Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award

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Prof. Din-Yuen Chan | Deep Learning | Best Scholar Award 

Prof. Din-Yuen Chan, National Chiayi University, Taiwan

Din-Yuen Chan is a prominent scholar in electrical engineering with extensive experience in visual signal processing and computer vision. He earned his Ph.D. in Electrical Engineering from National Cheng Kung University, Taiwan, in 1996. A member of the Visual Signal Processing and Communication Technical Committee (VSPC TC) since 2010, he served as the founding director of the Department of Electrical Engineering (2007–2011) and as Dean of the College of Science and Engineering at National Chiayi University (2017–2020). His research spans semantic object detection, video/audio coding, stereoscopic 3D, AI-based pattern recognition, and deep learning neural networks. In the past five years, he has published multiple SCI-indexed journal papers on topics such as stereo matching, instance segmentation, speaker diarization, depth estimation, and autonomous robotics. As a frequent corresponding author, he continues to lead innovations in applied AI and multimedia processing.

Professional Profile:

SCOPUS

Summary of Suitability for the Best Scholar Award

Dr. Din-Yuen Chan has maintained an outstanding academic career for over two decades, contributing significantly to the fields of electrical engineering and computer vision. His long-standing commitment to advancing knowledge is reflected in his leadership roles and consistent research output in areas such as semantic object detection, AI-based pattern recognition, video/audio coding, and stereoscopic 3D.

🎓 Education

  • Ph.D. in Electrical Engineering
    National Cheng Kung University, Taiwan 🇹🇼
    Completed in 1996

💼 Work Experience

  • 🧠 Member, Visual Signal Processing and Communication Technical Committee (VSPC TC)
    Since 2010

  • 🏛️ Founding Director, Department of Electrical Engineering, National Chiayi University
    2007 – 2011

  • 🎓 Dean, College of Science and Engineering, National Chiayi University
    2017 – 2020

🧪 Research Interests

  • 🔍 Computer Vision

  • 🎯 Semantic Object Detection

  • 🎞️ Video/Audio Coding

  • 🤖 AI-based Pattern Recognition

  • 🥽 Stereoscopic 3D

  • 🧠 Deep Learning Neural Networks

🏅 Achievements & Honors

  • ✍️ Published multiple SCI-indexed journal papers in high-impact venues, including:

    • EURASIP Journal on Image and Video Processing

    • IET Computer Vision

    • Multimedia Tools and Applications

    • Applied Sciences

  • ⭐ First or corresponding author in many significant papers on stereo matching, depth estimation, 3D object placement, and speaker diarization.

  • 🤖 Developed a low-cost autonomous outdoor robot with end-to-end deep learning navigation.

  • 🧏 Invented a new speaker-diarization technology using spectral-LSTM.

  • 🎓 Recognized leader in academia for establishing and leading research and administrative departments.

Publication Top Notes:

A new speaker-diarization technology with denoising spectral-LSTM for online automatic multi-dialogue recording

Natural-Prosodic Cross-Lingual Personalized TTS

New Efficient Depth Estimation and Real-Time Object 3D Recognition Models for Humanoid Robotic Environment Understanding

Rational 3D object placement based on deep learning based plane detection

INTEGRATED LIGHT-RESNET AND POOLFORMER NETWORKS FOR SHAPE-PRESERVING LANE DETECTION

Assist. Prof. Dr. Dumitru Radulescu | Machine Learning Awards | Top Researcher Award

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Assist. Prof. Dr. Dumitru Radulescu | Machine Learning Awards | Top Researcher Award 

Assist. Prof. Dr. Dumitru Radulescu, University of Medicine and Pharmacy Craiova (UMF Craiova), Romania

Dumitru Rădulescu, is a distinguished medical professional and researcher specializing in surgery and medical sciences. He earned his Bachelor’s degree in Medicine from UMF Craiova in 2009, followed by a Doctor of Medical Sciences degree, which he obtained in 2020 under the auspices of the Romanian Ministry of Health. Dr. Rădulescu’s academic journey is marked by his receipt of a competitive doctoral scholarship, highlighting his commitment to advancing his expertise in the medical field. Currently serving as a Specialist Surgeon at the Military Emergency Clinical Hospital “Dr. Ştefan Odobleja” in Craiova, he has accumulated extensive clinical experience through various residency programs in family medicine and general surgery. His professional roles include positions as a University Assistant at UMF Craiova, where he contributes to the education of future healthcare professionals in surgical specialties.

Professional Profile:

ORCID

Summary of Suitability for the Top Researcher Award

Dumitru Rădulescu is an accomplished researcher and specialist surgeon whose academic and professional journey highlights his commitment to advancing medical sciences, particularly in the areas of surgery and diagnostics. His education culminated in a Doctor of Medical Sciences degree from UMF Craiova, where he also received a doctoral scholarship, showcasing his academic excellence and dedication to research.

