Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

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Dr. Zhiqiang Wang | Computer Science and Artificial Intelligence | Best Academic Researcher Award

North China University of Science and Technology | China

Dr. Zhiqiang Wang is a dynamic researcher specializing in intelligent control, industrial process optimization, and data-driven modeling, with a particular emphasis on complex mineral processing systems such as copper flotation. His work integrates advanced control theory, artificial intelligence, subspace identification, transfer learning, and machine vision to enhance stability, efficiency, and real-time decision-making within large-scale industrial environments. He has contributed notable innovations in intelligent optimization strategies, expert-knowledge-assisted control frameworks, flotation-foam image analysis for process evaluation, and adaptive control algorithms designed for systems with nonlinear, highly variable operating conditions. His research extends across intelligent manufacturing, big-data-driven industrial analytics, and cross-disciplinary automation technologies, advancing next-generation solutions for process monitoring, fault diagnosis, and operational optimization. He has been actively involved in major national research initiatives focusing on full-process industrial evaluation, intelligent control system development, and 5G-enabled optimization technologies for metallurgy and beneficiation. According to Scopus, Wang has authored 35 indexed publications, accumulated 403 citations from 348 citing documents, and achieved an h-index of 9, reflecting his growing academic influence in industrial automation and intelligent systems engineering. His publications in leading journals including ISA Transactions, IEEE Transactions on Instrumentation and Measurement, Canadian Journal of Chemical Engineering, and the International Journal of Control, Automation and Systems demonstrate his contributions to advancing data-driven modeling, feature engineering, and real-time operational control. Through sustained collaboration, innovative methodologies, and impactful research, Wang continues to advance the field of intelligent industrial process control and smart manufacturing technologies.

Profile: Scopus

Featured Publications

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2025). Subspace identification method-based setpoints tracking control and its applications to the column cleaning process. ISA Transactions.

  • Wang, Z., He, D., Wang, Z., & Li, Q. (2023). Timeliness and stability-based operation optimization for copper flotation industrial process. IEEE Transactions on Instrumentation and Measurement.

  • Wang, Z., Peng, F., Li, Q., & He, D. (2023). State evaluation of copper flotation process based on transfer learning and a layered and blocked framework. Canadian Journal of Chemical Engineering.

  • Wang, Z., Zhang, X., & He, D. (2023). Dynamic global feature extraction and importance-correlation selection for the prediction of concentrate copper grade and recovery rate. Canadian Journal of Chemical Engineering.

  • Wang, Z., He, D., Zhang, Q., & Shi, J. (2020). Observer-based finite-time model reference adaptive state tracking control with actuator saturation. International Journal of Control, Automation and Systems.

Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

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Assist. Prof. Dr. Yinfeng Yang | Computer Science and Artificial Intelligence | Best Researcher Award

Anhui University of Chinese Medicine | China

Dr. Yinfeng Yang is a distinguished researcher specializing in artificial intelligence, bioinformatics, and traditional Chinese medicine (TCM), with a strong emphasis on AI-enabled drug discovery and biomedical big data analysis. Her work integrates multi-omics analytics, computational modeling, and machine learning to uncover disease biomarkers, elucidate therapeutic mechanisms, and accelerate the discovery of bioactive compounds from natural medicines. Dr. Yinfeng Yang has made significant contributions to AI-assisted drug discovery (AIDD), advancing methodologies in molecular docking, molecular dynamics simulations, high-throughput virtual screening, and quantitative structure–activity relationship modeling. She has authored 44 Scopus-indexed publications, accumulating 5,663 citations across 5,524 documents, supported by a Scopus h-index of 18, underscoring her impact in computational biology and integrative medicine research. Her studies, published in leading journals such as Journal of Advanced Research, Phytomedicine, ACS Omega, Drug Discovery Today, and Current Medicinal Chemistry, span diverse topics including cancer prognosis modeling, multi-scale mechanisms of herbal medicine, ADMET prediction frameworks, and the therapeutic potential of Ginkgo biloba in oncology and neurological disorders. Dr. Yinfeng Yang has led and contributed to numerous scientific research projects focused on TCM modernization, biomedical intelligence, and compound drug discovery. She also plays an active role in scholarly publishing as an editorial board member for journals such as PLOS ONE and Journal of Hebei Medical University, and serves as a recognized reviewer for more than 30 international journals, including Nature Communications, Phytomedicine, ACS Omega, and Journal of Ethnopharmacology. Her research excellence continues to advance innovation in AI-driven precision medicine and the global understanding of natural-product-based therapeutics.

