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.

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.

Yousef Asadi | Artificial Intelligence | Best Paper Award

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Mr. Yousef Asadi | Artificial Intelligence | Best Paper Award

Master Degree at Bu Ali Sina University | Iran

Mr. Yousef Asadi is a dedicated electrical engineer and researcher whose academic and professional pursuits center on advancing power systems, smart grids, and sustainable energy technologies. With a master’s degree in electrical engineering specializing in power systems from Buali Sina University, his expertise bridges theoretical insight with practical application in energy optimization, control, and artificial intelligence. His scholarly contributions have significantly enriched the field, with impactful publications in top-tier journals such as the Journal of Energy Storage, International Journal of Electrical Power & Energy Systems, Energies, Applied Sciences, and IEEE Access. His works focus on developing intelligent frameworks for energy management, universal models for power converters, and adaptive neural control techniques for active power filters—reflecting a strong interdisciplinary command of power electronics, control theory, and computational intelligence. Asadi’s research interests span microgrid stability, distributed generation, and reinforcement learning-based optimization, positioning him at the forefront of innovation in clean and resilient energy systems. His experiences in teaching, software-hardware setup, and internships across power distribution and aviation electronics have strengthened his technical and analytical capabilities. Fluent in English, Persian, and Kurdish, he demonstrates effective communication across diverse professional environments. Known for his proficiency in MATLAB, Python, and electrical design software, he applies computational modeling and automation to solve real-world energy challenges. His continuous pursuit of advanced, sustainable solutions reflects a commitment to bridging academia and industry for the development of smarter, more efficient energy infrastructures. Through his research and technical acumen, Yousef Asadi exemplifies a new generation of engineers dedicated to transforming the global energy landscape through innovation and intelligent system design.

Profile: Scopus

Featured Publications

Mansouri, M., Eskandari, M., Asadi, Y., & Savkin, A. (2024). A cloud-fog computing framework for real-time energy management in multi-microgrid system utilizing deep reinforcement learning.

Asadi, Y., Eskandari, M., Mansouri, M., Moradi, M. H., & Savkin, A. V. (2023). A universal model for power converters of battery energy storage systems utilizing the impedance-shaping concepts.

Asadi, Y., Eskandari, M., Mansouri, M., Savkin, A. V., & Pathan, E. (2022). Frequency and voltage control techniques through inverter-interfaced distributed energy resources in microgrids

Asadi, Y., Eskandari, M., Mansouri, M., Chaharmahali, S., Moradi, M. H., & Tahriri, M. S. (2022). Adaptive neural network for a stabilizing shunt active power filter in distorted weak grids.

Mansouri, M., Eskandari, M., Asadi, Y., Siano, P., & Alhelou, H. H. (2022). Pre-perturbation operational strategy scheduling in microgrids by two-stage adjustable robust optimization.

Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

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Prof. Dr. Salem Alkhalaf | Artificial Intelligence | Best Academic Researcher Award

Distinguished Researcher, Qassim University, Saudi Arabia

Prof. Dr. Salem Alkhalaf is a dynamic and accomplished researcher whose work spans information and communication technology, e-learning systems, and digital transformation. He holds a Ph.D. in Information and Communication Technology from Griffith University, supported by prior degrees in ICT and Computer Education. Prof. Dr. Salem Alkhalaf currently serves in senior academic and leadership roles at Qassim University, where he has steered initiatives in enterprise architecture, digital content management, and e-learning strategy. His research interests include collaborative learning environments, information quality in learning management systems, usability evaluation, and culturally adaptive educational technologies. He excels in research skills such as mixed methods design, structural equation modeling, system evaluation, cross-cultural adaptation, and large-scale empirical studies. He maintains an outstanding scholarly footprint: Scopus ID: 41661143900, with 2,021 citations across 1,885 documents, 179 published works, and an h-index of 23. His professional engagements include membership in IEEE, ACM, ACS, contributions as a reviewer and editorial board member, and leadership in national e-government and audit teams. Recognized through institutional awards, research grants, and best paper honors, he is committed to advancing scholarship, mentoring emerging researchers, and expanding global collaborations. Prof. Dr. Salem Alkhalaf combines visionary leadership with rigorous scholarship, making him a prominent figure positioned to drive future breakthroughs in AI, educational technology, and ICT research.

