Mr. Junde Lu | Artificial Neural Networks | Best Researcher Award

Published on by Computer Scientists Awards

Mr. Junde Lu | Artificial Neural Networks | Best Researcher Award

Beijing Information Science and Technology University | China

Mr. Junde Lu is a promising early-career researcher specializing in optical communication systems and signal processing, with a focus on developing efficient equalization algorithms for high-speed data transmission. His research interests center around enhancing the performance and reliability of optical communication links through advanced digital signal processing and AI-empowered equalization methods. He has contributed to the design of low-complexity receiver-side equalizers and has explored the potential of machine learning in nonlinear compensation for coherent optical systems. His scholarly contributions have been published in reputable international journals and conferences, particularly within the fields of photonics and communication technology. Junde Lu has authored and co-authored several scientific documents, with a citation record demonstrating growing recognition in his domain. According to Scopus and Google Scholar metrics, his academic record includes 13 research documents, 1 citation, and an h-index of 1, highlighting his emerging influence in optical communication research. His collaborative works with distinguished researchers underscore his commitment to advancing next-generation high-speed optical transmission technologies.

Profile

Scopus

Featured Publications

Lu, J., Sun, Y., Qin, J., & Lu, G.-W. (2025). A low-complexity receiver-side lookup table equalization method for high-speed short-reach IM/DD transmission systems. Photonics.

Chen, L., Sun, Y., Shi, J., Lu, J., & Qin, J. (2025). Exploring 400 Gbps/λ and beyond with AI-accelerated silicon photonic slow-light technology. Photonics.

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).

Ching-Lung Fan | Deep Learning | Best Researcher Award

Published on by Computer Scientists Awards

Assoc. Prof. Dr. Ching-Lung Fan | Deep Learning | Best Researcher Award

Associate Professor, ROC Military Academy, Taiwan

Ching-Lung Fan is an associate professor in Civil Engineering at the Republic of China Military Academy. He completed his Ph.D. in 2019 from the National Kaohsiung University of Science and Technology. His professional journey reflects a strong dedication to advancing technology in the construction and civil engineering sectors, particularly through the application of machine learning and deep learning methods. 🏫

Publication Profile

Education

Dr. Fan holds a Master of Science (M.S.) from National Taiwan University (2006) and a Ph.D. from National Kaohsiung University of Science and Technology (2019). His academic background underscores his commitment to both theoretical and practical contributions to the field. 🎓

Experience

Dr. Fan started his academic career as an assistant professor at the Republic of China Military Academy in January 2019 and was promoted to associate professor in June 2022. His teaching and research experience has significantly impacted the study of civil engineering, especially through the integration of machine learning and data mining. 🏢

Awards and Honors

Ching-Lung Fan has received several prestigious awards, including the Phi Tau Phi Scholastic Honor (2019), Outstanding Paper Award (2021), Excellent Paper Award (2022), and Best Researcher Award (2024). In 2023, he was honored with membership in Sigma Xi, an esteemed scientific organization. 🏅

Research Focus

Dr. Fan’s research interests are primarily centered around machine learning, deep learning, data mining, construction performance evaluation, and risk management. His work integrates cutting-edge computational methods with civil engineering applications to enhance the quality and efficiency of construction projects. 🤖📊

Conclusion

Dr. Fan’s innovative contributions to civil engineering, particularly in the realm of AI-driven solutions, continue to shape the future of construction and infrastructure development. His ongoing research and recognition in the academic community highlight his expertise and impact in the field. 🌟

Publications

 Integrating image processing technology and deep learning to identify crops in UAV orthoimages. CMC-Computers, Materials & Continua. (Accepted).

Predicting the construction quality of projects by using hybrid soft computing techniques. CMES-Computer Modeling in Engineering & Sciences. (Accepted).

 Evaluation model for crack detection with deep learning—Improved confusion matrix based on linear features. Journal of Construction Engineering and Management (ASCE), 151(3): 04024210. (SCI).

 Evaluating the performance of Taiwan airport renovation projects: An application of multiple attributes intelligent decision analysis. Buildings, 14(10): 3314. (SCI).

Deep neural networks for automated damage classification in image-based visual data of reinforced concrete structures. Heliyon, 10(19): e38104. (SCI).

Multiscale feature extraction by using convolutional neural network: Extraction of objects from multiresolution images of urban areas. ISPRS International Journal of GeoInformation, 13(1): 5. (SCI).

