Soufiane Bacha | Artificial Intelligence | Best Researcher Award

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Mr. Soufiane Bacha | Artificial Intelligence | Best Researcher Award

PhD Student, University of Science and Technology Beijing, Algeria

Mr. Soufiane Bacha is a promising young researcher in Artificial Intelligence and Data Quality with a strong academic background and growing international exposure. He is currently pursuing a Ph.D. in Data Quality at the University of Science and Technology Beijing (2023–ongoing) and a Ph.D. in Cancer Epidemiology at the Department of Computer Science, Ibn Khaldoun University of Tiaret, Algeria (2021–2025). He also holds a Master’s degree in Software Engineering (2019–2021), where he ranked first in his class and completed a thesis on imbalanced datasets and boosting methods, and a B.Sc. in Computer Science (2016–2019) with strong foundations in algorithms, cryptography and programming. Professionally, Mr. Soufiane Bacha gained valuable international research experience through an internship at the Faculty of Polytechnic Mons, UMONS University in Belgium, where he worked on Internet of Things (IoT) applications involving Raspberry Pi, Arduino and sensor technologies. He has served as a part-time lecturer in Graph Theory and as an ICT trainer in web development, demonstrating strong teaching, leadership, and communication skills. His research interests span artificial intelligence, data quality, machine learning for imbalanced datasets, cancer epidemiology, distributed applications and business analytics. He is proficient in Python, C/C++, Java, SQL and data analysis tools, with expertise in OLAP, data mining, and deep learning frameworks. His achievements include an NVIDIA Deep Learning Institute Certificate, participation in AI workshops, and a Scopus-indexed publication. With a dual doctoral training and interdisciplinary focus, Mr. Soufiane Bacha is well-positioned to make impactful contributions to AI-driven data quality research and healthcare analytics on a global scale.

Profile: ORCID | Google Scholar | ResearchGate

Featured Publications

1. Bacha, S., Ning, H., Mostefa, B., Sarwatt, D. S., & Dhelim, S. (2025). A novel double pruning method for imbalanced data using information entropy and roulette wheel selection for breast cancer diagnosis (arXiv:2503.12239).

 

Namita Bajpai | Artificial Intelligence | Best Researcher Award

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Ms. Namita Bajpai | Artificial Intelligence | Best Researcher Award

Research Scholar at Indian Institute of Technology, India.

Ms. Namita Bajpai is an AI researcher and academic, currently pursuing a PhD at IIT Kharagpur. With expertise in unsupervised learning and subclass classification, she is passionate about leveraging AI for healthcare applications and large-scale data analysis. She has prior experience as an Assistant Professor and Research Assistant, contributing to AI-based knowledge discovery platforms.

Publication Profile

Scopus

Google Scholar

Educational Details

Ms. Namita Bajpai is a dedicated researcher in Artificial Intelligence, specializing in unsupervised learning and subclass classification. She is currently pursuing a Doctor of Philosophy (PhD) in the Department of Artificial Intelligence at the Indian Institute of Technology (IIT) Kharagpur, India, focusing on hidden stratification in classification using scalable clustering. She holds a Master of Technology (M.Tech.) in Computer Science from IIT (Indian School of Mines), Dhanbad with an impressive GPA of 9.28 and a Bachelor of Engineering (B.E.) in Computer Science from Government Engineering College, Raipur, where she graduated with 75.21%.

Professional Experience

Ms. Namita has a strong background in academia and research. She served as a Research Assistant at IIT Kharagpur (2022–2023), where she contributed to the Integrated Information System and Knowledge Discovery Platform for ONGC (Oil and Natural Gas Corporation) of India. Before that, she worked as an Assistant Professor at C.V. Raman College of Engineering, Bhubaneswar (2017–2018), teaching Data Mining and C programming while also taking on administrative roles such as Time Table Coordinator and First-Year Coordinator.

Research Interest

Namita’s research focuses on unsupervised learning, subclass classification, and AI applications in healthcare. Her work aims to enhance AI models for more effective classification and pattern recognition, contributing to fields such as medical diagnostics, data-driven decision-making, and scalable AI solutions.

Author Metrics

Namita has contributed to AI and machine learning research, focusing on classification models, clustering techniques, and AI-driven insights. Her academic contributions are reflected in research publications and projects, advancing the field of unsupervised learning and its real-world applications.

Top Noted Publication

Ms. Namita Bajpai has contributed to high-impact AI and data science research, publishing in renowned journals and conferences. Her most cited works include:

  1. A Novel Distributed Energy-Efficient Routing Algorithm Based on Clustering Mechanism in WSN
    📄 2019 International Conference on Intelligent Computing and Remote Sensing
    🔹 Cited 4 times | Focus: Wireless Sensor Networks & Energy-Efficient Routing

  2. Raw Data Redundancy Elimination on Cloud Database
    📄 Computational Intelligence in Pattern Recognition (CIPR 2020)
    🔹 Cited 3 times | Focus: Cloud Computing & Data Redundancy Optimization

  3. Balanced Seed Selection for K-means Clustering with Determinantal Point Process
    📄 Pattern Recognition (2025)
    🔹 Cited 1 time | Focus: Clustering Optimization & AI Model Improvement

  4. A Stratified Seed Selection Algorithm for K-means Clustering on Big Data
    📄IEEE Transactions on Artificial Intelligence
    🔹 Upcoming | Focus: Big Data Processing & AI-Driven Clustering

Conclusion

Ms. Namita Bajpai is a highly suitable candidate for the Best Researcher Award based on her strong academic record, impactful AI research, and interdisciplinary contributions. Her work in AI-driven clustering, big data analysis, and healthcare applications makes her a promising leader in the field. By further expanding collaborations, citations, and funding opportunities, she can solidify her position as a leading researcher in AI and machine learning.

Her dedication to advancing AI for real-world applications, combined with her teaching experience and research achievements, makes her a top contender for this prestigious award.