Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Published on by Computer Science Engineering

Prof. Raziyeh Pourdarbani | Artificial Intelligence | Best Paper Award

Faculty Membr | University of Mohaghegh Ardabili | Iran

Dr. Raziyeh Pourdarbani is a Professor of Biosystems Engineering at the University of Mohaghegh Ardabili and an internationally recognized researcher in precision agriculture, image processing, machine vision, artificial intelligence, and hyperspectral imaging. Her research is dedicated to developing advanced computational approaches that enhance automation, sustainability, and non-destructive assessment within agricultural production systems. She has established a strong scholarly footprint through extensive publications that explore cutting-edge deep learning architectures, including the application of 2D and 3D convolutional neural networks, majority voting ensemble strategies, hybrid neural networks, and metaheuristic optimization techniques for quality evaluation and decision-making in crop and fruit management. Her studies have significantly advanced non-destructive methodologies for detecting bruises, internal defects, and ripening stages in fruits, as well as monitoring excessive nitrogen consumption and estimating chemical and physicochemical properties in plant leaves using hyperspectral, visible, and near-infrared spectral data. In addition to agricultural sensing and classification research, she has contributed impactful work on sustainable bioenergy, including biomethane production from agricultural residues, biodiesel engine performance enhancement using nanomaterials, and advanced exergy and life-cycle analysis of hybrid geothermal–solar power systems. She has authored multiple academic books addressing renewable energy and intelligent grading technologies and has led numerous research projects involving automated fruit identification algorithms, orchard-based robotic systems, video-based fruit maturity estimation, spectral wavelength optimization, agricultural development modeling, and geothermal heating-system design. Dr. Pourdarbani actively disseminates her findings through national and international conferences and contributes to the scientific community through reviewing and collaborative roles in multidisciplinary research initiatives. Her work is widely acknowledged for its scientific value and practical relevance in improving agricultural resource efficiency, enhancing food-quality monitoring, and promoting environmentally responsible production strategies. As a leading figure in the integration of computational intelligence with agricultural engineering, she continues to shape research directions that support global progress toward smart, sustainable, and technologically empowered agriculture.

Profile : Google Scholar

Featured Publication

Alibaba, M., Pourdarbani, R., Manesh, M. H. K., Ochoa, G. V., & Forero, J. D. (2020). Thermodynamic, exergo-economic and exergo-environmental analysis of hybrid geothermal–solar power plant based on ORC cycle using emergy concept. Heliyon, 6(4).

Pourdarbani, R., Sabzi, S., Kalantari, D., Hernández-Hernández, J. L., & Arribas, J. I. (2019). A computer vision system based on majority-voting ensemble neural network for the automatic classification of three chickpea varieties.

Pourdarbani, R., Sabzi, S., García-Amicis, V. M., García-Mateos, G., Hernández-Hernández, J. L., & Arribas, J. I. (2019). Automatic classification of chickpea varieties using computer vision techniques. Agronomy, 9(11), 672.

Ebrahimi, S., Pourdarbani, R., Sabzi, S., Rohban, M. H., & Arribas, J. I. (2023). From harvest to market: Non-destructive bruise detection in kiwifruit using convolutional neural networks and hyperspectral imaging. Horticulturae, 9(8), 936.

Pourdarbani, R., Sabzi, S., Rohban, M. H., Hernández-Hernández, J. L., & Arribas, J. I. (2021). One-dimensional convolutional neural networks for hyperspectral analysis of nitrogen in plant leaves. Applied Sciences, 11(24), 11853

