Zhi Liu | Artificial Intelligence | Research Excellence Award

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Prof. Zhi Liu | Artificial Intelligence | Research Excellence Award

Professor | Shandong University | China

Prof. Zhi Liu is a prominent researcher in Artificial Intelligence, specializing in machine learning, deep neural networks, and intelligent data analysis. His work focuses strongly on medical imaging, biomedical signal processing, and computer vision applications. He integrates domain knowledge with advanced AI models to enhance accuracy, robustness, and interpretability. His contributions include weakly supervised learning, multi-scale feature fusion, transformer-based models, and time-series analysis. Through interdisciplinary research, he advances impactful AI solutions for healthcare and intelligent systems.

Prof Zhi Liu
Shandong University
Artificial Intelligence | China

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4,806

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Featured Publications

Abylaikhan Myrzakhanov | Neural Networks | Research Excellence Award

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Mr. Abylaikhan Myrzakhanov | Neural Networks | Research Excellence Award

Institute of Smart Systems and Artificial Intelligence | Kazakhstan

Mr. Abylaikhan Myrzakhanov is a researcher at the Institute of Smart Systems and Artificial Intelligence, Kazakhstan, with specialization in neural networks and AI-driven intelligent sensing systems. His research focuses on the application of artificial intelligence, deep neural networks, and multispectral imaging for agricultural analytics and decision support. He has contributed to the development of AI-powered aerial imaging frameworks that integrate multispectral data with machine learning models to assess forage crop maturity with high accuracy and operational efficiency. His work demonstrates strong interdisciplinary impact by combining computer vision, remote sensing, and intelligent systems to address real-world challenges in precision agriculture. Through data-driven analysis and intelligent automation, his research supports sustainable agricultural practices, crop monitoring, and resource optimization, particularly in large-scale farming environments.

Profile: Orcid | Google Scholar

Featured Publications

Myrzakhanov, A., Baidalin, M., Rakhimzhanova, T., Akhet, A., Baidalina, S., Bogapov, I., Salikova, Z., & Varol, H. A. (2025). AI-powered aerial multispectral imaging for forage crop maturity assessment: A case study in Northern Kazakhstan. Agronomy.

Abdallah Alwawi | AI in Healthcare | Editorial Board Member

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Assist. Prof. Dr. Abdallah Alwawi | AI in Healthcare | Editorial Board Member

Al-Quds University | Palestine, State of

Assist. Prof. Dr. Abdallah Alwawi is a nursing education researcher at Al-Quds University whose work centers on palliative care education, trauma care training, simulation-based learning, and the integration of artificial intelligence in nursing education. His research emphasizes improving nursing students’ knowledge, attitudes, self-confidence, and clinical competence, particularly in sensitive areas such as end-of-life care and trauma management. He has contributed to evidence-based evaluation of educational interventions, including simulation modalities and primary trauma care courses, with a focus on healthcare education in the Palestinian context. More recently, his work explores nursing students’ perceptions, knowledge, and practices regarding generative AI technologies such as ChatGPT, highlighting the evolving role of digital innovation in health professions education. His research supports curriculum development, pedagogical reform, and policy-informed educational strategies in nursing and allied health sciences.

Profile: Google Scholar

Featured Publications

Alwawi, A. A., Abu-Odah, H., & Bayuo, J. (2022). Palliative care knowledge and attitudes towards end-of-life care among undergraduate nursing students at Al-Quds University: Implications for Palestinian education. International Journal of Environmental Research and Public Health, 19(15), 9563.
Citations: 41
Year: 2022

Salama, N., Bsharat, R., Alwawi, A., & Khlaif, Z. N. (2025). Knowledge, attitudes, and practices toward AI technology (ChatGPT) among nursing students at Palestinian universities. BMC Nursing, 24(1), 269.
Citations: 19
Year: 2025

Alwawi, A., Amro, N., & Inkaya, B. (2019). The effectiveness of the primary trauma care courses in West Bank, Palestine: Are the outcomes acceptable? Journal of Education and Practice.
Citations: 10
Year: 2019

Khlaif, Z. N., Salameh, N., Ajouz, M., Mousa, A., Itmazi, J., Alwawi, A., & Alkaissi, A. (2025). Using generative AI in nursing education: Students’ perceptions. BMC Medical Education, 25(1), 926.
Citations: 9
Year: 2025

Alwawi, A., & Inkaya, B. (2023). The effect of two different simulation modalities in palliative care teaching on nursing students’ knowledge, satisfaction, self-confidence, and skills: A randomized controlled trial. CIN: Computers, Informatics, Nursing, 41(4), 246–257.
Citations: 9
Year: 2023

