Dr. Nabil Bachagha | Remote Sensing | Best Researcher Award

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Dr. Nabil Bachagha | Remote Sensing | Best Researcher Award

University of Leeds | United Kingdom

Dr. Nabil Bachagha is a distinguished Research Fellow and global expert in remote sensing, GIS, and deep learning, with significant contributions to digital heritage preservation and archaeological landscape documentation. His interdisciplinary research integrates advanced geospatial technologies, including UAV photogrammetry, terrestrial 3D laser scanning, and machine learning models, to enhance the detection, classification, and conservation of archaeological and cultural heritage sites. A UK Global Talent Visa holder under the Exceptional Talent Route, Dr. Bachagha’s work bridges technology and heritage, focusing on data-driven approaches to protect endangered sites and reconstruct ancient civilizations through digital innovation. His expertise spans ENVI, ArcGIS, QGIS, and Earth Engine applications, combined with proficiency in Python, R, MATLAB, and JavaScript for geospatial analytics and automated system development. With over 430 citations from 374 documents in Scopus (h-index: 6) and 675 citations in Google Scholar (h-index: 8, i10-index: 7), Dr. Bachagha’s research demonstrates strong academic influence and global recognition. His projects, such as the “One Belt, One Road Heritage Protection” and “Endangered Wooden Architecture Programme,” exemplify his commitment to integrating AI, remote sensing, and geospatial intelligence in cultural heritage management.

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Featured Publications

Bachagha, N., Wang, X., Lasaponara, R., Luo, L., & Khatteli, H. (2020). Remote sensing and GIS techniques for reconstructing the military fort system of Roman boundary (Tunisia section) and identifying archaeological sites. Remote Sensing of Environment.

Bachagha, N., Luo, L., Wang, X., Masini, N., Tababi, M., Khatteli, H., & Lasaponara, R. (2020). Mapping the Roman water supply system of the Wadi el Melah Valley in Gafsa, Tunisia, using remote sensing. Sustainability.

Luo, L., Wang, X., Guo, H., Lasaponara, R., Zong, X., Masini, N., & Bachagha, N. (2019). Airborne and spaceborne remote sensing for archaeological and cultural heritage applications: A review of the century (1907–2017). Remote Sensing of Environment.

Bachagha, N., Xu, W., Luo, X., Brahmi, M., Wang, X., Souei, F., & Lasaponara, R. (2022). On the discovery of a Roman fortified site in Gafsa, southern Tunisia, based on high-resolution X-band satellite radar data. Remote Sensing.

Bachagha, N., Tababi, M., Selim, G., Shao, W., Xue, Y., Li, W., Bennour, A., Luo, L., Lasaponara, R., & Lao, Y. (2025). Facilitating archaeological discoveries through deep learning and space-based observations: A case study in southern Tunisia. Nature Communications.

Prof. Dr. Dachel Martínez Asanza | Medicine | Best Researcher Award

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Prof. Dr. Dachel Martínez Asanza | Medicine | Best Researcher Award

Professor/ Senior Researcher | University of Medical Sciences of Havana | Cuba

Prof. Dr. Dachel Martínez Asanza is a distinguished Cuban scholar and senior researcher in the field of dental sciences and medical education, recognized for her interdisciplinary expertise in comprehensive dentistry, health promotion, epidemiology, and pedagogical innovation in medical education. Her academic pursuits bridge clinical dentistry with public health, emphasizing preventive oral care, biopsychosocial health management, and the integration of digital and natural medicine within community health frameworks. A full professor at the University of Medical Sciences of Havana, Dr. Asanza has made substantial contributions to advancing dental education through work-based learning methodologies and curriculum development in health sciences. Her research explores the intersection of technology, digital health, and education, reflecting a deep commitment to enhancing healthcare delivery and educational practices in dentistry. With a Scopus record of 26 indexed publications, over 143 citations from 107 documents, and an h-index of 7, alongside Google Scholar metrics of 371 total citations, an h-index of 11, and an i10-index of 12, Dr. Asanza’s scholarly impact is widely recognized. Her works are featured in reputed international journals, often addressing themes such as digital health adoption, green innovation, and AI applications in healthcare.

