Tanmoy Parida
Biographic Data
| ID | 10130536 |
|---|---|
| NAME | Tanmoy Parida |
| GIVEN NAMES | Tanmoy |
| FAMILY NAME | Parida |
| SIGNATURE | PARIDA T |
| AFFILIATIONS | Siksha O Anusandhan University |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| AUTHOR COUNT | 2 |
| EDITOR COUNT | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2025 |
| H-INDEX | 0 |
Designing Intelligent Mentoring Systems for Art Learners
The use of Artificial Intelligence (AI) in art education has spawned Intelligent Mentoring Systems (IMS) that enable art learners to have personalized learning experiences. These systems are a combination of adaptive learning algorithms, visual analysis and affective computing to offer custom guidance, feedback and skill development paths. The proposed research paper discusses the development of an Intelligent Mentoring System based on AI and mul…
Object Detection in Photography Using Deep Learning
Object detection in photography has developed fast due to deep learning and has changed the manner in which visual content is photographed, arranged, and understood. This paper is a detailed examination of the current detection systems and how they can apply to the photographic process. Starting with the description of classical approaches like HOG, Haar cascades, and SVM-based networks, the paper compares the drawbacks of the mentioned methods w…
No prominent works on this page.
Designing Intelligent Mentoring Systems for Art Learners
The use of Artificial Intelligence (AI) in art education has spawned Intelligent Mentoring Systems (IMS) that enable art learners to have personalized learning experiences. These systems are a combination of adaptive learning algorithms, visual analysis and affective computing to offer custom guidance, feedback and skill development paths. The proposed research paper discusses the development of an Intelligent Mentoring System based on AI and mul…
Object Detection in Photography Using Deep Learning
Object detection in photography has developed fast due to deep learning and has changed the manner in which visual content is photographed, arranged, and understood. This paper is a detailed examination of the current detection systems and how they can apply to the photographic process. Starting with the description of classical approaches like HOG, Haar cascades, and SVM-based networks, the paper compares the drawbacks of the mentioned methods w…
Aesthetic Perception and Analysis (2 works) · Architecture (2 works) · Advanced Image and Video Retrieval Techniques (1 works) · Affective Computing (1 works) · Amateur (1 works) · Computational photography (1 works) · Convolutional neural network (1 works) · Deep learning (1 works) · Detector (1 works) · Digital art (1 works)