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

Mr. John de Britto Chinnappan | Computer Science | Editorial Board Member

Published on by Computer Scientists Awards

Mr. John de Britto Chinnappan | Computer Science | Editorial Board Member

Saveetha Engineering College| India

Dr. C. John De Britto is a dedicated researcher in Electrical and Electronics Engineering with a strong focus on power electronics, renewable energy systems, electric drives, optimization algorithms, and intelligent control strategies. His research work explores innovative solutions for improving power quality, enhancing the efficiency of renewable energy integration, and advancing smart energy systems. With contributions spanning image enhancement techniques, hybrid renewable systems, DC–DC converter architectures, electric vehicle impact mitigation, and intelligent control for photovoltaic systems, he brings a multidisciplinary approach bridging conventional power engineering with modern computational intelligence. His scholarly output includes 14 Scopus-indexed documents that have collectively received 40 citations with an h-index of 4 on Scopus. Additionally, his Google Scholar profile reflects 50 citations, an h-index of 4, and an i10-index of 1, highlighting the growing influence and visibility of his work. His publications demonstrate a strong commitment to developing sustainable engineering solutions, especially in areas such as quasi Z-source converters, hybrid renewable energy design, embedded platforms, fault recognition in industrial motors, and bio-inspired optimization for control systems. Dr. De Britto’s research impact is evident across peer-reviewed journals, international conferences, and interdisciplinary collaborations, with several studies addressing modern challenges such as electric vehicle charging impacts, microgrid performance, and automation for safety-critical applications. His continuous contributions to energy systems, computational approaches, and power conversion technologies position him as an emerging academic voice in renewable and intelligent power engineering research.

Profile

Scopus | ORCID | Google Scholar

Featured Publications

Venkatesh, S., De Britto, C. J., Subhashini, P., & Somasundaram, K. (2022). Image enhancement and implementation of CLAHE algorithm and bilinear interpolation. Cybernetics and Systems, 1–13.

Pradeep, M., Sathishkumar, S., & Subramanian, A. T. S. (2019). Recognition of fault and security of three phase induction motor by means of programmable logic controller. IOP Conference Series: Materials Science and Engineering, 623, 012017.

Yuvaraj, T., Prabaharan, N., De Britto, C. J., Thirumalai, M., Salem, M., & others. (2024). Dynamic optimization and placement of renewable generators and compensators to mitigate electric vehicle charging station impacts using the spotted hyena optimization algorithm. Sustainability, 16(19), 8458.

De Britto, C. J., Nagarajan, S., & Kumar, R. S. (2023). Effective design and implementation of hybrid renewable system using convex programming. International Journal of Green Energy, 20(13), 1473–1487.

De Britto, C. J., & Nagarajan, S. (2018). High performance quasi Z-source resonant converter with hybrid energy resources for rural electrification. International Journal of Engineering and Advanced Technology, 8(2C2), 132–135.