Niti Kant | Computational Theory | Best Researcher Award

Published on by Computer Science Engineering

Prof. Dr. Niti Kant | Computational Theory | Best Researcher Award

Professor | University of Allahabad | India

Prof. Dr. Niti Kant is a distinguished physicist currently serving in the Department of Physics, University of Allahabad, Prayagraj, India. With a Ph.D. from the Indian Institute of Technology (IIT) Delhi (2005) under the supervision of Dr. A. K. Sharma, his research focuses on laser–plasma interaction, self-focusing of lasers, harmonic generation, laser-induced electron acceleration, and terahertz (THz) radiation generation. Over the past two decades, Dr. Kant has made significant contributions to theoretical plasma physics, employing advanced analytical and numerical modeling approaches using Mathematica and Origin. He has published over 150 research papers in reputed international journals indexed by SCI, earning an H-index of 33 on Google Scholar, reflecting the global impact of his research. His academic journey includes postdoctoral research at POSTECH, South Korea, and academic leadership at Lovely Professional University, Punjab, where he served as Professor before joining the University of Allahabad. Dr. Kant has successfully led several sponsored research projects funded by CSIR, SERB, and DST, totaling over ₹50 lakhs, and has guided more than ten Ph.D. scholars in cutting-edge areas such as THz generation, nonlinear optics, and high-power laser–matter interaction. A life member of several prestigious scientific societies, including the Indian Science Congress Association, Optical Society of India, and Plasma Science Society of India, he also serves on editorial and review boards of international journals and as a peer reviewer for top publishers like Elsevier, IOP, and AIP. His work has been recognized with multiple honors, including the Merit Award (2024) by the University of Allahabad, Research Excellence Awards (2020, 2021), and the Outstanding Scientist Award (2020). With active international collaborations across the UK, Czech Republic, South Korea, and the USA, Dr. Kant’s research continues to advance the frontiers of laser–plasma physics, contributing to innovations in photonics, clean energy, and applied plasma technologies with profound implications for scientific and technological progress.

Featured Publication

Kamboj, O., Azad, T., Rajput, J., & Kant, N. (2025). The effect of density ramp on self-focusing of q-Gaussian laser beam in magnetized plasma. Journal of Optics (India). Citations: 2

Azad, T., Kant, N., & Kamboj, O. (2025). Efficient THz generation by Hermite–cosh–Gaussian lasers in plasma with slanting density modulation. Journal of Optics (India). Citations: 23

Singh, J., Kumar, S., Kant, N., & Rajput, J. (2025). Effect of frequency-chirped ionization laser on accelerated electron beam characteristics in plasma wakefield acceleration. European Physical Journal Plus. Citations: 1

Anshal, L., Kant, N., Azad, T., Rajput, J., & Kamboj, O. (2025). Propagation of Hermite–cosh–Gaussian laser beam in free-electron laser device under upward plasma density ramp. Laser Physics Letters. Citations: 1

Azad, T., Kant, N., & Kamboj, O. (2025). Enhanced third harmonic generation and SRS suppression in magnetized rippled plasma using Hermite cosh–Gaussian laser beam. Journal of Optics (India). Citations: 2

Prof. Dr. Niti Kant’s pioneering research in laser–plasma interaction, nonlinear optics, and terahertz generation has advanced the understanding of high-power laser applications, enabling innovations in photonics, clean energy, and next-generation communication technologies. His work bridges fundamental physics with practical technologies, fostering global scientific collaboration and contributing to sustainable technological progress.

Pascal Vollenweider | Software Engineering | Best Researcher Award

Published on by Computer Science Engineering

Mr. Pascal Vollenweider| Software Engineering | Best Researcher Award

Biomechanical Engineer | Straumann Group| Switzerland

Mr. Pascal Vollenweider is a highly skilled biomechanical engineer whose work bridges mechanical engineering and biomedical science, with a strong focus on orthodontic biomechanics and laser-based material processing. He currently serves as a Biomechanical Engineer at Ortho RDI, Institut Straumann AG in Basel, where he specializes in in-vitro testing of clear aligners and the development of advanced experimental methods. He holds a Master of Science in Life Science with a specialization in Biomedical Engineering from FHNW Muttenz (2020–2023), where his thesis on the Biomechanical Investigation of Orthodontic Tooth Movements Induced by Clear Aligners earned the highest distinction, demonstrating his ability to integrate engineering precision with biological understanding to enhance clinical orthodontic applications. His earlier academic training includes a Bachelor of Science in Mechanical Engineering from FHNW Brugg-Windisch, with a focus on Production Engineering, where his research on Laser Machining of PEEK for Security Features on Surfaces reflected his proficiency in high-precision manufacturing and materials science. Professionally, he has contributed to the Institute for Product and Production Engineering at FHNW, engaging in laser-based surface structuring, supervising student research, and supporting industrial collaborations. His multidisciplinary expertise enables him to contribute to the development of next-generation orthodontic devices and biomedical solutions that combine mechanical innovation with patient-centered design. Fluent in German, English, and French, Vollenweider demonstrates strong potential for global collaboration and continued research excellence. His work embodies a commitment to innovation, scientific rigor, and societal benefit through engineering-driven advancements in healthcare technology.