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Featured Publications

  • Golmankhaneh, A. K., Tunç, S., Schlichtinger, A. M., & Martínez Asanza, D. (2024). Modeling tumor growth using fractal calculus: Insights into tumor dynamics. Biosystems, 235, 105071.

  • Kamel Mouloudj, A. B., Bouarar, A. C., Martínez Asanza, D., & Linda, M. (2023). Factors influencing the adoption of digital health apps: An extended technology acceptance model (TAM). Integrating Digital Health Strategies for Effective Administration, 116–132.

  • Martínez Asanza, D. (2018). Traditional teaching in the 21st century? Neuronum Magazine, 4(1), 99–106.

  • Martínez-Asanza, D. (2021). Regarding work-based learning, a guiding principle of Cuban medical education. FEM: Journal of the Medical Education Foundation, 24(6), 325–325.

  • Njoku, A., Mouloudj, K., Bouarar, A. C., Evans, M. A., & Martínez Asanza, D. (2024). Intentions to create green start-ups for collection of unwanted drugs: An empirical study. Sustainability, 16(7), 2797.

Dr. Fan Zhang | Energy Technologies | Best Researcher Award

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Dr. Fan Zhang | Energy Technologies | Best Researcher Award

Research Associate | Queensland University of Technology | Australia

Dr. Fan Zhang is a distinguished researcher at the Queensland University of Technology whose work focuses on the advancement of next-generation aqueous zinc-ion batteries and sustainable energy storage technologies. Their research integrates bioinspired materials design, electrolyte optimization, and interfacial engineering to address key challenges such as dendrite formation, hydrogen evolution, and low reversibility in Zn-based systems. With significant contributions to materials science and electrochemistry, Dr. Zhang has established a strong reputation for innovative approaches that enhance the safety, energy density, and long-term stability of aqueous batteries. Their studies combine experimental synthesis with advanced characterization techniques, leading to impactful findings published in high-impact journals such as Advanced Materials, Journal of the American Chemical Society, National Science Review, and Nano Energy. Dr. Zhang’s scholarly influence is evidenced by a Scopus citation count of 370 (h-index: 12, 17 documents) and a Google Scholar citation count of 352 (h-index: 11, i10-index: 11). Their research continues to drive progress in electrochemical energy storage, contributing to the global shift toward sustainable and environmentally friendly power solutions.

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Featured Publications

Zhang, F., Liao, T., Liu, C., Peng, H., Luo, W., Yang, H., Yan, C., & Sun, Z. (2022). Biomineralization-inspired dendrite-free Zn-electrode for long-term stable aqueous Zn-ion battery. Nano Energy, 103, 107830.

Zhang, F., Liao, T., Peng, H., Xi, S., Qi, D. C., Micallef, A., Yan, C., Jiang, L., & Sun, Z. (2024). Outer sphere electron transfer enabling high-voltage aqueous electrolytes. Journal of the American Chemical Society, 146(15), 10812–10821.

Zhang, F., Liao, T., Qi, D. C., Wang, T., Xu, Y., Luo, W., Yan, C., Jiang, L., & Sun, Z. (2024). Zn-ion ultrafluidity via bioinspired ion channel for ultralong lifespan Zn-ion battery. National Science Review, 11(8), nwae199.

Zhang, F., Liao, T., Yan, C., & Sun, Z. (2024). Bioinspired designs in active metal-based batteries. Nano Research, 17(2), 587–601.

Zhang, F., Liao, T., Zhou, Q., Bai, J., Li, X., & Sun, Z. (2025). Advancements in ion regulation strategies for enhancing the performance of aqueous Zn-ion batteries. Materials Science and Engineering: R: Reports, 165, 101012.

