In this study, the authors conduct a survey to evaluate the impact of household socioeconomic status on effectiveness of distance learning for students.

The purpose of the study was to determine whether graph-based machine learning techniques, which have increased prevalence in the last few years, can accurately classify data into one of many clusters, while requiring less labeled training data and parameter tuning as opposed to traditional machine learning algorithms. The results determined that the accuracy of graph-based and traditional classification algorithms depends directly upon the number of features of each dataset, the number of classes in each dataset, and the amount of labeled training data used.

The authors develop a new method for training machine learning algorithms to differentiate between hate speech and cultural speech in online platforms.

In this study, the impact of shutting down schools on the emotional aspects of high school students was analyzed using survey responses.

In this study, three models are used to test the hypothesis that data-centric artificial intelligence (AI) will improve the performance of machine learning.

Cell segmentation is the task of identifying cell nuclei instances in fluorescence microscopy images. The goal of this paper is to benchmark the performance of representative deep learning techniques for cell nuclei segmentation using standard datasets and common evaluation criteria. This research establishes an important baseline for cell nuclei segmentation, enabling researchers to continually refine and deploy neural models for real-world clinical applications.

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

Using a convolution neural network, these authors show machine learning can clinically diagnose breast cancer with high accuracy.

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.

Here, seeking to understand the effects of emotion on memory recall, the authors used a study of 30 teenagers, comparing their ability to recall details from information or narrative writing. They found improved recall of narrative writing, suggesting emotional response can contribute to improved memory recall.