Browse Articles

Auto-Regressive Integrated Moving Average (ARIMA) models are known for their influence and application on time series data. This statistical analysis model uses time series data to depict future trends or values: a key contributor to crime mapping algorithms. However, the models may not function to their true potential when analyzing data with many different patterns. In order to determine the potential of ARIMA models, our research will test the model on irregularities in the data. Our team hypothesizes that the ARIMA model will be able to adapt to the different irregularities in the data that do not correspond to a certain trend or pattern. Using crime theft data and an ARIMA model, we determined the results of the ARIMA model’s forecast and how the accuracy differed on different days with irregularities in crime.

In this study, the authors survey middle and high school students in different states in the U.S. to evaluate stress levels, learning experiences, and activity levels during the COVID-19 pandemic.

Alzheimer’s disease (AD) is a common disease affecting 6 million people in the U.S., but no cure exists. To create therapy for AD, it is critical to detect amyloid-β protein in the brain at the early stage of AD because the accumulation of amyloid-β over 20 years is believed to cause memory impairment. However, it is difficult to examine amyloid-β in patients’ brains. In this study, we hypothesized that we could accurately predict the presence of amyloid-β using EEG data and machine learning.

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

In this study, the authors investigate the effects of the COVID-19 pandemic on South Korean international school students' anxiety, well being and their learning habits.

Sadly, around 800,000 people die by suicide worldwide each year. Dong and Pearce analyze health survey data to identify associations between suicidal ideation and relevant variables, such as sleep quality, hopelessness, and anxious behavior.

In this study, the authors examined the effects of commonly used pesticides (metolachlor, glyphosate, chlorpyrifos, and atrazine) on population size, motor function, and learning in Drosophila melanogaster.

The authors test different machine learning algorithms to remove background noise from audio to help people with hearing loss differentiate between important sounds and distracting noise.

This article discusses Alopecia areata, an autoimmune disorder causing sudden hair loss due to the immune system mistakenly attacking hair follicles. The article introduces the use of deep learning (DL) techniques, particularly convolutional neural networks (CNN), for classifying images of healthy and alopecia-affected hair. The study presents a comparative analysis of newly optimized CNN models with existing ones, trained on datasets containing images of healthy and alopecia-affected hair. The Inception-Resnet-v2 model emerged as the most effective for classifying Alopecia Areata.

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