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

Joung hwan mun | Machine learning | Best Scholar Award

Published on by Computer Science Engineering

Prof. Dr. Joung hwan mun | Machine learning | Best Scholar Award

Professor | Sungkyunkwan University | South Korea

Professor Joung Hwan Mun, Ph.D., is a distinguished Professor in the Department of Biomechatronic Engineering at Sungkyunkwan University, Korea, where he also serves as Director of the Institute of Biotechnology and Bioengineering and the Center for Bio-Information & Communication Technology. He earned his B.S. and M.S. degrees in Biomechatronic Engineering from Sungkyunkwan University and a Ph.D. in Mechanical Engineering from The University of Iowa, USA. With a prolific academic career spanning over two decades, Dr. Mun has significantly contributed to advancing biomechatronics, biomedical engineering, and intelligent healthcare technologies. His primary research interests encompass embedded systems in healthcare, artificial intelligence applications in medical devices, Internet of Things (IoT) integration for medical systems, and wearable sensor technologies for human motion analysis. He has authored more than 250 peer-reviewed publications, including 151 journal articles and 105 conference papers, reflecting his extensive influence in biomechanics, gait analysis, and machine learning-driven motion prediction. His work on AI-based gait and fall detection models, center of pressure trajectory prediction, and exoskeleton design has been widely recognized for improving human mobility, rehabilitation, and clinical diagnostics. Dr. Mun holds over 30 international and national patents, including innovations in surgical navigation, wearable exoskeletons, and fall detection systems, demonstrating his commitment to translational research with direct societal benefits. His leadership in integrating AI, sensor fusion, and biomechanical modeling has fostered interdisciplinary collaborations across Korea, the United States, and Japan. A former Adjunct Associate Professor at The University of Iowa and Invited Associate Professor at Tokyo Denki University, Dr. Mun continues to advance next-generation biomedical systems that merge artificial intelligence and human biomechanics to enhance healthcare accessibility, safety, and quality worldwide.

Featured Publication

Oh, S. E., Choi, A., & Mun, J. H. (2013). Prediction of ground reaction forces during gait based on kinematics and a neural network model. Journal of Biomechanics, 46(14), 2372–2380.

Mun, J. H., & Youn, S. H. (2020). Apparatus and method for discriminating biological tissue, surgical apparatus using the apparatus (U.S. Patent No. 10,864,037).

Choi, A., Kim, T. H., Yuhai, O., Jeong, S., Kim, K., Kim, H., & Mun, J. H. (2022). Deep learning-based near-fall detection algorithm for fall risk monitoring system using a single inertial measurement unit. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 30, 2385–2394.

Park, H. J., Sim, T., Suh, S. W., Yang, J. H., Koo, H., & Mun, J. H. (2016). Analysis of coordination between thoracic and pelvic kinematic movements during gait in adolescents with idiopathic scoliosis. European Spine Journal, 25(2), 385–393.

Choi, A., Lee, J. M., & Mun, J. H. (2013). Ground reaction forces predicted by using artificial neural network during asymmetric movements. International Journal of Precision Engineering and Manufacturing, 14(3), 475–483.

Choi, A., Joo, S. B., Oh, E., & Mun, J. H. (2014). Kinematic evaluation of movement smoothness in golf: Relationship between the normalized jerk cost of body joints and the clubhead. Biomedical Engineering Online, 13(1), 20.

Dr. Joung Hwan Mun’s pioneering research integrates artificial intelligence, biomechanics, and wearable sensing to advance intelligent healthcare systems and human–machine interaction. His innovations in gait analysis, fall detection, and exoskeleton technologies have significantly enhanced mobility, rehabilitation, and safety, driving global progress in personalized healthcare and biomedical engineering.

Muhammad Asif Munir | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Mr. Muhammad Asif Munir | Machine Learning | Best Researcher Award

