Assoc. Prof. Dr. Yuan. Zhi | Decision-making and Problem-solving | Best Researcher Award

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Assoc. Prof. Dr. Yuan. Zhi | Decision-making and Problem-solving | Best Researcher Award

Wuhan University of Technology | China

Zhi Yuan is a dedicated researcher specializing in intelligent shipping, traffic safety, and data-driven modeling, with strong contributions to maritime transportation analytics and vessel behavior prediction. He holds a bachelor’s degree in Electronic Information Science and Technology, a master’s degree in Software Engineering, and is completing a PhD in Traffic Information Engineering and Control at the School of Navigation, Wuhan University of Technology, including a joint PhD program at Liverpool John Moores University. His research experience spans numerous national and provincial projects on ship energy consumption prediction, vessel traffic flow modeling, multimodal data fusion, and intelligent navigation technologies. He has actively participated in major international conferences such as ISOPE, ICITE, IFSPA, CBD, and WTC, presenting work on AIS-based trajectory reconstruction, fuel consumption modeling, and spatio-temporal data analysis. His publications appear in leading journals including Environmental Modelling & Software, Ocean Engineering, Regional Studies in Marine Science, and IEEE Access. He has also contributed to several patents related to maritime safety and navigation systems. Recognized with multiple scholarships and academic awards, he continues to advance innovative solutions for maritime intelligence and sustainable navigation, aiming to enhance safety, efficiency, and environmental performance in complex waterways.

Profile:  Google Scholar

Featured Publications

1. Yuan, Z., Liu, J., Liu, Y., Zhang, Q., & Liu, R. W. (2020). A multi-task analysis and modelling paradigm using LSTM for multi-source monitoring data of inland vessels. Ocean Engineering, 213, 107604.

2. Yuan, Z., Liu, J., Liu, Y., & Li, Z. (2019). A novel approach for vessel trajectory reconstruction using AIS data. In Proceedings of the ISOPE International Ocean and Polar Engineering Conference (Paper No. ISOPE-I-19-364).

3. Li, Z., Zhang, T., Yuan, Z., Wu, Z., & Du, Z. (2018). Spatio-temporal pattern analysis and prediction for urban crime. In Proceedings of the Sixth International Conference on Advanced Cloud and Big Data (CBD).

4. Wang, X., Liu, J., Liu, Z., & Yuan, Z. (2020). Measurement and evaluation of marine intelligent transportation PNT data based on BDS and DGNSS. IOP Conference Series: Materials Science and Engineering, 719(1), 012069.

Dr. Uma Jothi | Team Building and Team Management | Excellence in Research

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Dr. Uma Jothi | Team Building and Team Management | Excellence in Research

Amrita University, India

Dr. J. Uma is an accomplished academic and researcher in the fields of information technology, cloud computing, artificial intelligence, and cybersecurity. She holds a B.Tech in Information Technology, an M.E. in Computer Science and Engineering with distinction, and has submitted her Ph.D. thesis in Information and Communication Engineering at Anna University. With more than twelve years of academic experience, she has served as Assistant Professor in leading engineering institutions, contributing significantly to teaching, curriculum development, and research mentorship. Her research focuses on cloud resource allocation, deep reinforcement learning, intelligent optimization algorithms, blockchain technologies, IoT-based systems, and data security. She has published impactful journal articles in reputed outlets like Transactions on Emerging Telecommunications Technologies and Springer’s Lecture Notes in Networks and Systems, along with several book chapters and conference papers. Her work includes innovations in heuristic optimization, adversarial defenses in deep learning, and smart healthcare IoMT solutions. She is also a published patent holder in IoT-based agriculture monitoring systems and employee training platforms. Dr. Uma has organized major AICTE- and RGNIYD-funded programs, contributing to national-level capacity building in data science, IoT, and smart city technologies. Her career reflects a strong commitment to advancing research, innovation, and academic excellence.

