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

Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award

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Dr. Mohassin Ahmad | Deep Learning | Best Researcher Award 

Dr. Mohassin Ahmad, Guru Nanak Institutions, India

Dr. Mohassin Ahmad is an accomplished academic and researcher currently serving as an Assistant Professor in the Department of Electronics and Communication Engineering at Guru Nanak Institutions, Hyderabad, since September 2023. He earned his Ph.D. in Image Forensics from the National Institute of Technology Srinagar in 2024, following an M.Tech in Communication and Information Technology from the same institute and a Bachelor of Engineering degree in Electronics and Communication from the University of Kashmir. Dr. Ahmad has extensive teaching and research experience, including a previous tenure as Assistant Professor at NIT Jammu and Kashmir from 2013 to 2017. His research interests focus on digital image forensics, image tampering detection, and communication systems, with multiple publications in reputed international journals. He has contributed significantly to curriculum development and laboratory setup and is known for his dedication to student mentorship and academic excellence. Dr. Ahmad is also recognized for his Young Researcher Award for work in copy-move forgery detection algorithms. Fluent in English, Urdu, and Kashmiri, he combines strong technical expertise with effective communication and leadership skills.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

Research and Academic Profile

Dr. Mohassin Ahmad has recently completed his PhD in Image Forensics (Electronics & Communication) from NIT Srinagar in 2024. His academic background is solid with a Master’s in Communication & Information Technology and a Bachelor’s in Electronics and Communication, showing a focused trajectory in communication technologies and electronics.

🎓 Education

  • PhD (2024) in Image Forensics (Electronics & Communication) — NIT Srinagar

  • M.Tech (2013) in Communication & Information Technology — NIT Srinagar (77.16%)

  • B.E (2010) in Electronics and Communication — University of Kashmir (79.3%)

💼 Work Experience

  • Assistant Professor, Guru Nanak Institutions, Hyderabad (ECE Dept.) — Since Sept 2023

  • Assistant Professor, Electronics & Communication Department, NIT Jammu & Kashmir — Sept 2013 to Aug 2017

    • Delivered lectures & coordinated courses

    • Established new labs & designed curriculum

    • Guided B.Tech & M.Tech research projects

    • Played key role in framing B.Tech & M.Tech curriculum

    • Mentored students with academic & personal support

🏆 Achievements & Awards

  • Young Researcher Award for paper:
    “A comparative analysis of Copy-Move forgery detection algorithms” — International Journal of Electronic Security and Digital Forensics, 2022

    • RSquarel score of 84, Award ID: RSL014

📚 Selected Research Publications

  • Detection and localization of image tampering with fused features — 2022

  • Comparative analysis of Copy-Move forgery detection algorithms — 2022

  • Novel image tamper detection using optimized CNN and firefly algorithm — 2021

  • Review on Digital Image Forgery Detection Approaches — 2021

  • FPGA implementation of convolution algorithms for image processing — 2019

Publication Top Notes:

Threats to medical diagnosis systems: analyzing targeted adversarial attacks in deep learning-based COVID-19 diagnosis

DS‐Net: Dual supervision neural network for image manipulation localization

A comparative analysis of copy-move forgery detection algorithms

Detection and localization of image tampering in digital images with fused features

A Comparative Analysis of Copy-Move Forgery Detection Algorithms

A novel image tamper detection approach by blending forensic tools and optimized CNN: Sealion customized firefly algorithm

Digital Image Forgery Detection Approaches: A Review

Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award

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Prof. Ming-Hsiang Su | Deep Learning | Best Researcher Award 

Prof. Ming-Hsiang Su, Data Science, Soochow University, Taiwan, Taiwan

Ming-Hsiang Su is an esteemed assistant professor in the Department of Data Science at Soochow University in Taipei, Taiwan. He earned his Ph.D. in Computer Science and Information Engineering from National Chung Cheng University and has an impressive academic background with an M.S. in Management Information Systems from National Pingtung University of Science and Technology and a B.S. in Computer Science from Tunghai University. His research expertise includes spoken dialogue systems, personality trait perception, speech emotion recognition, and speech signal processing. Before his current role, Ming-Hsiang conducted postdoctoral research at National Cheng Kung University and served as a lecturer at multiple institutions, including National Pingtung University of Science and Technology and National Chung Cheng University. His professional journey also includes a stint as an R&D engineer at Cino Group. His work in deep learning, natural language processing, and emotion and personality perception has significantly contributed to advancements in speech signal processing.

Professional Profile:

ORCID

 

🎓 Education

  • Ph.D. in Computer Science and Information Engineering, National Chung Cheng University
  • M.S. in Management Information Systems, National Pingtung University of Science and Technology
  • B.S. in Computer Science, Tunghai University

💼 Work Experience

  • Assistant Professor (August 2020 – Present)
    Department of Data Science at Soochow University, Taipei, Taiwan
  • Postdoctoral Fellow (August 2013 – July 2020)
    Department of Computer Science and Information Engineering (CSIE) at National Cheng Kung University, Tainan, Taiwan
  • Lecturer (June 2013 – July 2013)
    Skill Evaluation Center of Workforce Development Agency, Ministry of Labor, Taichung City, Taiwan
  • Lecturer (February 2012 – January 2013)
    Department of Management Information Systems at National Pingtung University of Science and Technology, Pingtung, Taiwan
  • Lecturer (September 2006 – January 2013)
    Department of Mathematics at National Chung Cheng University, Chiayi, Taiwan
  • R & D Engineer (August 2003 – September 2004)
    Cino Group, Taipei, Taiwan

Ming-Hsiang Su’s career reflects his dedication to advancing the field of computer science, particularly in speech and signal processing, through a blend of academic excellence and practical research. 🌟

Publication top Notes:

Few-Shot Image Segmentation Using Generating Mask with Meta-Learning Classifier Weight Transformer Network

Implementation of Sound Direction Detection and Mixed Source Separation in Embedded Systems

Semantic-Based Public Opinion Analysis System

Conditional Adversarial Learning for Empathetic Dialogue Response Generation

Speech Emotion Recognition Considering Nonverbal Vocalization in Affective Conversations