Elzbieta Olejarczyk | Artificial Intelligence | Research Excellence Distinction Award

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Assoc. Prof. Dr. Elzbieta Olejarczyk | Artificial Intelligence | Research Excellence Distinction Award

Senior Reasearcher at Nalecz Institute of Biocybernetics and Biomedical Engineering, Polish Academy of Sciences | Poland

Assoc. Prof. Dr. Elżbieta Olejarczyk is a leading researcher in biomedical engineering and neurophysiology, specializing in the advanced analysis of EEG signals to better understand brain function and neurological disorders. Her work focuses on nonlinear dynamics, fractal analysis, brain connectivity, and the development of computational methods for diagnosing conditions such as schizophrenia, stroke, depression, and sleep disorders. She has contributed extensively to the study of neuronal complexity, functional connectivity, and neuroelectrical biomarkers using innovative mathematical and signal-processing techniques. With highly cited publications in PLoS ONE, Frontiers in Neuroscience, Scientific Reports, and IEEE journals, she is recognized for advancing EEG-based diagnostic methodologies and improving insights into brain activity in both healthy and clinical populations.

 

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i10-index 29

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Featured Publications

Aaron Finley | Business Intelligence | Research Excellence Award

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Dr. Aaron Finley | Business Intelligence | Research Excellence Award

Assistant Professor at Macau University of Science and Technology | Macau

Dr. Aaron Finley is a researcher at the Macau University of Science and Technology, Macau, with expertise in Business Intelligence, data-driven policy analysis, and applied econometric modeling. His research focuses on the intersection of sustainability analytics, environmental economics, public health modeling, and advanced statistical methodologies. Dr. Finley has made significant scholarly contributions in evaluating carbon pricing instruments and their effectiveness in reducing emissions across major Asian economies, providing evidence-based insights for climate policy optimization. His work on environmental, social, and governance (ESG) factors in relation to business environments demonstrates the practical application of multivariate analysis techniques such as canonical correlation analysis in regional economic systems.

In addition to sustainability and economic modeling, Dr. Finley’s interdisciplinary research extends into public health analytics, where he applies predictive modeling, diffusion theory, and cost-effectiveness analysis to pandemic response strategies, vaccination behaviors, and lung cancer screening programs in Asia. His studies published in BMC Medicine, Journal of Thoracic Disease, Sustainable Futures, and Sustainability highlight his ability to translate complex data into actionable policy insights. Through the integration of business intelligence frameworks with health and environmental datasets, Dr. Finley’s research supports informed decision-making in government, healthcare, and sustainability-focused institutions. His growing citation impact reflects the relevance and applicability of his work across multiple high-impact domains.

Profiles: Scopus | Google Scholar

Featured Publications

  • Finley, A., He, W., Huang, H., & Hon, C. (2024). Analyzing the effectiveness of carbon pricing instruments in reducing carbon emissions in major Asian economies. Sustainability, 16(23), 10542.
    Citation Count: 5

  • Finley, A., He, W., Huang, H., & Hon, C. (2025). A canonical correlation analysis on the relation of environmental, social, governance (ESG) on business environment (paying taxes) in South China. Sustainable Futures, 10, 101369.

  • Zhang, X., Shi, W., Liu, Z., Finley, A., Cen, K., Xie, Z., Yang, P., Li, H., & Leong, U. (2025). Adaptive Fourier decomposition analysis of different pandemic stages in South Korean cities: Policies and trends. Journal of Thoracic Disease, 17(6), 3516–3531.

  • Zhang, T., Wang, Y., Chen, X., Yang, X., Zhang, L., Bazzi, N., Bai, L., & Finley, A. (2025). Cost-effectiveness of risk model-based lung cancer screening in smokers and nonsmokers in China. BMC Medicine, 23(1), 315.

  • He, W., Wu, J., Chen, C. H., Finley, A., Wang, H., Huang, H., Ng, C., & Chui, T. (2025). Predicting COVID-19 vaccination timing by integrating the theory of planned behavior and the diffusion of innovations: A cross-sectional survey in Macao, China. Journal of Thoracic Disease, 17(5), 2813.