Profiles:  ORCID

Featured Publications

Assist. Prof. Dr. Christos Kakarougkas | Motivation and Employee Engagement | Strategic Business Management Award

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Assist. Prof. Dr. Christos Kakarougkas | Motivation and Employee Engagement | Strategic Business Management Award

University of the Aegean | Greece

Christos S. Kakarougkas is an Assistant Professor specializing in Human Resource Management of Tourism Enterprises and Organizations. He holds a Doctoral Diploma from the University of the Aegean, where his research focused on the impact of reward systems on organizational culture change in Greek five-star hotels. He also earned a Master’s Degree in Hospitality Management from Thames Valley University and a Bachelor’s Degree in Tourism Business Management from ATEI Larisa. With extensive academic and professional experience, he has taught in undergraduate and postgraduate programs across Greek universities, including the University of the Aegean, University of West Attica, and Hellenic Open University, as well as international programs in collaboration with Metropolitan College and Queen Margaret University, Edinburgh. His teaching covers Human Resource Management, Strategic Management, Entrepreneurship, Global Booking Systems, and Hospitality Administration. Beyond teaching, he has supervised numerous graduate and postgraduate theses, developed vocational training curricula, and contributed to adult education programs in leadership, tourism management, and entrepreneurship. His research interests include organizational culture, human resource practices in tourism, and strategic management of hospitality enterprises. He has been recognized for his scholarly contributions and active engagement in educational innovation. Christos continues to advance knowledge in tourism management while fostering excellence in academic and professional development.

Profiles:  Google Scholar 

Featured Publications

Katsoni, V., Upadhya, A., & Stratigea, A. (2017). Tourism, culture and heritage in a smart economy. Springer Proceedings in Business and Economics, 38.

Stavrinoudis, T., & Kakarougkas, C. (2017). A theoretical model of weighting and evaluating the elements defining the change of organizational culture. In Tourism, culture and heritage in a smart economy: Third International Conference (pp. xx–xx).

Stavrinoudis, T., & Kakarougkas, C. (2017). Factors of human motivation in organizations: A first scientific modeling for a more effective application in the hospitality industry. International Journal of Cultural and Digital Tourism, 4(2), 20–30. https://doi.org/xxxx

Stavrinoudis, T., Kakarougkas, C., & Vitzilaiou, C. (2022). Hotel front line employees’ perceptions on leadership and workplace motivation in times of crisis. Tourism and Hospitality Management, 28(2), 257–276. https://doi.org/xxxx

Kakarougkas, C., & Stavrinoudis, T. (2021). COVID-19 impact on the human aspect of organizational culture and learning: The case of the Greek hospitality industry. In Organizational learning in tourism and hospitality crisis management (Vol. 8, pp. 49–xx).

Kakarougkas, C., & Stavrinoudis, T., & Psimoulis, M. (2023). Evaluating the COVID-19 pandemic changes on hotel organizational culture. Journal of Global Business Insights, 8(1), 80–94. https://doi.org/xxxx

Kakarougkas, C., & Papageorgakis, E. (2023). Evaluating the effectiveness of training methods on the performance of human resources in Greek hotel businesses. Journal of Advances in Humanities Research, 4863, 11–xx. https://doi.org/xxxx

Assoc. Prof. Dr. Surbhi Agrawal | Innovative Leadership | Research Excellence Award

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Assoc. Prof. Dr. Surbhi Agrawal | Innovative Leadership | Research Excellence Award

RV Institute of Technology and Management, India

Profiles:  Google Scholar 

Featured Publications

Bora, K., Saha, S., Agrawal, S., Safonova, M., Routh, S., & Narasimhamurthy, A. (2016). Cd-hpf: New habitability score via data analytic modeling. Astronomy and Computing, 17, 129–143.

Saha, S., Basak, S., Safonova, M., Bora, K., Agrawal, S., Sarkar, P., & Murthy, J. (2018). Theoretical validation of potential habitability via analytical and boosted tree methods: An optimistic study on recently discovered exoplanets. Astronomy and Computing, 23, 141–150.

Agrawal, S., Basak, S., Mathur, A., Theophilus, A. J., Deshpande, G., & Murthy, J. (2021). Habitability classification of exoplanets: A machine learning insight. The European Physical Journal Special Topics, 230, 2221–2251.

Viquar, M., Basak, S., Dasgupta, A., Agrawal, S., & Saha, S. (2018). Machine learning in astronomy: A case study in quasar-star classification. In Emerging Technologies in Data Mining and Information Security (pp. xxx–xxx). (Publisher details not provided).

Basak, S., Saha, S., Mathur, A., Bora, K., Makhija, S., Safonova, M., & Agrawal, S. (2020). Ceesa meets machine learning: A constant elasticity earth similarity approach to habitability and classification of exoplanets. Astronomy and Computing, 30, 100335.

Naik, P., Agrawal, S., & Murthy, S. (2015). A survey on various task scheduling algorithms toward load balancing in public cloud. American Journal of Applied Mathematics, 3(1-2), 14–17.

