Browse Articles

In this article, the authors identify the characteristics that make a book a best-seller. Knowing what, besides content, predicts the success of a book can help publishers maximize the success of their print products.

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

Using facial recognition as a use-case scenario, we attempt to identify sources of bias in a model developed using transfer learning. To achieve this task, we developed a model based on a pre-trained facial recognition model, and scrutinized the accuracy of the model’s image classification against factors such as age, gender, and race to observe whether or not the model performed better on some demographic groups than others. By identifying the bias and finding potential sources of bias, his work contributes a unique technical perspective from the view of a small scale developer to emerging discussions of accountability and transparency in AI.

Here, the authors aimed to apply home soil testing to identify the cause of the growth differences between two lemon trees. They hypothesized that differences in physical and chemical soil characteristics were influencing differences in soil productivity and plant growth. Overall, the study demonstrated the effectiveness of home soil testing to characterize soils and help homeowners solve common gardening problems.

Here the authors investigated how the "forever chemical" perfluorooctanoic acid binds to bovine serum albumin (BSA) using computational software to simulate its potential impact on essential human plasma proteins. They identify a possible, high-energy binding configuration that could persistently impair protein functions, underscoring the critical need for further research into the long-term health risks of per- and poly-fluoroalkyl substances exposure.

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 looked at the ability to navigate planes in space between randomly placed planets. They used machine and reinforcement learning to run simulations and found that they were able to identify optimal paths for travel. In the future these techniques may allow for safer travel in unknown spaces.

This study uses interpretable machine learning models, lasso and ridge regression with Shapley analysis, to identify key sales drivers for Corporación Favorita, Ecuador’s largest grocery chain. The results show that macroeconomic factors, especially labor force size, have the greatest impact on sales, though geographic and seasonal variables like city altitude and holiday proximity also play important roles. These insights can help businesses focus on the most influential market conditions to enhance competitiveness and profitability.

Obsessive-compulsive disorder (OCD) can cause significant impairment, and studies indicate that delays in diagnosis and treatment lead to worse outcomes. This study aimed to assess whether these delays have improved over the past three decades and to identify their causes.

Here, recognizing the difficulty that visually impaired people may have differentiating United States currency, the authors sought to use artificial intelligence (AI) models to identify US currencies. With a one-stage AI they reported a test accuracy of 89%, finding that multi-level deep learning models did not provide any significant advantage over a single-level AI.