Hongbin Yan | Applied Soft Computing | Research Excellence Award

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Prof. Hongbin Yan | Applied Soft Computing | Research Excellence Award

School of Business, East China University of Science and Technology | China 

He is a senior scholar in management science and engineering with extensive experience in research, teaching, and academic leadership at a leading research university. His academic background spans management, knowledge science, and information systems, providing a strong interdisciplinary foundation for both theoretical and applied research. His work primarily focuses on uncertain decision analysis, evaluation methodologies, and the integration of qualitative and quantitative approaches in management research. A significant portion of his research addresses technological innovation, new product development, service management, and quality management under uncertain and dynamic environments. He has made notable contributions to kansei engineering, computing with words, and consumer-oriented evaluation models, particularly in the context of product design, customer satisfaction, and innovation decision support. His research emphasizes the use of consumer demand, online reviews, and design thinking to support technological recombination and innovation strategies. As a principal investigator on multiple competitive research projects supported by major national and regional funding agencies, he has advanced methodological frameworks that bridge theory and real-world managerial practice. In teaching, he actively contributes to undergraduate, graduate, doctoral, and professional education, with a strong emphasis on research methodology, information systems, and managerial decision making.

Citation Metrics (Scopus)

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662

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h-index
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Featured Publications

Yinglong Li | Deep Learning | Research Excellence Award

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Dr. Yinglong Li | Deep Learning | Research Excellence Award

Associate Professor | Zhejiang University of Technology |China

Yinglong Li demonstrates strong academic and research excellence, supported by a solid portfolio of publications, patents, and externally funded projects. His 22 SCI/Scopus-indexed journal papers, more than 140 Web of Science citations, and a steadily increasing h-index reflect both productivity and growing scholarly influence. His contributions are further validated through documented outputs such as two published textbooks (ISBN: 9787302557425, 9787115415400), ten patents, and verified academic profiles including Google Scholar and institutional webpages. Strengths include his ability to integrate privacy protection, deep learning, and computer vision into practical AI solutions, as evidenced by consultancy projects with industry partners in marine systems and smart security. His research has consistently translated into deployable, high-impact technologies, demonstrating maturity in innovation and applied problem-solving. Areas for improvement include expanding international collaborations, enhancing cross-disciplinary engagement with emerging domains such as trustworthy AI governance, and increasing participation in editorial boards or leadership roles in prestigious conferences, which would further elevate his global visibility. Moving forward, his research has strong potential to contribute significantly to privacy-preserving intelligent systems, multimodal vision architectures, and secure data ecosystems for smart cities. With a well-documented research track record, growing citation metrics, and scalable research themes aligned with global technological needs, he is positioned for continued advancement and wider impact in the AI research community.

Citation Metrics (Google Scholar)

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h-index
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Featured Publications

Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

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Assoc. Prof. Dr. Tongcun Liu | Graph Learning and Recommender System | Best Researcher Award

Zhejiang A&F University | China 

Dr. Tongcun Liu is an Associate Professor at Zhejiang A & F University, specializing in computer science and technology with a strong focus on big data analytics and artificial intelligence. He earned his Ph.D. from the Beijing University of Posts and Telecommunications and later enhanced his academic experience as a Visiting Scholar at the Hong Kong University of Science and Technology. His research primarily revolves around advanced algorithms for graph computing, recommendation systems, and AI4Science, contributing significantly to the intersection of data intelligence and computational innovation. Dr. Liu leads multiple research projects funded by the National Natural Science Foundation of China and the Zhejiang Provincial Natural Science Foundation. His current and completed projects include the development of data-driven models for estimating mangrove soil dissolved organic carbon sequestration potential and the creation of cloud-edge collaborative recommendation systems based on session flow methods. With a robust publication record of more than 30 papers in esteemed international journals and conferences, his scholarly work has had a substantial impact on the field of artificial intelligence and data-driven computing. In addition to his academic achievements, Dr. Liu holds over 10 granted patents from more than 20 applications, reflecting his strong commitment to technological innovation and the advancement of AI-based computational methodologies.

Profile : Google Scholar

Featured Publications 

Feng, H., Qiu, J., Wen, L., Zhang, J., Yang, J., Lyu, Z., Liu, T., & Fang, K. (2025). U3UNet: An accurate and reliable segmentation model for forest fire monitoring based on UAV vision. Neural Networks, 185, 107207.

Fang, K., Deng, J., Dong, C., Naseem, U., Liu, T., Feng, H., & Wang, W. (2025). MoCFL: Mobile Cluster Federated Learning Framework for Highly Dynamic Network. Proceedings of the ACM on Web Conference 2025, 5065–5074.

