Objective: To predict Ki-67 expression levels in non-small cell lung cancer (NSCLC) using an interpretable model combining clinical-radiological, radiomic, and deep learning features. Methods: This ...

Choosing the right curve fit model is essential for revealing key data features, such as rate of change, asymptotes, and EC 50 /IC 50 values. The best model is the one that most faithfully reflects ...

Predicting performance for large-scale industrial systems—like Google’s Borg compute clusters—has traditionally required extensive domain-specific feature engineering and tabular data representations, ...

This project implements a quadratic nonlinear regression model to estimate the real-world distance between a hand and a camera based on the relative positions of hand landmarks in 2D images. The ...

Introduction: This study aims to evaluate the effectiveness of conventional metabolic parameters and radiomic features from 18 F-deoxyglucose(FDG) PET in predicting Ki-67 expression status in patients ...

ABSTRACT: This paper applies the novel adaptive learning methodology to forecast agricultural and energy prices in Greece’s volatile, data-scarce markets. We combine traditional ordinary least squares ...

Introduction and purpose Lupus nephritis (LN) is a major cause of morbidity and mortality in patients with SLE, a complex autoimmune disease characterised by loss of tolerance to self-nuclear antigens ...

Summary: A team of investigators from Dana-Farber Cancer Institute, The Broad Institute of MIT and Harvard, Google, and Columbia University have created an artificial intelligence model that can ...

1 Department of Statistics, College of Arts and Science, University of Benghazi, Benghazi, Libya. 2 Department of Mathematics, College Arts and Science, University of Benghazi, Benghazi, Libya. 3 ...