigation.

The authors studied optimized delivery routes to reduce travel times using different traveling salesman algorithms.

The authors looked at the difference in investing in stock vs. mutual funds.

The authors looked at how pharmacokinetics changed depending on the use of an in vitro or an in vivo model.

The authors looked at how students performed on standardized tests when they were taught material via memorization vs. conceptual based approaches.

The authors looked at the ability of different deep learning models to predict the presence of lung cancer from chest CT scans. They found that a pre-trained CNN model performed better than an autoencoder model.

Robot-assisted minimally invasive surgery (RMIS) benefits from increased precision and faster recovery, with force feedback from the surgical tool being critical for control. Researchers tested the use of neural networks for detecting the vanishing point of the tool, a key element for force feedback.

The authors looked at using machine learning to identify skills needed to apply for certain jobs, specifically looking at different techniques to parse apart the text. They found that Bidirectional Encoder Representation of Transforms (BERT) performed best.

Here the authors conducted a photometric analysis of Supernova (SN) 20234jvj. Through generating a light curve, they determined SN 2023jvj to be a Type Ia supernova located approximately 1.246e8 parasecs away from Earth.