Profile: GOOGLE SCHOLAR | SCOPUS  | ORCID

Tushar Kafare | Artificial Intelligence | Best Researcher Award

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Dr. Tushar Kafare | Artificial Intelligence | Best Researcher Award

Assistant Professor at Sinhgad College of Engineering, India

Dr. Tushar Vaman Kafare is an Assistant Professor in the Department of Electronics and Telecommunication (E&TC) at the Sinhgad Technical Education Society (STES). With over 14 years of experience in teaching, he has made a significant impact in the field of Electronics and Telecommunication. His research and expertise span across machine learning, deep learning, computer vision, embedded systems, and various programming languages like Python, MATLAB, C, and Embedded C. Dr. Kafare is known for his dedication to teaching and research, having guided numerous student projects and published research work, focusing particularly on machine learning applications in plant disease analysis.

Profile

Google Scholar

Education

Dr. Kafare holds an M.E. degree in Electronics and Telecommunication, as well as a B.E. in Electronics. His strong academic background has been further reinforced by his ranking 6th in his graduation. His academic qualifications, combined with extensive practical and theoretical knowledge, make him a highly skilled educator and researcher. His ongoing Ph.D. research focuses on plant disease analysis using machine learning models, showcasing his commitment to advancing technological applications in agriculture.

Experience

Having joined STES on September 7, 2022, Dr. Kafare brings with him a wealth of experience in academia and industry. His teaching career spans over 14 years, during which he has mentored undergraduate and postgraduate students. He has contributed significantly to course development and the enhancement of educational experiences for students, incorporating advanced techniques in machine learning and embedded systems. Additionally, Dr. Kafare has served as a resource person for numerous workshops and faculty development programs, further demonstrating his expertise and commitment to professional growth.

Research Interests

Dr. Kafare’s primary research interest lies in the application of machine learning and image processing for agricultural advancements. His Ph.D. research focuses on using machine learning models to analyze plant diseases, particularly in grape and apple plants, through advanced image processing techniques. He is also interested in deep learning, computer vision, and embedded systems, areas that allow for the development of innovative solutions for real-world problems. Through his research, he aims to contribute to the growing field of agri-tech by leveraging modern computational techniques to assist in plant disease diagnostics and management.

Awards

Dr. Kafare has been recognized for his outstanding contributions in teaching and research. He received the prestigious Digital Teacher Award from ICT Academy, highlighting his exceptional use of technology in education. Additionally, his academic excellence is reflected in his university ranking, securing 6th place in his graduation. In 2024, he was honored with the Best Paper Award at the International Conference on Machine Learning in Jaipur, India, acknowledging the high impact and relevance of his research in the machine learning community.

Publications

Dr. Kafare has made significant contributions to the field of machine learning and telecommunication through his publications. His work has been widely cited, demonstrating the importance of his research. Below is a list of selected publications:

Kafare, T.V. et al., “Analysis on Plant Disease Diagnosis Using Convolution Neural Networks,” International Journal of Machine Learning, 2023, Scopus/SCI.

Kafare, T.V. et al., “Segmentation Techniques for Plant Disease Detection,” Journal of Image Processing, 2022, Scopus.

Kafare, T.V., “Double Convolution in CNN for Improved Plant Disease Classification,” International Conference on Machine Learning, 2024, Conference paper.

Kafare, T.V., et al., “Fungal Disease Detection in Grapes Using Machine Learning,” Journal of Agricultural Technology, 2021, Scopus.

Conclusion

Dr. Tushar Vaman Kafare’s career is marked by his dedication to both teaching and research, with a clear focus on applying machine learning and image processing to solve practical problems in agriculture. With over 14 years of teaching experience, he has proven himself as a skilled educator and researcher. His ongoing Ph.D. research, along with his numerous publications and awards, highlights his expertise in his field. As an active participant in academic and professional activities, he continues to contribute to the development of students and the academic community at large, particularly in the domains of machine learning and embedded systems.

Diana Morales | Deep Learning | Best Researcher Award

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Dr. Diana Morales | Deep Learning | Best Researcher Award

Critical Care Fellow | University of Toronto | Canada

Dr. Diana Morales Castro, MD, MSc, is a renowned Costa Rican physician specializing in critical care medicine, echocardiography, and perioperative medicine. Currently serving as an Adult Critical Care Senior International Fellow at Toronto General Hospital, University Health Network, and University of Toronto, Dr. Morales Castro has an extensive academic and clinical background. With advanced training in critical care, anesthesiology, and echocardiography, her expertise has been shaped by prestigious fellowships and master’s programs in various global institutions, including the University of Toronto and University College London. She has contributed significantly to research in pharmacokinetics, critical care, and echocardiography, publishing in esteemed medical journals. Her dedication to education is evidenced by her role as a mentor for the European Diploma in Advanced Critical Care Echocardiography.

