Debajyoti Dhar | Computer Science | Best Researcher Award

Mr. Debajyoti Dhar | Computer Science | Best Researcher Award

Mr. Debajyoti Dhar, Atal Bihari Vajpayee Indian Institute of Information Technology and Management Gwalior, India

Debajyoti Dhar is an ambitious B.Tech student with a CGPA of 7.67/10, specializing in Computer Science. He has honed his skills through impactful internships, including as a Software Development Engineer at Defence Research and Development Establishment and a Full Stack Developer at Edilitics Private Limited. Debajyoti has contributed to projects like a Decentralized FPS Game with NFT Marketplace and a Ticket Management Platform, showcasing his expertise in blockchain, cloud systems, and machine learning. He has co-authored IEEE conference papers and a journal paper, demonstrating his strong research capabilities. ๐Ÿ’ป๐Ÿ“Š๐Ÿ”—

 

Publication Profile

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Education Background

Debajyoti Dhar is currently pursuing a Bachelor of Technology in Computer Science at the Indian Institute of Information Technology and Management Gwalior. He started his academic journey in December 2021 and is expected to graduate in July 2025. With a CGPA of 7.67/10.00, Debajyoti has demonstrated a strong academic performance, excelling in his coursework. His education has equipped him with a solid foundation in computer science, preparing him for advanced projects and research in areas such as software development, machine learning, and blockchain technology. ๐Ÿ“š๐Ÿ’ป๐Ÿš€

 

Professional Experience

Debajyoti Dhar has gained valuable experience through multiple internships, showcasing his expertise in software development. At Defence Research and Development Establishment (Dec 2022โ€“Oct 2023), he developed a heavy gas detection model in Java and created a 2D plotter in Python for data visualization. During his time at Edilitics Private Limited (Aprโ€“Jun 2023), he built a robust backend using FastAPI and enhanced development efficiency with CI/CD pipelines and Docker. At Mak Design Private Limited (Mayโ€“Jul 2024), he created a real-time chat module with Django and ReactJS, ensuring end-to-end encryption. ๐Ÿ’ป๐Ÿ”ง๐Ÿš€

 

Achievements

Debajyoti Dhar has demonstrated exceptional skills through various achievements. As a freelance developer for Metarootz, he built a full-stack blockchain social media project using NodeJS, ExpressJS, and MongoDB for the backend, and NextJS with TailwindCSS for the frontend. He delivered a comprehensive 5-day training bootcamp on web app deployment automation with Docker, Kubernetes, and Github Actions for industry professionals. Debajyoti has also co-authored two IEEE conference papers on computer vision and deep learning and contributed to a machine learning paper in MDPI Sensors journal. Additionally, he solved 300+ DSA questions on GFG and LeetCode. ๐Ÿ“ˆ๐Ÿ’ป๐Ÿ“š

 

Research Focus

Mr. Debajyoti Dhar has contributed significantly to machine learning and optimization techniques, particularly in the context of environmental prediction. His recent work, “Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale”, published in Sensors, demonstrates his expertise in applying advanced algorithms to solve agricultural and environmental challenges. The research focuses on soil organic carbon prediction using machine learning models, emphasizing scalability and efficiency. This aligns with his broader focus on data science, AI-driven predictions, and sustainable technologies to address complex real-world problems in various domains. ๐ŸŒ๐Ÿค–๐Ÿ“Š

 

Publication Top Notes ย 

  • Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale (2024) ๐Ÿ“š

Xiaozhou Lei | Computer Science | Best Researcher Award

Xiaozhou Lei | Computer Science | Best Researcher Award

Dr Xiaozhou Lei, shanghai university, China

Evaluation for the Best Researcher Award: Dr. Xiaozhou Lei.

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Research Contributions and Innovations

Dr. Xiaozhou Lei has made notable contributions to the field of image enhancement through his pioneering work on the cell vibration energy model. This model, which he first proposed, quantitatively describes the relationship between stimulus intensity and energy during cell photothermal conversion. His work has successfully applied this model to address significant challenges in low-light enhancement and image dehazing, offering a novel approach to these problems. This research represents a unique intersection of biological modeling and image processing, with potential applications across various scientific and technological domains.

Academic Achievements

Dr. Lei has demonstrated a solid academic foundation, having earned his B.S. and M.S. degrees in mechanical design and mechatronic engineering, respectively, from the Wuhan Institute of Technology. He is currently pursuing his Ph.D. in control science and engineering at Shanghai University, which underscores his commitment to advancing his expertise. Despite being early in his academic career, Dr. Lei has completed or is involved in 9 research projects, published 5 papers in SCI-indexed journals, and contributed to the field by serving as a reviewer for the Pattern Recognition Journal.

Industry and Professional Involvement

Dr. Leiโ€™s involvement in 11 consultancy and industry projects highlights his ability to bridge the gap between academic research and practical applications. Although he has not yet published books or patents, his work has significant implications for the fields of image processing and photothermal conversion. His professional network is also expanding, as seen in his reviewer role, although he does not currently hold any editorial appointments or professional memberships.

Conclusion

Dr. Xiaozhou Leiโ€™s innovative research on the cell vibration energy model and its application to image enhancement positions him as a strong candidate for the Best Researcher Award. His work is both original and impactful, demonstrating a deep understanding of both the theoretical and practical aspects of his field. While his academic and professional profile is still developing, his contributions thus far are promising and reflect significant potential for future advancements. Thus, he is a suitable candidate for recognition in this award category.

Publication top notes

Low-light image enhancement based on cell vibration energy model and lightness difference

Low-Light Image Enhancement Using the Cell Vibration Model