Junior Momo Ziazet | AI Advancements | Best Researcher Award

Published on 30/07/202630/07/2026 by Scientist Day

Global Scientist Day Awards

Junior Momo Ziazet
Concordia University, Canada

Junior Momo Ziazet
Affiliation	Concordia University
Country	Canada
Scopus ID	57219750482
Documents	5
Citations	34
h-index	3
Subject Area	AI Advancements
Event	Global Scientist Day Awards

Junior Momo Ziazet is a researcher affiliated with Concordia University, Canada, whose scholarly work contributes to the evolving field of artificial intelligence and computational research. Through peer-reviewed scientific publications indexed by Scopus, the researcher has demonstrated continued engagement with AI-related investigations and interdisciplinary technological innovation. The Global Scientist Day Awards recognizes researchers whose work supports scientific advancement through measurable academic output, research quality, and scholarly visibility.[1][2]

Contents

Abstract
Keywords
Introduction
Research Profile
Research Contributions
Publications
Research Impact
Award Suitability
Conclusion
External Links
References

Abstract

This article presents an academic overview of Junior Momo Ziazet and summarizes publicly available scholarly indicators associated with the researcher’s contributions to artificial intelligence. The profile highlights institutional affiliation, publication activity, citation metrics, and research visibility while providing a neutral overview suitable for academic recognition.[1][3]

Keywords

Artificial Intelligence, Machine Learning, Intelligent Systems, Computational Intelligence, AI Research, Scientific Publications, Research Evaluation, Concordia University, Citation Analysis, Global Scientist Day Awards.

Introduction

Artificial intelligence has become one of the fastest-growing scientific disciplines, influencing numerous sectors including healthcare, engineering, education, automation, and data science. Researchers working within this domain contribute to algorithmic development, intelligent decision-making systems, and computational methodologies that improve efficiency and knowledge discovery.[2][4]

Research Profile

Junior Momo Ziazet is affiliated with Concordia University in Canada and has established a documented research presence within Scopus-indexed literature. The available bibliometric indicators include five indexed publications, thirty-four citations, and an h-index of three, reflecting measurable scholarly engagement within the research community.[1]

Research Contributions

The researcher’s work contributes to advancing artificial intelligence through scholarly investigations that support computational methodologies and intelligent system development. Published studies demonstrate participation in contemporary scientific discussions while contributing evidence-based findings to the academic community.[2]

Publications

The available Scopus author profile records five indexed publications associated with the researcher. These publications collectively demonstrate scholarly productivity and contribute to citation-based assessment within the field of artificial intelligence. Representative DOI resources illustrating scholarly publishing standards are included for reference.[1]

Research Impact

Citation-based indicators provide one perspective on scholarly influence by reflecting how frequently published work is referenced by other researchers. Although bibliometric measures do not fully represent scientific quality, they remain widely used in institutional evaluations and research benchmarking.[3]

Award Suitability

Based on publicly available scholarly information, Junior Momo Ziazet demonstrates characteristics commonly considered during academic recognition processes, including peer-reviewed publication activity, indexed research output, citation performance, and institutional affiliation.Final award decisions remain subject to independent peer evaluation and the official assessment procedures established by the Global Scientist Day Awards.[4]

Conclusion

Junior Momo Ziazet represents an emerging scholarly profile within artificial intelligence through documented research activity and measurable academic metrics. Continued publication, collaboration, and scientific engagement are expected to further strengthen research visibility and long-term scholarly impact. This profile provides a structured academic summary suitable for recognition within the Global Scientist Day Awards framework.[1]

External Links

References

Elsevier (2026.). Scopus author details: Junior Momo Ziazet, Author ID 57219750482. Scopus.
https://www.scopus.com/pages/authors/57219750482
Google Scholar (2026.). Scholar profile for Junior Momo Ziazet.
https://scholar.google.com/citations?hl=fr&user=0HSGA14AAAAJ
Enhancing Energy Management and Efficiency of Asynchronous Federated Split Learning.
https://ieeexplore.ieee.org/document/11162158
Global Scientist Day Awards (2026.). Official Award Website.
https://scientistday.org/

Samia ZAOUI | AI Advancements | Women Researcher Award

Published on 18/10/202518/10/2025 by Scientist Day

Mrs. Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | Mohammed VI Foundation of Health and Sciences | Morocco

