Junior Momo Ziazet | AI Advancements | Best Researcher Award

Published on 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]

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]

References

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

Seenivasan M A | Neuromorphic Computing | Innovative Research Award

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Innovative Research Award

Seenivasan M A
National Institute of Technology Meghalaya, India

Seenivasan M A
Affiliation National Institute of Technology Meghalaya
Country India
Scopus ID 56340679300
Documents 6
Citations 6
h-index 2
Subject Area Neuromorphic Computing
Event Global Scientist Day Awards

Seenivasan M A of the National Institute of Technology Meghalaya has developed a research profile within the interdisciplinary domain of neuromorphic computing, an area focused on biologically inspired computational architectures and intelligent hardware systems. His publication and citation record provides evidence of active engagement in contemporary research themes relevant to future computing technologies.[1]

Abstract

This article presents an academic overview of Seenivasan M A in relation to consideration for the Innovative Research Award. The profile highlights research activities in neuromorphic computing, publication performance, citation metrics, and scholarly contributions. Neuromorphic systems represent an increasingly important field that seeks to emulate neural structures and adaptive learning mechanisms through advanced hardware and software architectures. The researcher’s documented scientific output demonstrates engagement with this evolving discipline and reflects ongoing participation in international research communication.[1][2]

Keywords

  • Neuromorphic Computing
  • Artificial Intelligence Hardware
  • Computational Neuroscience
  • Emerging Computing Technologies
  • Scientific Research Evaluation

Introduction

Neuromorphic computing is a rapidly developing scientific field that integrates concepts from neuroscience, electronics, artificial intelligence, and computer engineering. The objective is to create computational systems capable of efficient information processing through architectures inspired by biological neural networks. Researchers active in this area contribute to advancements in low-power computing, adaptive learning systems, and intelligent devices capable of real-time decision-making.[2]

Within this context, Seenivasan M A has contributed to scholarly activities that align with the broader goals of innovative and interdisciplinary research. His documented academic output provides measurable indicators of research engagement through publications, citations, and participation in scientific discourse.[1]

Research Profile

Seenivasan M A is affiliated with the National Institute of Technology Meghalaya, India. His Scopus author profile identifies six indexed documents, six citations, and an h-index of two. These metrics indicate an emerging scholarly presence within the scientific community and reflect ongoing research activity in advanced computing technologies.[1]

  • Affiliation: National Institute of Technology Meghalaya
  • Research Area: Neuromorphic Computing
  • Scopus Documents: 6
  • Citations: 6
  • h-index: 2
  • Country: India

Research Contributions

Research contributions within neuromorphic computing commonly involve the development of intelligent architectures, neural processing systems, adaptive algorithms, and energy-efficient computational frameworks. Work in this domain contributes to the advancement of machine intelligence and next-generation hardware design. Published research outputs attributed to Seenivasan M A demonstrate participation in these broader scientific objectives and contribute to the continuing evolution of bio-inspired computing systems.[1][3]

Publications

The researcher has produced multiple indexed scholarly documents contributing to the scientific literature. Publication activity serves as a key indicator of knowledge dissemination and peer-reviewed engagement. The documented publication record supports the evaluation of research productivity and scholarly communication within the field of advanced computing systems.[1]

  • Peer-reviewed scientific publications indexed in Scopus.
  • Research contributions associated with neuromorphic and intelligent computing systems.
  • Participation in scholarly dissemination through recognized academic channels.

Research Impact

Research impact may be assessed through citations, publication visibility, and influence on subsequent scientific work. Citation records indicate that published studies have been referenced within the academic literature. Although quantitative metrics represent only one dimension of research evaluation, they remain useful indicators of scholarly engagement and knowledge transfer.[1]

The interdisciplinary nature of neuromorphic computing enhances the potential relevance of research outcomes across artificial intelligence, robotics, embedded systems, and intelligent hardware development. Such areas continue to attract significant scientific and technological interest globally.[2]

Award Suitability

The Innovative Research Award recognizes researchers whose work demonstrates originality, scientific rigor, and relevance to emerging challenges. Based on the available scholarly indicators, Seenivasan M A exhibits characteristics associated with active research participation, including publication output, citation activity, and involvement in a strategically important research area. These factors support consideration for recognition within programs designed to acknowledge innovative scientific contributions.[1][4]

Conclusion

Seenivasan M A represents an emerging researcher contributing to the field of neuromorphic computing through documented scholarly output and participation in contemporary scientific inquiry. His research profile reflects engagement with technologically significant areas that continue to shape future computational systems. The combination of publication activity, citation evidence, and alignment with innovative research themes provides a basis for recognition through the Innovative Research Award and related academic honors.[1]

References

  1. Scopus author details: Seenivasan M A, Author ID 56340679300. Scopus. https://www.scopus.com/authid/detail.uri?authorId=56340679300
  2. Synaptic plasticity dynamics of a biological neuron from a model of excitatory and inhibitory neurotransmitter releases
    towards hardware implementation of stdp in cmos. Neurocomputing. https://www.sciencedirect.com/science/article/abs/pii/S0925231226015365
  3. Design of synaptic interconnection of a sensory lif neuron in cmos for high-speed in-sensor neuromorphic systems.
    https://www.researchgate.net/publication/404098575_Design_of_Synaptic_Interconnection_of_a_Sensory_LIF_Neuron_in_CMOS_for_High-Speed_In-sensor_Neuromorphic_Systems
  4. Mathematical model and its realization of subthreshold internal membrane dynamics and implementation in cmos neuromorphic systems. The International Journal of Numerical Modeling: Electronic Networks, Devices and Field. https://onlinelibrary.wiley.com/doi/abs/10.1002/jnm.70143

Ali Cantürk | AI Advancements | Research Excellence Award

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Dr. Ali Cantürk | AI Advancements | Research Excellence Award 

Abdulhamıd Khan Research Hospital | Turkey 

Ali Cantürk, MD, EDiR is a distinguished radiologist specializing in interventional radiology and advanced imaging. He has authored 10 scientific documents, which have collectively received 36 citations across 35 publications, reflecting the growing impact of his research in the field. His work, particularly in radiomics, artificial intelligence, and clinical decision support, has contributed to advancements in modern radiology practices. With an h-index of 4, his scholarly influence continues to expand.

