Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Dr. Keyong Hu | artificial intelligence | Best Researcher Award

Teacher, Hangzhou Normal University, China

Dr. KeYong Hu is an accomplished academic and researcher specializing in artificial intelligence and new energy technology. He earned his Ph.D. from the Zhejiang University of Technology in 2016 and is currently serving as an Associate Professor at Hangzhou Normal University, within the School of Information Science and Technology. Dr. Hu has contributed significantly to the intersection of AI and energy systems, with numerous publications in international journals, showcasing his expertise in predictive modeling and intelligent optimization.

Publication Profile

ORCID

🎓 Education Background

Dr. KeYong Hu completed his doctoral studies at the Zhejiang University of Technology, Hangzhou, China, where he received his Ph.D. in 2016. His academic training laid a strong foundation in computational intelligence and energy-related engineering applications.

💼 Professional Experience

Dr. Hu holds the position of Associate Professor at Hangzhou Normal University, Hangzhou, Zhejiang, China, affiliated with the School of Information Science and Technology. He has been actively involved in teaching, mentoring, and high-impact research since earning his doctorate.

🏆 Awards and Honors

While specific awards are not listed, Dr. Hu’s prolific publishing record in top-tier peer-reviewed journals like Mathematics, Heliyon, Sustainability, and Computers and Electrical Engineering underscores his recognition and influence in the fields of AI and energy optimization.

🔬 Research Focus

Dr. Hu’s research centers on the integration of artificial intelligence with new energy technologies, particularly photovoltaic power forecasting, energy system optimization, and cross-modal data analysis. His innovative use of algorithms such as Copula functions, Transformers, and Dung Beetle Optimization showcases his depth in AI-driven energy analytics.

✅ Conclusion

Dr. KeYong Hu stands out as a forward-thinking researcher contributing impactful work at the intersection of artificial intelligence and sustainable energy. Through his academic leadership and research contributions, he continues to shape the future of intelligent energy systems in China and beyond. 🌍📈

📚 Top Publications 

🔗 Two-Stage Distributionally Robust Optimal Scheduling for Integrated Energy Systems Considering Uncertainties in Renewable Generation and Loads
Journal: Mathematics | Year: 2025
Cited by: Check on Google Scholar

🔗 Short-term Photovoltaic Forecasting Model with Parallel Multi-Channel Optimization Based on Improved Dung Beetle Algorithm
Journal: Heliyon | Year: 2024
Cited by: Check on Google Scholar

🔗 Distributed Regional Photovoltaic Power Prediction Based on Stack Integration Algorithm
Journal: Mathematics | Year: 2024
Cited by: Check on Google Scholar

🔗 Automatic Depression Prediction via Cross-Modal Attention-Based Multi-Modal Fusion in Social Networks
Journal: Computers and Electrical Engineering | Year: 2024
Cited by: Check on Google Scholar

🔗 Short-Term Photovoltaic Power Generation Prediction Based on Copula Function and CNN-CosAttention-Transformer
Journal: Sustainability | Year: 2024
Cited by: Check on Google Scholar

Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

Dr. Seyed Abolfazl Aghili | Deep Learning | Best Review Paper Award

lecturer, iran university of science and technology, Iran

Seyed Abolfazl Aghili is a passionate civil engineer with a strong focus on construction engineering and management. With a Ph.D. in Civil Engineering from the prestigious Iran University of Science and Technology (IUST), he specializes in using artificial intelligence for enhancing the resilience of HVAC systems in hospitals. His research integrates cutting-edge technologies such as machine learning and deep learning to optimize building systems and improve decision-making in construction projects. Seyed’s dedication to his field has earned him a reputation as a driven academic and professional in the civil engineering community. 🏗️🤖

Publication Profile

ORCID

Education Background

Seyed Abolfazl Aghili completed his Ph.D. in Civil Engineering with a specialization in Construction Engineering and Management from Iran University of Science and Technology (IUST) between 2019 and 2024. His doctoral thesis focused on developing a framework to assess the long-term resilience of hospital air conditioning systems using artificial intelligence. Prior to that, he earned his M.Sc. in Civil Engineering with a focus on Construction Engineering and Management at IUST, where he investigated employee selection methods in construction firms. He also holds a B.Sc. in Civil Engineering from Isfahan University of Technology (IUT). 🎓📚

Professional Experience

Seyed Abolfazl Aghili has extensive experience in both academic research and practical applications of civil engineering, particularly in construction management. He has worked on various projects involving energy management, risk management, and resilience within the construction industry. His academic journey has seen him contribute significantly to the research community, particularly in the areas of AI in construction systems and HVAC performance. Furthermore, he has been an integral part of various conferences and publications, sharing his insights on improving construction management processes through technology. 💼🏢

Awards and Honors

Seyed Abolfazl Aghili has earned several prestigious awards throughout his academic journey. He was ranked 5th among 2200 participants in the Nationwide University Entrance Exam for the Ph.D. program in Iran in 2019. Additionally, he ranked 2nd among all construction management students at Iran University of Science and Technology during his M.Sc. studies. He was also ranked in the top 1% (220th out of 32,663) in the Nationwide University Entrance Exam for the M.Sc. program in Iran in 2013. 🏆🥇

Research Focus

Seyed’s primary research interests lie in the application of machine learning and deep learning techniques in construction engineering. His work focuses on enhancing the resilience of building systems, especially HVAC systems in healthcare settings. He also explores risk management, sustainability, lean construction, and decision-making systems for project managers. His interdisciplinary research combines civil engineering with advanced AI methodologies, driving innovations in construction management and systems optimization. 🔍💡

Conclusion

Seyed Abolfazl Aghili’s academic and professional journey reflects his commitment to advancing civil engineering through innovative solutions. His focus on integrating artificial intelligence into construction systems is helping to create smarter, more sustainable, and resilient built environments. Through his work, he continues to contribute valuable insights to both the academic world and the practical sector of construction engineering. 🌍🔧

Publications Top Notes

Artificial Intelligence Approaches to Energy Management in HVAC Systems: A Systematic Review. Journal of Buildings, 15.7 (2025).

