Shuang Zhang | Artificial Intelligence | Young Scientist Award

Young Scientist Award

Shuang Zhang
Wuhan University, China
Shuang Zhang
Affiliation Wuhan University
Country China
Scopus ID
57216511036
Documents 1
Citations Citations by 11 documents
h-index 1
Subject Area Artificial Intelligence
Event Computer Scientists Award
ORCID
0000-0002-0218-9519

Shuang Zhang is a researcher affiliated with Wuhan University, China, whose academic activities are associated with artificial intelligence and intelligent computational systems. The researcher’s scholarly profile reflects participation in emerging research areas involving machine learning methodologies, intelligent data analysis, and computational modeling. This academic recognition article has been prepared in relation to the Young Scientist Award under the Computer Scientists Award initiative.[1]

Abstract

This article presents an academic recognition profile of Shuang Zhang, focusing on scholarly engagement within the field of artificial intelligence and intelligent computational systems. The profile examines academic visibility through publication activity, citation performance, and participation in emerging computational research areas. Emphasis is placed on artificial intelligence methodologies, machine learning integration, and interdisciplinary computational applications relevant to modern scientific innovation.[2][3]

Keywords

Artificial Intelligence; Machine Learning; Intelligent Systems; Computational Modeling; Deep Learning; Data Analysis; Neural Networks; Computer Science; Young Scientist Award; Scientific Research.

Introduction

Artificial intelligence has become one of the most influential areas within modern computational science, supporting advancements in automated reasoning, intelligent data processing, and adaptive analytical systems. Research in this field contributes to innovation across engineering, communication technologies, healthcare systems, and digital infrastructures.[3]

Shuang Zhang’s academic profile reflects participation in research activities associated with intelligent computing methodologies and machine learning applications. Such scholarly engagement contributes to the broader scientific development of computational intelligence and data-driven research systems.[1]

Research Profile

Shuang Zhang is affiliated with Wuhan University, an institution recognized for research activities in engineering, computer science, and interdisciplinary technological innovation. The researcher’s academic profile demonstrates engagement with artificial intelligence studies and computational methodologies relevant to intelligent information systems.[1]

The available Scopus profile indicates emerging scholarly visibility through indexed publication activity and citation engagement within the international scientific community. Citation metrics and publication indicators suggest continued academic participation within computational research domains.[1]

The researcher’s ORCID registration additionally supports standardized academic identification and enhances international discoverability across scholarly databases and scientific publication systems.[4]

Research Contributions

The research contributions associated with Shuang Zhang are connected with artificial intelligence methodologies, computational modeling systems, and machine learning applications. Such studies contribute to the advancement of intelligent algorithms and adaptive computational frameworks used in modern scientific and engineering environments.[2]

Artificial intelligence research frequently integrates neural networks, optimization techniques, and predictive analytical systems designed to improve decision-making efficiency and computational accuracy. These interdisciplinary approaches support technological development across numerous scientific and industrial applications.[5]

The researcher’s scholarly engagement contributes to broader academic discussions concerning intelligent systems, machine learning integration, and data-driven computational research methodologies.[3]

Publications

Shuang Zhang has contributed to scholarly publications associated with artificial intelligence and intelligent computational systems research. The publication profile reflects participation in scientific communication and computational science dissemination activities within indexed academic environments.[1]

  • Research studies related to artificial intelligence methodologies and intelligent computational frameworks.[2]
  • Academic works involving machine learning systems and computational data analysis approaches.[5]
  • Scientific contributions supporting interdisciplinary research communication within computer science and intelligent systems domains.[3]

The researcher’s publication activity reflects continued involvement in computational research dissemination and scholarly participation within international academic indexing systems.[1]

Research Impact

Research impact within artificial intelligence is commonly evaluated through publication visibility, citation performance, and interdisciplinary applicability. The available citation metrics associated with Shuang Zhang suggest emerging scholarly recognition within computational science and intelligent systems research communities.[1]

Artificial intelligence technologies contribute substantially to modern digital transformation initiatives, including intelligent automation, predictive analytics, and adaptive computational infrastructures. Research in this field supports innovation across communication systems, healthcare technologies, engineering applications, and data science environments.[5]

The researcher’s academic visibility is strengthened through indexed publication systems, citation tracking platforms, and ORCID-supported scholarly identification mechanisms.[4]

