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Attention Deficit Hyperactivity Disorder (ADHD) is characterized by impulsivity, hyperactivity, and inattention. The authors hypothesized that people with ADHD would display more inattentional blindness in perceptually simple tasks and less inattentional blindness in perceptually complex tasks. The results indicate that there is no significant correlation between ADHD and inattentional blindness in either type of task.

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 looked at different factors, such as age, pre-existing conditions, and geographic region, and their ability to predict what an individual's health insurance premium would be.

Here the authors sought to use three machine learning models to predict poverty levels in Cambodia based on available household data. They found teat multilayer perceptron outperformed the other models, with an accuracy of 87 %. They suggest that data-driven approaches such as these could be used more effectively target and alleviate poverty.

Lung cancer is highly fatal, largely due to late diagnoses, but early detection can greatly improve survival. This study developed three models to enhance early diagnosis: an MLP for clinical data, a CNN for imaging data, and a hybrid model combining both.

The authors looked at different ways to build computational resources that would analyze shooting form for basketball players.

The authors test various machine learning models to improve the accuracy and efficiency of pneumonia diagnosis from X-ray images.

In this study the authors looked at the ability of artificial intelligence to detect tempo, rhythm, and intonation of a piece played on violin. Technology such as this would allow for students to practice and get feedback without the need of a teacher.

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.

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.