Abstract: In this paper, we propose a new clustering-based binary-class classification framework that integrates the clustering technique into a binary-class classification approach to handle the ...

The goal of a machine learning binary classification problem is to predict a variable that has exactly two possible values. For example, you might want to predict the sex of a company employee (male = ...

JSON Prompting is a technique for structuring instructions to AI models using the JavaScript Object Notation (JSON) format, making prompts clear, explicit, and machine-readable. Unlike traditional ...

Abstract: Diabetes poses a significant global health challenge due to its rising prevalence and the high number of undiagnosed cases. Early detection is crucial, and machine learning presents ...

Physics and Python stuff. Most of the videos here are either adapted from class lectures or solving physics problems. I really like to use numerical calculations without all the fancy programming ...

Physical frailty is a pressing public health issue that significantly increases the risk of disability, hospitalization, and mortality. Early and accurate detection of frailty is essential for timely ...

This repository compares the performance of Adaline, Logistic Regression, and Perceptron models on binary classification tasks using linearly, non-linearly, and marginally separable datasets from the ...

ABSTRACT: The rapid evolution of land use patterns in Lusaka presents significant challenges for sustainable urban development and resource management. This study employs a time-series analysis of ...

Cluster analysis can be used on symptom and behavior data to identify groups of similar individuals who may share underlying disease etiology or health risks. However, there are few clustering methods ...