Data visualization has become an essential component of modern data analytics, enabling users to identify patterns, trends, correlations, and anomalies within large datasets. Scatter plots are among the most effective visualization techniques for representing relationships between numerical variables. This research presents the design and development of interactive 2D and 3D scatter plot visualizations using D3.js and Three.js technologies. D3.js is employed to construct dynamic two-dimensional scatter plots with interactive features such as zooming, filtering, and tooltips, while Three.js is utilized to render three-dimensional scatter plots using WebGL for enhanced spatial representation. The proposed approach demonstrates how multidimensional datasets can be effectively visualized and explored through modern web-based technologies. Experimental analysis indicates that D3.js offers superior simplicity and responsiveness for two-dimensional data representation, whereas Three.js provides greater depth perception and multidimensional exploration capabilities. The integration of these technologies creates a scalable framework for scientific, business, and analytical applications.
Managing workforce stability during organizational changes is a critical challenge for modern enterprises. This study proposes an intelligent prediction system to identify employees who are at potential risk of layoffs by analysing historical employee data and workplace interacti…
In recent years, the integration of machine learning and data mining techniques in sports analytics has significantly improved decision-making processes in team management. This project focuses on the application of machine learning algorithms to analyze football player performan…
Tuberculosis (TB) remains a major global health challenge, particularly in regions with limited access to rapid and reliable diagnostic facilities. Traditional diagnostic methods are often time-consuming, expensive, and require specialized infrastructure, which delays early detec…
Chronic kidney disease impacts millions worldwide, and delayed diagnosis results in unfavorable outcomes and higher healthcare expenses. Recent advancements in machine learning present promising diagnostic features, but their “black box” nature restricts clinical uptake. This rev…
In this study, we aimed to create a system that uses machine learning to detect and classify diabetes in an e-healthcare setting. We used Ensemble Decision Tree algorithms for selecting important features from a large set of data. Detecting diabetes accurately is a big challenge…
The present study examines the impact of Artificial Intelligence (AI) on Human Resource (HR) decision-making and employee experience in modern organisations. The rapid integration of AI technologies into HR functions has significantly transformed traditional practices such as rec…