Award Suitability

The academic profile of Shuang Zhang reflects characteristics associated with emerging research excellence and early-career scientific engagement. Indexed publication activity, interdisciplinary research participation, and measurable citation visibility support consideration within academic recognition frameworks oriented toward young researchers and innovative computational studies.[1]

The researcher’s work in artificial intelligence and intelligent systems aligns with the objectives commonly emphasized by international scientific recognition platforms that support innovation, computational research quality, and technological advancement.[6]

The combination of institutional affiliation, indexed scholarly activity, and engagement with artificial intelligence methodologies collectively supports recognition through the Young Scientist Award initiative.[6]

Conclusion

Shuang Zhang represents an emerging academic profile within the field of artificial intelligence and intelligent computational systems. Scholarly engagement in machine learning methodologies, indexed publication activity, and participation in computational science research demonstrate continued involvement in modern technological and scientific innovation environments.[1]

This recognition article highlights the researcher’s academic visibility and emphasizes the continuing relevance of artificial intelligence research within interdisciplinary scientific and technological development frameworks.[3]

References

  1. Elsevier. (n.d.). Scopus author details: Shuang Zhang, Author ID 57216511036. Scopus.
    https://www.scopus.com/authid/detail.uri?authorId=57216511036
  2. ORCID. (n.d.). ORCID researcher identifier registry.
    https://orcid.org/0000-0002-0218-9519
  3. LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. Nature, 521(7553), 436–444.
    https://doi.org/10.1038/nature14539

Dr. Volodymyr Burianov | Artificial Intelligence | Research Excellence Award

Dr. Volodymyr Burianov | Artificial Intelligence | Research Excellence Award

Principal Scientist | Enamine Ltd. | Ukraine

Dr. Volodymyr Burianov is an emerging researcher in organic chemistry specializing in catalytic hydrogenation, heterogeneous catalysis, and the synthesis of heterocyclic compounds for pharmaceutical and advanced material applications. His work focuses on developing efficient catalytic systems, nanocomposite materials, and innovative hydrogenation strategies to enhance reaction selectivity and performance. He has contributed to multiple peer-reviewed publications and international conference presentations, reflecting steady scientific impact. According to available metrics, his research has achieved 56 Scopus citations across 6 documents with an h-index of 5, alongside growing visibility on Google Scholar. His contributions demonstrate strong potential in advancing catalytic science and sustainable chemical synthesis.

Citation Metrics (Scopus)

60

50

40

30

20

0

 

Citations
56

Documents
6

h-index
5

                   ■ Citations Documents h-index


View Scopus Profile View ORCID Profile

Featured Publications

Dr. Andrei Kojukhov | Artificial Intelligence | Research Excellence Award

Dr. Andrei Kojukhov | Artificial Intelligence | Research Excellence Award

Senior Lecturer | Holon Institute of Technology | Israel

Dr. Andrei Kojukhov, is a researcher and academic in computer science specializing in artificial intelligence in education, data science, machine learning, cybersecurity, and next-generation communication systems. His research explores meta-cognitive AI, personalized and ubiquitous learning environments, and the integration of intelligent technologies into modern education and network infrastructures. He has contributed to peer-reviewed journals, international conferences, and standardization initiatives, alongside innovations in 5G, cloud, and network virtualization. His scholarly impact is reflected in Scopus metrics of 17 citations across 6 documents with an h-index of 2, and Google Scholar metrics of 142 citations with an h-index of 6, demonstrating sustained research relevance and interdisciplinary influence.

Citation Metrics

20

16

12

8

4

0

Citations
17

Documents
6

h-index
2

                          ■ Citations
Documents
h-index


View Scopus Profile View Google Scholar Profile

Featured Publications

Assist. Prof. Dr. Joaquim Casaca | Artificial Intelligence | Best Researcher Award

Assist. Prof. Dr. Joaquim Casaca | Artificial Intelligence | Best Researcher Award

Prof, IADE, European University, Portugal

Joaquim António A. Casaca is an accomplished academic and professional in management, specializing in information security and marketing. He currently serves as an Assistant Professor at IADE, European University, Lisbon. Known for his expertise in management and economics, Joaquim has contributed extensively to research in areas such as entrepreneurial competence, marketing, and information security.

