Deep Learning Classification Models with AG News The growth of online news has led to a large volume of digital data. Natural language processing can help leverage this data for useful applications such as topic classification. Deep learning classification models are an attractive option to be used in these applications as the many layers in deep networks allow for hierarchal processing of the data (Glassner, 2021). The field of natural language processing has seen rapid growth since the advent of deep learning models with recurrent neural networks leading the charge (Ketkar & Moolayil, 2021).
Prediction of Bodyfat using a Linear Regression Model and Body Measurements Abstract
Bodyfat percentage is an important estimator for health. The most accurate method for measuring bodyfat is by underwater weighing which is time-intensive. Predictive analytics (linear regression) using indirect measurements offer a faster method to compute bodyfat percentage. This study analyzes bodyfat data determined by underwater weighing with their corresponding indirect measurements. To predict the bodyfat using these measurements, we compare a traditional linear model, regularized model, subset models (indicator, dichotomous, piecewise, polynomial), and feature engineering models (principal components analysis).
Predicting the Diagnosis of Autism using Classification Models based on fMRI Abstract
Autism spectrum disorder is a lifelong neurodevelopmental disorder that is diagnosed based on behavioral and social interaction patterns. Predictive algorithms provide a novel approach in identifying key neurological biomarkers and subsequent psychiatric diagnosis using functional magnetic resonance imaging (fMRI) data. This study analyzes a dataset collected from studies at 17 international locations as part of the Autism Brain Imaging Dataset Exchange (ABIDE).
