Vivek Dwivedi | Computer Science | Research Excellence Award

Mr. Vivek Dwivedi | Computer Science | Research Excellence Award

Research Scholar | The University of Slovak University of Technology | Slovakia

Mr. Vivek Dwivedi is an emerging researcher in the field of Computer Science, specializing in machine learning, robotics, and intelligent computational systems. His research emphasizes the development of real-time applications using computer vision, natural language processing, and advanced programming frameworks. He has worked on innovative solutions such as adaptive multi-camera systems for virtual environments and intelligent robotic mechanisms, showcasing strong technical expertise and research potential. With 12 published documents, 25 citations, and an h-index of 3, his contributions reflect steady academic growth and relevance. His work aims to bridge the gap between theoretical research and practical implementation, contributing to advancements in automation, smart technologies, and next-generation digital systems that address real-world challenges.

                            Citation Metrics ( Scopus )

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25
documents
12
h-index
3

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Li Mingxuan | Engineering | Research Excellence Award

Mr. Li Mingxuan | Engineering | Research Excellence Award

Artificial Intelligence Division | The University of  Beijing Smart-Chip Microelectronics Technology Company Ltd | China

Mr. Li Mingxuan is an emerging author contributing to the advancement of artificial intelligence applications in modern power systems. His research focuses on integrating machine learning techniques with energy infrastructure to improve system efficiency, reliability, and intelligent monitoring. His published work explores innovative approaches such as enhanced image processing algorithms for transmission line inspection and intelligent fault detection methodologies. With a growing academic presence, he has authored 11 research documents, receiving 2 citations and achieving an h-index of 1. His contributions emphasize the practical implementation of AI-driven solutions in complex engineering environments, particularly in optimizing distributed energy systems and smart grid technologies. His research reflects a commitment to advancing intelligent automation and supporting the evolution of sustainable and resilient power networks through engineering innovation and interdisciplinary collaboration.

                            Citation Metrics ( Scopus )

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2
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Moumita Ghosh | Computer Science | Best Researcher Award

Dr. Moumita Ghosh | Computer Science | Best Researcher Award

Assistant Professor, Heritage Institute of Technology, India

Dr. Moumita Ghosh (PhD, Engg.) is a passionate researcher from Kolkata, India 🇮🇳, currently working as an Assistant Professor in the Department of Computer Science and Engineering at the Heritage Institute of Technology. Her core research interests lie at the intersection of Data Science and Computational Biodiversity. With a deep commitment to innovation and academia, she integrates machine learning and data mining techniques to address biodiversity conservation and complex ecological data analysis. 👩‍🏫🌿📊

Profile

Orcid

Education 🎓

Dr. Ghosh holds a Ph.D. in Engineering (2019–2024) from Jadavpur University, Kolkata, with her thesis focusing on “Algorithms for Data Mining: Applications in Biodiversity” 🧠🌱. She earned her M.E. in Multimedia Development from the same university (2011–2013) and completed her B.Tech. in Info Technology from WBUT in 2011. She also achieved outstanding academic performance in both her higher secondary and secondary education at Ichapur Girls’ High School. 🎓

Experience 💼

With over a decade of academic experience, Dr. Ghosh has served in key teaching roles at several premier institutions. She currently teaches Data Structures at Heritage Institute of Technology (2024–Present). Previously, she worked at Narula Institute of Technology (2022–2024), Jadavpur University (as a guest faculty and later PI for a DST-funded project), and held Assistant Professor roles at Institute of Engineering and Management (2015–2017) and Bengal College of Engineering and Technology (2013–2015). 💻📚

Research Interest 🔍

Dr. Ghosh’s research bridges Data Science and Ecology through Computational Biodiversity 🌍🧬. Her work includes pattern mining, remote sensing data, complex networks, and biodiversity modeling using advanced machine learning algorithms. She explores how AI and statistical methods can help mitigate biodiversity loss, emphasizing ecological data interpretation and predictive modeling. Her interests extend to deep learning, natural language processing, and ecological network analysis. 📈🌐

Awards 🏆

Dr. Ghosh is a UGC NET qualifier (2017 & 2018) and was awarded the prestigious DST Women Scientists Fellowship (2019–2022), where she led a ₹22 lakh project on biodiversity data mining. She collaborates internationally with Universitas Islam Indonesia and has served as a reviewer and TPC member for various global conferences. She is a proud member of the Computer Society of India (CSI) since 2021. 🏅🌟

Publications 📄

📖 Ghosh et al. (2023). “An Irregular CLA-based Novel Frequent Pattern Mining Approach.” International Journal of Data Mining, Modelling and Management. DOI

📖 Ghosh et al. (2022). “Recognition of Coexistence Pattern of Salt Marshes and Mangroves.” Ecological Informatics. DOI

📖 Ghosh et al. (2022). “Frequent itemset mining using FP-tree.” Innovations in Systems and Software Engineering.

📖 Ghosh et al. (2021). “Knowledge Discovery of Sundarban Mangrove Species.” SN Computer Science. DOI

📖 Ghosh et al. (2021). “Prediction of Interaction between SARS-CoV-2 and Human Protein.” Journal of The Institution of Engineers (India): Series B. DOI

📖 Mondal, Ghosh et al. (2022). “Suffix forest for mining tri-clusters from time-series data.” Innovations in Systems and Software Engineering.

📖 Ghosh & Parekh (2013). “Fish shape recognition using multiple shape descriptors.” International Journal of Computer Applications.

Conclusion

Dr. Moumita Ghosh is a highly suitable candidate for the Best Researcher Award. Her innovative integration of machine learning and biodiversity studies, coupled with a solid record of publications, a granted patent, and a DST fellowship, reflects both depth and societal relevance in her research. With continued international exposure and independent research leadership, she is poised to make significant contributions to science and sustainability.