Automated Author ProfileSevarakhon Egamnazarova Xasanboy kizi
, PhD Candidate, Kokand State University
Sevarakhon Egamnazarova Xasanboy kizi
Current S-Index
Sum of Dataset Indices for all datasets
Average Dataset Index per Dataset
Average Dataset Index per dataset
Total Datasets
Total datasets for this author
Average FAIR Score
Average FAIR Score per dataset
Total Citations
Total citations to the author's datasets
Total Mentions
Total mentions of the author's datasets
S-Index Interpretation
The S-Index (Sharing Index) is a comprehensive metric that represents the cumulative impact of all your datasets. It is calculated as the sum of Dataset Index scores across all your claimed datasets.
What it means:
- A higher S-index indicates greater overall impact of your datasets relative to typical datasets in their fields of research
- The S-Index grows as you add more datasets or as existing datasets gain more citations and mentions
- It provides a single number to track your research data impact over time
Current S-Index: 0.0 (sum of 2 datasets Dataset Index scores)
More information here.
S-Index Over Time
Cumulative Citations Over Time
Cumulative Mentions Over Time
Datasets
This paper presents a multimodal intelligent system that integrates Speech-to-Text (STT) technology into the automatic generation of Islamic geometric patterns. The proposed framework transforms user voice commands into structured parametric representations and synthesizes visual patterns through generative algorithms. The approach is based on the integration of computer graphics and artificial intelligence models, enabling a more intuitive and efficient human–computer interaction.The software implementation is developed in Python using open-source STT and graphics libraries. Experimental evaluation reports an average speech recognition accuracy of WER = 7.8%, while pattern generation latency ranges between 120–250 ms under GPU acceleration. The results confirm that the system maintains high visual consistency and design diversity while achieving real-time performance.
Authors
- Sevarakhon Egamnazarova Xasanboy kizi
This paper presents a multimodal intelligent system that integrates Speech-to-Text (STT) technology into the automatic generation of Islamic geometric patterns. The proposed framework transforms user voice commands into structured parametric representations and synthesizes visual patterns through generative algorithms. The approach is based on the integration of computer graphics and artificial intelligence models, enabling a more intuitive and efficient human–computer interaction.The software implementation is developed in Python using open-source STT and graphics libraries. Experimental evaluation reports an average speech recognition accuracy of WER = 7.8%, while pattern generation latency ranges between 120–250 ms under GPU acceleration. The results confirm that the system maintains high visual consistency and design diversity while achieving real-time performance.
Authors
- Sevarakhon Egamnazarova Xasanboy kizi