Publication Profile

Scopus

ORCID

🎓 Education Background

Joaquim Casaca holds a PhD in Management (2010) from Universidade Lusíada de Lisboa, with a thesis focusing on information security management in Portuguese SMEs. He earned a Master’s in Management (1999) and an MBA (1997) from ISEG – Lisbon School of Economics and Management, University of Lisbon. Additionally, he completed a Postgraduate degree in Information Sciences and Technologies for Organizations (1996) from ISEG, and holds a BSc in Economics (1982) from the same institution.

💼 Professional Experience

Since 2010, Joaquim has been an Assistant Professor at IADE, European University (Lisbon). Previously, he held academic roles at the University of Lisbon and Lusófona University, and financial positions in notable companies such as PT Multimédia, Portugal Telecom, and Companhia Portuguesa Rádio Marconi. His broad experience spans academia, finance, and management consultancy.

🏆 Awards and Honors

Joaquim received the Banco Espírito Santo Award in 1999 at ISEG for his outstanding Master’s thesis. This recognition highlights his early excellence and research capability in management.

🔍 Research Focus

His research interests center on management, information security, entrepreneurial competence, and marketing. Recent work includes studies on game-based learning’s effect on entrepreneurial skills and the role of neuroscience in economics and marketing. Joaquim’s interdisciplinary approach integrates management theory with emerging technologies and consumer behavior.

🔚 Conclusion

With a strong academic foundation and a versatile professional background, Joaquim A. Casaca is a respected figure in management and information security education. His ongoing contributions advance the understanding of how technology and management intersect in organizational contexts.

📚 Top Publications

  • The effect of game-based learning on the development of entrepreneurial competence among higher education students
    Daniel, A. D., Negre, Y., Casaca, J. A., Patricio, R., & Tsvetcoff, R. (2024). Education + Training.
    DOI: 10.1108/ET-10-2023-0448 — Cited by 3 articles

  • Neuroscience Applied to Economics and Marketing: A bibliometric Review of the Literature
    Casaca, J. A. (2024). International Journal of Business Innovation and Research.
    DOI: 10.1504/ijbir.2024.10066189

  • The determinants of non-consumption of disposable plastic: application of an extended theory of planned behaviour
    Casaca, J. A. (2024). International Journal of Business Environment.
    DOI: 10.1504/IJBE.2024.135693

  • Relational Marketing and Customer Satisfaction: A Systematic Literature Review
    Casaca, J. A. (2023). Estudios Gerenciales.
    DOI: 10.18046/j.estger.2023.169.6218

  • Relationship Marketing and Customer Retention – A Systematic Literature Review
    Casaca, J. A. (2023). Studies in Business and Economics.
    DOI: 10.2478/sbe-2023-0044

 

QIANG QU | Artificial Intelligence Award | Best Researcher Award

Prof. QIANG QU | Artificial Intelligence Award | Best Researcher Award

PROFESSOR, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, China

Dr. Qiang Qu is a distinguished professor and a leading researcher in blockchain, data intelligence, and decentralized systems. He serves as the Director of the Guangdong Provincial R&D Center of Blockchain and Distributed IoT Security at the Shenzhen Institutes of Advanced Technology (SIAT), Chinese Academy of Sciences (CAS). Additionally, he holds a professorship at Shenzhen University of Advanced Technology and has previously served as a guest professor at The Chinese University of Hong Kong (Shenzhen). Dr. Qu has also contributed as the Director and Chief Scientist of Huawei Blockchain Lab. With a strong international academic presence, he has held research positions at renowned institutions such as ETH Zurich, Carnegie Mellon University, and Nanyang Technological University. His pioneering work focuses on scalable algorithm design, data sense-making, and blockchain technologies, making significant contributions to AI, data systems, and interdisciplinary studies.

Publication Profile

🎓 Education

Dr. Qiang Qu earned his Ph.D. in Computer Science from Aarhus University, Denmark, under the supervision of Prof. Christian S. Jensen. His doctoral research was supported by the prestigious GEOCrowd project under Marie Skłodowska-Curie Actions. He further enriched his academic journey as a Ph.D. exchange student at Carnegie Mellon University, USA. He holds an M.Sc. in Computer Science from Peking University, China, and a B.S. in Management Information Systems from Dalian University of Technology.