Sarkar, J., Saha, S., & Agrawal, S. (2014). An efficient use of principal component analysis in workload characterization—a study. AASRI Procedia, 8, 68–74.

Saha, S., Agrawal, S., Bora, K., Routh, S., & Narasimhamurthy, A. (2015). ASTROMLSKIT: A new statistical machine learning toolkit: A platform for data analytics in astronomy. arXiv preprint arXiv:1504.07865.

Safonova, M., Mathur, A., Basak, S., Bora, K., & Agrawal, S. (2021). Quantifying the classification of exoplanets: In search for the right habitability metric. The European Physical Journal Special Topics, 230(10), 2207–2220.

Mr. Gabriel Amaizu | Charismatic Leadership | Best Researcher Award

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Mr. Gabriel Amaizu | Charismatic Leadership | Best Researcher Award

Towson University | United States

Profile:  Google Scholar 

Featured Publications

Njoku, J. N., Nwakanma, C. I., Amaizu, G. C., & Kim, D. S. (2023).
Prospects and challenges of Metaverse application in data‐driven intelligent transportation systems. IET Intelligent Transport Systems, 17(1), 1–21.

Amaizu, G. C., Nwakanma, C. I., Bhardwaj, S., Lee, J. M., & Kim, D. S. (2021).
Composite and efficient DDoS attack detection framework for B5G networks. Computer Networks, 188, Article 107871.

Amaizu, G. C., Nwakanma, C. I., Lee, J. M., & Kim, D. S. (2020).
Investigating network intrusion detection datasets using machine learning. In Proceedings of the 2020 International Conference on Information and Communication Technology Convergence (ICTC). IEEE.
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Sampedro, G. A. R., Agron, D. J. S., Amaizu, G. C., Kim, D. S., & Lee, J. M. (2022).
Design of an in-process quality monitoring strategy for FDM-type 3D printer using deep learning. Applied Sciences, 12(17), Article 8753.

Amaizu, G. C., Njoku, J. N., Lee, J. M., & Kim, D. S. (2024).
Metaverse in advanced manufacturing: Background, applications, limitations, open issues & future directions. ICT Express, 10(2), 233–255.

Prof. Burcu Hudaverdi | Risk Management | Best Researcher Award

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Prof. Burcu Hudaverdi | Risk Management | Best Researcher Award

Dokuz Eylul University | Turkey

Profile: Scopus

Featured Publications

  • Vine Bayes classifier based on truncated copula with application to gene expression data
  • Copula-based conditional reliability with application to rocket motor data

Assoc. Prof. Dr. Ankit Chaudhary | Strategic Leadership | Best Researcher Award

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Assoc. Prof. Dr. Ankit Chaudhary | Strategic Leadership | Best Researcher Award

Jawaharlal Nehru University | India

Profiles: Google Scholar | Scopus

Featured Publications

Raheja, J. L., Chaudhary, A., & Singal, K. (2011). Tracking of fingertips and centers of palm using KINECT. 2011 IEEE 3rd International Conference on Computational Intelligence, Modelling and Simulation (CIMSIM), 248–252.

Pandy, H., Chaudhary, A., & Mehrotra, D. (2014). A comparative review of approaches to prevent premature convergence in GA. Applied Soft Computing, 24, 1047–1077.

Chaudhary, A., Raheja, J. L., Das, K., & Raheja, S. (2011). Intelligent approaches to interact with machines using hand gesture recognition in natural way: A survey. International Journal of Computer Science and Engineering Survey (IJCSES), 2(1), 122–133.

Raheja, J. L., Kumar, S., & Chaudhary, A. (2013). Fabric defect detection based on GLCM and Gabor filter: A comparison. Optik, 124(23), 6469–6474.

Raheja, J. L., Mishra, A., & Chaudhary, A. (2016). Indian Sign Language recognition using SVM. Pattern Recognition and Image Analysis, 26(2), 434–441.

Mr. Luis Martin Pomares | Resource Assessment| Best Researcher Award

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Mr. Luis Martin Pomares | Resource Assessment| Best Researcher Award

