Assoc. Prof. Dr. Yun-Cheng Tsai | Data Analytics | Best Researcher Award

Assoc. Prof. Dr. Yun-Cheng Tsai | Data Analytics | Best Researcher Award

Associate Professor, National Taiwan Normal University, Taiwan

Dr. Yun-Cheng Tsai is a distinguished researcher and educator specializing in blockchain technology, financial vision, artificial intelligence, and educational analytics. He is currently a faculty member at the National Taiwan Normal University, Department of Technology Application and Human Resource Development. With a strong background in computer science and information engineering, Dr. Tsai has contributed significantly to various domains, including educational metaverse environments, reinforcement learning in finance, and data privacy protection. His interdisciplinary research integrates technology and human resource development, making a substantial impact on academia and industry. ๐ŸŒ๐Ÿ’ก

Publication Profile

๐ŸŽ“ Education

Dr. Tsai earned his Ph.D. in Computer Science and Information Engineering from National Taiwan Normal University (2009-2016) ๐ŸŽ“. His academic journey also includes prestigious research stays at the Max Planck Institute for the History of Science and Humboldt-Universitรคt zu Berlin, where he collaborated on pioneering technological advancements in data science and blockchain applications. ๐ŸŒ๐Ÿ“–

๐Ÿ’ผ Experience

With a rich academic career spanning multiple institutions, Dr. Tsai has held faculty positions at several esteemed universities in Taiwan. Before joining National Taiwan Normal University in 2022, he was affiliated with Soochow University (2019-2022), National Taiwan University (2017-2019), and National Taipei University of Business (2016-2017). His expertise in blockchain and AI applications has also led to extensive research collaborations globally. ๐Ÿซ๐Ÿ”ฌ

๐Ÿ† Awards and Honors

Dr. Tsaiโ€™s contributions to blockchain technology, financial data security, and educational analytics have earned him recognition in the research community. His invited research positions at the Max Planck Institute and Humboldt-Universitรคt zu Berlin highlight his international reputation. ๐Ÿ…๐Ÿ“œ

๐Ÿ”ฌ Research Focus

Dr. Tsaiโ€™s research spans blockchain applications in financial systems, reinforcement learning for trading strategies, and AI-driven educational environments. He has developed innovative solutions for transparency in carbon credit markets, interactive learning tools for blockchain education, and privacy-preserving financial vision models. His work is widely cited and influences both academic and industry advancements. ๐Ÿš€๐Ÿ“Š

๐Ÿ” Conclusion

Dr. Yun-Cheng Tsai is a leading academic in blockchain technology, AI, and educational analytics, making significant contributions to transparency in financial markets, metaverse learning, and AI-powered trading strategies. His global collaborations and impactful research continue to shape the future of technology and education. ๐ŸŒŸ๐Ÿ“ก

๐Ÿ”— Publications

Enhancing Transparency and Fraud Detection in Carbon Credit Markets Through Blockchain-Based Visualization Techniquesย โ€“ Electronics (2025) ๐Ÿ”— DOI: 10.3390/electronics14010157

Empowering Young Learners to Explore Blockchain with Userโ€Friendly Tools: A Method Using Google Blockly and NFTsย โ€“ IET Blockchain (2024) ๐Ÿ”— DOI: 10.1049/blc2.12055

Empowering Students Through Active Learning in Educational Big Data Analyticsย โ€“ Smart Learning Environments (2024) ๐Ÿ”— DOI: 10.1186/s40561-024-00300-1

Learner-Centered Analysis in Educational Metaverse Environments: Exploring Value Exchange Systems Through Natural Interaction and Text Miningย โ€“ Journal of Metaverse (2023) ๐Ÿ”— DOI: 10.57019/jmv.1302136

Financial Vision-Based Reinforcement Learning Trading Strategyย โ€“ Analytics (2022) ๐Ÿ”— DOI: 10.3390/analytics1010004

The Protection of Data Sharing for Privacy in Financial Visionย โ€“ Applied Sciences (2022) ๐Ÿ”— DOI: 10.3390/app12157408

Dynamic Deep Convolutional Candlestick Learnerย โ€“ arXiv (2022) ๐Ÿ”— Scopus ID: 85123711664

A Pricing Model with Dynamic Credit Rating Transition Matricesย โ€“ Journal of Risk Model Validation (2021) ๐Ÿ”— DOI: 10.21314/JRMV.2021.007

Christopher Ekeocha | Machine learning | Best Researcher Award

Mr. Christopher Ekeocha | Machine learning | Best Researcher Award

Graduate Research Assistant, Africa Centre of Excellence in Future Energies and Electrochemical Systems (ACE-FUELS), Nigeria

Christopher Ikechukwu Ekeocha is a dedicated Assistant Research Fellow at the National Mathematical Centre in Abuja, Nigeria, with a keen interest in corrosion mitigation and environmental pollution. His extensive research focuses on developing innovative eco-friendly materials and computational simulation techniques to address corrosion and pollution challenges. He has represented Nigeria internationally at the International Chemistry Olympiad, guiding students to success in countries like Vietnam, Azerbaijan, Georgia, France, and China. ๐ŸŒ๐Ÿ”ฌ