Danheng Gao | Deep Learning | Research Excellence Award

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Prof. Dr. Danheng Gao | Deep Learning | Research Excellence Award

Associate Researcher at Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences | China

Prof. Dr. Danheng Gao is a distinguished researcher at the Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, China, specializing in deep learning and its integration with advanced optical and photonic systems. His research bridges the disciplines of machine learning, surface-enhanced Raman spectroscopy (SERS), nonlinear optics, and ultrafast photonics, with a strong emphasis on intelligent data-driven strategies for real-world analytical applications. Prof. Gao has made notable contributions to the development of rapid identification and sensing technologies by combining artificial intelligence with spectroscopic techniques, significantly enhancing accuracy, speed, and automation in chemical and food analysis. His work in ultrafast photonics further explores the convergence of nonlinear optical phenomena with intelligent control systems, enabling breakthroughs in high-speed optical signal processing and precision measurement. Through high-impact publications in leading journals such as Food Chemistry, his research demonstrates strong interdisciplinary value across photonics, artificial intelligence, and applied chemistry. With growing citation impact, Prof. Gao is recognized for advancing intelligent optical sensing, machine-learning-driven spectroscopy, and next-generation photonic technologies.

Profile: Scopus

Featured Publications

  1. Gao, D., et al. (2025). A rapid wine brand identification method based on the joint application of SERS and machine learning techniques.

  2. Gao, D., et al. (2025). Advancements in ultrafast photonics: Confluence of nonlinear optics and intelligent strategies.
    Citation Count: 6

Libo Zhou | Digital Implantology | Research Excellence Award

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Dr. Libo Zhou | Digital Implantology | Research Excellence Award

Associate Chief Physician at Jiamusi University Affiliated Stomatological Hospital | China

Dr. Libo Zhou is a highly respected specialist in digital implantology whose career reflects a strong commitment to advancing dental technology, clinical innovation, and interdisciplinary collaboration. Recognized as an influential figure in the integration of robotics within oral implantology, he has significantly contributed to the development, refinement, and clinical application of robotic surgical systems, demonstrating exceptional expertise in guiding complex digital workflows and precision-based implant procedures. His leadership in pioneering robotic full-arch implant interventions and expanding the use of dynamic navigation has played a vital role in elevating clinical standards and shaping the future of technologically enhanced dental care. As an academic leader and research mentor, he fosters a culture of scientific inquiry, guiding projects that explore advanced imaging techniques, digital planning methodologies, and innovations in automated surgical guidance. His research portfolio includes numerous peer-reviewed publications in reputable indexed journals, reflecting a strong focus on improving procedural accuracy, enhancing patient outcomes, and addressing key engineering challenges in medical-dental integration. He has also contributed to national and regional advancements through patents, collaborative multidisciplinary initiatives, and active participation in professional committees dedicated to implantology and medical-engineering innovation. His work has earned recognition from scientific communities for excellence in research and contributions to clinical practice, further establishing his influence in transforming traditional approaches to implant dentistry. With a steadfast vision for the future of digital healthcare, he continues to drive translational research, promote industry-academia partnerships, and support the evolution of intelligent surgical systems that bridge technological capability with clinical expertise. His professional journey reflects a dedication not only to personal advancement but also to elevating the standards of practice, education, and innovation within the broader field of oral implantology.

Profiles: Scopus | ORCID

Featured Publications

Zhao, W., Teng, W., Su, Y., & Zhou, L. (2024). Accuracy of dental implant surgery with freehand, static computer-aided, dynamic computer-aided, and robotic computer-aided implant systems: An in vitro study. The Journal of Prosthetic Dentistry.

Zhou, L., Wu, F., Wang, J., Zhao, Y., Wu, G., & Su, Y. (2024). Effects of endoplasmic reticulum stress on chondrocyte apoptosis via the PI3K/AKT signaling pathway. Tissue and Cell.

Zhou, L., Teng, W., Li, X., & Su, Y. (2023). Accuracy of an optical robotic computer-aided implant system and the trueness of virtual techniques for measuring robot accuracy evaluated with a coordinate measuring machine in vitro. The Journal of Prosthetic Dentistry.

Ma, R., Liu, Q., Zhou, L., & Wang, L. (2023). High porosity 3D printed titanium mesh allows better bone regeneration. BMC Oral Health.

Zhou, L., Chen, D., Liu, P., Chen, L., & Su, Y. (2022). miR-132-3p participates in the pathological mechanism of temporomandibular joint osteoarthritis by targeting PTEN. Archives of Oral Biology.

Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

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Dr. Peik Foong Yeap | Artificial Intelligence | Best Academic Researcher Award

Senior Lecturer at University of Newcastle | Singapore

Dr. Yeap Peik Foong is a distinguished academic and researcher whose career reflects a deep commitment to advancing knowledge in strategic management, organisational development, cross-cultural management, sustainability practices, and innovation within higher education and industry. Renowned for her interdisciplinary perspective, she has contributed extensively to scholarly literature through impactful journal articles, book chapters, and international conference presentations that explore themes such as digital transformation, human–AI collaboration, leadership effectiveness, consumer behaviour, knowledge management, environmental sustainability, and community-based tourism. Her work is recognized for its ability to merge theoretical frameworks with real-world applications, offering insights that guide policy development, organisational strategy, and educational leadership. She has played influential roles in shaping academic programs, strengthening research culture, and supporting curriculum innovation, while also contributing actively as a reviewer, editorial board member, and examiner for reputable journals, conferences, and institutions worldwide. Her research leadership is further demonstrated through her involvement in numerous funded projects that address emerging challenges in digital well-being, workplace resilience, global responsibility, cybersecurity, internationalisation of higher education, and interorganisational collaboration. Known for her mentorship and supervision of postgraduate candidates, she has supported research that spans management, marketing, organisational behaviour, and industry-specific strategic studies, helping shape future scholars and professionals. Her consistent engagement with global academic communities, coupled with her ability to foster collaborative networks, reflects her dedication to elevating research standards and promoting sustainable, innovative, and culturally aware practices across sectors. Dr. Yeap’s body of work positions her as a respected thought leader whose scholarly contributions and service continue to influence contemporary debates and future directions in management, education, and organisational sustainability.

Profile: Scopus

Featured Publications

Ha, H., Yeap, P. F., Loh, H. S., & Pidani, R. (2025). Environmental sustainability and CSR practices by banks in Indonesia, Malaysia, and Singapore.

Tan, K. L., Yeap, P. F., Cheong, K. C. K., & Shanu, R. (2025). Crafting an organizational strategy for the new era: A qualitative study of artificial intelligence transformation in a homegrown Singaporean hotel chain.

Tan, K.-L., Loganathan, S. R., Pidani, R. R., Yeap, P.-F., Ng, D. W. L., Chong, N. T. S., Liow, M. L. S., Cheong, K. C.-K., & Yeo, M. M. L. (2024). Embracing imperfections: A predictive analysis of factors alleviating adult leaders’ digital learning stress on Singapore’s lifelong learning journey.

Yeap, P. F., & Liow, M. L. S. (2023). Tourist walkability and sustainable community-based tourism: Conceptual framework and strategic model.

Ong, H. B., Chong, L. L., Choon, S. W., Tan, S. H., Yeap, P. F., & Kasuma, N. M. H. (2022). Retaining skilled workers through motivation: The Malaysian case.

Lee, Y. W., Dorasamy, M., Ahmad, A. A., Jambulingam, M., Yeap, P. F., & Harun, S. (2021). Synchronous online learning during movement control order in higher education institutions: A systematic review.

Kuai Zhou | Computer Vision | Young Researcher Award

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Dr. Kuai Zhou | Computer Vision | Young Researcher Award

Lecturer at School of Aeronautical Engineering | Nanjing University of Industry Technology | China

Kuai Zhou is an emerging researcher in advanced aerospace manufacturing whose work integrates computer vision, deep learning, robotic automation, and precision aircraft assembly, positioning him as a promising contributor to the evolution of intelligent manufacturing systems. With a strong academic foundation in aerospace manufacturing engineering, he has developed deep expertise in visual measurement, robotic manipulation, and metrology for complex assembly tasks, building a portfolio of impactful publications and patented innovations that highlight both technical rigor and forward-looking research ambition. His scholarly contributions span high-quality scientific journals, where he has advanced methods for monocular visual measurement, high-precision six-degree-of-freedom pose estimation, super-resolution-enhanced assembly accuracy, convolutional-neural-network-based calibration techniques, adaptive insertion strategies, and robust machine-vision algorithms designed for the precise alignment and assembly of intricate components. These works collectively contribute to overcoming long-standing challenges in accuracy, automation, and reliability within large-scale aircraft assembly environments. Beyond his academic achievements, he has played an important role in national research initiatives focused on aerospace innovation, contributing to technological development in areas requiring high-precision visual sensing, automated alignment, and intelligent robotic assistance. His research and patented solutions consistently emphasize the integration of theoretical modeling with practical engineering, enabling more efficient workflows, reducing human dependence in critical assembly processes, and strengthening the foundational technologies required for future aerospace manufacturing ecosystems. With recognized expertise in computer vision, robotics, automation, and image processing, he continues to push the boundaries of intelligent aircraft assembly, helping shape the next generation of smart manufacturing and autonomous industrial systems while establishing himself as a rising figure in the field of aerospace engineering.

