Riyazahemed A Jamadar
Biographic Data
| ID | 8968284 |
|---|---|
| NAME | Riyazahemed A Jamadar |
| GIVEN NAMES | Riyazahemed A |
| FAMILY NAME | Jamadar |
| SIGNATURE | JAMADAR R A |
| AFFILIATIONS | Centre for Artificial Intelligence and Robotics |
| VERIFIED | No |
| TOTAL WORKS | 3 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 3 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2025 |
| LATEST PUBLICATION YEAR | 2026 |
| H-INDEX | 0 |
Real-Time Ai Feedback for Performance Students
Performance-based instruction depends on the timeliness, accuracy, and pedagogically valuable feedback in order to assist in the acquisition of embodied skills. Nevertheless, the conventional instructor-based feedback can be delayed, subjective, and not scaling and restrictive in use during live practice. This article proposes a real-time AI feedback system to performance learning which combines multimodal sensing, low-latency artificial intellig…
Machine Translation for Folk Narratives in Education
Machine translation (MT) has become an imperative facilitator of the increase in availability of culturally enriched learning resources in multilingual learning settings. Folk narratives as carriers of indigenous knowledge, moral codes, and linguistic innovation are still overrepresented in the informal sphere of learning because, despite the language barrier and the lack of quality translations, the number of them is limited. This paper will sug…
Exploring Emotional Expression in Digital Art Through Deep Learning Techniques
The expression of emotion is a characteristic but difficult feature of digital art that is most frequently expressed in abstract visual features instead of direct semantics. This paper explores how deep learning methods can be used to learn and analyze emotional expression in digital artwork. The proposed hybrid model that integrates Convolutional Neural Networks and Vision Transformers will be able to capture local visual features, including col…
No prominent works on this page.
Exploring Emotional Expression in Digital Art Through Deep Learning Techniques
The expression of emotion is a characteristic but difficult feature of digital art that is most frequently expressed in abstract visual features instead of direct semantics. This paper explores how deep learning methods can be used to learn and analyze emotional expression in digital artwork. The proposed hybrid model that integrates Convolutional Neural Networks and Vision Transformers will be able to capture local visual features, including col…
Real-Time Ai Feedback for Performance Students
Performance-based instruction depends on the timeliness, accuracy, and pedagogically valuable feedback in order to assist in the acquisition of embodied skills. Nevertheless, the conventional instructor-based feedback can be delayed, subjective, and not scaling and restrictive in use during live practice. This article proposes a real-time AI feedback system to performance learning which combines multimodal sensing, low-latency artificial intellig…
Machine Translation for Folk Narratives in Education
Machine translation (MT) has become an imperative facilitator of the increase in availability of culturally enriched learning resources in multilingual learning settings. Folk narratives as carriers of indigenous knowledge, moral codes, and linguistic innovation are still overrepresented in the informal sphere of learning because, despite the language barrier and the lack of quality translations, the number of them is limited. This paper will sug…
Aesthetic Perception and Analysis (1 work) · Ambiguity (1 work) · Convolutional neural network (1 work) · Creativity (1 work) · Deep learning (1 work) · Digital Storytelling and Education (1 work) · Embodied and Extended Cognition (1 work) · Embodied cognition (1 work) · Emotional expression (1 work) · Experiential learning (1 work)