Profile

Scholar

Education

Dr. Morales Castro’s educational background is rooted in excellence and dedication to advancing medical knowledge. She graduated with a Licentiate in Medicine and Surgery from the University of Costa Rica in 2011, followed by a Specialty in Anesthesiology and Recovery in 2015 from the same institution. Seeking to deepen her knowledge in critical care, she completed a Master in Perioperative Medicine at University College London in 2018. Her journey continued with a series of fellowships, including the Adult Critical Care Medicine Fellowship and Adult Critical Care Echocardiography Fellowship at the University of Toronto in 2018 and 2020, respectively. Dr. Morales Castro further expanded her expertise by pursuing a Master in Pharmaceutical Sciences at the University of Toronto, which she is expected to complete in 2024.

Experience

Dr. Morales Castro’s clinical experience spans across several high-profile institutions in Costa Rica and Canada. She began her career as a General Physician at the El Caoba EBAIS in Costa Rica, where she served in mandatory social service. She then advanced to become an Attending Anesthesiologist at Trauma Hospital and Hospital Calderón Guardia, before further specializing in adult critical care at the University of Toronto. Her role as an Attending Intensivist at the National Transplant and ECMO Center in Costa Rica was a significant milestone, where she provided critical care to patients undergoing complex treatments like ECMO. Currently, she balances her work as an attending physician with her position as a mentor for advanced critical care echocardiography at the European Society of Intensive Care Medicine.

Research Interests

Dr. Morales Castro’s research primarily focuses on pharmacokinetics and pharmacodynamics in critically ill patients, particularly those undergoing extracorporeal membrane oxygenation (ECMO). Her work delves into optimizing sedative and anesthetic pharmacokinetics during critical illness and exploring the role of therapeutic drug monitoring for drugs like propofol and fentanyl in patients on ECMO. She also investigates the impact of echocardiography and ultrasound techniques in the management of critically ill patients, with a special interest in COVID-19-related complications. Her work not only contributes to improving clinical outcomes but also advances the education of healthcare providers through innovative teaching methods like self-learning videos in transthoracic echocardiography.

Awards

Dr. Morales Castro has received numerous accolades throughout her career, recognizing her excellence in research, education, and clinical care. She was awarded the 2023 Allan Spanier Award for the best education study on simulator-based echocardiography training. In 2022, she received the MD Program Teaching Award of Excellence from the Temerty Faculty of Medicine at the University of Toronto. Her dedication during the COVID-19 pandemic was recognized with a certificate from the Costa Rican Social Security. Further demonstrating her academic prowess, she received honors for her master’s degree in perioperative medicine from University College London in 2019 and honors for her specialty in anesthesiology from the University of Costa Rica in 2015.

Publications

Dr. Morales Castro has authored several impactful publications in leading medical journals, reflecting her research contributions in critical care and pharmacokinetics. Key publications include:

Morales Castro D, Wong I, Panisko D, Najeeb U, Douflé G. Self-Learning Videos in Focused Transthoracic Echocardiography Training. Clin Teach. 2025 Feb;22(1):e70014.

Morales Castro D, Balzani E, Abdul-Aziz MH, et al. Propofol and Fentanyl Pharmacokinetics and Pharmacodynamics in Extracorporeal Membrane Oxygenation. Annals of the American Thoracic Society. 2025;22(1):121-9.

Morales Castro D, Granton J, Fan E. Ceftobiprole and Cefiderocol for Patients on Extracorporeal Membrane Oxygenation: The Role of Therapeutic Drug Monitoring. Current Drug Metabolism. 2024;25:1-5.

Morales Castro D, Ferreyro B.L., McAlpine D, et al. Echocardiographic Findings in Critically Ill COVID-19 Patients Treated with and Without ECMO. J Cardiothorac Vasc Anesth. 2024.

Douflé G, Dragoi L, Morales Castro D, et al. Head-to-Toe Bedside Ultrasound for ECMO Patients. Intensive Care Med. 2024.

Morales Castro D, Dresser L, Granton J, Fan E. Pharmacokinetic Alterations in Critical Illness. Clin Pharmacokinet. 2023; 62(2):209-220.

Morales Castro D, Abdelnour-Berchtold E, Urner M, et al. Transesophageal Echocardiography-Guided ECMO Cannulation in COVID-19. J Cardiothorac Vasc Anesth. 2022;36(12):4296-4304.

Conclusion

Dr. Diana Morales Castro stands out as a dedicated physician, educator, and researcher with a profound impact on the fields of critical care medicine and pharmacokinetics. Through her academic achievements, clinical experience, and innovative research, she has contributed to improving the quality of care in critical settings, especially for patients undergoing complex treatments like ECMO. Her commitment to education and mentorship further elevates the standards of healthcare. As she continues to explore the intersections of critical care, pharmacokinetics, and echocardiography, Dr. Morales Castro’s work promises to shape the future of intensive care and pharmacological management in critically ill patients.