Assist. Prof. Dr. Lotfi Jlali | Mathematics | Best Researcher Award

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Assist. Prof. Dr. Lotfi Jlali | Mathematics | Best Researcher Award

Imam Mohammad Ibn Saud Islamic University | Tunisia

Dr. Lotfi Mohamed Alhosine Jlali is an accomplished Tunisian mathematician and Assistant Professor at Imam Mohammad Ibn Saud Islamic University, Saudi Arabia. His research primarily focuses on Partial Differential Equations (PDEs) and Nonlinear Analysis, with particular expertise in the mathematical modeling of fluid dynamics, including the Navier–Stokes, Euler, and Magnetohydrodynamic (MHD) systems. Dr. Jlali’s work delves into the local and global existence, uniqueness, and regularity of solutions for incompressible fluid equations, as well as the asymptotic behavior of problems influenced by small or large parameters, such as rotating and anisotropic fluid systems. His studies also address the blow-up criteria for non-regular solutions and the stability of global solutions, applying advanced mathematical tools like Strichartz inequalities, energy estimates, and Sobolev embeddings. Dr. Jlali has made significant contributions to understanding the long-term dynamics of fluid equations, particularly in Sobolev–Gevrey and Fourier–Lei–Lin spaces. His research output includes 13 indexed publications with 51 citations in Scopus (h-index: 4) and 85 citations on Google Scholar (h-index: 5, i10-index: 3), reflecting the growing impact of his work in mathematical fluid mechanics.

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Featured Publications:

Benameur, J., & Jlali, L. (2016). Long time decay for 3D Navier-Stokes equations in Sobolev-Gevrey spaces. Electronic Journal of Differential Equations.

Benameur, J., & Jlali, L. (2016). On the blow-up criterion of 3D-NSE in Sobolev–Gevrey spaces. Journal of Mathematical Fluid Mechanics.

Jlali, L. (2017). Global well posedness of 3D-NSE in Fourier–Lei–Lin spaces. Mathematical Methods in the Applied Sciences.

Benameur, J., & Jlali, L. (2020). Long time decay of 3D-NSE in Lei-Lin-Gevrey spaces. Mathematica Slovaca.

Jlali, L., & Benameur, J. (2024). Long time decay of incompressible convective Brinkman-Forchheimer in L2(R3). Demonstratio Mathematica.

Dr. Yonglin Ren | Computer Science | Innovative Research Award

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Dr. Yonglin Ren | Computer Science | Innovative Research Award

Senior Project Engineer & Researcher | Concordia University | Canada

Dr. Yonglin Ren is a distinguished Senior Project Engineer and Researcher at Concordia University, recognized for his interdisciplinary expertise in mathematical modeling, logistics optimization, and sustainable engineering systems. His research bridges theoretical optimization frameworks and industrial applications, focusing on metaheuristic algorithms, CAD/CAE-based modeling, and supply chain design for humanitarian and sustainable logistics. Dr. Ren’s contributions have advanced methodologies for capacitated location allocation problems, high-speed rail freight transport, and dynamic mechanical system modeling. His work integrates computational intelligence with real-world challenges in water resource management, transportation networks, and crisis logistics, making a significant impact in both academia and industry. His publications are widely cited, reflecting his influence in the fields of operational research and applied optimization, with a Scopus record of 3 indexed documents, 6 citations, and an h-index of 1, alongside a Google Scholar citation count of 26. Dr. Ren has collaborated on multiple international engineering and research projects, driving innovations that contribute to sustainable development and global resource optimization.

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Featured Publications 

Ren, Y., & Awasthi, A. (2014). Investigating metaheuristics applications for capacitated location allocation problem on logistics networks. Chaos Modeling and Control Systems Design, 213–238.

Ren, Y., & Awasthi, A. (2012). Location allocation planning of logistics depots using genetic algorithm. Research in Logistics & Production, 2, 247–257.

