Browse Articles

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.

Lithium-ion batteries, a breakthrough in chemistry that enabled the electronic revolution we live today have become an essential part of our day-to-day life. A phone battery running out after a heavy day of use with limited opportunities for recharging is a well-known and resented experience by almost everyone. How then can we make batteries more efficient? This paper proposes the use of a different type of separator, that improves the charging and discharging capacities of lithium ions compared to the classical separator. This and similar attempts to improve Lithium-ion battery function could facilitate the development of higher-performance batteries that work longer and withstand harsher use.

In this study, the authors use existing mathematical models to how high school athletes pace 800 m, 1600 m, and 3200 m distance track events compared to elite athletes.

To best identify tuberculosis and pneumonia diagnoses in chest x-rays, the authors compare different deep learning convolution neural networks.

The authors looked at how the length of an audio clip used of a song impacted the ability to properly classify it by musical genre.

The authors developed a quantum inspired model for stock market fluctuations.

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.

This article describes the classification of medical text data using vector databases and text embedding. Various large language models were used to generate this medical data for the classification task.

In this paper, Thomas et al. introduce a new, affordable way to study characteristics of rocket motors using small-scale rocket motors.

The authors develop and test a machine learning algorithm for predicting diabetes diagnoses.