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

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

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 (2007โ€“2011) and as Dean of the College of Science and Engineering at National Chiayi University (2017โ€“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.

๐ŸŽ“ Education

  • Ph.D. in Electrical Engineering
    National Cheng Kung University, Taiwan ๐Ÿ‡น๐Ÿ‡ผ
    Completed in 1996

๐Ÿ’ผ Work Experience

  • ๐Ÿง  Member, Visual Signal Processing and Communication Technical Committee (VSPC TC)
    Since 2010

  • ๐Ÿ›๏ธ Founding Director, Department of Electrical Engineering, National Chiayi University
    2007 โ€“ 2011

  • ๐ŸŽ“ Dean, College of Science and Engineering, National Chiayi University
    2017 โ€“ 2020

๐Ÿงช Research Interests

  • ๐Ÿ” Computer Vision

  • ๐ŸŽฏ Semantic Object Detection

  • ๐ŸŽž๏ธ Video/Audio Coding

  • ๐Ÿค– AI-based Pattern Recognition

  • ๐Ÿฅฝ Stereoscopic 3D

  • ๐Ÿง  Deep Learning Neural Networks

๐Ÿ… Achievements & Honors

  • โœ๏ธ 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

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

  • ๐Ÿค– Developed a low-cost autonomous outdoor robot with end-to-end deep learning navigation.

  • ๐Ÿง Invented a new speaker-diarization technology using spectral-LSTM.

  • ๐ŸŽ“ Recognized leader in academia for establishing and leading research and administrative departments.

Publicationย 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

Dr. Shuaa Alharbi | Deep learning Awards | Women Researcher Award

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Dr. Shuaa Alharbi | Deep learning Awards | Women Researcher Awardย 

Dr. Shuaa Alharbi, Qassim University, Saudi Arabia

Shuaa S. Alharbi is an Assistant Professor at the College of Computer Science, Qassim University, Saudi Arabia. She holds a B.Sc. and M.Sc. in Computer Science from Qassim University, and a Ph.D. in Computer Science from Durham University, UK. Her research expertise lies in machine learning, deep learning, and image processing, particularly in the biomedical domain. She specializes in developing novel deep learning architectures and techniques for analyzing medical images to enhance diagnostic accuracy. Her work focuses on curvilinear structure extraction and bioimage informatics, and she has published impactful research in esteemed journals, including Signal, Image and Video Processing and Methods. Dr. Alharbi has also contributed extensively to academic committees, curriculum development, and postgraduate supervision, reflecting her dedication to education and research excellence.

Professional Profile:

ORCID

ย Suitability for the Women Researcher Award

Dr. Shuaa S. Alharbi has demonstrated substantial contributions in computer science, with a focus on machine learning, image processing, and medical image analysis. Her research is interdisciplinary, addressing key challenges in the fields of bioimage informatics, medical diagnostics, and AI-based deep learning applications. These align with global priorities in health technology and AI-driven innovation.

๐ŸŽ“ Education

  • B.Sc. in Computer Science (2007)
    ๐Ÿ“ Qassim University, Saudi Arabia
  • M.Sc. in Computer Science (2014)
    ๐Ÿ“ Qassim University, Saudi Arabia
  • Ph.D. in Computer Science (2020)
    ๐Ÿ“ Durham University, United Kingdom
    ๐Ÿง‘โ€๐Ÿ’ป Specialization: Bioimage Informatics, Machine Learning, and Image Processing

๐Ÿ’ผ Work Experience

  • Teaching Assistant (2008-2016)
    ๐Ÿ“ Qassim University – College of Computer Science
  • Lecturer (2016-2020)
    ๐Ÿ“ Qassim University – College of Computer Science
  • Assistant Professor (2020โ€“Present)
    ๐Ÿ“ Qassim University – College of Computer Science
  • Administrative Roles:
    • E-Content Supervisor (2020-2022)
    • IT Department Coordinator (2020-2022)
    • Member of various academic and examination committees

๐Ÿ† Achievements, Awards, and Honors

  • Published Research:
    • ๐Ÿ“˜ Sequential Graph-Based Extraction of Curvilinear Structures (2019)
      ๐Ÿ”— Signal, Image, and Video Processing Journal
    • ๐Ÿ“˜ The Multiscale Top-Hat Tensor (2019)
      ๐Ÿ”— Methods Journal
  • Research Contributions:
    ๐ŸŒŸ Expertise in machine learning, deep learning, and medical image processing
    ๐ŸŒŸ Development of novel architectures for analyzing curvilinear structures in biological and medical images
  • Committee Memberships:
    ๐Ÿ… Standing Committees in the Scientific Council (2023-2024)
  • Supervision:
    ๐ŸŽ“ Postgraduate Supervisor at the College of Computer Science