Ren, Y. (2011). Metaheuristics for multiobjective capacitated location allocation on logistics networks. Concordia University.

Ren, Y., Hajiebrahimi, S., Azad, M., Awasthi, A., & Salah, S. (2020). Humanitarian aid for Wuhan with crisis logistics management approach. Proceedings of the International Conference on Industrial Engineering and Operations Management.

Ren, Y., & Awasthi, A. (2025). Logistics hub location for high-speed rail freight transport—Case Ottawa–Quebec City corridor. Logistics, 9(4), 158.

Dr. Malaya Nath | Signal Processing | Best Researcher Award

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Dr. Malaya Nath | Signal Processing | Best Researcher Award

Assistant Professor | National Institute of Technology Puducherry | India

Dr. Malaya Kumar Nath is an accomplished researcher and academician in the field of Electronics and Communication Engineering, specializing in Biomedical Signal and Image Processing, Pattern Recognition, Deep Learning, and Computational Neuroscience. His research primarily focuses on developing advanced computational models for medical image analysis, disease diagnosis, and intelligent healthcare systems using signal and image processing techniques integrated with artificial intelligence. Dr. Nath has significantly contributed to diagnostic automation through the application of deep learning architectures such as CNNs and EfficientNet for skin cancer, glaucoma, and retinal image analysis. His scholarly contributions have earned him recognition among the Top two percentage most influential scientists worldwide, as reported by Stanford University and Elsevier in 2025. He has an extensive publication record, with 69 Scopus-indexed documents and over 1,291 citations by 902 documents, achieving an h-index of 21 on Scopus. On Google Scholar, he has accumulated 2,185 citations with an h-index of 24 and an i10-index of 47, reflecting his impactful research influence. His interdisciplinary research integrates biomedical data analytics with machine learning and deep neural frameworks, addressing challenges in medical imaging and healthcare informatics.

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Featured Publications

Keerthana, D., Venugopal, V., Nath, M. K., & Mishra, M. (2023). Hybrid convolutional neural networks with SVM classifier for classification of skin cancer. Biomedical Engineering Advances, 5, 100069.

Anbalagan, T., Nath, M. K., Vijayalakshmi, D., & Anbalagan, A. (2023). Analysis of various techniques for ECG signal in healthcare, past, present, and future. Biomedical Engineering Advances, 6, 100089.

Elangovan, P., & Nath, M. K. (2021). Glaucoma assessment from color fundus images using convolutional neural network. International Journal of Imaging Systems and Technology, 31(2), 955–971.

Vijayalakshmi, D., & Nath, M. K. (2020). A comprehensive survey on image contrast enhancement techniques in spatial domain. Sensing and Imaging, 21(1), 40.

Venugopal, V., Raj, N. I., Nath, M. K., & Stephen, N. (2023). A deep neural network using modified EfficientNet for skin cancer detection in dermoscopic images. Decision Analytics Journal, 8, 100278.

Dr. Jan Muhammad | Mathematics | Best Researcher Award

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Dr. Jan Muhammad | Mathematics | Best Researcher Award

Shanghai University | China

Dr. Jan Muhammad is a distinguished mathematician and postdoctoral researcher specializing in nonlinear partial differential equations (PDEs), soliton theory, and mathematical physics. His research primarily focuses on analytical and semi-analytical approaches for exploring nonlinear dynamical systems, fractional calculus, and optical wave propagation in applied mathematics and engineering contexts. With over 50 peer-reviewed publications in prestigious international journals, Dr. Muhammad has significantly contributed to advancing the theoretical and applied aspects of nonlinear PDEs and fractional models. His studies often explore the mathematical structures governing complex fluid mechanics, magnetohydrodynamics, and fractional optical systems, offering new insights into the behavior of nonlinear waves, stability, and multistability phenomena. His scholarly impact is reflected by 294 Scopus citations across 149 documents with an h-index of 11, showcasing the depth and reach of his contributions. His work is also widely recognized on Google Scholar, emphasizing his growing influence within the global mathematical community. Dr. Muhammad’s ongoing research bridges mathematical theory with real-world physical systems, demonstrating excellence in mathematical modeling, analytical methods, and interdisciplinary applications.

