ABSTRACT: Accurate prediction of malaria incidence is indispensable in helping policy makers and decision makers intervene before the onset of an outbreak and potentially save lives. Various ...

In an era of rapidly growing multimedia data, the need for robust and efficient classification systems has become critical, specifically the identification of class names and poses or styles. This ...

Recently, many machine learning techniques have been presented to detect brain lesions or determine brain lesion types using microwave data. However, there are limited studies analyzing the location ...

Introduction: The unmanned aerial vehicle -based light detection and ranging (UAV-LiDAR) can quickly acquire the three-dimensional information of large areas of vegetation, and has been widely used in ...

ABSTRACT: This paper presents a comprehensive machine learning approach for credit score classification, addressing key challenges in financial risk assessment. We propose an optimized CatBoost-based ...

This project explores and evaluates multiple classification algorithms, including K-Nearest Neighbors (KNN), Logistic Regression, Support Vector Machines (SVM), and ensemble methods (Boosting and ...

Abstract: The K-Nearest Neighbor algorithm is a supervised machine learning algorithm that is used for classification problems. The execution time of this algorithm could be extremely high, especially ...