๐ŸŒŸ Areas of Interest

  • Machine Learning & Deep Learning ๐Ÿค–
  • Medical Image Analysis ๐Ÿฅ
  • Computer Graphics and Signal Processing ๐ŸŽจ

Publicationย Top Notes:

Arabic Speech Recognition: Advancement and Challenges

Date Fruit Detection and Classification Based on Its Variety Using Deep Learning Technology

Exploring the Applications of Artificial Intelligence in Dental Image Detection: A Systematic Review

E-DFu-Net: An efficient deep convolutional neural network models for Diabetic Foot Ulcer classification

Integration of machine learning bi-modal engagement emotion detection model to self-reporting for educational satisfaction measurement

Prof. Jar-Ferr Yang | Machine Learning Awards | Best Researcher Award

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Prof. Jar-Ferr Yang | Machine Learning Awards | Best Researcher Awardย 

Prof. Jar-Ferr Yang, National Cheng Kung University, Taiwan

Jar-Ferr (Kevin) Yang, Ph.D., an IEEE Fellow, is a Distinguished Professor at the Institute of Computer and Communication Engineering, Department of Electrical Engineering, National Cheng Kung University in Tainan, Taiwan. He earned his Ph.D. in Electrical Engineering from the University of Minnesota in 1988 and has since held various academic and administrative positions, including Vice Dean of the Miin Wu School of Computing and Director of multiple research centers focused on ubiquitous computing and multimedia technologies. Dr. Yang has been recognized for his contributions to fast algorithms and efficient realization of video and audio coding, receiving numerous accolades such as the Best Presentation Award and Best Paper Awards at international conferences. He has also served on editorial boards for several prestigious journals and participated in numerous professional activities within the IEEE community. His extensive research and leadership in electrical engineering and computer science continue to impact both academia and industry.

Professional Profile:

SCOPUS

Suitability Summary for Jar-Ferr Ferr Kevin Yang for the Best Researcher Award

Dr. Jar-Ferr Ferr Kevin Yang has demonstrated significant contributions to the field of Electrical Engineering, particularly in the areas of computer and communication engineering. With a robust publication record of 269 documents and over 3,347 citations, his work has garnered substantial recognition within the academic community. His h-index of 27 indicates a solid impact in his field, reflecting both the quantity and quality of his research outputs.

๐Ÿ“š Education

  • ๐ŸŽ“ Ph.D. in Electrical Engineering (1988) – University of Minnesota, USA
  • ๐ŸŽ“ M.S. in Electrical Engineering (1979) – National Taiwan University, Taiwan
  • ๐ŸŽ“ B.S. in Electrical Engineering (1977) – Chung Yuan Christian University, Taiwan

๐Ÿ’ผ Employment and Related Experiences

  • ๐Ÿ›๏ธ Distinguished Professor (2004โ€“Present) – Institute of Computer and Communication Engineering, National Cheng Kung University, Taiwan
  • ๐Ÿข Vice Dean (2021โ€“2023) – Miin Wu School of Computing, National Cheng Kung University, Taiwan
  • ๐Ÿ”ฌ Adjunct Research Fellow (2015โ€“2020) – Office of Science and Technology, Executive Yuan, Taiwan
  • ๐Ÿ“Š Director
    • TOUCH Center (2012โ€“2019) – National Cheng Kung University
    • AR/VR and 3D Multimedia Cross-University Resource Center (2015โ€“2017) – Ministry of Education, Taiwan
  • ๐Ÿ“š Chairperson (2005โ€“2008) – Institute of Computer and Communication Engineering, National Cheng Kung University
  • ๐ŸŒŽ Visiting Scholar (2002) – University of Washington, USA
  • ๐Ÿข Professor and Associate Professor (1988โ€“2004) – Department of Electrical Engineering, National Cheng Kung University, Taiwan
  • ๐Ÿ› ๏ธ Assistant Researcher (1981โ€“1984) – Transmission Research Group, Chung-Hwa Telecommunication Research Laboratories, Taiwan

