Soumia Benkou | AI in Life Science Research | Best Researcher Award

Mrs. Soumia Benkou | AI in Life Science Research | Best Researcher Award

Ibnou Zohr University | Morocco

Dr. Soumia Benkou is an accomplished Data Scientist and cybersecurity researcher with a strong academic foundation and professional experience in mathematics, machine learning, and blockchain-based E-health systems. Her current Ph.D. research at Ibnou Zohr University focuses on advanced Cloud Computing models and Blockchain architectures for healthcare data security and integrity. Over the years, she has authored and co-authored 9 research documents in reputed journals and international conferences, gaining significant scholarly recognition. Her impactful work on “Healthcare Blockchain Data Integrity Schemes Verification on Storage Cloud” and “E-health Blockchain: Conception of a New Smart Healthcare Architecture” demonstrates innovative integration of deep reinforcement learning with data integrity mechanisms. Dr. Benkou’s contributions have earned more than 120 citations across various indexed platforms, reflecting the growing influence of her research in the domains of data science, cryptography, and E-health innovation. With an h-index of 6, her research excellence highlights consistent productivity and influence within her field. Her work continues to pave the way for intelligent healthcare systems built on trust, transparency, and advanced analytics—making her a leading candidate for recognition in the realm of Life Science and Computing Research Awards.

Featured Publications

Benkou, S., Asimi, A., & Mbarek, L. (2023). E-Health Blockchain: Conception of a new smart healthcare architecture based on deep reinforcement learning. In The International Conference on Artificial Intelligence and Smart Environments (ICAISE 2023).

Benkou, S., & Asimi, A. (2023). Artificial Intelligence and Smart Environment: ICAISE’2022 635, 282. In Proceedings of the International Conference on Artificial Intelligence and Smart Environments (ICAISE 2022).

Benkou, S., & Asimi, A. (2025). DCMHTS: A deep clustering model for healthcare based on trigger service. Network Modeling Analysis in Health Informatics and Bioinformatics, 14(1), 1–18.

Benkou, S., & Asimi, A. (2025). A new perspective on E-health perforated blockchain. In Recent Advances in Internet of Things Security (p. 56).

Ait El Mouden, R., Asimi, A., & Benkou, S. (2025). A multi-objective optimization problem. In Intersection of Artificial Intelligence, Data Science, and Cutting-Edge Technologies.

Matteo Testi | AI in Life Science Research | Best Researcher Award

Mr. Matteo Testi | AI in Life Science Research | Best Researcher Award

AIVB|Italy

Matteo Testi, CEO & Founder of Deep Learning Italia and Artificial Intelligence Venture Builder (AIVB), is not only a leader in the Italian AI & edtech scene but also an active researcher with quantifiable academic impact. According to his Google Scholar profile, he has an 19, indicating that 19 of his publications have each been cited at least 19 times. Google Scholar His body of work comprises 33 documents listed on his Scholar profile—these include peer-reviewed articles, conference papers, and technical reports. Google Scholar His total citation count, summing across these works, is 1,666 citations as of now. Google Scholar These metrics reflect a solid research footprint, especially considering that Matteo’s primary role is entrepreneurial and educational leadership. His research interests include machine learning, deep learning, and MLOps, and he has authored works such as “MLOps: A Taxonomy and a Methodology”, which notably has been cited over 100 times. ResearchGate Combined with his roles at Deep Learning Italia since 2018, and more recently at AIVB since 2024, these numbers underscore both his academic credibility and his influence in translating AI research into education, business strategy, and applied technology.

Profile:  GOOGLE SCHOLAR

Featured Publications

MLOps: A taxonomy and a methodology

FetalMLOps: operationalizing machine learning models for standard fetal ultrasound plane classification

Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis

Enhancing Cell Counting through MLOps: A Structured Approach for Automated Cell Analysis