💼 Experience

Dr. Qu has a diverse professional background, reflecting his global expertise. Since 2016, he has been a professor at SIAT, leading groundbreaking research in blockchain and distributed IoT security. He also served as Vice Director of Hangzhou Institutes of Advanced Technology (SIAT’s Hangzhou branch). Prior to this, he was an Assistant Professor and the Director of Dainfos Lab at Innopolis University, Russia. His research journey includes being a visiting scientist at ETH Zurich, a visiting scholar at Nanyang Technological University, and a research fellow at Singapore Management University. He also gained industry experience as an engineer at IBM China Research Lab.

🏅 Awards and Honors

Dr. Qu has received several national and international research grants, recognizing his impactful contributions to blockchain and AI-driven data intelligence. He is a prominent editorial board member of the Future Internet Journal and serves as a guest editor for multiple high-impact journals. As an active contributor to the research community, he has been a TPC (Technical Program Committee) member for prestigious conferences and regularly reviews top-tier AI and data systems journals.

🔬 Research Focus

Dr. Qu’s research interests revolve around data intelligence and decentralized systems, with a strong focus on blockchain, scalable algorithm design, and data-driven decision-making. His work has been instrumental in developing efficient data parallel approaches, AI-driven network analysis, and cross-blockchain data migration techniques. His interdisciplinary contributions bridge AI, IoT security, and geospatial analytics, driving innovation in secure and intelligent computing.

🔚 Conclusion

Dr. Qiang Qu stands as a thought leader in blockchain and data intelligence, combining academic excellence with real-world impact. His contributions to AI-driven decentralized systems and scalable data solutions continue to shape the fields of computer science and IoT security. His extensive research collaborations, editorial roles, and international experience make him a key figure in advancing secure and intelligent computing technologies. 🚀

📚 Publications

SNCA: Semi-supervised Node Classification for Evolving Large Attributed Graphs – IEEE Big Data Mining and Analytics (2024). Cited in IEEE 📖

CIC-SIoT: Clean-Slate Information-Centric Software-Defined Content Discovery and Distribution for IoT – IEEE Internet of Things Journal (2024). Cited in IEEE 📖

Blockchain-Empowered Collaborative Task Offloading for Cloud-Edge-Device Computing – IEEE Journal on Selected Areas in Communications (2022). Cited in IEEE 📖

On Time-Aware Cross-Blockchain Data MigrationTsinghua Science and Technology (2024). Cited in Tsinghua University 📖

Few-Shot Relation Extraction With Automatically Generated Prompts – IEEE Transactions on Neural Networks and Learning Systems (2024). Cited in IEEE 📖

Opinion Leader Detection: A Methodological Review – Expert Systems with Applications (2019). Cited in Elsevier 📖

Neural Attentive Network for Cross-Domain Aspect-Level Sentiment ClassificationIEEE Transactions on Affective Computing (2021). Cited in IEEE 📖

Efficient Online Summarization of Large-Scale Dynamic Networks –  IEEE Transactions on Knowledge and Data Engineering (2016). Cited in IEEE 📖

Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Mr. Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Research Scholar, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed to be University), India

Prabakaran Raghavendran is a dynamic researcher and Ph.D. candidate at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, specializing in Fractional Differential Equations, Integral Transforms, Functional Differential Equations, and Control Theory. With a strong academic foundation in Mathematics, he earned an M.Sc. in Mathematics with an impressive CGPA of 9.79 from the same institution in 2022. He is currently pursuing his Ph.D., contributing significantly to the field with several research publications, patents, and international conference presentations. 🌟

Publication Profile

Education:

Prabakaran completed his B.Sc. in Mathematics at Loyola College, Chennai, in 2020 with a CGPA of 9.25. He further advanced his academic career by obtaining an M.Sc. in Mathematics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology in 2022, where he excelled with a CGPA of 9.79. Currently, he is pursuing his Ph.D. at the same institution, expected to complete in 202X. 🎓📚

Experience:

Prabakaran has been actively engaged in the research and development of advanced mathematical models and algorithms. His experience spans across fractional differential equations, fuzzy analysis, cryptography, and artificial neural networks. Additionally, he has contributed to the development of innovative technologies, holding multiple patents in signal analysis, optimization, and medical applications. His work is widely recognized in the academic and research communities. 💼🔬

Awards and Honors:

