Keuho Park | Data Science and Deep Learning | Excellence in Research Award

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Dr. Keuho Park | Data Science and Deep Learning | Excellence in Research Award

Principal Researcher at Korea Electronics Technology Institute | South Korea

Dr. Keuho Park is a dedicated researcher in advanced computer engineering applications, recognized for his multidisciplinary contributions that span smart agriculture, drone-based disease detection, hyperspectral image analysis, and innovative hybrid image-recognition solutions, and he currently serves as a Senior Researcher at the Korea Electronics Technology Institute in Seongnam-si within the IT Application Research Center, where he focuses on transforming real-world challenges into practical, technology-driven solutions through intelligent imaging systems, AI-powered analysis frameworks, and applied computational methods, and his academic foundation is strengthened through his ongoing doctoral work in Computer Engineering at Chonbuk National University in Jeonju, where he continuously expands his expertise in machine learning, sensor data interpretation, and digital transformation technologies, and throughout his career he has authored influential works including Comparison of Effects of Foliar Fertilizer Application of Hydrogen Water on Leaf Lettuce, which explores agricultural enhancement through innovative water-based treatments, Automated Detection of Rice Bakanae Disease via Drone Imagery, which showcases how drone platforms and visual analytics can modernize disease surveillance, Tunnel Emergence Detection Technology based on Hybrid Image Recognition, which presents practical image-based safety solutions integrating hybrid recognition techniques, and Classification of Apple Leaf Conditions in Hyper-Spectral Images for Diagnosis of Marssonina Blotch using mRMR and Deep Neural Network, which demonstrates his expertise in hyperspectral data classification and deep neural network modeling, and through this diverse portfolio Keunho Park has emerged as a leading contributor at the intersection of AI, agriculture, imaging science, and smart-system innovation, consistently advancing research that bridges technical sophistication with real-world impact.

Profile: Orcid

Featured Publications:

Park, K., Jung, S., Kim, H., Kim, S., Kang, D., Choi, J., & Park, K. S. (2025). Comparison of effects of foliar fertilizer application of hydrogen water on leaf lettuce.

Kim, D., Jeong, S., Kim, B., Kim, S., Kim, H., Jeong, S., Yun, G., Kim, K.-Y., & Park, K. (2022). Automated detection of rice Bakanae disease via drone imagery.

Kim, S., Jeong, S., Park, K., Kim, D., Yoo, C.-J., & Shin, J. (2021). Tunnel emergence detection technology based on hybrid image recognition.

Park, K., Hong, Y. K., Kim, G., & Lee, J. (2018). Classification of apple leaf conditions in hyper-spectral images for diagnosis of Marssonina blotch using mRMR and deep neural network.

Kartik Charania | Data Science and Deep Learning | Best Researcher Award

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Mr. Kartik Charania | Data Science and Deep Learning | Best Researcher Award

Senior Research Fellow at Sardar Vallabhbhai National Institute of Technology Surat | India

Kartik Charania is a dedicated Water Resources Engineer and researcher whose work focuses on hydrological modeling, rainfall variability, and sustainable water distribution systems. Pursuing his Ph.D. in Water Resources Engineering at SVNIT, Surat, his doctoral research emphasizes the spatiotemporal analysis of rainfall variability to support efficient and equitable water distribution network design in semi-arid basins. His expertise integrates advanced statistical and innovative trend analysis techniques with GIS-based spatial mapping to assess temporal rainfall shifts and their hydrological implications. Through his research, he aims to enhance water management practices, optimize reservoir operations, and promote climate-resilient water supply systems. His academic journey includes a Master’s in Water Resources Engineering and a Bachelor’s in Civil Engineering from Gujarat Technological University, where he built a strong foundation in hydraulic and environmental systems. Proficient in tools such as EPANET, ArcGIS, Python, HEC-RAS, HEC-HMS, and Q-GIS, he combines computational and analytical approaches to develop data-driven solutions for sustainable water infrastructure. Kartik has contributed to leading journals like Environmental Science and Pollution Research and World Water Policy, presenting innovative methods for rainfall trend analysis in the Shetrunji Basin, India. His active participation in conferences on hydrology and climate variability highlights his commitment to advancing knowledge in the field. Additionally, he qualified for the GATE examination and participated in specialized training programs like the “Training of Trainer (ToT)” under the MARVI project, reflecting his dedication to groundwater visibility and community-based water management.