Citation Metrics (Scopus)

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30
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10
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Citations
36

Documents
10

h-index
4

Citations

Documents

h-index


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Featured Publications

Longfei Yue | AI Advancements | Best Researcher Award

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Dr. Longfei Yue | AI Advancements | Best Researcher Award

Dr. Longfei Yue | NUE | China

Longfei Yue is an influential researcher in the fields of unmanned aerial vehicles (UAVs), reinforcement learning, multi-agent systems, and intelligent autonomous control. His work focuses on advancing next-generation autonomous flight technologies, with major contributions to cooperative decision-making, swarm intelligence, guidance laws, and mission-planning strategies for aerial and aerospace systems. He has produced an extensive body of work, reflected through a strong publication record and impactful citation metrics. His research outputs include dozens of journal articles and conference papers, spanning high-quality platforms such as international aeronautical and aerospace journals, IEEE publications, machine learning proceedings, and multidisciplinary scientific journals. His citation 300, H-index 11, and 286 publication data highlight the growing influence and visibility of his contributions in the global research community. Yue’s research emphasizes cutting-edge reinforcement learning approaches such as hierarchical learning, multi-agent reinforcement learning, soft actor-critic frameworks, and constrained learning techniques. These methods are applied to challenging aerospace scenarios including exoatmospheric evasion, missile guidance, cooperative multi-target tracking, aerial confrontation strategies, dual-UAV reconnaissance, and intelligent route planning for UAV swarms. His studies integrate autonomy, control theory, optimization, and machine learning to develop efficient, safe, and robust decision-making mechanisms for complex flight environments. His work also extends to the development of unsupervised learning techniques for grouping aerial swarms and dynamic policy learning for combat maneuvering. Many of his publications have received substantial citations, demonstrating wide academic and practical relevance. Beyond UAVs, Yue has collaborated on interdisciplinary studies in applied sciences, psychology, medical engineering, and data-driven modeling, further broadening his research impact. Overall, Longfei Yue’s research significantly advances autonomous aerial systems, cooperative robotics, and intelligent control engineering. His contributions play a pivotal role in shaping the future of UAV autonomy, multi-agent intelligence, and high-level aerospace decision-making technologies.

Profile: Scopus

Featured Publications

Collaborative energy-saving path planning of unmanned surface vehicle cluster based on multi-head attention mechanism and multi-agent deep reinforcement learning. (2025). Engineering Applications of Artificial Intelligence.

 CAP planning method based on elliptic fitting of optimal detection routes. (2025). Beijing Hangkong Hangtian Daxue Xuebao (Journal of Beijing University of Aeronautics and Astronautics).

Exoatmospheric evasion guidance law with total energy limit via constrained reinforcement learning. (2024). International Journal of Aeronautical and Space Sciences.

Samia ZAOUI | AI Advancements | Women Researcher Award

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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

Saleem Ramadan | Data Science | Best Researcher Award

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Assoc. Prof. Dr. Saleem Ramadan | Data Science | Best Researcher Award

Assoc. Prof. Dr. Saleem Ramadan | Al Hussein Technical University | Jordan

Dr. Saleem Z. Ramadan is an accomplished Data Analyst and Business Analyst with a strong interdisciplinary background in industrial engineering, systems optimization, and data science. With academic and consulting experience across the U.S. and Jordan, he has applied data-driven decision-making, predictive analytics, and optimization modeling to complex problems in healthcare, manufacturing, and finance. Dr. Ramadan holds a Ph.D. in Systems Engineering from Ohio University and has served as Acting Chair and Associate Professor at Al Hussein Technical University, leading research and teaching initiatives integrating machine learning and operations analytics. He has developed impactful analytics solutions—ranging from hydroponic resource optimization and radiology workflow improvement to financial risk dashboards—using tools such as Python, SQL, Power BI, Tableau, and Minitab. His consulting work with Healthcare Operations & Performance Excellence (HOPE) led to measurable improvements in hospital performance through Six Sigma and process control techniques. A Certified Analytics Professional (CAP) and Microsoft Power BI Data Analyst Associate, Dr. Ramadan has authored 20 peer-reviewed publications, accumulating 236 citations from 230 documents with an h-index of 6. His recent works focus on machine learning–driven optimization, surgical scheduling prediction, and additive manufacturing parameter tuning. Dr. Ramadan’s combination of technical proficiency, academic leadership, and applied research impact uniquely positions him at the intersection of analytics innovation and business performance excellence.

Profiles:  Scopus | Google Scholar | LinkedIn

Featured Publications

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2025). A data-driven approach for predicting remaining intra-surgical time and enhancing operating room efficiency. Journal of Industrial Engineering and Management.

Ramadan, S., Abushams, M., Al-Dahidi, S., & Odeh, I. (2024). Optimizing tensile strength and energy consumption for FDM through mixed-integer nonlinear multi-objective optimization and design of experiments. Heliyon.