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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.

The authors looked at ways that autonomous vehicles could be trained to better perform in adverse weather without the use of actual images taken during adverse weather events. They were able to improve performance, but models using actual images still perform the best.

With the advance of technology, artificial intelligence (AI) is now applied widely in society. In the study of AI, machine learning (ML) is a subfield in which a machine learns to be better at performing certain tasks through experience. This work focuses on the convolutional neural network (CNN), a framework of ML, applied to an image classification task. Specifically, we analyzed the performance of the CNN as the type of neural activation function changes.

This study investigates the effect of water saturation in a two-layer fluidization behavior of an overlying dry sand bed. Building on prior work showing that moisture inhibits fluidization in granular media, the authors address an underexplored configuration involving layered dry and wet sand. The experiments demonstrate that even minimal water saturation in the sub-layer prevents uniform fluidization of the dry upper layer, leading to the conclusion that two-layer fluidized beds with moisture are not viable under the conditions tested.

Studying exoplanets, or planets that orbit a star other than the Sun, is critical to a greater understanding the formation of planets and how Earth's solar system differs from others. In this study the authors analyze the transit light curves of three hot Jupiter exoplanets to ultimately determine if and how these planets have changed since their discovery.

Plant diseases can cause up to 50% crop yield loss for the popular tomato plant. A mobile device-based method to identify diseases from photos of symptomatic leaves via computer vision can be more effective due to its convenience and accessibility. To enable a practical mobile solution, a “shallow” convolutional neural networks (CNNs) with few layers, and thus low computational requirement but with high accuracy similar to the deep CNNs is needed. In this work, we explored if such a model was possible.

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

In this study the authors looked at developing a more efficient particle collision classification method with the goal of being able to more efficiently analyze particle trajectories from large-scale particle collisions without loss of accuracy.

The authors studied the chemoreception of moon jellyfish in response to food, and developed an AI tool to identify track and quantify the pulsation of swimming jellyfish.

In this study, the authors tested the ability and accuracy of a neural net to identify patterns in complex number matrices.