Assistant Professor| Swedish College of Engineering and Technology | Pakistan

Dr. Muhammad Asif Munir is an accomplished researcher and academic in the field of Electrical Engineering, currently serving as an Assistant Professor at the Swedish College of Engineering and Technology, District Rahim Yar Khan, Punjab, Pakistan, and pursuing his Ph.D. at The Islamia University of Bahawalpur. His research primarily focuses on machine learning and deep learning applications in biomedical image analysis, with a particular emphasis on addressing the challenges of small and imbalanced radiomics datasets. With six peer-reviewed publications indexed in SCI and Scopus journals, including IEEE Access and Future Internet (MDPI), and a growing citation record of 56 citations (h-index: 4, i10-index: 2), Dr. Munir has demonstrated consistent academic excellence and research innovation. His notable contribution, the GSRA-KL framework, introduces a novel sparse regularized autoencoder–based methodology that significantly enhances synthetic data generation and improves the predictive accuracy of gene mutation analysis in lung cancer radiomics. This work not only contributes to the evolution of precision oncology but also exemplifies the integration of AI-driven data synthesis with clinical applications. His ongoing research explores the incorporation of explainable artificial intelligence (XAI) into radiomics for more interpretable, transparent, and reliable predictive modeling, fostering clinically explainable AI systems in healthcare. Dr. Munir’s interdisciplinary approach bridges data science, medical imaging, and clinical decision support, aiming to make AI tools both scientifically robust and ethically transparent. A member of professional organizations such as IEEE and IAENG, he remains actively engaged in promoting research collaboration and advancing the global discourse on intelligent healthcare systems. Through his scholarly contributions, Dr. Munir is significantly impacting the development of data-efficient, interpretable, and patient-centered AI frameworks, reinforcing the global transition toward smart healthcare technologies and next-generation precision medicine. His commitment to research excellence and translational impact continues to position him as a promising figure in the convergence of engineering and medical AI research.

Featured Publication

Aslam, M. A., Munir, M. A., & Cui, D. (2020). Noise removal from medical images using hybrid filters of technique. Journal of Physics: Conference Series, 1518(1), 012061.

Aslam, M. A., Xue, C., Wang, K., Chen, Y., Zhang, A., Cai, W., Ma, L., Yang, Y., Sun, X., & Munir, M. A. (2020). SVM based classification and prediction system for gastric cancer using dominant features of saliva. Nano Biomedicine and Engineering, 12(1), 1–13.

Munir, M. A., Aslam, M. A., Shafique, M., Ahmed, R., & Mehmood, Z. (2022). Deep stacked sparse autoencoders – A breast cancer classifier. Mehran University Research Journal of Engineering and Technology, 41(1), 41–52.

Aslam, M. A., Munir, M. A., Ahmad, R., Samiullah, M., Hassan, N. M., & Mahnoor, S. (2022). Deep neural networks for prediction of cardiovascular diseases. Nano Biomedicine and Engineering, 14(1).

Munir, M. A., Shah, R. A., Ali, M., Laghari, A. A., Almadhor, A., & Gadekallu, T. R. (2024). Enhancing gene mutation prediction with sparse regularized autoencoders in lung cancer radiomics analysis. IEEE Access.

Dr. Muhammad Asif Munir’s research advances intelligent healthcare by integrating machine learning and explainable AI to enhance diagnostic accuracy and transparency in medical imaging. His innovations in radiomics and synthetic data generation foster data-efficient, interpretable, and globally applicable solutions that strengthen precision oncology and next-generation healthcare systems.

Hawazin Elani | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Dr. Hawazin Elani | Machine Learning | Best Researcher Award

Harvard University | United States

Dr. Hawazin W. Elani, Ph.D., is an accomplished scholar and academic leader whose research integrates dentistry, epidemiology, and health policy to advance oral health equity through data-driven, interdisciplinary approaches. She serves as an Associate Professor in the Department of Health Policy and Management at the Harvard T.H. Chan School of Public Health and in the Department of Oral Health Policy and Epidemiology at the Harvard School of Dental Medicine, with additional affiliations at the Harvard Data Science Initiative and the Kempner Institute for the Study of Natural and Artificial Intelligence. Dr. Elani earned her Ph.D. in Dental Sciences with a concentration in Epidemiology and Population Health and an M.Sc. from McGill University, as well as an MMSc in Oral Biology and a Clinical Certificate in Prosthodontics from Harvard. Her research explores health disparities, oral health policy, and the application of artificial intelligence and machine learning in predicting oral health outcomes. She has authored over 30 peer-reviewed publications in high-impact journals such as Health Services Research, JAMA Network Open, and Journal of Dental Research, with her work cited widely for shaping discussions on healthcare access and reform. As principal investigator on multiple NIH and foundation-funded projects, including R01 and K-series grants, she has led innovative studies assessing the effects of Medicaid expansion and socioeconomic factors on dental care utilization. Recognized with Harvard’s Young Mentor Award and Distinguished Junior Faculty Award in 2024, Dr. Elani also contributes to national and international committees, including the NIH, the National Academies of Sciences, and the Medicaid Policy Research Advisory Group. Through her leadership, global collaborations, and dedication to mentoring, she continues to advance the intersection of artificial intelligence, population health, and oral health policy, driving forward equitable and sustainable improvements in healthcare delivery worldwide.

