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.
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…
Estimating the number of people present in a crowded scene from an image is a challenging computer vision problem, particularly under conditions of severe occlusion, scale variation, and non-uniform crowd distribution. This paper presents a deep learning framework for crowd densi…
Parkinson's disease is a progressive neurodegenerative disorder that primarily affects movement, balance, and motor coordination due to the gradual loss of dopamine-producing neurons. Early identification of the disease is essential for timely medical intervention and improved pa…
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…
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…
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…