Joung hwan mun | Machine learning | Best Scholar Award

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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

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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

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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.

Marco Capogni | Data Science | Best Researcher Award

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Prof. Dr. Marco Capogni | Data Science | Best Researcher Award

Researcher | ENEA – National Institute for Ionizing Radiation Metrology | Italy

Prof. Dr. Marco Capogni’s research focuses on the precise measurement and standardization of radionuclides, with a strong emphasis on ionizing radiation metrology and its applications in medicine, industry, and environmental monitoring. He has developed and maintained primary national standards for radioactivity, collaborating with international institutions such as the Bureau International des Poids et Mesures (BIPM) and contributing to interlaboratory comparisons to ensure global consistency in radionuclide measurements. His work includes the implementation of absolute measurement techniques and computational codes such as GEANT, MCNP, Penelope, and Fluka for both direct and indirect activity determination. Marco has been actively involved in projects producing medical radionuclides like Mo-99 and Cu-64, utilizing neutron activation and absolute or relative measurement systems, and has contributed to the development of innovative sources of fusion neutrons for radioisotope production under the Sorgentina-RF project. His expertise spans gamma spectrometry, liquid scintillation counting, and coincidence counting methods, and he has served as a member of international working groups including the International Committee for Radionuclide Metrology (ICRM) and the European Metrology Network for Radiation Protection (EURAMET). Marco has led and coordinated numerous European research projects funded by EMRP and EMPIR, focusing on robust production chains for medical radionuclides, radiological early warning networks, and metrology for decommissioning nuclear facilities. He has also contributed to the training of students at the master’s and doctoral levels in physics, engineering, and medical physics, supervising multiple theses on radionuclide metrology and measurement techniques. His work has resulted in significant publications, patents, and participation in international conferences, reflecting his leadership in metrological science and nuclear applications. Marco Capogni’s contributions demonstrate a blend of experimental expertise, computational proficiency, and collaborative engagement with international metrology and research networks, addressing challenges in nuclear measurement, radioprotection, and medical isotope production. He has achieved 1,882citations, authored 133 documents, and holds an h-index of 21.

Profiles: Scopus | ORCID
Featured Publication

Capogni, M., … (2024). Assessment of impurity production upon 14 MeV fusion neutron irradiation of both natural and isotopically enriched 100Mo samples. European Physical Journal Plus.
Citations: 1

Capogni, M., … (2024). Measurements of the absolute gamma-ray emission intensities from the decay of 166Ho. Applied Radiation and Isotopes.
Citations: 2

Capogni, M., … (2024). Future of 99Mo reactor-independent supply. Nature Reviews Physics.
Citations: 3

Capogni, M., … (2023). Analytical study of low energy proton interactions in the SORGENTINA’s fusion ion source-Part II: beam-gas: SORGENTINA ion beam interactions. European Physical Journal Plus.
Citations: 2

Capogni, M., … (2023). The international reference system for beta-particle emitting radionuclides: Validation through the pilot study CCRI(II)-P1.Co-60. Applied Radiation and Isotopes.
Citations: 5

Capogni, M., … (2023). The importance of uncertainty analysis and traceable measurements in routine quantitative 90Y-PET molecular radiotherapy: A multicenter experience. Pharmaceuticals.
Citations: 1

Capogni, M., … (2023). Experimental campaign on ordinary and baritic concrete samples for the SORGENTINA-RF plant: The SRF-bioshield tests. European Physical Journal Plus.
Citations: 3

Dr. Devbrat Pundhir | Artificial Intelligence | Best Researcher Award

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Dr. Devbrat Pundhir | Artificial Intelligence | Best Researcher Award

Assoc.Prof.Dr at Raja Balwant Singh Engineering Technical Campus, India

Dr. Devbrat Pundhir has consistently contributed to the understanding of ionospheric behavior under seismic influences through a series of well-structured, peer-reviewed studies. His commitment to exploring the physics behind electromagnetic precursors and TEC variations makes his research both scientifically rigorous and societally relevant. His innovations in methodology, especially his move towards AI integration and data-centric modeling, reflect his adaptability and forward-thinking approach.

Professional Profile

Google Scholar | Scopus | Orcid Profile

Education

Dr. Devbrat Pundhir holds a Ph.D. in Physics from Banasthali Vidyapith (Deemed University), awarded in 2018, with his thesis focused on ionospheric perturbations due to earthquakes using GPS-based Total Electron Content (TEC) measurements. He earned his M.Sc. in Physics from Dayalbagh Educational Institute (Deemed University), Agra, in 2012 with 70.1% marks. Prior to that, he completed his B.Sc. (Hons.) in Physics from the same institute in 2010, securing 68.9%. His early education was completed under the U.P. Board, where he passed his Intermediate in 2007 and High School in 2005, both with First Division.

