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

Posted on 31/05/2025 by ScienceFather

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

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Posted on 18/11/2024 by ScienceFather

Mr. Seyed matin malakouti | Deep learning Awards | Best Researcher Award

Mr. Seyed matin malakouti, University of Rijeka, Croatia

Seyed Matin Malakouti is an accomplished electrical engineer and researcher specializing in control systems engineering and machine learning. He completed his Master of Science in Electrical Engineering from the University of Tabriz, Iran, after earning his Bachelor’s degree from Isfahan University of Technology. His research spans various applications of machine learning, including wind power generation prediction, heart disease classification using ECG data, and solar farm power generation forecasting. Seyed’s work has resulted in several high-impact publications in prestigious journals, with his research on wind energy and machine learning techniques receiving significant citations. He has also been involved in cutting-edge projects such as predicting global temperature change and advancing renewable energy solutions. In recognition of his contributions, Seyed has received multiple awards, including the Best Researcher Award at the International Conference on Cardiology and Cardiovascular Medicine in 2023, and nominations for Best Paper and Best Researcher Awards in other international conferences. Additionally, he actively contributes to the scientific community as a peer reviewer for numerous journals in the fields of artificial intelligence, environmental sciences, and electrical engineering.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Seyed Matin Malakouti is a highly qualified and accomplished researcher in the field of Electrical Engineering, specializing in Control Systems, Machine Learning, and Data Science. His impressive academic background includes a Master’s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training 🎓

2020 – 2022: M.Sc. in Electrical Engineering – Control System Engineering, University of Tabriz, Iran
2014 – 2019: B.Sc. in Electrical Engineering, Isfahan University of Technology, Iran

Awards & Honors 🏆

2023: Best Researcher, International Conference on Cardiology and Cardiovascular Medicine
2023: Nominated for Best Paper Award, International Research Awards on Mathematics and Optimization Methods
2024: International Young Scientist Awards, Best Researcher Category

Technical Skills 🛠️

Machine Learning 🤖
Data Science 📊
Programming Languages: MATLAB, Python 💻

Peer Review Activities 🧐

Seyed has reviewed articles for prestigious journals, such as:

IEEE Access
Artificial Intelligence Review
BMC Public Health
Environmental Monitoring and Assessment 🌱

Publication top Notes:

Machine learning and transfer learning techniques for accurate brain tumor classification

ML: Early Breast Cancer Diagnosis

Advanced techniques for wind energy production forecasting: Leveraging multi-layer Perceptron + Bayesian optimization, ensemble learning, and CNN-LSTM models

Babysitting hyperparameter optimization and 10-fold-cross-validation to enhance the performance of ML methods in predicting wind speed and energy generation

Discriminate primary gammas (signal) from the images of hadronic showers by cosmic rays in the upper atmosphere (background) with machine learning

Estimating the output power and wind speed with ML methods: A case study in Texas

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Posted on 15/11/2024 by ScienceFather

Ms. Rachel Stephen Mollel | Machine Learning Awards | Best Scholar Award

Ms. Rachel Stephen Mollel, University of Strathclyde, United Kingdom

Rachel Stephen Mollel is a Ph.D. student in Electrical and Electronic Engineering at the University of Strathclyde, UK. Her research focuses on machine learning, explainable AI, energy demand-side management, smart metering, and non-intrusive load monitoring (NILM). She holds a Master of Engineering from Arkansas Tech University, USA, and a Bachelor’s degree in Telecommunication Engineering from Visvesvaraya Technological University, India. Rachel has contributed significantly to the energy sector, exploring the role of smart meters in reducing energy costs and enhancing communication between energy providers and consumers. Her recent work, which investigates the potential of NILM to reveal hidden demand flexibility in residential energy consumption, has been published in various peer-reviewed journals and conferences. Additionally, she is actively involved in improving the interpretability of NILM models to enhance algorithm performance. Her contributions have been recognized with a Commonwealth Scholarship in 2020.

Professional Profile:

ORCID

Summary of Suitability for the Best Scholar Award:

Rachel Stephen Mollel is a highly suitable candidate for the Best Research Scholar Award based on her significant contributions to the fields of machine learning, explainable AI, and energy demand-side management. As a PhD student at the University of Strathclyde, her research aims to address critical energy issues through innovative approaches like Non-Intrusive Load Monitoring (NILM), which helps uncover hidden demand flexibility in residential energy consumption.

Education:

2021 – Present: PhD in Electrical and Electronic Engineering, University of Strathclyde, UK
2010 – 2012: Master of Engineering, Arkansas Tech University, USA (GPA: 3.75/4.0)
2006 – 2010: Bachelor’s degree in Telecommunication Engineering, Visvesvaraya Technological University, India (First Class)

Work Experience:

2011 – 2012: Graduate Assistant, Arkansas Tech University, USA
Assisted in the Digital Logic and Robotics Course & Lab; delivered tutorials, graded lab reports and exams, and supported the development of course materials under faculty supervision.
2014 – 2020: Assistant Lecturer, University of Dar es Salaam, Tanzania
Delivered lectures, prepared and graded exams in Control Systems Engineering and Fundamentals of Electrical Engineering. Supervised undergraduate student projects, practical training, and fieldwork. Managed various administrative duties, such as student registration and coordination of departmental examinations.