Ground surface structure classification using UAV remote sensing images and machine learning algorithms. Applied Geomatics, 15: 919-931. (ESCI).

 Using convolutional neural networks to identify illegal roofs from unmanned aerial vehicle images. Architectural Engineering and Design Management, 20(2): 390-410. (SCI).

Evaluation of machine learning in recognizing images of reinforced concrete damage. Multimedia Tools and Applications, 82: 30221-30246. (SCI).

 Supervised machine learning–Based detection of concrete efflorescence. Symmetry, 14(11): 284. (SCI).

 

Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Published on by Computer Scientists Awards

Mr. Prabakaran Raghavendran | Artificial Neural Network | Young Scientist Award

Research Scholar, Vel Tech Rangarajan Dr.Sagunthala R&D Institute of Science and Technology (Deemed to be University), India

Prabakaran Raghavendran is a dynamic researcher and Ph.D. candidate at Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, specializing in Fractional Differential Equations, Integral Transforms, Functional Differential Equations, and Control Theory. With a strong academic foundation in Mathematics, he earned an M.Sc. in Mathematics with an impressive CGPA of 9.79 from the same institution in 2022. He is currently pursuing his Ph.D., contributing significantly to the field with several research publications, patents, and international conference presentations. 🌟

Publication Profile

Education:

Prabakaran completed his B.Sc. in Mathematics at Loyola College, Chennai, in 2020 with a CGPA of 9.25. He further advanced his academic career by obtaining an M.Sc. in Mathematics from Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology in 2022, where he excelled with a CGPA of 9.79. Currently, he is pursuing his Ph.D. at the same institution, expected to complete in 202X. 🎓📚

Experience:

Prabakaran has been actively engaged in the research and development of advanced mathematical models and algorithms. His experience spans across fractional differential equations, fuzzy analysis, cryptography, and artificial neural networks. Additionally, he has contributed to the development of innovative technologies, holding multiple patents in signal analysis, optimization, and medical applications. His work is widely recognized in the academic and research communities. 💼🔬

Awards and Honors:

Prabakaran’s academic excellence and dedication to research have earned him several prestigious awards, including the Best Paper Presentation Award for his work on Fractional Integro Differential Equations at the 7th International Conference on Mathematical Modelling, Applied Analysis, and Computation (ICMMAAC-24) in Beirut, Lebanon. He is also a life member of both the International Association of Engineers (IAENG) and the International Organization for Academic and Scientific Development (IOASD). 🏅🌍

Research Focus:

Prabakaran’s research focuses on Fractional Differential Equations, Integral Transforms, and Control Theory, with particular attention to their applications in various fields such as cryptography, artificial neural networks, and fuzzy analysis. He has developed new methodologies for solving complex mathematical models and is deeply involved in finding practical solutions for issues such as Parkinson’s disease prognosis, noise reduction in signals, and optimization in robotics. 🔍🔢

Conclusion:

Prabakaran Raghavendran is a passionate and dedicated researcher in the field of Mathematics, with a strong focus on fractional differential equations and control theory. His groundbreaking work in both theoretical and applied mathematics has earned him recognition through publications and patents. With his ongoing research contributions, he continues to push the boundaries of mathematical modeling and its applications in real-world problems. 🌐💡

Publications:

A Study on the Existence, Uniqueness, and Stability of Fractional Neutral Volterra-Fredholm Integro-Differential Equations with State-Dependent Delay. Fractal Fractional, 9 (1), 1-23. (2024) (SCIE-WoS & Scopus) (Q1).

Analytical Study of Existence, Uniqueness, and Stability in Impulsive Neutral Fractional Volterra-Fredholm Equations.  Journal of Mathematics and Computer Science, 38 (3), 313-329. (2024) (WoS & Scopus) (Q1).

Application of Artificial Neural Networks for Existence and Controllability in Impulsive Fractional Volterra-Fredholm Integro-Differential Equations. Applied Mathematics in Science and Engineering, 32 (1), 1-21. (2024) (SCIE-WoS-Scopus).

Existence and Controllability for Second-Order Functional Differential Equations With Infinite Delay and Random Effects.  International Journal of Differential Equations, 5541644, 2024, 1-9. (2024) (WoS & Scopus).

Solving the Chemical Reaction Models with the Upadhyaya Transform. Orient J Chem, 2024; 40(3). (WoS) (WoS).