Pratik Mungekar
Biographic Data
| ID | 10130985 |
|---|---|
| NAME | Pratik Mungekar |
| GIVEN NAMES | Pratik |
| FAMILY NAME | Mungekar |
| SIGNATURE | MUNGEKAR P |
| AFFILIATIONS | Society for Applied Microwave Electronics Engineering & Research |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Ai-Enhanced Animation Techniques for Art Education
The introduction of Artificial Intelligence (AI) into creative education has redefined the pedagogic environment, and provides new tools that make the artistic world more expressive and learning more productive. The paper will discuss how AI-enhanced methods of animation can be applied to art education and why they have the potential to enhance creativity, simplify the process of production, and help to generate a personalized learning experience…
Data Science for Measuring Visual Influence in Art
The paper discusses the applications of data-driven approaches to quantify visual influence in art which combines computational means with art-historical theory to identify stylistic and conceptual connections between artists and works of art. The paper investigates the way data science can give objective schemes of the artistic evolution by quantifying the visual similarities and the pathways of influence. Based on online art repositories massiv…
No prominent works on this page.
Ai-Enhanced Animation Techniques for Art Education
The introduction of Artificial Intelligence (AI) into creative education has redefined the pedagogic environment, and provides new tools that make the artistic world more expressive and learning more productive. The paper will discuss how AI-enhanced methods of animation can be applied to art education and why they have the potential to enhance creativity, simplify the process of production, and help to generate a personalized learning experience…
Data Science for Measuring Visual Influence in Art
The paper discusses the applications of data-driven approaches to quantify visual influence in art which combines computational means with art-historical theory to identify stylistic and conceptual connections between artists and works of art. The paper investigates the way data science can give objective schemes of the artistic evolution by quantifying the visual similarities and the pathways of influence. Based on online art repositories massiv…
Aesthetic Perception and Analysis (1 work) · Animation (1 work) · Art History and Market Analysis (1 work) · Art, Technology, and Culture (1 work) · Artificial neural network (1 work) · Artistic and Creative Research (1 work) · Computer animation (1 work) · Computer facial animation (1 work) · Convolutional neural network (1 work) · Deep learning (1 work)