Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Published on by NESIN Awards

Mr. Sarbajit Paul Bappy | Computer Science | Research Excellence Award

Teaching Assistant | Daffodil International University | Bangladesh

Sarbajit Paul Bappy is an emerging researcher in computer science with a growing focus on applied machine learning, medical image analysis, and agricultural informatics. He is currently serving as a Teaching Assistant in the Department of Computer Science and Engineering at Daffodil International University, Bangladesh, where he has been contributing to academic instruction and research support since 2025. Alongside his professional role, he is pursuing his undergraduate degree in Computer Science and Engineering at the same institution, demonstrating a strong integration of academic excellence and early-career research productivity. His scholarly work includes peer-reviewed publications and openly accessible datasets that address critical challenges in healthcare diagnostics and smart agriculture. Notably, he co-authored SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images (MAKE, 2025), which combines deep learning with explainable AI techniques to enhance early disease detection. He also contributed significantly to the dataset Jackfruit AgroVision, a comprehensive benchmark for disease detection in jackfruit and its leaves, supporting advancements in precision agriculture and food-security research. His collaborations span multidisciplinary teams involving experts such as Amir Sohel, Rittik Chandra Das Turjy, Md Assaduzzaman, Ahmed Al Marouf, Jon George Rokne, and Reda Alhajj, illustrating his ability to contribute within diverse international research groups. Through his ongoing work in AI-driven health diagnostics, dataset development, and sustainable agricultural technology, Bappy aims to advance research that supports societal well-being, improves disease detection accuracy, and contributes to innovation within global machine learning communities.

Profiles: Google Scholar | ORCID | LinkedIn

Featured Publications

1. Sohel, A., Turjy, R. C. D., Bappy, S. P., Assaduzzaman, M., Marouf, A. A., Rokne, J. G., & Alhajj, R. (2025). SkinVisualNet: A Hybrid Deep Learning Approach Leveraging Explainable Models for Identifying Lyme Disease from Skin Rash Images. Machine Learning and Knowledge Extraction, 7(4), 157. https://doi.org/10.3390/make7040157  MDPI+1

2. Sohel, A., Bijoy, M. H. I., Turjy, R. C. D., & Bappy, S. P. (2025). Jackfruit AgroVision: A Extensive Dataset for Jackfruit Disease and Leaf Disease Detection using Machine Learning. Mendeley Data. https://doi.org/10.17632/pt647jfn52.1

Mohammed Alenazi | Computer Engineering | Best Researcher Award

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Mr. Mohammed Alenazi | Computer Engineering | Best Researcher Award

Assistant Professor | University of Tabuk | Saudi Arabia

Mr. Mohammed M. Alenazi is an accomplished academic and researcher with expertise in electrical and electronics engineering, computer engineering, and artificial intelligence applications in energy-efficient networks. He earned his Ph.D. in Electrical and Electronics Engineering from the University of Leeds, UK (2018–2022), focusing on energy efficiency in AI-powered communication systems. Prior to this, he completed his M.Eng. in Computer Engineering at Florida Institute of Technology, USA (2016–2017), and a B.Eng. in Computer Engineering from University Sultan Bin Fahad (2007–2011), along with an Associate’s degree in Electrical/Electronics Equipment Installation and Repair from Tabuk College of Technology (2002–2004). Professionally, Mr. Alenazi began his career as a Senior Engineer at Saudi Telecom Company (2006–2011), where he gained practical experience in optical fiber networks, before transitioning to academia as a Teaching Assistant at Northern Border University (2012–2013) and later at the University of Tabuk, where he continues to serve since 2013, eventually advancing into an assistant professorship. His research interests include machine learning, IoT networks, energy optimization, and intelligent systems, with key contributions in developing models for energy-efficient ML-based service placement, neural network embedding in IoT, and intelligent sterilization systems, reflected in several IEEE and Scopus-indexed publications. In addition to publications, he has contributed innovative patents, such as systems for vehicle communication during accidents. His research skills encompass advanced AI modeling, simulation of communication networks, and interdisciplinary problem-solving in sustainable technologies. Mr. Alenazi is an active member of IEEE, AAAI (USA), AISB (UK), PMI, and the Saudi Council of Engineers, and he holds prestigious certifications including CCNA, CompTIA Security+ CE, and PMP. He has consistently demonstrated leadership in academia and professional communities, bridging industry and research while mentoring students. With a growing academic profile of 28 citations, 7 documents, and an h-index of 3, he is well-positioned for continued impact and recognition in his field.

Profiles: Google Scholar | Scopus | ORCID  | ResearchGate

Featured Publications

  1. Alenazi, M. M., Yosuf, B. A., El-Gorashi, T., & Elmirghani, J. M. H. (2020). Energy efficient neural network embedding in IoT over passive optical networks. 2020 22nd International Conference on Transparent Optical Networks (ICTON), 1–6. Cited by: 13

  2. Yosuf, B. A., Mohamed, S. H., Alenazi, M. M., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient AI over a virtualized cloud fog network. Proceedings of the Twelfth ACM International Conference on Future Energy Systems. Cited by: 11

  3. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2021). Energy-efficient distributed machine learning in cloud fog networks. 2021 IEEE 7th World Forum on Internet of Things (WF-IoT), 935–941. Cited by: 9

  4. Banga, A. S., Alenazi, M. M., Innab, N., Alohali, M., Alhomayani, F. M., Algarni, M. H., & others. (2024). Remote cardiac system monitoring using 6G-IoT communication and deep learning. Wireless Personal Communications, 136(1), 123–142. Cited by: 4

  5. Alenazi, M. M., Yosuf, B. A., Mohamed, S. H., El-Gorashi, T. E. H., & Elmirghani, J. M. H. (2022). Energy efficient placement of ML-based services in IoT networks. 2022 IEEE International Mediterranean Conference on Communications and Networking (MeditCom). Cited by: 4