Education 📚

  • Doctor of Medical Sciences
    University of Medicine and Pharmacy Craiova (UMF Craiova)
    2014 – 2020
  • Doctoral Scholarship
    UMF Craiova (POSDRU/187/1.5/S/156069)
    2014 – 2015
  • Bachelor’s Degree in Medicine
    UMF Craiova
    2003 – 2009
  • High School Diploma
    Balş Theoretical High School
    1999 – 2003

Professional Development 🎓

  • Specialist Surgeon
    Ministry of Health Order no. 721/04.06.2018
    2018 – Present
  • General Surgery Resident
    2012 – 2018
  • Family Medicine Resident
    2010 – 2012

Areas of Competence 💪

  • DPPD Module (2008)
  • English for Specific Purposes – Medical English B2 (2021)

Professional Experience 🏥

  • Current Position:
    University Assistant, Military Emergency Clinical Hospital “Dr. Ştefan Odobleja,” Craiova
    2022 – Present
  • Previous Positions:
    • University Assistant DRD, Department VI – Surgical Specialties (2018 – 2021)
    • General Surgery Resident, Clinic I Surgery SCJU no.1 Craiova (2013 – 2018)
    • Family Medicine Resident, Filantropia Clinical Hospital Craiova (2010 – 2012)

Research Contributions 🔬

Dr. Rădulescu is a dedicated researcher who recently received a grant for his project titled:
“Discovery and validation of a new leukocyte formula marker for predicting mortality in patients with tuberculosis and malnutrition using machine learning.” 🤖
This project highlights his commitment to leveraging modern technology in medical research to address critical health issues.

Publication Top Notes

Enhancing the Understanding of Abdominal Trauma During the COVID-19 Pandemic Through Co-Occurrence Analysis and Machine Learning

Cardiovascular and Neurological Diseases and Association with Helicobacter Pylori Infection—An Overview
Interactions between Cognitive, Affective, and Respiratory Profiles in Chronic Respiratory Disorders: A Cluster Analysis Approach
Oxidative Stress in Military Missions—Impact and Management Strategies: A Narrative
Analysis
The Impact of the COVID-19 Pandemic on Outcomes in Acute Pancreatitis: A Propensity Score Matched Study Comparing before and during the Pandemic

 

 

Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award

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Mr. Zhongwen Hao | Deep learning Award | Best Researcher Award 

Mr. Zhongwen Hao, Cranfield University, China

Zhongwen Hao is a Master’s candidate in Aerospace Manufacturing at Cranfield University, UK, and concurrently pursuing a Master of Mechanical Engineering at Nanjing University of Aeronautics and Astronautics, China. He completed his Bachelor’s degree in Electronic Information with a focus on Image Processing from China University of Mining and Technology. His research interests include robot control, visual servoing, image processing, and deep learning. Zhongwen has led notable projects such as visual servoing of robotic arms using deep learning techniques and galaxy image classification. His proficiency in programming with C++, Python, and MATLAB, coupled with his skills in deep learning and image processing, underscores his technical expertise. He has published research on motion prediction and object detection in visual servoing systems. Zhongwen is known for his strong project execution abilities, team spirit, and resilience.

Professional Profile:

Summary of Suitability:

Hao’s research direction aligns well with cutting-edge fields such as robot control, visual servoing, image processing, and deep learning. These areas are highly relevant and significant in contemporary technological advancements. Hao has a solid educational foundation with advanced studies in Aerospace Manufacturing and Mechanical Engineering, complemented by a bachelor’s degree in Electronic Information with a focus on Image Processing. This diverse yet interconnected educational background enhances his research capabilities.

Education

  1. Cranfield University, Bedford, UK
    Master’s Candidate of Aerospace Manufacturing
    Major: Deep Learning and Image Processing
    September 2023 – September 2024
  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China
    Master of Mechanical Engineering
    Major: Mechanical
    September 2022 – June 2025 (Expected)
  3. China University of Mining and Technology, Xuzhou, China
    Bachelor of Electronic Information
    Major: Image Processing
    September 2017 – June 2021

Work Experience

  1. Project Leader
    Research on Visual Servoing of Robotic Arms Based on Deep Learning
    June 2024 – September 2024

    • Led research on target detection using the DETR model, trajectory planning with the PSO algorithm, and motion prediction using BiLSTM and KAN neural networks.
    • Integrated and simulated algorithms in ROS using Gazebo to validate their effectiveness.
  2. Participator
    Galaxy Image Classification Based on Deep Learning
    February 2024 – March 2024

    • Handled image preprocessing and reconstruction, and implemented galaxy image classification using the VIT model, achieving a classification accuracy of 90%.

Publication top Notes:

Motion Prediction and Object Detection for Image-Based Visual Servoing Systems Using Deep Learning