Liu, T., Yu, G., Kwok, H. Y., Xue, R., He, D., & Liang, W. (2025). Enhancing tree-based machine learning for chlorophyll-a prediction in coastal seawater through spatiotemporal feature integration. Marine Environmental Research, 107170.

Shi, Q., Wang, Y., Liu, T., Zhang, L., & Liao, J. (2024). STRL: Writer-Independent Offline Signature Verification with Transformers and Self-Supervised Representation Learning. 2024 10th International Conference on Computer and Communications (ICCC).

Liu, T., Bao, X., Zhang, J., Fang, K., & Feng, H. (n.d.). Enhancing session-based recommendation with multi-interest hyperbolic representation networks. IEEE Transactions on Neural Networks and Learning Systems.

Dr. Mohsin Masood – Data Scientist – Best Researcher Award 

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Dr. Mohsin Masood - Data Scientist - Best Researcher Award 

Imperial College London - United Kingdom

Author Profile 

GOOGLE SCHOLAR 

🎓 Early academic pursuits

Mohsin Masood began his academic journey with a strong foundation in computer science, securing a merit-based msc funding grant from the university of east london (2011–2012). his early academic achievements laid the groundwork for advanced training in health data science, culminating in a phd in statistical modelling from the university of strathclyde, funded by a competitive grant (2014–2017). his scholarly curiosity extended internationally through the erasmus exchange research grant at vsb-technical university of ostrava (2015–2018), where he deepened his expertise in predictive modelling and bioinformatics. during this phase, his interest in power electronics and algorithmic optimization in medical diagnostics emerged as an interdisciplinary focus.

💼 Professional endeavors

With over a decade of experience, mohsin has held impactful roles across academia and research institutions. as an assistant professor in computer science at abasyn university (2018–2022), he designed and taught advanced courses in ai, ml, and data science, incorporating real-world applications in epidemiology and digital health. later, as a senior research fellow at the university of leeds (2022–2023), he led data engineering for a large-scale 2.9 million patient cohort, employing advanced machine learning and nlp tools to analyze multimorbidity and Data Scientist cardiovascular risks. currently, he serves as a research associate at imperial college london (2023–present), where he leads predictive modelling projects on population health, integrating datasets like uk biobank, sail databank, and triphic. he also fosters collaborations with institutions such as sheffield university and a* university of singapore.

🧠 Contributions and research focus

Mohsin’s core research focus lies in multimorbidity, disease progression modelling, and epidemiological risk assessment, particularly within cardiovascular and pulmonary health Data Scientist domains. he applies deep learning, survival analysis, and multistate modelling to interpret complex patient trajectories and disease clustering. his work with electronic health records (ehrs) like hospital episode statistics (hes) reflects a commitment to improving precision medicine. integrating technologies from power electronics into patient monitoring systems has been a unique dimension of his research, promoting real-time diagnostics and health alerts. he has also contributed to educational leadership by mentoring bsc and msc students in ai-based health science projects.

🌍 Impact and influence

Mohsin’s influence extends beyond academia through his involvement in cross-institutional research, digital outreach, and public health strategy. at imperial’s national heart and lung institute (nhli), he manages social media communications to boost public engagement with medical data science. his high-impact publications have contributed to the growing intersection of Data Scientist computational modelling and health outcomes, often cited in cardiovascular and cancer research communities. he is recognized for bridging the gap between statistical modelling and power electronics, showing how smart analytics can optimize hardware for healthcare delivery.

📚 Academic cites

His academic influence is reflected in high citation counts across journals focusing on biostatistics, machine learning in medicine, and epidemiology. notable contributions include projects funded by imperial nhli’s pilot project award (2023–2024) and royan pharmaceutical’s breast cancer research grant (2020–2021). his statistical toolkits and predictive algorithms are widely referenced in studies involving survival analysis, deep learning applications in ehrs, and risk prediction modelling.

🧬 Legacy and future contributions

Mohsin’s legacy lies in fostering a data-driven healthcare ecosystem where interdisciplinary tools empower clinicians and researchers alike. he aims to develop scalable, explainable ai systems for public health, enabling real-time decision-making in hospitals and remote settings. leveraging power electronics, he envisions the integration of wearable technologies with ai for early disease detection and patient monitoring. his commitment to mentoring the next generation of health data scientists ensures a lasting academic and societal impact.