Experience

Currently, Dr. Pundhir serves as an Assistant Professor of Physics in the Department of Applied Sciences & Humanities at Raja Balwant Singh Engineering Technical Campus, Bichpuri, Agra, since January 2019. Previously, he worked as a Senior Research Fellow on a Ministry of Earth Sciences-sponsored project related to electromagnetic earthquake precursors at the Seismo-electromagnetics and Space Research Laboratory (SESRL), Agra, from 2016 to 2018, where he also taught undergraduate Engineering Physics. He earlier served as a Junior Research Fellow in a Department of Science & Technology project on Schumann Resonance phenomena. His early research experience also includes work in fiber optics, nonlinear behavior of inorganic materials, and nanostructures under the guidance of Dr. Sukhdev Roy during his M.Sc. Additionally, he has experience as a video editor in an MHRD project at Dayalbagh Educational Institute.

Skills and Expertise

Dr. Pundhir possesses strong analytical and technical skills in atmospheric physics, signal processing, and modeling using AI/ML techniques. He has hands-on expertise in GPS-TEC data analysis, ULF/VLF ground measurements, and ionospheric modeling. He has also designed a biosensor in fiber optics and conducted research on carbon nanotubes and iron oxide materials. His computer skills include certified proficiency in Computer Concepts by NIELIT and three years of practical experience in computer applications. He has developed attainment calculation software and has served as a resource for IPR and innovation-related activities in his institution.

Research Focus

Dr. Pundhir’s primary research areas lie in seismo-electromagnetics, low-latitude ionospheric modeling, and earthquake precursor detection using TEC and electromagnetic signals. His ongoing projects focus on AI-based prediction of ionospheric behavior and synthesis of metallic nanostructures. His work also explores the coupling of atmospheric and ionospheric parameters during seismic events and the use of satellite and ground-based tools for early warning systems. He has published over 35 international journal articles, co-supervised Ph.D. students, and contributed significantly to interdisciplinary research involving geophysics, space weather, and machine learning applications.

Awards and Honors

Dr. Pundhir has been recognized widely for his academic and research contributions. He has served on the Technical Program Committees of various international conferences in China and India and was appointed as Chair for multiple events. He is a life member of the Indian Geophysical Union and serves on editorial boards of international journals like the SCIREA Journal of Environment and Geosciences. He has received multiple certifications from AICTE, the Ministry of Education, and international FDPs on AI, teaching methods, and instrumentation. He has mentored students for projects funded by INSPIRE and AICTE and contributed actively to institutional innovation and IPR policies.

Publication

  • Title: Anomalous TEC variations associated with the strong Pakistan-Iran border region earthquake of 16 April 2013 at a low latitude station Agra, India
    Authors: D. Pundhir, B. Singh, O.P. Singh
    Journal: Advances in Space Research
    Year: 2014
    Citations: 30

 

  • Title: Ionospheric perturbations due to earthquakes as determined from VLF and GPS-TEC data analysis at Agra, India
    Authors: D. Singh, B. Singh, D. Pundhir
    Journal: Advances in Space Research
    Year: 2018
    Citations: 21

 

  • Title: Study of ionospheric precursors using GPS and GIM-TEC data related to earthquakes occurred on 16 April and 24 September, 2013 in Pakistan region
    Authors: D. Pundhir, B. Singh, O.P. Singh, S.K. Gupta, S.P. Karia, K.N. Pathak
    Journal: Advances in Space Research
    Year: 2017
    Citations: 19

 

  • Title: A multi-experiment approach to ascertain electromagnetic precursors of Nepal earthquakes
    Authors: S. Sharma, R.P. Singh, D. Pundhir, B. Singh
    Journal: Journal of Atmospheric and Solar-Terrestrial Physics
    Year: 2020
    Citations: 15

 

  • Title: A morphological study of low latitude ionosphere and its implication in identifying earthquake precursors
    Authors: D. Pundhir, B. Singh, O.P. Singh, S.K. Gupta
    Journal: J. Ind. Geophys. Union
    Year: 2017
    Citations: 10

Conclusion

In conclusion, Dr. Pundhir is a highly deserving candidate for the Best Researcher Award. His pioneering work on seismo-ionospheric precursors, broad publication record, and his ongoing evolution into interdisciplinary modeling highlight his scientific maturity and future leadership potential. Recognizing him with this award would not only honor his current achievements but also encourage further innovation in disaster prediction science.

