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. 💻📊🔗

Publication Profile

Orcid

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. 📚💻🚀

Professional Experience

Debajyoti Dhar has gained valuable experience through multiple internships, showcasing his expertise in software development. At Defence Research and Development Establishment (Dec 2022–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 (Apr–Jun 2023), he built a robust backend using FastAPI and enhanced development efficiency with CI/CD pipelines and Docker. At Mak Design Private Limited (May–Jul 2024), he created a real-time chat module with Django and ReactJS, ensuring end-to-end encryption. 💻🔧🚀

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. 📈💻📚

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. 🌍🤖📊

Publication Top Notes

Highly Efficient JR Optimization Technique for Solving Prediction Problem of Soil Organic Carbon on Large Scale (2024) 📚

Prof. Larbi Boubchir | Computer Science Award | Best Researcher Award

Prof. Larbi Boubchir, University of Paris 8, France

Prof. Larbi Boubchir appears to be a strong candidate for the “Research for Best Researcher Award” based on several key factors:

Publication profile

Google Scholar

Academic and Professional Achievements

Prof. Boubchir is a Full Professor of Computer Science at the University of Paris 8, France, where he has held multiple significant roles, including Deputy Director of the LIASD laboratory and Head of the IUSD research group. His academic background includes a Ph.D. in Signal and Image Processing and an HDR degree in Computer Science, showcasing a solid foundation in his field.

Research Expertise

His research interests are diverse and highly relevant, covering artificial intelligence, biometrics, biomedical signal processing, and image processing. His expertise in advanced areas such as machine learning, deep learning, and feature engineering, coupled with practical applications in biometric security and health-related fields, highlights his significant contributions to cutting-edge technology.

Publication

Analytical form for a Bayesian wavelet estimator of images using the Bessel K form densities 📉 – Cited by 155, 2005
Face–iris multimodal biometric identification system 🕵️‍♂️ – Cited by 104, 2020
Multi-spectral palmprint recognition based on oriented multiscale log-Gabor filters ✋ – Cited by 89, 2016
Multivariate statistical modeling of images with the curvelet transform 📊 – Cited by 79, 2005
A methodology for time-frequency image processing applied to the classification of non-stationary multichannel signals using instantaneous frequency descriptors with … ⏱️ – Cited by 74, 2012
A closed-form nonparametric Bayesian estimator in the wavelet domain of images using an approximate α-stable prior 📈 – Cited by 64, 2006
Wavelet Denoising Based on the MAP Estimation Using the BKF Prior With Application to Images and EEG Signals 🧠 – Cited by 57, 2013
EEG epileptic seizure detection and classification based on dual-tree complex wavelet transform and machine learning algorithms ⚡ – Cited by 50, 2020
A review of feature extraction for EEG epileptic seizure detection and classification 🔬 – Cited by 49, 2017
Epileptic seizure prediction based on EEG spikes detection of ictal-preictal states 🔍 – Cited by 45, 2020
Robust model-free gait recognition by statistical dependency feature selection and globality-locality preserving projections 🚶‍♂️ – Cited by 39, 2016
Human gait recognition based on Haralick features 🚶‍♀️ – Cited by 38, 2017
Face–iris multi-modal biometric system using multi-resolution Log-Gabor filter with spectral regression kernel discriminant analysis 📸 – Cited by 37, 2018
Palm vein recognition based on competitive coding scheme using multi-scale local binary pattern with ant colony optimization 🖐️ – Cited by 36, 2020
Human gait recognition using GEI-based local multi-scale feature descriptors 🕺 – Cited by 36, 2019

Awards and Recognition

He has received several prestigious awards, including IEEE Access Outstanding Associate Editor accolades and Best Paper awards at international conferences. These honors reflect his high impact and recognition in the research community.

Leadership and Teaching

In addition to his research, Prof. Boubchir has made substantial contributions to education as the head of Master’s programs in Cyber Security, Data Science, and Big Data. His leadership in these programs demonstrates his commitment to advancing knowledge and mentoring future professionals.

Conclusion

Prof. Boubchir’s extensive research contributions, leadership roles, and accolades make him a highly suitable candidate for the Research for Best Researcher Award.