Profile: Scopus | Orcid | Google Scholar

Featured Publications:

Charania, K. M., & Patel, J. N. (n.d.). Spatiotemporal trends and variability of rainfall patterns using innovative polygon trend analysis method for Shetrunji Basin, India. Environmental Science and Pollution Research, 1–11.

Charania, K. M., & Patel, J. N. (n.d.). Comprehensive trend analysis of monthly and seasonal rainfall in the Shetrunji Basin, India using statistical and innovative techniques. World Water Policy.

Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

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Assoc. Prof. Dr. Lubna Aziz | Data Science and Deep Learning | Best Researcher Award

Associate Professor at Iqra University Karachi | Pakistan

Assoc. Prof. Dr. Lubna Aziz is an accomplished AI and MLOps Engineer, Researcher, and Academic Leader with over fifteen years of multidisciplinary experience in artificial intelligence, machine learning, and higher education leadership, currently serving as Assistant Professor and Head of Artificial Intelligence at Iqra University, Karachi. She holds a PhD in Computer Science from Universiti Teknologi Malaysia and has earned dual Gold Medals in both her MS and BS in Computer Engineering from BUITEMS, reflecting her consistent record of academic excellence. Her professional expertise spans AI model development, scalable ML pipeline automation, MLOps deployment, Explainable AI, Computer Vision, and Generative AI, integrating research-driven innovation with real-world engineering impact. Dr. Aziz has designed and led AI curricula, supervised numerous student projects, and directed institutional initiatives aligned with HEC, NCEAC, and ABET accreditation standards. Her research advances Computer Vision, Large Language Models (LLMs), and Explainable AI (XAI) with applications across healthcare, finance, and creative AI, focusing on interpretable, multimodal, and human-centric intelligent systems. She has contributed to IEEE Access, Nature Scientific Reports, Springer, and MDPI journals, with publications exploring object detection, medical imaging, energy optimization, multimodal AI, and generative modeling. As an active reviewer for leading international journals and a keynote and technical chair for major AI and engineering conferences, she has significantly shaped discourse in emerging technologies. Her research projects include AI-driven healthcare diagnostics, cardiovascular risk modeling, and LLM intelligence benchmarking, funded by HEC, NIH, and the Royal Academy of Engineering UK. Known for her academic leadership, technical depth, and commitment to inclusive innovation, Lubna Aziz continues to bridge the gap between AI research and practical deployment, fostering the next generation of intelligent systems and ethical AI solutions.

Profile: Orcid 

Featured Publications:

Deebani, W., Aziz, L., Alawad, W. M., Alahmari, L. A., Al‐Ahmary, K. M., Alqurashi, Y., & Alwabel, A. S. A. (2025). Advancing electronic noses with transformers: Real‐time classification of hazardous odors and food freshness. Journal of Food Science.

Aziz, L., Adil, H., & Sarwar, R. (2025). Artificial sensing: AI-driven electronic nose for real-time gas leak detection and food spoilage monitoring. Sir Syed University Research Journal of Engineering & Technology.

Deebani, W., Aziz, L., Aziz, A., Basri, W. S., Alawad, W. M., & Althubiti, S. A. (2025). Synergistic transfer learning and adversarial networks for breast cancer diagnosis: Benign vs. invasive classification. Scientific Reports.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., Khan, S., Ayub, H., & Ayub, S. (2021). Multi-level refinement feature pyramid network for scale imbalance object detection. IEEE Access.

Arfeen, Z. A., Sheikh, U. U., Azam, M. K., Hassan, R., Shehzad, H. M. F., Ashraf, S., Abdullah, M. P., & Aziz, L. (2021). A comprehensive review of modern trends in optimization techniques applied to hybrid microgrid systems. Concurrency and Computation: Practice and Experience.

Aziz, L., Salam, M. S. B. H., Sheikh, U. U., & Ayub, S. (2020). Exploring deep learning-based architecture, strategies, applications and current trends in generic object detection: A comprehensive review. IEEE Access.