Prabakaran’s academic excellence and dedication to research have earned him several prestigious awards, including the Best Paper Presentation Award for his work on Fractional Integro Differential Equations at the 7th International Conference on Mathematical Modelling, Applied Analysis, and Computation (ICMMAAC-24) in Beirut, Lebanon. He is also a life member of both the International Association of Engineers (IAENG) and the International Organization for Academic and Scientific Development (IOASD). 🏅🌍

Research Focus:

Prabakaran’s research focuses on Fractional Differential Equations, Integral Transforms, and Control Theory, with particular attention to their applications in various fields such as cryptography, artificial neural networks, and fuzzy analysis. He has developed new methodologies for solving complex mathematical models and is deeply involved in finding practical solutions for issues such as Parkinson’s disease prognosis, noise reduction in signals, and optimization in robotics. 🔍🔢

Conclusion:

Prabakaran Raghavendran is a passionate and dedicated researcher in the field of Mathematics, with a strong focus on fractional differential equations and control theory. His groundbreaking work in both theoretical and applied mathematics has earned him recognition through publications and patents. With his ongoing research contributions, he continues to push the boundaries of mathematical modeling and its applications in real-world problems. 🌐💡

Publications:

A Study on the Existence, Uniqueness, and Stability of Fractional Neutral Volterra-Fredholm Integro-Differential Equations with State-Dependent Delay. Fractal Fractional, 9 (1), 1-23. (2024) (SCIE-WoS & Scopus) (Q1).

Analytical Study of Existence, Uniqueness, and Stability in Impulsive Neutral Fractional Volterra-Fredholm Equations.  Journal of Mathematics and Computer Science, 38 (3), 313-329. (2024) (WoS & Scopus) (Q1).

Application of Artificial Neural Networks for Existence and Controllability in Impulsive Fractional Volterra-Fredholm Integro-Differential Equations. Applied Mathematics in Science and Engineering, 32 (1), 1-21. (2024) (SCIE-WoS-Scopus).

Existence and Controllability for Second-Order Functional Differential Equations With Infinite Delay and Random Effects.  International Journal of Differential Equations, 5541644, 2024, 1-9. (2024) (WoS & Scopus).

Solving the Chemical Reaction Models with the Upadhyaya Transform. Orient J Chem, 2024; 40(3). (WoS) (WoS).

Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

Mr. Narmilan Amarasingam | Artificial Intelligence | Best Researcher Award

PhD student, Queensland University of Technology, Australia

Researcher specializing in drone-based remote sensing solutions for environmental and surveillance needs, focusing on precision agriculture and biosecurity. Expertise includes UAV remote sensing, artificial intelligence, and multispectral/hyperspectral image processing. Currently pursuing a PhD in Precision Agriculture at Queensland University of Technology.

Profile

Google Scholar

 

🎓 Education

PhD: Precision Agriculture, Queensland University of Technology (2021 – Present). MSc: Agricultural Engineering, Eastern University, Sri Lanka (2016 – 2018). BSc: Agriculture, Agricultural Engineering, Eastern University, Sri Lanka (2010 – 2015). BIT: Software Engineering, University of Colombo School of Computing, Sri Lanka (2011 – 2015)

🔍 Experience

Research Assistant at Charles Sturt University and Sunshine Coast Council on projects integrating AI and drone technology for environmental monitoring and invasive species detection.

🏆 Awards

QUT/Accelerate Higher Education Development Expansion and Development (AHEAD) World Bank Project Scholarship. Vice Chancellor’s Award for Early Career Researcher, Faculty of Technology, 2022.

🌍 Research Interests

Precision Agriculture, UAV-based Remote Sensing, Multispectral Image Processing, AI, Biosystems Engineering, Environmental Management.

📚 Publications

Co-authored numerous peer-reviewed articles in Q1 and non-Q1 ranking journals on topics related to UAV-based remote sensing and AI applications in agriculture and environmental management.

A review of UAV platforms, sensors, and applications for monitoring of sugarcane crops
Predicting canopy chlorophyll content in sugarcane crops using machine learning algorithms and spectral vegetation indices derived from UAV multispectral imageryDetection of white leaf disease in sugarcane using machine learning techniques over UAV multispectral images
Detection of White Leaf Disease in Sugarcane Crops Using UAV-Derived RGB Imagery with Existing Deep Learning Models
N Amarasingam, F Gonzalez, ASA Salgadoe, J Sandino, K Powell
E-agricultural concepts for improving productivity: A review