Featured Publication

Vollenweider, P. (2023). Validation of the quantitative case analysis method for measuring orthodontic tooth movement. Journal of Biomedical Engineering and Orthodontic Research, 15(2), 145–156.

Rana Ghazali | Data Science | Best Researcher Award

Published on by Computer Science Engineering

Dr. Rana Ghazali | Data Science | Best Researcher Award

Researcher |McMaster University | Iran

Dr. Rana Ghazali focuses on advancing intelligent computing systems through the integration of machine learning, reinforcement learning, and large language models to optimize big data and distributed computing environments. Her work bridges the domains of cloud computing, Hadoop-based systems, and intelligent caching to enhance computational performance and resource utilization in large-scale data frameworks. She has contributed to innovative algorithms such as CLQLMRS and H-SVM-LRU for improving cache locality and intelligent cache replacement in MapReduce job scheduling, combining machine learning with distributed system optimization. Rana’s research also extends to the design and analysis of routing protocols in mobile ad hoc networks, leveraging bio-inspired algorithms such as the Ant Colony Optimization method to improve network efficiency. Her current exploration includes the application of reinforcement learning in scheduling and performance enhancement for distributed computing platforms, with additional attention to emerging paradigms like edge, fog, and serverless computing. As a researcher affiliated with the Resource Allocation and Stochastic Systems Lab at McMaster University, she contributes to cutting-edge discussions on adaptive data management, cyber and network security, and intelligent system design. Rana’s expertise further encompasses data analytics, large language models, and the intersection of artificial intelligence with real-world computing challenges. She has served as a reviewer for leading international journals including Elsevier and Wiley publications and has participated in academic collaborations that explore deep learning and resource optimization in distributed architectures. Her research endeavors consistently emphasize scalable, secure, and intelligent computational systems that advance the performance of modern data-intensive applications. Rana Ghazali has 13 citations, 2 documents, and an h-index of 2.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Ghazali, R., Down, D. G. (2025). Smart data prefetching using KNN to improve Hadoop performance. EAI Endorsed Transactions on Scalable Information Systems, 12(3). Cited by 1

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2023). Hadoop-oriented SVM-LRU (H-SVM-LRU): An intelligent cache replacement algorithm to improve MapReduce performance. arXiv preprint arXiv:2309.16471. Cited by 2

Ghazali, R., Adabi, S., Rezaee, A., Down, D. G., & Movaghar, A. (2022). CLQLMRS: Improving cache locality in MapReduce job scheduler using Q-learning. Journal of Cloud Computing, 9. Cited by 9

Ghazali, R., Adabi, S., Down, D. G., & Movaghar, A. (2021). A classification of Hadoop job schedulers based on performance optimization approaches. Cluster Computing, 24(4), 3381–3403. Cited by 11

Ghazali, R., Down, D. G. (2025). A systematic overview of caching mechanisms to improve Hadoop performance. Concurrency and Computation: Practice and Experience, 37(25–26), e70337.

Caitlin Kent | Open Source | Best Researcher Award

Published on by Computer Science Engineering

Ms. Caitlin Kent | Open Source | Best Researcher Award

Honours Student | University of Wollongong | Australia

Ms. Caitlin Kent has built a distinguished career in nursing and health research, with a particular focus on global and tropical health, immunisation programs, and community-based research. Her role at the Menzies School of Health Research demonstrates expertise in coordinating participant recruitment, conducting mixed-methods and qualitative research, and presenting findings to stakeholders and policy partners. Caitlin’s work contributes to the advancement of public health initiatives aimed at addressing disease prevention and improving healthcare outcomes in diverse populations. Her professional experience spans both clinical and research settings, including emergency departments, acute care, and neurological wards, where she has developed critical skills in patient management, leadership, and interdisciplinary collaboration. As part of the “B Part of it NT” Meningococcal B vaccine study, Caitlin engaged in public health research focused on vaccination coverage and disease control in Northern Territory communities. Her work as a Nurse Immuniser during the Covid-19 and Influenza vaccination rollout further reflects her dedication to preventive healthcare and rural outreach. In addition to her clinical expertise, Caitlin’s engagement in leadership and mentorship programs—such as the Menzies Leadership Development Program, Emerging Research Leadership Program, and Catalyse Mentorship Program—highlights her commitment to advancing research capacity and promoting women in STEMM. Her recognition through academic awards and participation in professional development programs underscores a strong foundation in evidence-based practice and applied health research. Through her ongoing Honours in Nursing and active involvement in multidisciplinary research initiatives, Caitlin continues to contribute to improving healthcare delivery, fostering community health awareness, and strengthening the integration of clinical practice with research innovation across Australia’s tropical and remote health landscape.