Profiles: Scopus | Google Scholar | ORCID

Featured Publications

  • Fan, N., Chen, J., Wang, J., Chen, Z. S., & Yang, Y. (2025). Bridging data and drug development: Machine learning approaches for next-generation ADMET prediction. Drug Discovery Today, Article 104487.

  • Han, Z., Liu, Q., Yang, J., Wang, X., Song, W., Wang, J., & Yang, Y. (2025). Exploration of the mechanism of Ginkgo biloba leaves targeted angiogenesis against gastric cancer. ACS Omega, 10(35), 40460–40476.

  • Li, H., Fu, S., Shen, P., Zhang, X., Yang, Y., & Guo, J. (2025). Mitochondrial pathways in rheumatoid arthritis: Therapeutic roles of traditional Chinese medicine and natural products. Phytomedicine, Article 157106.

  • Yang, P., Wang, X., Yang, J., Yan, B., Sheng, H., Li, Y., Yang, Y., & Wang, J. (2025). AI-driven multiscale study on the mechanism of Polygonati Rhizoma in regulating immune function in STAD. ACS Omega, 10(19), 19770–19796.

  • Zhang, H., Xu, Q., Kan, H., Yang, Y., & Cai, Y. (2025). Exploration of the clinicopathological and prognostic significance of BRCA1 in gastric cancer. Discover Oncology, 16(1), 381.

Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

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Prof. Ana Josselinne Alegre Mondragón | Data Science and Analytics | Editorial Board Member

Geospatial Information Sciences Research Center | Mexico

Prof. Ana Josselinne Alegre Mondragón is a distinguished geospatial information sciences researcher whose scholarly contributions advance the understanding of violence dynamics, clandestine graves detection, and geospatial applications for public security and forensic investigations. Her research integrates geographic profiling, spatial statistics, remote sensing, UAV-based photogrammetry, and geointelligence modeling to address critical national challenges, particularly the search for missing persons and the analysis of organized crime patterns. She has authored influential book chapters, peer-reviewed journal articles, and scientific reports published by Springer, Forensic Sciences International, and leading academic institutions in Mexico. Her work includes the development of spectral indices for detecting clandestine graves, multivariate regional analyses of burial sites, geospatial modeling of crime, assessment of soil alterations linked to criminal activities, and advanced methodologies for drone-based terrain morphology assessment. She has actively contributed to high-impact research projects such as Espacio Clandestino, geospatial infrastructures for the Ayotzinapa investigation, and scientific applications supporting national systems for the search of missing persons. Her publications and research tools are widely referenced, shaping new standards for evidence-based forensic search protocols. She has collaborated on cross-disciplinary initiatives involving forensic science, anthropology, criminology, and remote sensing, generating innovative methodologies and academic outputs recognized both nationally and internationally. Her scholarly trajectory demonstrates a sustained commitment to advancing geospatial science for human rights, security analysis, and public policy, positioning her as a leading figure in Mexico’s emerging field of forensic geospatial analytics.

Profile: Scopus | ORCID

Featured Publications

  • Alegre-Mondragón, A. J. (2024). Cambios organizacionales en la policía de la Ciudad de México. Del modelo CompStat a la evaluación con transparencia y apertura hacia la ciudadanía. Revista Digital de Estudios Organizacionales, Universidad de Xalapa, Veracruz, México, 53–92. DOI: https://doi.org/10.69509/wxj14d34

  • Silván-Cárdenas, J. L., Alegre-Mondragón, A. J., Ramírez Aceves E. D., Campos Cornejo, D. R. & Bautista Nadalón, M. (2024). Diseño y aplicación de índices espectrales para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 355–389.

  • Alegre-Mondragón, A. J. & Silván-Cárdenas, J. L. (2024). Morfología del terreno mediante fotogrametría con drones: oportunidades y limitaciones para la detección de fosas clandestinas. En Interpretar la naturaleza para encontrar a quienes nos faltan. COBUPEJ & CentroGeo, 323–350.

  • Silván-Cárdenas, J. L. & Alegre-Mondragón, A. J. (2024). Espacio Clandestino: A Nationwide Platform to Support Clandestine Graves Search in Mexico. In Recent Developments in Geospatial Information Sciences. Springer, 175–186.

