ABSTRACT: This study investigates projectile motion under quadratic air drag, focusing on mass-dependent dynamics using the Runge-Kutta (RK4) method implemented in FreeMat. Quadratic drag, predominant ...

Hyperspectral imaging (HSI) captures rich spectral data across hundreds of contiguous bands for diverse applications. Dimension reduction (DR) techniques are commonly used to map the first three ...

Dimensionality reduction techniques like PCA work wonderfully when datasets are linearly separable—but they break down the moment nonlinear patterns appear. That’s exactly what happens with datasets ...

Abstract: Principal Component Analysis (PCA) is perhaps the most popular linear projection technique for dimensionality reduction. We consider PCA under the assumption that the high-dimensional data ...

Prerequisite: Introduction to Python for Absolute Beginners or some experience using Python. You’ve cleaned and analyzed your data, now learn how to visualize it. Visualizing data is critical for both ...

1 University of Dallas, Computer Science Department, Irving, TX, United States 2 University of Dallas, Biology Department, Irving, TX, United States T-cell receptor (TCR) sequencing has emerged as a ...

Have you ever wondered how businesses sift through mountains of customer feedback to uncover what truly matters? Imagine receiving hundreds, if not thousands, of ...

In today’s data-rich environment, business are always looking for a way to capitalize on available data for new insights and increased efficiencies. Given the escalating volumes of data and the ...

I attempted to run the demo notebook to reproduce the PCA visualization, but the results I obtained are different from what is shown in the demo. Below is the output I obtained using the same slice ...