Mr. Rakhmon Saparbaev | Deep Learning | Research Excellence Award

Published on by Computer Scientists Awards

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

Muhammad Irfan Khan | Deep Learning | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Muhammad Irfan Khan | Deep Learning | Best Researcher Award

University of Electronic Science and Technology of China | China

Muhammad Irfan Khan is a dedicated ML Security Engineer, researcher, and academic professional specializing in artificial intelligence, cybersecurity, and image processing, currently pursuing his M.S. in Information and Communication Engineering at the University of Electronic Science and Technology of China (UESTC), Chengdu. He has worked as a Machine Learning & Security Engineer at Victoriam.ai Solution, USA, where he developed threat detection models and optimized real-time security frameworks, and as a Research Intern at LinkDoc Technology, contributing to medical image segmentation advancements. At Namal University, Pakistan, he gained substantial experience as a Research Assistant, Teaching Assistant, and Lab Engineer, supporting AI/ML research, supervising projects, and co-authoring multiple peer-reviewed publications. His research contributions include journal articles such as “Genetic Algorithm Based Hybrid Deep Learning Framework for Stability Prediction of ABO3 Perovskites in Solar Cell Applications” (Energies, 2025), “Forecasting Fluctuations in Cryptocurrency Trading Volume Using a Hybrid LSTM-DQN Reinforcement Learning” (Digital Finance Journal, 2025), “Machine Learning-Powered Malware Detection in Encrypted IoT Traffic” (IEEE Journal of IoT, 2024), and “Decoding Emotions: U-Net-Driven Pattern Recognition for fMRI Analysis” (IEEE Transactions on Medical Imaging, 2025), along with conference proceedings in ICICT and IBCAST. He has served as a reviewer for international journals and conferences, including Computational Economics (Springer), Scientific Reports (Nature), and AAAI-26. His technical strengths span deep learning, reinforcement learning, cybersecurity, computer vision, and data-driven optimization, while also excelling in leadership and collaborative research. Despite his growing recognition, his current Scopus/Google Scholar profile records 2 documents reflecting his early yet impactful stage in research.

Profile: Scopus | LinkedIn

Featured Publication

Wali, S., Khan, M. I., & Zulfiqar, N. (2025). Forecasting fluctuations in cryptocurrency trading volume using a hybrid LSTM–DQN reinforcement learning. Digital Finance Journal.