Dubai Electricity and Water Authority (DEWA) | United Arab Emirates

Luis Martín-Pomares is a leading expert in solar energy with extensive experience in solar resource assessment, remote sensing, and solar forecasting. He has held positions as Principal Scientist at the Dubai Electricity and Water Authority (DEWA) and as a Scientist at the Qatar Foundation’s QEERI, contributing to the development of regional solar atlases and advanced predictive models for photovoltaic and concentrated solar power plants. As President and Senior Project Developer at Investigaciones y Recursos Solares Avanzados S.L. (IrSOLaV), he led multi-disciplinary teams in the design and implementation of multi-MW solar projects worldwide, integrating satellite-derived irradiance data, statistical downscaling models, and deep learning for solar forecasting. His research encompasses solar irradiance estimation from geostationary and polar satellites, nowcasting systems, energy audits, and GIS-based solar resource mapping. Luis actively participates in international collaborations including the International Energy Agency (IEA) Solar Heating and Cooling tasks, COST Action 1002, and the FP7 DNIcast project. He is also a reviewer for journals such as Solar Energy, Journal of Solar Energy Engineering, and Atmospheric Measurement Techniques. His work has advanced predictive solar modeling and operational strategies for renewable energy integration, establishing him as a key contributor to the global solar energy community.

Profile: Google Scholar 

Featured Publications

Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R., & Cony, M. (2010). Prediction of global solar irradiance based on time series analysis: Application to solar thermal power plants energy production planning. Solar Energy, 84(10), 1772–1781.

Perez, R., Lorenz, E., Pelland, S., Beauharnois, M., Van Knowe, G., et al. (2013). Comparison of numerical weather prediction solar irradiance forecasts in the US, Canada and Europe. Solar Energy, 94, 305–326.

Polo, J., Wilbert, S., Ruiz-Arias, J. A., Meyer, R., Gueymard, C., Suri, M., … (2016). Preliminary survey on site-adaptation techniques for satellite-derived and reanalysis solar radiation datasets. Solar Energy, 132, 25–37.

Espinar, B., Ramírez, L., Drews, A., Beyer, H. G., Zarzalejo, L. F., Polo, J., & Martín, L. (2009). Analysis of different comparison parameters applied to solar radiation data from satellite and German radiometric stations. Solar Energy, 83(1), 118–125.

Lorenz, E., Remund, J., Müller, S. C., Traunmüller, W., Steinmaurer, G., Pozo, D., … (2009). Benchmarking of different approaches to forecast solar irradiance. Proceedings of the 24th European Photovoltaic Solar Energy Conference, 21–25.

Jahangiri, M., Shamsabadi, A. A., Mostafaeipour, A., Rezaei, M., Yousefi, Y., … (2020). Using fuzzy MCDM technique to find the best location in Qatar for exploiting wind and solar energy to generate hydrogen and electricity. International Journal of Hydrogen Energy, 45(27), 13862–13875.

Mr. Venkatesh Guntreddi | Machine Learning and Deep Learning | Best Researcher Award

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Mr. Venkatesh Guntreddi | Machine Learning and Deep Learning | Best Researcher Award

Vellore Institute of Technology | India

Venkatesh Guntreddi is an emerging AI professional with a strong academic and industry background in Artificial Intelligence and Machine Learning. He is currently pursuing an M.Tech in Computer Science and Engineering with a specialization in AI and ML at Vellore Institute of Technology, where he has maintained an excellent academic record. He previously earned his B.Tech in Computer Science from Andhra University, building a solid foundation in programming, algorithms, and systems. Professionally, he has gained two years of experience as an AI Engineer at Tech Mahindra, where he developed and deployed end-to-end AI solutions spanning natural language processing, computer vision, and generative AI. His expertise covers the entire data science lifecycle, including data preprocessing, model building, optimization, and deployment using cloud platforms such as AWS and GCP, supported by MLOps best practices. He has worked extensively with advanced techniques in deep learning, transfer learning, and large language models, including Retrieval-Augmented Generation (RAG) and Agentic AI workflows. His research interests lie in scalable AI systems, applied NLP, generative AI, and real-world enterprise applications. Recognized for delivering impactful, production-ready solutions, he is committed to advancing data-driven innovation and seeks opportunities to contribute as an AI/ML Engineer or Data Scientist.

Profile:  ORCID

Featured Publications

“Deep Learning based Glaucoma Detection using Majority Voting Ensemble of ResNet50, VGG16, and Swin Transformer”

Dr. Hai Xue | Edge computing | Best Researcher Award

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Dr. Hai Xue | Edge computing | Best Researcher Award

University of Shanghai for Science and Technology,

Profile

Google Scholar

🎓 Early Academic Pursuits

Dr. Hai Xue embarked on his academic journey in the field of computer engineering with a Bachelor of Science in Information and Communication Engineering from Konkuk University, Seoul, South Korea, in 2014. Driven by an insatiable curiosity for software and computing, he pursued his Master’s degree at Hanyang University, Seoul, where he specialized in Computer and Software under the guidance of Prof. Inwhee Joe. This period was crucial in shaping his foundational knowledge and research skills, which later fueled his contributions to edge computing and network science. Dr. Xue culminated his formal education with a Ph.D. in Computer Engineering from Sungkyunkwan University, Suwon, in 2020, where he worked under the mentorship of Prof. Hee Yong Youn. His doctoral research laid the groundwork for his future breakthroughs in dynamic resource allocation and federated learning.