Profile: Google Scholar

Featured Publications

Kong, S. H. J., Huang, X., & Zhou, K. (2023). Online measurement method for assembly pose of gear structure based on monocular vision. Measurement Science and Technology, 34(6), 065110.

Kong, S. H. J., Huang, X., Zhou, K., & Li, H. Y. (2021). Detection method of addendum circle of gear structure based on machine vision. Chinese Journal of Scientific Instrument, 42(4), 247–255.

Li, H., Huang, X., Chu, W., Zhou, K., & Zhao, Z. (2021). 一种面向齿形结构装配的视觉测量方法. Laser & Optoelectronics Progress, 58(16), 1610003.

Zhou, K., Huang, X., Li, S., Li, H., & Kong, S. (2021). 6-D pose estimation method for large gear structure assembly using monocular vision. Measurement, 183, 109854.

Zhou, K., Huang, X., Li, S., & Li, G. (2023). Convolutional neural network-based pose mapping estimation as an alternative to traditional hand–eye calibration. Review of Scientific Instruments, 94(6).

Zhou, K., Huang, X., Li, S., & Li, G. (2023). Improving pose estimation accuracy for large hole shaft structure assembly based on super-resolution. Review of Scientific Instruments, 94(6).

Eric Howard | Artificial Intelligence | Research Excellence Award

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Dr. Eric Howard | Artificial Intelligence | Research Excellence Award

Honorary Research Fellow at Macquarie University | Australia

Dr. Eric Howard is a distinguished multidisciplinary scholar whose contributions span quantum computing, artificial intelligence, data science, cybersecurity, theoretical physics, and scientific philosophy, recognized for advancing both foundational research and transformative technological innovation. His work integrates quantum information theory with machine learning, leading to pioneering developments in quantum-classical neural networks, AI-enhanced intrusion detection models, quantum Bayesian inference frameworks, and advanced simulation methods for exploring molecular systems and emergent physical phenomena. With expertise that bridges scientific rigor and applied innovation, he has contributed significantly to research on quantum graph neural networks, holographic beam shaping, variational algorithm design, and AI-driven optimization for next-generation computational systems. His scholarly output includes a substantial body of peer-reviewed publications across major scientific outlets, along with editorial leadership in physics and theoretical sciences, where he supports global research through special issues, journal editing, and peer-review responsibilities. As an author and thought leader, he has produced influential academic texts and continues to develop works that deepen the understanding of machine learning theory and the evolution of quantum scientific paradigms. His professional impact extends into industry through leadership roles in AI-enabled cybersecurity and digital intelligence ventures, translating advanced theoretical models into practical solutions for threat analytics, secure digital infrastructures, cloud intelligence, and automated decision systems. Actively involved in leading scientific societies across computing, optics, physics, mathematics, and interdisciplinary research, he contributes to knowledge communities that shape the future of computational science and emerging technologies. Across academia, research, and innovation ecosystems, he is recognized for his ability to unify quantum science, intelligent computation, and high-impact problem solving, establishing a reputation as an influential figure driving progress at the intersection of advanced physics, machine intelligence, and next-generation technological development.

Profile: Google Scholar

Featured Publications

Ackley, K., Adya, V. B., Bailes, M., Blair, D., Lasky, P., & Howard, E. (2020). Neutron Star Extreme Matter Observatory: A kilohertz-band gravitational-wave detector in the global network.

Xue, X., Bian, L., Shu, J., Yuan, Q., Zhu, X., Bhat, N. D. R., Dai, S., Feng, Y., … (2021). Constraining cosmological phase transitions with the Parkes pulsar timing array.

Yoshiura, S., Pindor, B., Line, J. L. B., Barry, N., Trott, C. M., Beardsley, A., … (2021). A new MWA limit on the 21 cm power spectrum at redshifts ∼13–17.

Xue, X., Xia, Z. Q., Zhu, X., Zhao, Y., Shu, J., Yuan, Q., Bhat, N. D. R., Cameron, A. D., … (2022). High-precision search for dark photon dark matter with the Parkes Pulsar Timing Array.

Rahimi, M., Pindor, B., Line, J. L. B., Barry, N., Trott, C. M., Webster, R. L., Jordan, C. H., … (2021). Epoch of reionization power spectrum limits from Murchison Widefield Array data targeted at EoR1 field.

Devarajan, H. R., Singh, S. B., & Howard, E. (2024). Explainable AI for cloud-based machine learning interpretable models and transparency in decision making.