Data-driven approach to fault detection for hospital HVAC system. Journals of Smart and Sustainable Built Environment, ahead-of-print (2024).

Feasibility Study of Using BIM in Construction Site Decision Making in Iran. International Conference on Civil Engineering, Architecture and Urban Infrastructure, July 2015.

Review of digital imaging technology in safety management in the construction industry. 1st National Conference on Development of Civil Engineering, Architecture, Electricity and Mechanical in Iran (December, 2014).

The role of insurance companies in managing the crisis after earthquake. 1st National Congress of Engineering, Construction, and Evaluation of Development Projects, May 2013.

The need for a new approach to pre-crisis and post-crisis management of earthquake. 1st National Conference on Seismology and Earthquake, February 2013.

Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Dr. Sikandar Ali | Artificial Intelligence Award | Best Researcher Award

Postdoc Fellow, Inje University, South Korea

🎓 Sikandar Ali is a passionate AI researcher and educator specializing in Artificial Intelligence applications in healthcare. Currently pursuing a PhD at Inje University, South Korea, he has a strong academic background and extensive research experience in digital pathology, medical imaging, and machine learning. As a team leader of the digital pathology project, he develops innovative AI algorithms for cancer diagnosis while collaborating with a global team of researchers. Sikandar is a recipient of prestigious scholarships, accolades, and recognition for his contributions to AI and healthcare innovation.

Publication Profile

Google Scholar

Education

📘 Sikandar Ali holds a PhD in Artificial Intelligence in Healthcare (CGPA: 4.46/4.5) from Inje University, South Korea, where his thesis focuses on integrating pathology foundation models with weakly supervised learning for gastric and breast cancer diagnosis. He earned an MS in Computer Science from Chungbuk National University, South Korea (GPA: 4.35/4.5), with research on AI-based clinical decision support systems for cardiovascular diseases. His undergraduate degree is a Bachelor of Engineering in Computer Systems Engineering from Mehran University of Engineering and Technology, Pakistan, with a CGPA of 3.5/4.0.

Experience

💻 Sikandar is an experienced researcher and AI specialist. Currently working as an AI Research Assistant at Inje University, he focuses on cutting-edge projects in digital pathology, cancer detection, and medical imaging. Previously, he worked as a Research Assistant at Chungbuk National University, focusing on cardiovascular disease diagnosis using AI. His industry experience includes roles such as Search Expert at PROGOS Tech Company and Software Developer Intern at Hidaya Institute of Science and Technology.

Awards and Honors

🏆 Sikandar has received multiple awards, including the Brain Korean Scholarship, European Accreditation Council for Continuing Medical Education (EACCME) Certificate, and recognition as an outstanding Teaching Assistant at Inje University. He has also earned full travel grants for international conferences, extra allowances for R&D industry projects, and certificates for reviewing research papers in leading journals. Additionally, he is a Guest Editor at Frontiers in Digital Health.

Research Focus

🔬 Sikandar’s research focuses on developing AI algorithms for medical imaging, with expertise in weakly supervised learning, self-supervised learning, and digital pathology. His projects include designing AI systems for cancer detection, COVID-19 prediction, and IPF severity classification. He also works on object detection applications using YOLO models and wearable sensor-based activity detection for pets. His commitment to explainability and interpretability in AI models ensures their practical utility in healthcare.

Conclusion

🌟 Sikandar Ali is a dedicated AI researcher driving innovation in healthcare through artificial intelligence. With his strong educational foundation, diverse research experience, and impactful contributions, he aims to bridge the gap between AI and medicine, making healthcare more efficient and accessible.

Publications

Detection of COVID-19 in X-ray Images Using DCSCNN
Sensors 2022, IF: 3.4

A Soft Voting Ensemble-Based Model for IPF Severity Prediction
Life 2021, IF: 3.2

Metaverse in Healthcare Integrated with Explainable AI and Blockchain
Sensors 2023, IF: 3.4

Weakly Supervised Learning for Gastric Cancer Classification Using WSIs
Springer 2023

Classifying Gastric Cancer Stages with Deep Semantic and Texture Features
ICACT 2024

Computer Vision-Based Military Tank Recognition Using YOLO Framework
ICAISC 2023

Activity Detection for Dog Well-being Using Wearable Sensors
IEEE Access 2022

Cat Activity Monitoring Using Wearable Sensors
IEEE Sensors Journal 2023, IF: 4.3

Deep Learning for Algae Species Detection Using Microscopic Images
Water 2022, IF: 2.9

Comprehensive Review on Multiple Instance Learning
Electronics 2023

Hybrid Model for Face Shape Classification Using Ensemble Methods
Springer 2021

Cervical Spine Fracture Detection Using Two-Stage Deep Learning
IEEE Access 2024

 

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