We will be using ML algorithms such as Artificial Neural Network (ANN), K-nearest neighbors (KNN) algorithm, Support Vector Machine (SVM) algorithm, Random Forest algorithm, and Lo-gistic Regression algorithm. By comparing all the algorithm Ran …

Mar 8, 2022 · This paper describes various Supervised Machine Learning (ML) classification techniques, compares various supervised learning algorithms as well as determines the most efficient...

This project aims to classify glass samples using machine learning techniques implemented in a Jupyter notebook, by analyzing various physical and chemical properties of the glass to accurately predict its type.

Glass is used in a variety of ways, and investigators should be able to identify which type of glass is present during a crime scene assessment. We'll utilize the internet dataset and machine learning to resolve the greater than disadvantage in order to find out what type of glass it is.

This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the …

Apr 1, 2023 · In order to classify and identify glass, we used logistic regression and various machine learning algorithms to classify glass of known composition and class, and used various metrics to...

Mar 25, 2025 · This project develops a machine learning model to classify glass types based on their attributes. It includes data preprocessing, visualization, and modeling using algorithms like Logistic Regression, KNN, Random Forest, and Gradient Boosting.

In this paper, we offer an efficient malware detection approach using classification algorithms in machine learning such as support vector machine, random forest and extreme gradient boosting. We employ an extensive feature selection process …

May 9, 2024 · This study demonstrates the great potential of machine learning for glass chemical component estimation and glass type and weathering status identification, providing technical guidance for the appraisal of ancient glass artifacts.

In the present study machine learning (ML) models were used to predict the Glass Forming Ability (GFA) of various bulk metallic glass (BMG) systems. A careful feature selection procedure was employed to identify important parameters that exert influence on glass formation.