๐Ÿ† Awards and Honors

  • ๐Ÿ… IEEE Fellow (2007) – Contributions to fast algorithms and efficient realization of video and audio coding
  • ๐Ÿ† Best Paper Awards (Multiple Years: 2015, 2017, 2019) – Recognitions at International Conferences on 3D Systems and Applications
  • ๐Ÿฅ‡ Golden Medal (2015) – Kwoh-Ting Li Foundation of Science and Literature
  • ๐ŸŽ–๏ธ Outstanding Electrical Engineering Professor Award (2010) – Chinese Institute of Electrical Engineering, Taiwan
  • ๐ŸŒŸ Excellent Research Awards (1998โ€“2004) – National Science Council, Taiwan (Consecutive years)
  • ๐Ÿ… Best Industrial Cooperation Professor Award (2011, 2014) – National Cheng Kung University
  • ๐Ÿ† Best Presentation and Technical Awards (2020, 2016) – Recognitions for Intelligent Information Processing and Circuit Systems

Publicationย Top Notes:

CTDP Depacking with Guided Depth Upsampling Networks for Realization of Multiview 3D Video

Enhancing Fan Engagement in a 5G Stadium With AI-Based Technologies and Live Streaming

An image-guided network for depth edge enhancement

Improved vehicle detection systems with double-layer LSTM modules

Improved quadruple sparse census transform and adaptive multi-shape aggregation algorithms for precise stereo matching

Convolutional Layers Acceleration By Exploring Optimal Filter Structures

Prof. Catalin Dumitrescu | Artificial Intelligence Awards | Excellence in Research

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Prof. Catalin Dumitrescu | Artificial Intelligence Awards | Excellence in Research

Prof. Catalin Dumitrescu, University Politehnica of Bucharest, Romania

Dr. Cฤƒtฤƒlin Dumitrescu is an Associate Professor and R&D Scientific Adviser specializing in Computing and Artificial Intelligence at the Department of Electronics & Telecommunications, Transportation Engineering Faculty, University Politehnica of Bucharest (UPB), Romania. With a Ph.D. in Digital Signal Processing and Machine Learning from UPB, he possesses extensive expertise in artificial intelligence, machine learning, and digital signal processing, particularly in applications related to defense, cybersecurity, and multimedia security. Dr. Dumitrescu has over 20 years of R&D experience in the defense industry, including roles in machine learning systems for IMINT & SIGINT. He is also a certified expert in Critical Infrastructure Risk Management and Competitive Intelligence.

 

Professional Profile:

Summary of Suitability for Excellence in Research: Dr. Catalin Dumitrescu

Dr. Catalin Dumitrescu exemplifies excellence in research through his extensive expertise, academic credentials, professional experience, and impactful contributions in the fields of Artificial Intelligence, Machine Learning, and Digital Signal Processing, particularly in applications for defense, transportation, and security.

Education

๐ŸŽ“ Ph.D. in Digital Signal Processing & Machine Learning โ€“ University Politehnica of Bucharest.
๐Ÿ“œ Engineering Degree in Signal and Image Processing โ€“ Transportation Engineering Faculty, UPB.
๐ŸŽ“ Postgraduate Degree in International Business & Economics โ€“ Bucharest University of Economic Studies.
๐Ÿ“‘ Certified Expert in:

  • Critical Infrastructure Risk Management โš ๏ธ
  • Competitive Intelligence ๐Ÿง 

Professional Experience

๐Ÿ”น 2015 โ€“ Present: Associate Professor, R&D Adviser in AI & Computing, UPB.
๐Ÿ”น 2018 โ€“ Present: R&D Consultant, SOLIDUS AI TECH.
๐Ÿ”น 2015 โ€“ 2018: Software Systems Architect, UTI GROUP.
๐Ÿ”น 1995 โ€“ 2015: R&D Military Officer, Defense Advanced Technology Institute.
๐Ÿ”น 1986 โ€“ 1995: Electronics Engineer, Transport Research Institute.

๐Ÿ’ก Career Highlights:

  • 20+ years of experience in Machine Learning, AI, and Cyber Defence.
  • Expertise in IMINT & SIGINT for the defence sector ๐Ÿ›ก๏ธ.
  • Development of advanced algorithms and software architecture for signal processing and AI systems.

