Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Sachin Sravan Kumar Komati | Deep Learning | Best Researcher Award

AI Engineer | Florida International University | United States

Sachin Sravan Kumar Komati is an accomplished researcher in Artificial Intelligence and Machine Learning, specializing in biomedical applications, particularly in gastrointestinal disease diagnosis, cancer prognosis, and postoperative complication prediction. His research integrates deep learning, computer vision, and multimodal AI frameworks to develop intelligent healthcare solutions. He has contributed significantly to the fields of predictive analytics, medical imaging, and surgical AI, creating advanced models using LSTM, Vision Transformers, and Autoencoders for enhanced diagnostic precision. His works explore AI-driven insights in clinical and imaging datasets, focusing on improving real-time disease detection and patient-specific treatment strategies. Sachin’s scholarly contributions include numerous peer-reviewed publications in reputed international journals such as PLOS One, Gastroenterology, Gastrointestinal Endoscopy, Critical Care Medicine, and the Journal of Clinical Oncology. His research has earned global recognition through multiple conference acceptances, including at ACG, AASLD, and UEG Week. According to Google Scholar, he has received 2 citations, with an h-index of 1 and an i10-index of 0, reflecting his emerging influence in AI-driven healthcare research. His Scopus metrics also indicate growing visibility and scholarly impact. Sachin’s research continues to advance the integration of artificial intelligence into clinical decision-making and medical imaging, aiming to bridge the gap between AI innovation and patient-centered healthcare.

Profile

Google Scholar | ORCID

Featured Publications

Boppana, S. H., Tyagi, D., Komati, S. S. K., Boppana, S. L., Raj, R., & Mintz, C. D. (2025). AI-delirium guard: Predictive modeling of postoperative delirium in elderly surgical patients. PLOS One, 20(6), e0322032.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., Aakash, F., & Dang, A. K. (2025). Enhancing gastrointestinal bleeding detection in wireless capsule endoscopy using convolutional autoencoders. American College of Gastroenterology, 120(10S2).

Boppana, S. H., Chitturi, R. H., Komati, S. S. K., Raj, R., & Mintz, C. D. (2025). DiabCompSepsAI: Integrated AI model for early detection and prediction of postoperative complications in diabetic patients using a Random Forest Classifier. Journal of Clinical Medicine, 14(20), 7173.

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Predictive modeling of GI disease: GastroEndo-Seq for progression and outcome forecasting. Gastroenterology, 120(10S2).

Boppana, S. H., Thota, M., Maddineni, G., Komati, S. S. K., & Mintz, C. D. (2025). Vision Transformer-based framework for risk stratification and prognostic assessment in gastrointestinal lesion management. Gastrointestinal Endoscopy, 120(10S2).

Prof. Chen Juan | Deep learning | Best Researcher Award

Published on by Computer Scientists Awards

Prof. Chen Juan | Deep learning | Best Researcher Award

Shanghai University, China

Dr. Juan Chen is a distinguished researcher and educator in the field of big data analytics, autonomous driving, and computer vision, currently serving as a faculty member at SILC Business School, Shanghai University since 2009. With over two decades of academic and research experience, she specializes in developing cutting-edge AI models, especially for transportation and e-commerce applications. Her expertise in deep learning and intelligent transportation systems has earned her recognition in core academic journals and scientific communities.

Publication Profile

ORCID

🎓 Education Background

Dr. Chen obtained her Ph.D. in Control Science and Engineering from Tongji University, China in 2008. She previously completed her Master’s degree at the School of Automation, Xi’an Jiaotong University in 2003, and earned her Bachelor’s degree in Energy and Power Engineering from Shanghai University of Technology in 1996. Her robust academic background laid the foundation for her interdisciplinary work across AI, engineering, and data science.

🏫 Professional Experience

Dr. Chen began her academic career as a lecturer at the School of Electronic and Information Engineering, Northern University for Nationalities from 1996 to 1998 and returned to the same school from 2001 to 2002. Since 2009, she has been actively contributing to teaching and research at SILC Business School, Shanghai University. Her teaching portfolio includes essential courses such as Python Program Design, Fundamentals of Data Analysis, and Deep Learning Practice in Computer Vision, which bridge theory with real-world AI practices.

🏆 Awards and Honors

Dr. Chen has consistently published in prestigious journals indexed in SCI and ESCI, such as the International Journal of Distributed Sensor Networks, IET Intelligent Transport Systems, and Algorithms. Her research achievements, including core journal recognition by Peking University, reflect her impactful contributions to intelligent systems and optimization in traffic networks.

🔬 Research Focus

Dr. Chen’s research is centered on big data analysis applied to transportation and e-commerce, autonomous vehicle control, computer vision, and deep learning. She has developed advanced models such as graph convolutional networks and spatiotemporal LSTM to address challenges in vehicle trajectory prediction, traffic congestion, and signal optimization. Her work integrates reinforcement learning, fuzzy logic, and multi-objective optimization to improve real-world systems’ efficiency and sustainability.

🔚 Conclusion

With an unwavering commitment to advancing AI applications in intelligent transportation, Dr. Juan Chen exemplifies interdisciplinary excellence. Her blend of academic rigor, research innovation, and practical teaching continues to inspire the next generation of engineers and data scientists. 🚗💡📊

📚 Top Publications :

Urban expressway on-ramp control based on improved NSGA-Ⅱ algorithm of reinforcement learning
Journal of Shanghai University (Natural Science Edition), 2023
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Vehicle Trajectory Prediction Based on Local Dynamic Graph Spatiotemporal-LSTM Model
World Electric Vehicle Journal, 2024
Cited by: Search in Google Scholar

KGCN-LSTM: A graph convolutional network considering knowledge fusion of point of interest for vehicle trajectory prediction
IET Intelligent Transport Systems, 2023
Cited by: Search in Google Scholar

Connected and automated vehicle control at unsignalized intersection based on deep reinforcement learning in vehicle-to-infrastructure environment
International Journal of Distributed Sensor Networks, 2022
Cited by: Search in Google Scholar

Multi-class expressway traffic control for reducing congestion and emissions based on fuzzy NSGA
Journal of Shanghai University (Natural Science Edition), 2021
Cited by: Search in Google Scholar

Freeway Traffic Congestion Reduction and Environment Regulation via Model Predictive Control
Algorithms, 2019
Cited by: Google Scholar

Traffic congestion prediction based on GPS trajectory data
International Journal of Distributed Sensor Networks, 2019
Cited by: Search in Google Scholar