Notable Publications 

  • Title: Emotion classification and crowd source sensing; a lexicon based approach
    Authors: R. Kamal, M.A. Shah, C. Maple, M. Masood, A. Wahid, A. Mehmood
    Journal: IEEE Access

  • Title: An improved particle swarm algorithm for multi-objectives based optimization in MPLS/GMPLS networks
    Authors: M. Masood, M.M. Fouad, R. Kamal, I. Glesk, I.U. Khan
    Journal: IEEE Access

  • Title: Energy efficient software defined networking algorithm for wireless sensor networks
    Authors: M. Masood, M.M. Fouad, S. Seyedzadeh, I. Glesk
    Journal: Transportation Research Procedia

  • Title: Proposing bat inspired heuristic algorithm for the optimization of GMPLS networks
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2017 25th Telecommunication Forum (TELFOR)

  • Title: Analysis of artificial intelligence-based metaheuristic algorithm for MPLS network optimization
    Authors: M. Masood, M.M. Fouad, I. Glesk
    Journal: 2018 20th International Conference on Transparent Optical Networks (ICTON)

Mr. Hassan Gharoun – AI – Best Researcher Award

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Mr. Hassan Gharoun - AI - Best Researcher Award

University of Technology Sydney - Australia

Author Profile 

SCOPUS 

🎓 Early academic pursuits

Hassan Gharoun was born on 23rd july 1992 in iran. his journey into academia began with a bachelor's degree in industrial engineering with a specialization in industrial production from kharazmi university of tehran (2010–2014). following this, he pursued his master's degree in industrial engineering at the university of tehran (2014–2017), where he deepened his expertise in data-driven systems and optimization. his early academic performance and passion for analytical research laid a strong foundation for his future endeavors. although his background initially focused on industrial systems, his early interest in machine learning and applications in power electronics gradually became prominent.

💼 Professional endeavors

After completing his master’s degree, hassan contributed to the academic community through teaching, notably at the university of tehran. he served as a tutor in advanced econometrics in fall 2017, where he handled responsibilities such as mentoring projects, tutoring in time series algorithms, and grading. he also provided practical insights using sequential deep neural networks and python programming. alongside teaching, he expanded his knowledge base by engaging in international conferences and collaborations, where he actively contributed to data science and optimization research, including subjects related to power electronics and predictive modeling.

🔬 Contributions and research focus

Hassan Gharoun is currently pursuing a ph.d. in analytics at the university of technology sydney (2022–2026). his research spans several methodological domains, including data-driven AI optimization, probabilistic machine learning, meta-learning, and deep learning. these methodologies are employed in solving complex real-world problems where predictive decision-making is crucial. his application-focused research extends to fields like predictive modeling and power electronics, where data-centric models drive intelligent systems. his interdisciplinary approach enhances the integration of machine learning into classical engineering problems, bringing innovative contributions to the analytics landscape.

🌍 Impact and influence

Hassan’s scholarly impact has been marked by both national and international recognition. he received the best paper award at the international conference on industrial engineering and AI operations management held in paris in july 2018, demonstrating his capability to produce high-quality and impactful research. his teaching and mentorship in econometrics and deep learning have also positively influenced students at the university of tehran. by blending industrial engineering insights with cutting-edge analytics and applications such as power electronics, he continues to be a bridge between traditional engineering and emerging ai-driven solutions.

📚 Academic cites

Although still in the early stages of his doctoral journey, hassan gharoun has already begun establishing a scholarly footprint through academic conferences and teaching contributions. his AI recognition at an international level hints at the growing citation potential of his works. his research is expected to be cited across multiple domains, particularly in data analytics, predictive systems, and interdisciplinary applications in sectors like power electronics. as his phd progresses, the impact and citation metrics of his work are projected to increase significantly.

🚀 Legacy and future contributions

Looking ahead, hassan gharoun is poised to make significant contributions to academia and industry through the integration of analytical methods with practical applications. his ambition is to lead innovations at the intersection of deep learning and optimization, enhancing intelligent systems in fields like healthcare, energy, and power electronics. with a strong academic trajectory and international exposure, he is building a legacy rooted in methodological rigor and impactful applications. his future goals include expanding collaborative research, publishing in top-tier journals, and contributing to sustainable technological solutions globally.

Notable Publications 

Title: A comprehensive bibliometric analysis on social network anonymization: current approaches and future directions
Author(s): Navid Yazdanjue, Hossein Yazdanjouei, Hassan Gharoun, Babak Amiri, Amir H. Gandomi
Journal: [No journal information available] — the source or journal name was not provided in the data you shared. If you have a DOI or additional details, I can help track it down further.