Ms Hind MEZIANE | IoT security | Best Researcher Award

PhD Student at ACSA Lab, FSO, UMP, Oujda, Morocco , Morocco

Hind Meziane is a dedicated PhD candidate in Computer Science at Mohammed Premier University, specializing in IoT security systems. With a strong academic foundation and diverse teaching experience, Hind is passionate about leveraging artificial intelligence to enhance cybersecurity protocols in the Internet of Things.

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Academic Background 🎓

PhD in Computer Science (2019-Present), Mohammed Premier University, Oujda.
Specialized Master in Computer Engineering (2017-2019), Honors, Mohammed Premier University, Oujda.
License in Mathematics and Computer Science (2014-2016), Mohammed Premier University, Oujda.
DEUG in Mathematics and Computer Science (2012-2014), Mohammed Premier University, Oujda.
Baccalaureate in Science, Mathematics Option B (2011-2012), Mehdi Ben Berka High School, Oujda.

Professional Experience 💼

Permanent Teacher at Sup MTI Higher School of Management, Oujda (Feb 2024-Present).
Temporary Teacher at the School of Advanced Technology and Faculty of Sciences, Mohammed Premier University (2020-2024).
Trainer at OFPPT (May 2024).

Research Interests 🔍

Internet of Things (IoT) security.
Artificial Intelligence in cybersecurity.
Performance evaluation of AI algorithms.

Projects 💻

Developed various applications including payroll management, chat applications, and a multilingual search engine.
Involved in educational projects focusing on database management and web applications.

Awards and Achievements 🏆

Hind has received recognition for her contributions to the field, including various certifications such as Huawei’s HCIA AI courses and a Coursera certification in Cybersecurity and the Internet of Things. She is also a keynote speaker, having presented on AI’s role in IoT security at international conferences.

Publications📚

Meziane, H., Ouerdi, N.
Journal: International Journal of Advanced Computer Science and Applications (IJACSA)
Year: 2022
Title: A Study of Modelling IoT Security Systems with Unified Modelling Language (UML).
Meziane, H., Ouerdi, N., Abraham, A.
Journal: International Journal of Computer Information Systems and Industrial Management Applications
Year: 2023
Title: Modeling IoT based Forest Fire Detection System with IoTsec.
Meziane, H., Ouerdi, N.
Journal: Scientific Reports (Sci Rep)
Year: 2023
Title: A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems
Meziane, H., Ouerdi, N., Kasmi, M. A., Mazouz, S.
Book Chapter: Emerging Trends in ICT for Sustainable Development
Publisher: Springer, Cham
Year: 2021
Title: Classifying Security Attacks in IoT Using CTM Method.
Hind, M., Ouerdi, N., Sanae, M., Abraham, A.
Book Chapter: Intelligent Systems Design and Applications
Publisher: Springer
Year: 2023
Title: A comparative study for modeling IoT security systems
Lecture Notes in Networks and Systems.
Hind, M., Ouerdi, N., Amine, K. M., Sanae, M.
Conference Proceedings: Proceedings of the 3rd International Conference on Networking, Information Systems & Security
Year: 2020
Title: Internet of Things: Classification of attacks using CTM method.

Dr Shi-Kai Jian | rock physics | Best Researcher Award

post-doctoral researcher at R&D Center for Ultra-Deep Complex Reservoir Exploration and Development Technology, CNPC , China

Shi-Kai Jian is a post-doctoral researcher at the R&D Center for Ultra-Deep Complex Reservoir Exploration and Development Technology, CNPC. With a robust background in petroleum exploration and geological engineering, he has made significant contributions to rock physics and seismic inversion, enhancing oil and gas production methodologies.

Profile

Education 🎓

Shi-Kai holds a B.S. degree in Petroleum Exploration from Yangtze University, an M.S. in Geodetection and Information Technology from Chengdu University of Technology, and a Ph.D. in Geological Resources and Geological Engineering from the China University of Petroleum (East China). His comprehensive education underpins his innovative research in the field.

Professional Experience 💼

Currently serving as a post-doctoral researcher, Shi-Kai has successfully completed five research programs, contributing to the understanding and development of ultra-deep reservoirs. His experience spans both academic research and practical applications in the oil and gas industry, with a focus on fracture prediction and seismic modeling.

Technical Skills ⚙️

His technical expertise includes rock physics modeling, numerical simulations, and advanced seismic analysis. He has also contributed to the development of multi-scale fracture characterization theories.

Awards and Achievements 🏆

Dr. Jian has been recognized for his research contributions, with 10 SCI papers and 3 EI papers published to date. He is also a member of the Young Editorial Board of the World Petroleum Industry and actively reviews for several reputable journals

Publications 📚

Rock-physics modeling and pre-stack seismic inversion for the Cambrian superdeep dolomite reservoirs in Tarim Basin, Northwest China (Marine and Petroleum Geology, 2024)

Elastic equivalent numerical modeling based on the dynamic method of Longmaxi Formation shale digital core (Chinese Journal of Geophysics, 2023)

3D finite-element modeling of effective elastic properties for fracture density and multiscale natural fractures (Journal of Geophysics and Engineering, 2021)

Anisotropic Effective Elastic Properties for Multi-Dimensional Fractured Models (Applied Sciences, 2022)

Elastic characteristics of fault damage zones within superdeep carbonates in Tarim Basin, Northwest China (Journal of Geophysics and Engineering, 2022)