Ching-Lung Fan | Deep Learning | Best Researcher Award

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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).

 

Sara Tehsin | Deep learning | Best Researcher Award

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Ms. Sara Tehsin | Deep learning | Best Researcher Award

PhD Student, National University of Sciences and Technology, Islamabad, Pakistan

Sara Tehsin is a motivated and results-driven professional with over ten years of experience in Image Processing and Machine Learning. As an Engineering Lecturer at HITEC University in Taxila, Pakistan, she excels in delivering high-quality educational experiences and has a proven track record of producing outstanding results through her strong work ethic, adaptability, and effective communication skills. She is passionate about academic development and seeks opportunities to contribute her expertise while furthering her professional growth. 📚💻

Publication Profile

Google Scholar

Education

Sara Tehsin is currently pursuing a PhD in Computer Engineering at the National University of Sciences and Technology (NUST), Islamabad, where she has achieved a remarkable GPA of 3.83/4.00. Her research focuses on Digital Forensics, Deep Learning, and Digital Image Processing. She holds a Master’s degree in Computer Engineering from NUST, where she graduated with a GPA of 3.7/4.0, and a Bachelor’s degree from The Islamia University of Bahawalpur, with a GPA of 3.36/4.00. 🎓🌟

Experience

Sara has extensive teaching experience, currently serving as an Engineering Lecturer at HITEC University since September 2019, where she develops engaging curriculum and delivers lectures aligned with international standards. Previously, she was a Computer Science Lecturer at Sharif College of Engineering and Technology, and she also served as a Teaching Assistant at NUST and a Lab Engineer at Foundation University. Her roles have encompassed curriculum development, practical instruction, and student support in various computer science subjects. 👩‍🏫🔧

Research Interests

Sara’s research interests encompass Digital Forensics, Deep Learning, Digital Image Processing, and Machine Learning. She focuses on developing innovative solutions for image recognition and forgery detection, contributing significantly to the fields of computer vision and machine learning. Her work aims to enhance the accuracy and efficiency of image processing systems. 🧠🔍

Publications

Self-organizing hierarchical particle swarm optimization of correlation filters for object recognition
S. Tehsin, S. Rehman, M.O.B. Saeed, F. Riaz, A. Hassan, M. Abbas, R. Young, …
IEEE Access, 5, 24495-24502 (2017)
Cited by: 21

Improved maximum average correlation height filter with adaptive log base selection for object recognition
S. Tehsin, S. Rehman, A.B. Awan, Q. Chaudry, M. Abbas, R. Young, A. Asif
Optical Pattern Recognition XXVII, 9845, 29-41 (2016)
Cited by: 18

Fully invariant wavelet enhanced minimum average correlation energy filter for object recognition in cluttered and occluded environments
S. Tehsin, S. Rehman, F. Riaz, O. Saeed, A. Hassan, M. Khan, M.S. Alam
Pattern Recognition and Tracking XXVIII, 10203, 28-39 (2017)
Cited by: 12

Comparative analysis of zero aliasing logarithmic mapped optimal trade-off correlation filter
S. Tehsin, S. Rehman, A. Bilal, Q. Chaudry, O. Saeed, M. Abbas, R. Young
Pattern Recognition and Tracking XXVIII, 10203, 22-37 (2017)
Cited by: N/A