🌟 Professional Endeavors

Dr. Xue’s professional career is marked by a series of prestigious positions that reflect his growing influence in the field of computer engineering. After earning his Ph.D., he served as a Research Professor at Korea University, Seoul, from September 2020 to September 2021. During this tenure, he collaborated with renowned researcher Prof. Sangheon Pack, contributing significantly to the domains of edge computing and network optimization. In September 2021, he transitioned to his current role as an Assistant Professor at the University of Shanghai for Science and Technology (USST), Shanghai, China. Here, he continues to engage in high-impact research, mentoring young scholars, and advancing cutting-edge technological solutions.

🔮 Contributions and Research Focus

Dr. Xue’s research interests are deeply rooted in dynamic resource allocation, federated learning, and edge computing. His contributions have led to substantial advancements in these areas, including:

  • Dynamic Pricing in Edge Offloading: His recent work on dynamic pricing-based near-optimal resource allocation is set to redefine how computational resources are distributed efficiently across networks.
  • Energy Harvesting in Edge Computing: His paper on dynamic differential pricing-based edge offloading systems with energy harvesting devices has been accepted by IEEE Transactions on Network Science and Engineering, highlighting his expertise in sustainable and energy-efficient computing.
  • Federated Learning Incentive Mechanisms: His study on Yardstick-Stackelberg pricing-based incentive mechanisms for federated learning in edge computing, accepted by Computer Networks, sheds light on optimizing collaborative learning models.
  • Neural Network Optimization: His work on dynamic pseudo-mean mixed-precision quantization (DPQ) for pruned neural networks, published in Machine Learning, underscores his ability to push the boundaries of artificial intelligence efficiency.

🏆 Accolades and Recognition

Dr. Xue’s contributions have not gone unnoticed. His publications in high-impact journals such as IEEE Transactions, Computer Networks, and Machine Learning underscore his academic excellence. His research has been classified under prestigious rankings, including CAS Q2 and JCR Q1, affirming its significance within the scientific community. These accolades reflect his unwavering commitment to innovation and the quality of his scholarly output.

🌐 Impact and Influence

Dr. Xue’s research has far-reaching implications in both academia and industry. His work in dynamic pricing mechanisms is influencing how network providers optimize their resource allocation, while his advancements in federated learning are paving the way for more secure and efficient decentralized AI applications. His insights into energy harvesting in edge computing hold promise for sustainable technological solutions, a pressing need in today’s energy-conscious world.

🌟 Legacy and Future Contributions

Looking ahead, Dr. Xue is poised to make even more significant contributions to computer engineering. His ongoing projects aim to refine the synergy between AI and edge computing, ensuring smarter, more adaptive network solutions. As an educator, he remains dedicated to nurturing the next generation of computing professionals, equipping them with the knowledge and skills necessary to tackle future challenges in technology.

📝Notable Publications

Dynamic load balancing of software-defined networking based on genetic-ant colony optimization

Author(s): H. Xue, K.T. Kim, H.Y. Youn
Journal: Sensors
Year: 2019

 Detection of falls with smartphone using machine learning technique

Author(s): X. Chen, H. Xue, M. Kim, C. Wang, H.Y. Youn
Journal: 2019 8th International Congress on Advanced Applied Informatics (IIAI-AAI)
Year: 2019

Packet Scheduling for Multiple‐Switch Software‐Defined Networking in Edge Computing Environment

Author(s): H. Xue, K.T. Kim, H.Y. Youn
Journal: Wireless Communications and Mobile Computing
Year: 2018

 Dynamic pricing based near-optimal resource allocation for elastic edge offloading

Author(s): Y. Xia, H. Xue, D. Zhang, S. Mumtaz, X. Xu, J.J.P.C. Rodrigues
Journal: arXiv preprint arXiv:2409.18977
Year: 2024

DPQ: dynamic pseudo-mean mixed-precision quantization for pruned neural network

Author(s): S. Pei, J. Wang, B. Zhang, W. Qin, H. Xue, X. Ye, M. Chen
Journal: Machine Learning
Year: 2024