Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

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Prof. Zhiguo Zhao | Machine Learning | Best Researcher Award

Professor | Huaiyin Institute of Technology | China

Prof. Zhiguo Zhao is a distinguished academic and researcher in automotive engineering, currently serving as Dean at the School of Traffic Engineering, Huaiyin Institute of Technology. His research primarily focuses on automotive system dynamics and control, intelligent connected vehicles, new energy vehicle technology, and energy equipment fault diagnosis. He has made significant contributions to battery State of Health (SOH) estimation, vehicle safety, and energy management systems, developing advanced models integrating artificial intelligence and optimization algorithms. Professor Zhao has authored over 20 high-impact publications in leading SCI and EI journals, alongside securing 10 invention patents. His research outputs have received provincial and national recognition, particularly for their practical applications in intelligent transportation and energy-efficient vehicle systems. He has successfully led multiple national and provincial research projects and has cultivated innovative industry-university collaboration models for talent development. According to Scopus, his academic record includes 36 indexed documents with 147 citations and an h-index of 7, while Google Scholar reports higher citation metrics, reflecting his growing international academic influence. His interdisciplinary expertise bridges theoretical modeling and industrial applications, fostering advancements in intelligent mobility, new energy systems, and vehicular safety technology.

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Featured Publications

Zhao, Z. (2025). Estimation of lithium battery state of health using hybrid deep learning with multi-step feature engineering and optimization algorithm integration. Energies, 18(21), 5849.

Zhao, Z. (2019). Construction and verification of equivalent mechanical model for liquid sloshing in hazardous material tankers. Journal of Huaiyin Institute of Technology, 5, 1–10.

Zhao, Z. (2023). Integrated energy management strategy for hybrid electric vehicles based on adaptive control and machine learning. Journal of Energy Storage, 59, 106781.

Zhao, Z. (2022). Fault diagnosis of power equipment using hybrid neural network and sensor fusion techniques. IEEE Transactions on Industrial Electronics, 69(8), 8123–8134.

Zhao, Z. (2021). Dynamic modeling and control optimization for intelligent connected vehicles in complex traffic environments. Vehicle System Dynamics, 59(4), 613–631.

Assist. Prof. Dr. Hanen Marzouki | Biotechnology | Best Researcher Award

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Assist. Prof. Dr. Hanen Marzouki | Biotechnology | Best Researcher Award

University of Monastir | Tunisia

Dr. Hanen Marzouki is an accomplished Assistant Professor in Biological Sciences from Tunisia, specializing in the study of essential oils, plant extracts, and their biological activities. Her research focuses on the chemical characterization, chromatographic separation, and bioactivity evaluation of natural compounds derived from medicinal and aromatic plants. She has significantly contributed to understanding the biochemical composition, allelopathic potential, and pharmacological properties of essential oils—particularly those of Laurus nobilis L., Eucalyptus species, and Artemisia herba-alba. Her interdisciplinary expertise spans phytochemistry, in vitro propagation, and molecular analysis, integrating traditional botanical knowledge with modern biotechnological and analytical techniques. Dr. Marzouki has collaborated internationally on research exploring supercritical CO₂ extraction, GC/MS profiling, and in silico molecular docking to investigate bioactive substances for potential therapeutic and agricultural applications. Her scholarly impact is reflected in Scopus with 135 citations across 123 documents and an h-index of 4. On Google Scholar, she continues to build an expanding citation base highlighting her contributions to natural product chemistry and sustainable bioresources.

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Featured Publications

Marzouki, H., Horchani, M., Chaieb, I., M’Rabet, Y., Ben Jannet, H., & Saadaoui, E. (2025). Chemical characterization, in silico investigations, in vitro evaluation of allelopathic potential and insecticidal activity of Laurus nobilis L. essential oil. Chemistry & Biodiversity.

Piras, A., Marzouki, H., Falconieri, D., Porcedda, S., Gonçalves, M. J., & Salgueiro, L. (2017). Chemical composition and biological activity of volatile extracts from leaves and fruits of Schinus terebinthifolius Raddi from Tunisia. Records of Natural Products, 11(1), 9–16.

Floris, S., Fais, A., Rosa, A., Piras, A., Marzouki, H., & Era, B. (2019). Phytochemical composition and enzyme inhibitory properties of seed extracts from the Washingtonia filifera palm. RSC Advances, 9, 21278.

Marzouki, H., Falconieri, D., Piras, A., & Porcedda, S. (2015). Chemical composition of essential oils from needles of Pinus pinaster from Italy and Tunisia. Asian Journal of Chemistry, 27(7).

Marzouki, H., Khaldi, A., Piras, A., & Marongiu, B. (2009). Biological activity evaluation of the oils from Laurus nobilis of Tunisia and Algeria extracted by supercritical carbon dioxide. Natural Products Research, 23, 230–237.

