Mr. Rakhmon Saparbaev | Deep Learning | Research Excellence Award

Mr. Rakhmon Saparbaev | Deep Learning | Research Excellence Award

Urgench State University | Uzbekistan

Mr. Raxmon Saparbayev Komiljonovich is a telecommunications engineering researcher specializing in information transmission systems, network modeling, and signal processing. His work focuses on modeling virus propagation in telecommunication networks, LTE channel resource optimization, and FIR-based signal analysis using MATLAB. He has contributed to peer-reviewed journals and international conference proceedings, including IEEE and AIP publications, reflecting interdisciplinary expertise in IoT, electromagnetic systems, and network traffic analysis. His research integrates machine learning and simulation approaches to improve network reliability and performance. According to Scopus metrics, he has 3 indexed documents, 2 citations, and an h-index of 1, demonstrating emerging scholarly impact.

Citation Metrics (Scopus)

5

4

3

2

1

0

Citations
2

Documents
3

h-index
1

          Citations    Documents    h-index


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Featured Publications

Multi-use Models of Channel Resources of LTE Technology
– Conference Paper

Method for the Correction of Spectral Distortions in X-Ray Photon-Counting Detectors
– Research Work

Modeling of Virus Spread Processes in Telecommunication Networks
– Research Contribution

Mr. Andi Chen | Deep Learning | Excellence in Research Award

Mr. Andi Chen | Deep Learning | Excellence in Research Award

Nanjing University | China

Mr. Andi Chen is an interdisciplinary researcher specializing in quantum-inspired neural networks, tensorized deep learning, and artificial intelligence for pattern recognition and multimodal generation. His research integrates quantum computing concepts with modern neural architectures, including convolutional, residual, and diffusion-based models. He has contributed to high-impact journals and conferences in neural computation and applied physics, while actively engaging in innovation projects on large language models, reinforcement learning fine-tuning, and AI-driven scientific applications across mathematics, engineering, and economics.

Citation Metrics (Google Scholar)

20

15

10

5

0

Citations
9

Documents
6

h-index
2

Citations
Documents
h-index


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 Featured Publications