Profile:  ORCID
Featured Publication

Kent, C., & Halcomb, E. (2025). Nurses’ experiences of mental health care in the emergency department: An integrative review. Journal of Clinical Nursing, 34(9–10), 1–15.

Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Published on by Computer Science Engineering

Dr. Ying Yi Tan | Smart Manufacturing | Best Researcher Award

Research Fellow | Singapore University of Technology and Design | Singapore

Dr. Tan Ying Yi is a Research Fellow at the Singapore University of Technology and Design (SUTD) whose research lies at the intersection of digital fabrication, smart textiles, and computational design. The focus of his work is the development of digital knitting technologies and multi-material additive manufacturing methods for creating functional, mechanically graded, and intelligent textile-based systems. His investigations explore how knitted fabrics can be engineered with integrated electrical and mechanical properties, transforming traditional textiles into high-performance materials applicable to both architectural and biomedical domains. Ying Yi has played a significant role in advancing customized technical textiles for applications such as structural membranes, façade systems, prosthetic interfaces, and wearable technologies. His leadership in projects involving smart garments for body joint monitoring has contributed to innovations in digital health and human–machine interaction, demonstrating the potential of computational design and materials research to improve quality of life. Collaborative projects with institutions like SingHealth Polyclinics, Tan Tock Seng General Hospital, and Hyundai Motor Group have led to impactful real-world solutions such as smart knee braces for gait assessment and smart shirts for motion tracking. His work is characterized by an interdisciplinary approach, blending engineering precision, material science, and architectural design principles to create responsive systems that interact dynamically with users and environments. Recognized with awards for excellence in architectural membranes and advanced manufacturing, Ying Yi continues to contribute to the integration of digital fabrication, computational modeling, and soft robotics in technical textile research. His studies have been featured by major media outlets for their innovation and societal relevance, showcasing how fabric-based systems can bridge the gap between engineering and design. Citations 19 Documents 5 h-index View.

Profiles: Google Scholar | Scopus | ORCID
Featured Publication

Weeger, O., Sakhaei, A. H., Tan, Y. Y., Quek, Y. H., Lee, T. L., Yeung, S. K., & Kaijima, S. (2018). Nonlinear multi-scale modelling, simulation and validation of 3D knitted textiles. Applied Composite Materials, 25(4), 797–810. Citations: 43

Sakhaei, A. H., Kaijima, S., Lee, T. L., Tan, Y. Y., & Dunn, M. L. (2018). Design and investigation of a multi-material compliant ratchet-like mechanism. Mechanism and Machine Theory, 121, 184–197. Citations: 31

Gupta, S. S., Tan, Y. Y., Chia, P. Z., Pambudi, C. P., Quek, Y. H., Yogiaman, C., & Tracy, K. J. (2020). Prototyping knit tensegrity shells: A design-to-fabrication workflow. SN Applied Sciences, 2(6), 1062. Citations: 25

Do, H., Tan, Y. Y., Ramos, N., Kiendl, J., & Weeger, O. (2020). Nonlinear isogeometric multiscale simulation for design and fabrication of functionally graded knitted textiles. Composites Part B: Engineering, 202, 108416. Citations: 20

Gupta, U., Lau, J. L., Chia, P. Z., Tan, Y. Y., Ahmed, A., Tan, N. C., Soh, G. S., & Low, H. Y. (2023). All knitted and integrated soft wearable of high stretchability and sensitivity for continuous monitoring of human joint motion. Advanced Healthcare Materials, 12(21), 2202987. Citations: 17

Pal, A., Chan, W. L., Tan, Y. Y., Chia, P. Z., & Tracy, K. J. (2020). Knit concrete formwork. Proceedings of the 25th CAADRIA Conference, 1, 213–222. Citations: 7