Dr. Hui Yu | Data Science | Best Innovation Award

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Dr. Hui Yu | Data Science | Best Innovation Award

Assoc.Prof.Dr at Institute of Mountain Hazards and Environment, CAS, China

Assoc. Prof. Dr. Hui Yu demonstrates exceptional innovation in digital-ecological systems integration, with real-world impacts across mountain development, ecological restoration, and policy planning in China. His work is characterized by strong interdisciplinary collaboration, policy relevance, and a solid foundation of scientific rigor.

Professional Profile

Education

While specific degree details were not explicitly listed, Assoc. Prof. Dr. Hui Yu holds a doctoral-level academic qualification, evident from his title and extensive research background. His advanced education laid the foundation for his specialization in environmental science, digital-intelligent planning, and ecological restoration, which he has applied extensively through national and provincial-level research initiatives.

Experience

Assoc. Prof. Dr. Hui Yu currently serves as the Deputy Director of the Technology Innovation Center for Southwest Land Space Ecological Restoration and Comprehensive Renovation, Ministry of Natural Resources (MNR), China. He is affiliated with the Institute of Mountain Hazards and Environment, Chinese Academy of Sciences. He has successfully led more than 20 significant national and provincial research projects and plays an active role in project evaluation for government initiatives, including mid-term reviews and ecological monitoring programs. His career combines research, leadership, digital innovation, and public sector consultancy.

Skills and Expertise

Dr. Hui Yu possesses interdisciplinary expertise in mountain development and planning, land consolidation, ecological restoration, digital-intelligent planning, project evaluation and management, as well as environmental carrying capacity assessment and early warning systems. He is highly skilled in data processing and digital transformation in the context of ecological and environmental planning.

Research Focus

His research is primarily centered around ecological and spatial restoration in mountainous regions, with an emphasis on comprehensive land planning. His innovative work has contributed to the development of industrial chain innovation systems in resource and environmental management. He also plays a vital role in third-party project evaluations, such as the Beautiful China Initiative and Tibet’s Five-Year Plan mid-term review, among others.

Awards and Honors

Dr. Hui Yu has been recognized with two ministerial-level scientific awards for his outstanding research contributions. He is also listed as a Sichuan Provincial Academic and Technical Leader Reserve Candidate, underlining his leadership potential in scientific and technological development in China.

Publication

  • Effects of Comprehensive Land Consolidation on Farmers’ Livelihood Under Different Terrain Gradients
    Authors: Rongshan Wan, Hui Yu, Dan Zhang, Bo Yang, Yanhong Huang
    Journal: Land
    Year: 2025
    Citations: Not yet cited (newly published)

 

  • Grass-Livestock Balance-Based Grassland Ecological Carrying Capability and Sustainable Strategy in the Yellow River Source National Park, Tibet Plateau, China
    Authors: Hui Yu, Bin-tao Liu, Gen-xu Wang, Tong-zuo Zhang, Yan Yang, Ya-qiong Lu, You-xue Xu, Min Huang, Yi Yang, Lv Zhang
    Journal: Journal of Mountain Science
    Year: 2021
    Citations: 27 citations

 

  • Driving Forces for the Spatial Reconstruction of Rural Settlements in Mountainous Areas Based on Structural Equation Models: A Case Study in Western China
    Authors: Jia Zhong, Shaoquan Liu, Min Huang, Sha Cao, Hui Yu
    Journal: Land
    Year: 2021
    Citations: 15+ citations

 

  • Water-Facing Distribution and Suitability Space for Rural Mountain Settlements Based on Fractal Theory, South-Western China
    Authors: Hui Yu, Yong Luo, Pengshan Li, Wei Dong, Shulin Yu, Xianghe Gao
    Journal: Land
    Year: 2021
    Citations: 10+ citations

 

  • Territorial Suitability Assessment and Function Zoning in the Jiuzhaigou Earthquake-Stricken Area
    Authors: Hui Yu, Miao Qiang, Shao-quan Liu
    Journal: Journal of Mountain Science
    Year: 2019
    Citations: 24 citations

Conclusion

Highly Recommended for the Best Innovation Award. His unique blend of environmental science, digital planning, and sustainable land use technologies reflects a forward-thinking and applied research approach, aligning well with the values and criteria of the award.