Vijaykumar Bhanuse
Biographic Data
| ID | 8968269 |
|---|---|
| NAME | Vijaykumar Bhanuse |
| GIVEN NAMES | Vijaykumar |
| FAMILY NAME | Bhanuse |
| SIGNATURE | BHANUSE V |
| AFFILIATIONS | International Institute of Information Technology |
| VERIFIED | No |
| TOTAL WORKS | 4 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 4 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Predictive Dropout Analysis in Art Education Management
Student drop out of the art education programs in the academic institutions is a major problem because the students tend to drop out of the programs due to a complex interplay of creative, behavioral, psychological, and the socio-economic factors and not as a result of their work performance. This research paper introduces a predictive dropout analysis model that suits the field of art education management, and which can be used to predict potent…
Data-Driven Insights for Performing Arts Teaching
The convergence of artificial intelligence and education of performing arts has introduced possibilities of improving creativity, assessment and pedagogy. This work is a proposal to develop a Data-Driven Pedagogical Model of Performing Arts (DDPMPA) that combines multimodal analytics such as motion, audio, emotional and textual data to create holistic information about the performance of learners. The framework uses the technique of feature extra…
Cultural Identity Management Through Digital Art Tools
The quick development of digital artistic means has changed the way people and societies build, communicate and maintain cultural identity in networked space. With the rise of digital humanities and cultural informatics, artistic practices are becoming more and more involved with virtual spaces, in which identity is a hybrid, fluid, and collaboratively constructed space. The proposed study explores how digital illustration, 3D modeling, immersive…
Ai-Enabled Market Forecasting for Folk Art Industries
The study provides a holistic framework of AI-enabled market forecasting within the folk-art industry based on a combination of machine learning, statistical modeling, and cultural data analytics to forecast the market forces and facilitate sustainable artisan livelihoods. The study presents a unified ensemble framework that integrates Long Short-Term Memory (LSTM) networks, Prophet time-series models, and Gradient Boosting Regression (GBR) to so…
No prominent works on this page.
Data-Driven Insights for Performing Arts Teaching
The convergence of artificial intelligence and education of performing arts has introduced possibilities of improving creativity, assessment and pedagogy. This work is a proposal to develop a Data-Driven Pedagogical Model of Performing Arts (DDPMPA) that combines multimodal analytics such as motion, audio, emotional and textual data to create holistic information about the performance of learners. The framework uses the technique of feature extra…
Cultural Identity Management Through Digital Art Tools
The quick development of digital artistic means has changed the way people and societies build, communicate and maintain cultural identity in networked space. With the rise of digital humanities and cultural informatics, artistic practices are becoming more and more involved with virtual spaces, in which identity is a hybrid, fluid, and collaboratively constructed space. The proposed study explores how digital illustration, 3D modeling, immersive…
Ai-Enabled Market Forecasting for Folk Art Industries
The study provides a holistic framework of AI-enabled market forecasting within the folk-art industry based on a combination of machine learning, statistical modeling, and cultural data analytics to forecast the market forces and facilitate sustainable artisan livelihoods. The study presents a unified ensemble framework that integrates Long Short-Term Memory (LSTM) networks, Prophet time-series models, and Gradient Boosting Regression (GBR) to so…
Predictive Dropout Analysis in Art Education Management
Student drop out of the art education programs in the academic institutions is a major problem because the students tend to drop out of the programs due to a complex interplay of creative, behavioral, psychological, and the socio-economic factors and not as a result of their work performance. This research paper introduces a predictive dropout analysis model that suits the field of art education management, and which can be used to predict potent…
Analytics (2 works) · Artistic and Creative Research (2 works) · Cultural heritage (2 works) · Gradient boosting (2 works) · Predictive analytics (2 works) · Aesthetic Perception and Analysis (1 work) · Art Education and Development (1 work) · Art History and Market Analysis (1 work) · Art, Technology, and Culture (1 work) · Arts in education (1 work)