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Featured Publications

Muhammad, J., Fang, L., & Guo, Z. (2020). Global weak solutions to a class of compressible non-Newtonian fluids with vacuum. Mathematical Methods in the Applied Sciences, 43, 5234–5249.

Zhu, H., Fang, L., Muhammad, J., & Guo, Z. (2020). Global weak solutions to a Vlasov–Fokker–Planck/compressible non-Newtonian fluid system of equations. ZAMM, 100, e201900091.

Muhammad, J., Ali, Q., & Younas, U. (2024). Three component coupled fractional nonlinear Schrödinger equations: Diversity of exact optical solitonic structures. Modern Physics Letters B, 2450373.

Muhammad, J. (2024). On the global existence for a class of compressible non-Newtonian fluids with inhomogeneous boundary data. Russian Journal of Mathematical Physics, 31, 276–298.

Muhammad, J., Younas, U., & Nasreen, N. (2024). Multicomponent nonlinear fractional Schrödinger equation: Optical wave propagation in fiber optics. Partial Differential Equations in Applied Mathematics, 100805.

Prof. Joongrock Kim | Computer Vision | Best Researcher Award

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Prof. Joongrock Kim | Computer Vision | Best Researcher Award

Associate Professor | Changwon National University | South Korea

Prof. Joongrock Kim is an accomplished researcher and Associate Professor in Artificial Intelligence Convergence Engineering at Changwon National University, Republic of Korea. His expertise spans computer vision, 3D scene understanding, deep learning-based perception, and intelligent systems for automotive and consumer applications. Over his distinguished career, he has contributed significantly to the development of advanced AI technologies, including driver monitoring systems, 3D reconstruction, food recognition, and smart V2X perception systems. His research focuses on integrating multimodal sensing, neural rendering, and adaptive feature extraction for robust real-world perception, bridging academia and industry to advance AI deployment in smart vehicles and appliances. Dr. Kim’s prolific output includes numerous high-impact publications and international patents on AI-based sensing and perception systems. According to Scopus, he has achieved 212 citations across 207 documents with an h-index of 7, while his Google Scholar profile reflects broader academic engagement and influence. His work continues to drive innovation in perception AI, human–machine interaction, and computational imaging, establishing him as a leading figure in applied artificial intelligence and computer vision research.

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Featured Publications

Park, M., Do, M., Shin, Y. J., Yoo, J., Hong, J., Kim, J., & Lee, C. (2024). H2O-SDF: Two-phase learning for 3D indoor reconstruction using object surface fields. International Conference on Learning Representations (ICLR).

Kim, J., Yu, S., Kim, D., Toh, K.-A., & Lee, S. (2017). An adaptive local binary pattern for 3D hand tracking. Pattern Recognition.

Kim, J., Yoon, C. (2016). Three-dimensional head tracking using adaptive local binary pattern in depth images. International Journal of Fuzzy Logic and Intelligent Systems.

Kim, K., Kim, J., Choi, J., Kim, J., & Lee, S. (2015). Depth camera-based 3D hand gesture controls with immersive tactile feedback for natural mid-air gesture interactions. Sensors.

Kim, J., Yu, S., & Lee, S. (2014). Random-profiles-based 3D face recognition system. Sensors.

Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

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Mr. Siddhant Srinivas | Cyber Security | Best Researcher Award

California State University | United States

Siddhant Srinivas is an emerging researcher in Artificial Intelligence and Cybersecurity, currently contributing to the advancement of AI-augmented Security Operations Centers (SOC) through the integration of Large Language Models (LLMs) and AI agents. His research primarily focuses on developing intelligent frameworks that enhance the efficiency, scalability, and trustworthiness of SOC workflows. As the first author of a peer-reviewed publication in MDPI, Siddhant has presented a comprehensive taxonomy of AI-driven applications across SOC processes, highlighting their potential in transforming traditional alert triage, threat detection, and incident response systems. His work introduces a capability-maturity model that outlines the evolution from manual to autonomous SOC operations while addressing the challenges of explainability, safety, and reliability in AI deployments. Siddhant’s contributions emphasize bridging the gap between theoretical AI models and their practical implementation in cybersecurity domains. He has been recognized for his scholarly excellence through published research and active involvement in Dr. Alzahrani’s AI Research Lab. His published works are cited in indexed databases such as Scopus and Google Scholar, reflecting a growing academic footprint and influence in the emerging intersection of AI and security research. His citation records and h-index metrics from both Scopus and Google Scholar demonstrate his contributions to advancing secure, transparent, and automated AI systems.

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Featured Publication

Srinivas, S., Kirk, B., Zendejas, J., Bari, A., Dajani, K., & Alzahrani, N. (2025). AI-Augmented SOC: A survey of LLMs and agents for security automation. MDPI Informatics, 5(4), 95.

Prof. Piotr Kaminski | Biological Sciences | Best Researcher Award

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Prof. Piotr Kaminski | Biological Sciences | Best Researcher Award

Nicolaus Copernicus University | Poland

Dr. Piotr Kamiński, PhD, is a distinguished Professor and Head of the Division of Ecology and Environmental Protection at the Department of Medical Biology and Biochemistry, Nicolaus Copernicus University in Toruń, Poland. He also serves at the Department of Biotechnology, University of Zielona Góra, contributing significantly to interdisciplinary studies that bridge environmental sciences and medical biology. His scientific expertise lies in environmental ecophysiology, with research encompassing the physiological responses of humans, birds, mammals, fish, and plants to varying environmental conditions. Dr. Kamiński has made impactful contributions to understanding how ecological and biochemical factors influence organismal health, oxidative stress, and adaptive mechanisms in diverse ecosystems. His collaborations span numerous countries, reflecting a global approach to ecophysiological and environmental safety studies. He has supervised over 180 master’s and bachelor’s theses, and 23 doctoral dissertations, fostering the next generation of scientists. With over 190 peer-reviewed publications, his work appears in reputable journals and edited volumes, often addressing bioenergetics, antioxidant mechanisms, and ecological physiology. His academic excellence is reflected in Scopus metrics with 63 documents, 782 citations from 713 sources, and an h-index of 16. On Google Scholar, his citations exceed 1200, with an h-index of 19, underscoring his international scientific influence and recognition.

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Featured Publications

Dietrich-Muszalska, A., Kamiński, P., Kontek, B., & Gorzelańczyk, E. J. (2025). Curcumin as an antioxidant against ziprasidone-induced lipid peroxidation in human plasma: Potential relevance to cortico-subcortical circuit function. International Journal of Molecular Sciences.

Kurhaluk, N., Buyun, L., Kołodziejska, R., Kamiński, P., & Tkaczenko, H. (2025). Effect of phenolic compounds and terpenes on the flavour and functionality of plant-based foods. Nutrients.

Tkaczenko, H., Buyun, L., Kołodziejska, R., Kamiński, P., & Kurhaluk, N. (2025). Neuroactive phytochemicals as multi-target modulators of mental health and cognitive function: An integrative review. International Journal of Molecular Sciences.

Kurhaluk, N., Kamiński, P., Bilski, R., Kołodziejska, R., Woźniak, A., & Tkaczenko, H. (2025). Role of antioxidants in modulating the microbiota–gut–brain axis and their impact on neurodegenerative diseases. International Journal of Molecular Sciences.

Bilski, R., Kamiński, P., Kupczyk, D., Jeka, S., Baszyński, J., Tkaczenko, H., & Kurhaluk, N. (2024). Environmental and genetic determinants of ankylosing spondylitis. International Journal of Molecular Sciences.