Profiles: Scopus | ORCID
Featured Publication

lani, H. W., Kawachi, I., & Sommers, B. D. (2020). Changes in emergency department dental visits after Medicaid expansion. Health Services Research, 55(1), 76–84.

Elani, H. W., Simon, L., Ticku, S., Bain, P. A., Barrow, J., & Riedy, C. A. (2018). Does providing dental services reduce overall health care costs? A systematic review of the literature. Journal of the American Dental Association (1939), 149(6), 430–438.e10.

Elani, H. W., Starr, J. R., Da Silva, J. D., & Gallucci, G. O. (2018). Trends in dental implant use in the U.S., 1999–2016, and projections to 2026. Journal of Dental Research, 97(13), 1424–1430.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., Cleber, S., & Bittner, N. (2019). Comparison of the color appearance of peri-implant soft tissue with natural gingiva using anodized pink-neck implants and pink abutments: A prospective clinical trial. The International Journal of Oral & Maxillofacial Implants, 34(1), 168–175.

Gil, M. S., Ishikawa-Nagai, S., Elani, H. W., Da Silva, J. D., Kim, D. M., Tarnow, D., Schulze-Späte, U., & Bittner, N. (2017). A prospective clinical trial to assess the optical efficacy of pink neck implants and pink abutments on soft tissue esthetics. Journal of Esthetic and Restorative Dentistry, 29(3), 213–219.

Ye Tao | Machine Learning | Best Researcher Award

Published on by Computer Science Engineering

Dr. Ye Tao | Machine Learning | Best Researcher Award

PhD Student | China University of Petroleum, Beijing| China

Dr Ye Tao is a dedicated researcher focusing on sedimentology, unconventional oil and gas exploration, and the integration of artificial intelligence into geological studies. His work emphasizes fine characterization and sweet spot evaluation of shale gas reservoirs, tectonic evolution, sedimentary system reconstruction, and deepwater hydrocarbon accumulation models. Ye Tao has served as principal investigator and key researcher on multiple funded projects, including studies on shale reservoir heterogeneity in the Wufeng–Longmaxi Formations, tectonic evolution of the North Uscult Basin, and migration and accumulation mechanisms in the Guyana Basin. His expertise spans seismic data interpretation, fracture classification, mechanical modeling, and stress field simulation, contributing to accurate prediction of reservoir sweet spots and caprock sealing capacity. Ye Tao has actively published in peer-reviewed journals, presenting significant contributions such as deep learning-aided shale reservoir analysis, isotope-based sea-level reconstructions, and machine learning-based carbonate fossil recognition. His interdisciplinary approach bridges geology with computer vision and artificial intelligence, providing innovative methodologies for improving exploration accuracy. Ye Tao has been awarded multiple national and institutional prizes, including first prizes at China University of Petroleum’s Graduate Academic Forum and the National Doctoral Student Academic Forum, showcasing his academic excellence and leadership. His skillset includes seismic processing, petrographic thin section analysis, carbon and oxygen isotope testing, and restoration of paleoenvironments, enabling comprehensive understanding of sedimentary processes. By applying deep learning techniques to geological data, Ye Tao is contributing to next-generation exploration strategies that enhance prediction of hydrocarbon distribution and optimize resource development. His work demonstrates strong potential for advancing both theoretical sedimentology and applied petroleum exploration, making significant impact on energy resource evaluation and development strategies in complex geological settings.

Profile:  ORCID
Featured Publication

Tao, Y., Bao, Z., & Ma, F. (2025). Analyzing key controlling factors of shale reservoir heterogeneity in “thin” stratigraphic settings: A deep learning-aided case study of the Wufeng-Longmaxi Formations, Fuyan Syncline, Northern Guizhou. Applied Computing and Geosciences, 100293.

Tao, Y., Bao, Z., Yu, J., & Li, Y. (2025). The petrophysical characteristics and controlling factors of the Wufeng Formation–Longmaxi Formation shale reservoirs in the Fuyan Syncline, Northern Guizhou. Geological Journal.

Tao, Y., Gao, D., He, Y., Ngia, N. R., Wang, M., Sun, C., Huang, X., & Wu, J. (2023). Carbon and oxygen isotopes of the Lianglitage Formation in the Tazhong area, Tarim Basin: Implications for sea-level changes and palaeomarine conditions. Geological Journal, 58, 967–980.

Tao, Y., He, Y., Zhao, Z., Wu, D., & Deng, Q. (2023). Sealing of oil-gas reservoir caprock: Destruction of shale caprock by micro-fractures. Frontiers in Earth Science, 10, 1065875.