Dr. Samia Zaoui, based in Rabat, Morocco, is a multidisciplinary researcher and project leader bridging artificial intelligence, aeronautics, and healthcare systems innovation. She is currently pursuing her Ph.D. in Computer Science Engineering (AI & Aeronautics) at the Higher Institute of Aeronautics and Space (ISAE-SUPAERO) and INP Toulouse, France. Her research focuses on the application of AI technologies for supply chain resilience, predictive modeling, and sustainable industrial systems, with a strong emphasis on pharmaceutical and healthcare logistics. Dr. Zaoui has an extensive background in strategic project development, digital transformation, and industrial management, having held leadership roles at the Mohammed VI Foundation of Health and Sciences and the Cheikh Zaid Foundation. She has spearheaded projects in sports medicine innovation, pharmaceutical manufacturing, and medical technology transfer, fostering collaborations with international organizations such as WHO, LCIF, and Smile Train. Her scientific contributions include several peer-reviewed publications in international journals, such as the Global Journal of Flexible Systems Management and Production Planning & Control, covering topics like AI-driven supply chain viability, sustainability in Industry 5.0, and pharmaceutical risk prediction using machine learning. Dr. Zaoui’s research integrates AI-based decision systems with aeronautical and industrial engineering principles, contributing to global efforts in intelligent, resilient, and sustainable supply networks. She also actively participates in international technology exhibitions and collaborative industrial initiatives across Europe, Asia, and Africa.

Profile: Google Scholar

Featured Publications

Zaoui, S., Foguem, C., Tchuente, D., Fosso-Wamba, S., & Kamsu-Foguem, B. (2023). The viability of supply chains with interpretable learning systems: The case of COVID-19 vaccine deliveries. Global Journal of Flexible Systems Management, 24(4), 633–657. https://doi.org/10.1007/s40171-023-00357-w

Zaoui, S., Foguem, C., Tchuente, D., & Kamsu-Foguem, B. (2025). The application of artificial intelligence technologies in the resilience and the viability of supply chains: A systematic literature review. Production Planning & Control, 1–18.

Zaoui, H., Zaoui, S., Kamsu-Foguem, B., & Tchuente, D. (2024). Sustainability: The main pillar of Industry 5.0. Oklahoma International Publishing (OkIP) Books. https://doi.org/10.55432/978-1-6692-0007-9_13

StEER – Structural Engineering Extreme Event Reconnaissance. (2024). Hualien City, Taiwan Earthquake: Preliminary Virtual Reconnaissance Report (PVRR). https://doi.org/10.17603/ds2-0d2z-9682

Muhammad Arshad | AI Advancements | Best Researcher Award

Published on 18/07/202518/07/2025 by Scientist Day

Dr. Muhammad Arshad | AI Advancements | Best Researcher Award

Dr. Muhammad Arshad | Yeez Consultants, Pakistan

Dr. Muhammad Zeshan Arshad is a distinguished data scientist and academic with a Ph.D. in Statistics from the University of Agriculture, Faisalabad, specializing in mathematical statistics and advanced probability distributions. His expertise lies in machine learning, time complexity analysis, and predictive modeling, with applications spanning public health, engineering, and environmental sciences. With peer-reviewed publications, numerous ongoing collaborative projects, and experience in both academic and applied research settings, Dr. Arshad contributes significantly to interdisciplinary data-driven solutions. He is currently serving as a Data Scientist at Yeez Consultants and has held teaching positions at several renowned institutions in Pakistan.

Academic Profile

ORCID

Education

Dr. Muhammad Zeshan Arshad holds a Ph.D. in Statistics from the University of Agriculture, Faisalabad, where he graduated. His doctoral research focused on “A New Transformed G Class of Distributions with Theory and Applications,” which made substantial theoretical and applied contributions to statistical modeling and distribution theory. He also holds an M.Phil. in Statistics from the National College of Business Administration and Economics (NCBA&E), Lahore, where he worked under the mentorship of the late Prof. Dr. Munir Ahmad. His M.Phil. thesis dealt with a transmuted exponentiated moment Pareto distribution, contributing to probability theory. These qualifications are further strengthened by his participation in advanced workshops and short courses on computational modeling, data analysis using statistical software, and instructional methods. This educational journey reflects his strong grounding in both theory and application of statistics across multiple domains.

Experience

Dr. Arshad brings over 15 years of academic and professional experience in teaching, research, and data science. He is currently employed as a Data Scientist at Yeez Consultants in Faisalabad, where he has been leading machine learning and predictive modeling projects since 2020. In addition to his industry role, he has served as a visiting lecturer and tutor at several academic institutions including the University of Agriculture Faisalabad, Government College University Faisalabad, NCBA&E Lahore, and Allama Iqbal Open University. He has taught a wide range of subjects including probability and statistics, business mathematics, regression analysis, and statistical inference to students of economics, engineering, agricultural sciences, and business. His teaching responsibilities also included thesis supervision at M.Phil. and Ph.D. levels. His academic service has been complemented by administrative duties such as examination control and curriculum development, making him a versatile academic professional with a deep understanding of higher education systems.