Research Interests

๐Ÿ” Core Areas:

  • Artificial Intelligence & Machine Learning ๐Ÿค–
  • Digital Signal Processing ๐Ÿ“ก
  • Neural Networks for Audio & Image Analysis ๐ŸŽง๐Ÿ–ผ๏ธ
  • Cyber Security & Forensics ๐Ÿ•ต๏ธโ€โ™‚๏ธ
  • Cognitive Radio Systems ๐Ÿ“ป

๐Ÿ” Specialized Focus:

  • Deep Learning for object detection and classification ๐Ÿ–ฅ๏ธ
  • Brain-Computer Interfaces ๐Ÿง 
  • EEG, EKG, and EMG signal analysis ๐Ÿ“Š
  • Cryptography & Multimedia Security ๐Ÿ”’

Teaching Expertise

๐Ÿ“š Courses include:

  • Cyber Security & Defence ๐Ÿ”
  • Digital Image Processing ๐Ÿ“ท
  • Real-Time Signal Processing โฑ๏ธ
  • Multimedia Forensics and Security ๐ŸŽฅ

Publication top Notes:

Fuzzy logic for intelligent control system using soft computing applications

CITED:61

Development of an acoustic system for UAV detection

CITED:60

Using brain-computer interface to control a virtual drone using non-invasive motor imagery and machine learning

CITED:21

Aircraft trajectory tracking using radar equipment with fuzzy logic algorithm

CITED:21

Internal Auditing & Risk Management, No. 4 (56)

CITED:17

Monitoring system with applications in road transport

CITED:17

Dr. Tara P Banjade | Artificial Intelligence Awards | Best Researcher Award

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Dr. Tara P Banjade | Artificial Intelligence Awards | Best Researcher Awardย 

Dr. Tara P Banjade, East China University of Technology, Nanchang, China

Dr. Tara P. Banjade is an Associate Professor at the East China University of Technology, Nanchang, China, specializing in applied mathematics, seismic signal processing, and artificial intelligence applications for seismic data processing. He completed his Ph.D. in Applied Mathematics at Harbin Institute of Technology in China in 2020, following a Master’s and Bachelor’s in Mathematics from Tribhuvan University, Nepal. Dr. Banjadeโ€™s research focuses on developing mathematical algorithms for denoising seismic data, including 1D earthquake signals and 2D geophysical data like oil, gas, and ground-penetrating radar (GPR) data. His innovative approaches employ techniques such as variational mode decomposition, wavelet transforms, and artificial intelligence, including DARE U-Net for seismic noise attenuation and self-guided singular value decomposition for data edge detection.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Dr. Tara P. Banjade demonstrates an impressive academic and research profile, particularly within Applied Mathematics and Seismic Signal Processing, fields which align closely with the scope of the Best Researcher Award. His doctoral education from Harbin Institute of Technology and ongoing research position at East China University of Technology position him as a strong candidate.

Education

  1. Harbin Institute of Technology, Harbin, China
    • Ph.D. in Applied Mathematics
    • Duration: September 2015 โ€“ January 2020
  2. Tribhuvan University, Kathmandu, Nepal
    • Masterโ€™s in Mathematics
    • Duration: 2012 โ€“ 2014
  3. Tribhuvan University, Kathmandu, Nepal
    • Bachelorโ€™s in Mathematics
    • Duration: 2006 โ€“ 2010

Work Experience

  1. Associate Professor
    • Institution: East China University of Technology, School of Geophysics and Measurement-Control Technology, Nanchang, Jiangxi, China
    • Duration: March 2023 โ€“ Present
  2. Founder/Chairperson
    • Organization: Intellisia Institute for Research and Development, Nepal
  3. Research Director
    • Organization: Girija Prasad Koirala Foundation
    • Duration: 2020 โ€“ Present
  4. Visiting Scientist
    • Institution: Research Centre for Applied Science and Technology (RECAST), Tribhuvan University, Nepal
  5. Founding Member and Mathematics Lecturer
    • Institution: Arunima College, Tribhuvan University, Nepal
    • Duration: 2020 โ€“ 2023
  6. Executive Member
    • Organization: Nepal Mathematical Society
    • Duration: 2021 โ€“ 2024
  7. Visiting Faculty
    • Institution: School of Mathematical Science, Tribhuvan University, Nepa.

Publication top Notes:

Seismic Random Noise Attenuation Using DARE U-Net

Enhancing seismic data by edge-preserving geometrical mode decomposition