  • Alegre-Mondragón, A. J., Vilalta-Perdomo, C. J., Silván-Cárdenas, J. L. & Silva-Arias, C. (2024). Characteristics of Clandestine Burial Sites in Mexico: A Regional Overview with Multivariate Analysis. In Recent Developments in Geospatial Information Sciences. Springer, 53–64.

Wael Badawy | Computer Science | Best Researcher Award

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Prof. Dr. Wael Badawy | Computer Science | Best Researcher Award

Egyptian Russian University | Egypt

Prof. Wael Badawy, is a distinguished engineer, researcher, and academic leader with over 28 years of experience in higher education, research, technology commercialization, and innovation management. He earned his Ph.D. in Computer Engineering from the University of Louisiana at Lafayette, USA, and an equivalent Ph.D. in Electrical Engineering recognized by the Egyptian Higher Council of Universities, complemented by M.Sc. and B.Sc. degrees in Computer Science and Automatic Control Engineering from Alexandria University, Egypt. Prof. Wael Badawy has held senior academic and leadership positions, including Executive Director of ABM College, Canada, Program Head of Data Science and Cybersecurity at the Egyptian Russian University, and professorships at Nile University, Badr University, and the American University in Cairo, where he has taught and supervised students in Artificial Intelligence, Deep Learning, Multimedia Engineering, Cybersecurity, and Information Technology Management. His research contributions encompass over 400 publications in high-impact journals and conferences, 56 books and proceedings, and 34 co-invented patents, with highly cited work including the IEEE Transactions on Circuits and Systems for Video Technology (2018). Prof. Badawy has received more than 90 prestigious awards and honors, including the QS Reimagine Education Awards (2023, shortlisted), Silicon Review “30 Innovative Brands of the Year” (2022), and multiple distinctions in STEM, business innovation, and leadership. He actively serves on international standardization committees, professional organizations such as IEEE and ACM, and national research councils, contributing to curriculum development, program design, and strategic planning in higher education. Prof. Wael Badawy’s extensive contributions to research, innovation, and education demonstrate his sustained impact on technology, society, and the global academic community, making him an exemplary candidate for the Best Researcher Award.

Profile: Google Scholar | ORCID | LinkedIn | Staff Page

Featured Publications

  • Du, S., Ibrahim, M., Shehata, M., & Badawy, W. (2012). Automatic license plate recognition (ALPR): A state-of-the-art review. IEEE Transactions on Circuits and Systems for Video Technology, 23(2), 311–325.

  • Rahman, C. A., Badawy, W., & Radmanesh, A. (2003). A real-time vehicle’s license plate recognition system. In Proceedings of the IEEE Conference on Advanced Video and Signal Based Surveillance.

  • Shehata, M. S., Cai, J., Badawy, W. M., Burr, T. W., Pervez, M. S., Johannesson, R. J., … (2008). Video-based automatic incident detection for smart roads: The outdoor environmental challenges regarding false alarms. IEEE Transactions on Intelligent Transportation Systems, 9(2), 349–360.

  • Ghallab, Y. H., Badawy, W., Kaler, K. V. I. S., & Maundy, B. J. (2005). A novel current-mode instrumentation amplifier based on operational floating current conveyor. IEEE Transactions on Instrumentation and Measurement, 54(5), 1941–1949.

  • Du, S., Shehata, M., & Badawy, W. (2011). Hard hat detection in video sequences based on face features, motion and color information. In 2011 3rd International Conference on Computer Research and Development, 4, 25–29.

  • Ghallab, Y., & Badawy, W. (2004). Sensing methods for dielectrophoresis phenomenon: From bulky instruments to lab-on-a-chip. IEEE Circuits and Systems Magazine, 4(3), 5–15.

  • Badawy, W., & Gomaa, H. (2015). Analyzing a segment of video. U.S. Patent No. 9,014,429.

  • Ghallab, Y. H., & Badawy, W. (2010). Lab-on-a-chip: Techniques, circuits, and biomedical applications. Artech House.

  • Badawy, W. (2009). Mesh based frame processing and applications. U.S. Patent No. 7,616,782.

  • Badawy, W. (2009). Video based monitoring system. U.S. Patent No. 7,612,666.