Publication Profile

ORCID

Strengths for the Award:

  1. Academic Excellence: Christopher Ikechukwu Ekeocha has consistently performed at a high academic level throughout his education. His Ph.D. in Corrosion Technology (CGPA: 4.60/5.0) and Master’s in Environmental Chemistry (CGPA: 3.92/5.0) demonstrate his dedication to research and academic rigor.
  2. Innovative Research: His focus on developing eco-friendly, biomass-based anti-corrosion materials and using machine learning models for corrosion prediction is cutting-edge. His work combines experimental and computational techniques, pushing the boundaries of corrosion technology.
  3. Strong Publication Record: Ekeocha has published extensively in reputable, high-impact journals, with topics ranging from corrosion inhibitors to environmental chemistry. This demonstrates the relevance and quality of his work. Key publications include machine learning models and computational simulations for anti-corrosion research, which have been well-received in the scientific community.
  4. Interdisciplinary Collaboration: He has collaborated on multidisciplinary projects promoting circular economy and eco-friendly techniques for corrosion mitigation. His ability to work across various fields shows adaptability and leadership in research.
  5. Community Contribution: In addition to his academic work, Ekeocha has made significant contributions to the Chemistry Olympiad, leading Nigerian teams and authoring textbooks. His role in this capacity speaks to his leadership and commitment to education and knowledge dissemination.

Areas for Improvement:

  1. Research Diversification: While Ekeocha has made strong contributions in corrosion technology, expanding his research to other areas of environmental chemistry or further enhancing the practical applications of his work could strengthen his overall profile. Engaging in more diverse projects could showcase his versatility.
  2. Industry Engagement: Although his research is well-grounded in academia, there could be a stronger connection with industry to ensure his innovations, especially in corrosion mitigation, are applied in real-world settings. Collaborations with companies focusing on corrosion prevention or environmental impact assessments could enhance the practical impact of his research.
  3. International Recognition: While his publications are gaining recognition, presenting his research at more international conferences or collaborating with foreign institutions could boost his global visibility and increase the influence of his work.

Education

Christopher Ekeocha is affiliated with the Africa Centre of Excellence in Future Energies and Electrochemical Systems (ACE-FUELS) at the Federal University of Technology, Owerri (FUTO). His research emphasizes the permeation of ions across semi-permeable membranes, focusing on membrane thickness, permeation time, and electrolyte concentration. ๐ŸŽ“โš›๏ธ

Experience

With over a decade of experience, Christopher Ekeocha has served as an Assistant Research Fellow at the National Mathematical Centre, Abuja, since 2011. He leads Nigeriaโ€™s participation in the International Chemistry Olympiad, having represented the country in multiple international events. His expertise lies in corrosion studies, computational modeling, and eco-friendly corrosion inhibitors. ๐ŸŒฑ๐Ÿ”ง

Research Focus

Christopher’s research centers on the development of mathematical and predictive models for novel corrosion inhibitors. He specializes in using computational simulations and eco-friendly materials to mitigate metallic corrosion and conducting ecological risk assessments of environmental pollution. His work also covers adsorption kinetics, water and solvent treatment using nanoparticles, and pollutant removal with agricultural waste. ๐Ÿ“Š๐Ÿ”

Awards and Honours

Ekeocha has gained recognition for his contributions to corrosion research and environmental protection. His participation in the International Chemistry Olympiad as a Nigerian team leader is notable, alongside his extensive academic publications and active role in global scientific conferences. ๐Ÿ†๐ŸŒŸ

Publication Top Notes

Christopher Ikechukwu Ekeocha has authored several influential articles in prestigious journals, including Materials Today Communications, Structural Chemistry, and African Scientific Reports. His works primarily focus on corrosion inhibition, eco-friendly materials, and environmental pollution. ๐Ÿ“šโœจ

Ekeocha, C. I., et al. (2024). Data-Driven Machine Learning Models and Computational Simulation Techniques for Prediction of Anti-Corrosion Properties of Novel Benzimidazole Derivatives. Materials Today Communications https://doi.org/10.1016/j.mtcomm.2024.110156

Ekeocha, C. I., et al. (2024). Theoretical Study of Novel Antipyrine Derivatives as Promising Corrosion Inhibitors for Mild Steel in an Acidic Environment. Structural Chemistry https://doi.org/10.1007/s11224-024-02368-4

Ekeocha, C. I., et al. (2023). Review of Forms of Corrosion and Mitigation Techniques: A Visual Guide. African Scientific Reports, 2(3): 117. https://doi.org/10.46481/asr.2023.2.3.117

Conclusion:

Christopher Ikechukwu Ekeocha is an excellent candidate for the Research for Best Research Award. His innovative contributions in the field of corrosion technology, combined with his interdisciplinary approach and strong academic background, position him well for recognition. His research aligns with global trends toward eco-friendly solutions and computational advancements, making him a strong contender. However, increased industry engagement and further research diversification would further elevate his impact in both academic and practical domains.

 

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

 

๐ŸŽ“ 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)

๐Ÿ” 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.

๐Ÿ† 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.

๐ŸŒ Research Interests

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

๐Ÿ“š 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