Research Interests

Dr. Arshad’s research interests lie at the intersection of theoretical statistics and applied data science. He has a strong focus on the development of new probability distributions, entropy modeling, and statistical inference techniques. He is also deeply engaged in machine learning applications, particularly in time complexity analysis, noise-resilient predictive modeling, and shrinkage regression. His recent work includes forecasting COVID-19 cases, analyzing heart disease patterns, environmental modeling, and predictive analytics in public health and agriculture. With published journal articles and several under review in reputed international journals such as PLOS ONE, Heliyon, and AIMS Mathematics, his research has practical significance in health sciences, engineering, climate studies, and financial analytics. He actively collaborates with researchers across Saudi Arabia, Egypt, Canada, and Finland, and his publications reflect a blend of statistical innovation and real-world application.

Awards

Dr. Arshad has received several acknowledgments and awards for his academic excellence and research output. Notably, he was awarded an Appreciation Letter by the Higher Education Commission (HEC) of Pakistan for his successful completion of Ph.D. with distinction and impactful research. His scholarly contributions have earned him reviewer roles in internationally recognized journals including PLOS ONE, the Pakistan Journal of Statistics, and the International Journal of Data Science and Analysis. He has presented his work at national conferences such as the 21st International Conference on Statistical Sciences and previous events held in Faisalabad and Lahore. In addition to these academic recognitions, he has also received positive feedback and citations for his applied research in predictive modeling and statistical computing. These honors underscore his growing influence in the field of data science and statistics, both nationally and internationally.

Publications

Exploring Time Complexity and Machine Learning Scalability for COVID-19 Predictions: A Case Study from Saudi Arabia (2025) – Computational and Applied Mathematics

A NEW TRANSFORMED G CLASS OF DISTRIBUTIONS WITH THEORY AND APPLICATIONS (2023) – Statistics

Modeling potato data with the DUS modified Lehmann-type II power function distribution: Inference and applications (2023) – AIP Advances

An Alternative Statistical Model to Analysis Pearl Millet (Bajra) Yield in Province Punjab and Pakistan (2023) – Complexity

EFFECTS OF SOWING TIMES AND RICE VARIETAL RESISTANCE ON THE SEVERITY OF NARROW BROWN LEAF SPOT IN RELATION TO ENVIRONMENTAL CONDITIONS (2022) – Phytopathology

On Some Properties of a New Truncated Model With Applications to Lifetime Data (2022) – International Journal of Analysis and Applications

A new extended model with bathtub-shaped failure rate: Properties, inference, simulation and applications (2021) – Mathematics

A new generalization of Lehmann type-II distribution: Theory, simulation, and applications to survival and failure rate data (2021) – Scientific African

Conclusion

Dr. Muhammad Zeshan Arshad stands out as a dynamic and impactful researcher whose contributions span both theoretical innovation and practical application in statistics and data science. With a robust academic background, over a decade of interdisciplinary teaching and research experience, and a growing portfolio of international collaborations and high-quality publications, he exemplifies academic excellence and scientific integrity. His work continues to influence fields such as healthcare analytics, environmental modeling, and machine learning, reflecting a deep commitment to solving real-world problems through advanced statistical methods. These accomplishments make him a highly deserving candidate for recognition in any leading research or academic award platform.

Junior Momo Ziazet | AI Advancements | Best Researcher Award

Abstract

Keywords

Introduction

Research Profile

Research Contributions

Publications

Research Impact

Award Suitability

Conclusion

External Links

References

Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | AI Advancements | Women Researcher Award

Mrs. Samia ZAOUI | Mohammed VI Foundation of Health and Sciences | Morocco

Profile: Google Scholar

Featured Publications

Muhammad Arshad | AI Advancements | Best Researcher Award

Dr. Muhammad Arshad | AI Advancements | Best Researcher Award

Dr. Muhammad Arshad | Yeez Consultants, Pakistan

Academic Profile

Education

Experience

Research Interests

Awards

Publications

Exploring Time Complexity and Machine Learning Scalability for COVID-19 Predictions: A Case Study from Saudi Arabia (2025) – Computational and Applied Mathematics

A NEW TRANSFORMED G CLASS OF DISTRIBUTIONS WITH THEORY AND APPLICATIONS (2023) – Statistics

Modeling potato data with the DUS modified Lehmann-type II power function distribution: Inference and applications (2023) – AIP Advances

An Alternative Statistical Model to Analysis Pearl Millet (Bajra) Yield in Province Punjab and Pakistan (2023) – Complexity

EFFECTS OF SOWING TIMES AND RICE VARIETAL RESISTANCE ON THE SEVERITY OF NARROW BROWN LEAF SPOT IN RELATION TO ENVIRONMENTAL CONDITIONS (2022) – Phytopathology

On Some Properties of a New Truncated Model With Applications to Lifetime Data (2022) – International Journal of Analysis and Applications

A new extended model with bathtub-shaped failure rate: Properties, inference, simulation and applications (2021) – Mathematics

A new generalization of Lehmann type-II distribution: Theory, simulation, and applications to survival and failure rate data (2021) – Scientific African

Conclusion

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