Mrs. Andsera Adugna Mekonen | Remote Sensing | Young Scientist Award

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Mrs. Andsera Adugna Mekonen | Remote Sensing | Young Scientist Award

University of Naples | Italy

Andsera Adugna Mekonen is an emerging Earth and Environmental Scientist specializing in remote sensing, geoinformatics, and precision agroforestry systems. His research focuses on leveraging drone and satellite imagery for above-ground biomass estimation and sustainable agroforestry ecosystem monitoring. He integrates advanced remote sensing technologies, GIS applications, photogrammetry, and machine learning to improve environmental assessment and agricultural productivity. His expertise extends to UAS-based data acquisition, multispectral and RGB imagery analysis, and the application of artificial intelligence and data science in Earth observation. He has presented his work at leading international conferences, including IEEE MetroAerospace, and contributed to advancements in sustainable land management and ecosystem monitoring. His innovative approach combines Earth observation with AI-driven analytical frameworks to enhance accuracy in biomass modeling and environmental risk assessment. He has authored impactful research in peer-reviewed journals, with a Scopus record of 2 documents and an h-index of 1, and a Google Scholar profile reflecting 59 citations, an h-index of 2, and an i10-index of 1. His contributions demonstrate a growing influence in geospatial and agro-environmental research, emphasizing interdisciplinary integration of technology and sustainability science.

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Featured Publications

Mekonen, A. A., Raghuvanshi, T. K., Suryabhagavan, K. V., & Kassawmar, T. (2022). GIS-based landslide susceptibility zonation and risk assessment in a complex landscape: A case study of the Beshilo watershed, northern Ethiopia. Environmental Challenges, 8, 100586.

Mekonen, A. A., Accardo, D., & Renga, A. (2024). Above-ground biomass estimation in an agroforestry environment by UAS and RGB imagery. In IEEE International Workshop on Metrology for Aerospace, 272–277.

Mekonen, A. A., Accardo, D., & Renga, A. (2025). Above-Ground Biomass Prediction in Agroforestry Areas Using Machine Learning and Multispectral Drone Imagery. In IEEE International Workshop on Metrology for Aerospace, 63–68.

Mekonen, A. A., Accardo, D., & Claudia, C. (2025). An effective process to use drones for above-ground biomass estimation in agroforestry landscapes. Aerospace, 12(11), 26.

Sisay, S. B., Melkamu, M. B., Birhan, B. A., & Mekonen, A. A. (2019). Inoculation and phosphorus fertilizer improve food-feed traits of grain legumes in mixed crop-livestock systems of Ethiopia.

Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Mohanned M. H. AL-Khafaji | Artificial Intelligence | Best Researcher Award

Engineering | University of Technology | Iraq

Dr. Mohanned Mohammed Hussein Al-Khafaji is an accomplished researcher and academic leader in production engineering, specializing in intelligent manufacturing systems, laser material processing, neural network modeling, and fuzzy logic control applications. As Dean of the College of Production Engineering and Metallurgy at the University of Technology, Baghdad, his research integrates computational modeling, automation, and artificial intelligence to enhance production efficiency and precision engineering. He has made significant contributions to the development of computer-controlled manufacturing systems, laser-based material processing, and predictive modeling using advanced algorithms. His work on CO₂ laser processing, neural network-based machining analysis, and hybrid intelligent systems has advanced industrial automation and smart manufacturing processes. Dr. Al-Khafaji’s research also explores mechatronics, robotic systems, and additive manufacturing, emphasizing simulation tools like Abaqus, COMSOL Multiphysics, and MATLAB. His scientific output reflects substantial academic influence, with 15 Scopus-indexed documents, 41 citations from 37 documents, and an h-index of 3. On Google Scholar, he has accumulated 125 citations, an h-index of 6, and an i10-index of 4, underscoring his growing impact in engineering research.

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Featured Publications

Al-Khafaji, M. M. H., & Hubeatir, K. A. (2021). CO2 laser micro-engraving of PMMA complemented by Taguchi and ANOVA methods. Journal of Physics: Conference Series, 1795(1), 012062.

Al-Khafaji, M. M. H. (2018). Neural network modeling of cutting force and chip thickness ratio for turning aluminum alloy 7075-T6. Al-Khwarizmi Engineering Journal, 14(1), 67–76.

Khayoon, M. A., Hubeatir, K. A., & Al-Khafaji, M. M. (2021). Laser transmission welding is a promising joining technology technique – A recent review. Journal of Physics: Conference Series, 1973(1), 012023.

Momena, T. F. A., Mohammed, M. M. H., & Al-Khafaji, M. M. H. (2023). Smart robot vision for a pick and place robotic system. Engineering and Technology Journal, 40(6), 1–15.

Shaker, F., Al-Khafaji, M., & Hubeatir, K. (2020). Effect of different laser welding parameters on welding strength in polymer transmission welding using semiconductor. Engineering and Technology Journal, 38(5), 761–768.*

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.