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

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

Assoc. Prof. Dr. Waleed Mahmoud Elsayed | Machine learning Awards | Best Researcher Award

Assoc. Prof. Dr. Waleed Mahmoud Elsayed | Machine learning Awards | Best Researcher Awardย 

Assoc. Prof. Dr. Waleed Mahmoud Elsayed, Beni-suef university, Saudi Arabia

Dr. Waleed Mahmoud Ead is an accomplished Assistant Professor in the Faculty of Computing and Information at Al-Baha University, Saudi Arabia, with over 15 years of experience in digital business transformation, data science, and applied research. He holds a Ph.D. in Computers and Informatics from Menoufia University, Egypt, where he focused on privacy-preserving techniques in social networks. Throughout his career, Dr. Ead has developed expertise in business intelligence, data mining, machine learning, cloud computing, and big data analytics, and he is SAS-certified in multiple disciplines, including machine learning and visual analytics. His research interests span social network analysis, distributed databases, precision medicine, and cybersecurity. He has served in various academic roles across prominent Egyptian institutions and has co-supervised doctoral and master’s research in genetics, AI, and privacy in healthcare. A dedicated peer reviewer for renowned journals such as Springer Nature and Inderscience, Dr. Ead is also an active contributor to academic conferences and international workshops. Beyond academia, he is a technology enabler, STEM judge, and entrepreneur, with projects focused on sustainable agriculture and digital innovation.

Professional Profile:

GOOGLE SCHOLAR

ORCID

SCOPUS

โœ… Summary of Suitability for Best Researcher Award: Dr. Waleed Mahmoud Ead

Dr. Waleed Mahmoud Ead is highly suitable for the Best Researcher Award, given his exceptional combination of research depth, academic leadership, interdisciplinary engagement, and societal impact. His qualifications are supported by the following key strengths

๐ŸŽ“ Education

  • ๐Ÿฅ‡โ€ฏ2004: B.Sc. (Honor) in Information and Technology Systems โ€“ Zagazig University, Egypt

  • ๐Ÿ“šโ€ฏ2012: M.Sc. in Computers and Informatics โ€“ Menoufia University, Egypt
    โ€ƒโ€ƒ๐Ÿ“˜ Thesis: “Developing an Intelligent Technique in Web Mining”

  • ๐ŸŽ“โ€ฏ2018: Ph.D. in Computers and Informatics โ€“ Menoufia University, Egypt
    โ€ƒโ€ƒ๐Ÿ“— Thesis: “Privacy Preserving in Social Networks”

๐Ÿ‘จโ€๐Ÿซ Academic Work Experience

  • ๐Ÿ‡ธ๐Ÿ‡ฆโ€ฏ2024โ€“Present: Assistant Professor, Faculty of Computing and Information โ€“ Al-Baha University, Saudi Arabia

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2022โ€“2023: Assistant Professor, CSIT โ€“ Egypt-Japan University of Science and Technology

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2018โ€“2022: Assistant Professor, Faculty of Computers & AI โ€“ Beni-Suef University

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2015โ€“2018: Lecturer Associate, Faculty of Information Technology โ€“ MUST University

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2014: Lecturer Associate, Faculty of Computers & Information โ€“ Beni-Suef University

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2012: Lecturer Associate, CSC โ€“ October 6 University

  • ๐Ÿ‡ช๐Ÿ‡ฌโ€ฏ2006โ€“2012: Teaching Assistant, CSC โ€“ October 6 University

๐Ÿ† Achievements & Honors

  • ๐Ÿง  SAS Certified: Machine Learning, Visual Analytics, Business Planning

  • ๐Ÿ’ก Developed systems for international conferences

  • ๐ŸŒ Peer Reviewer for top journals & publishers (Inderscience, Springer, EAI, etc.)

  • ๐Ÿงฌ Co-supervisor for Ph.D. and Master’s students in AI, bioinformatics, and precision medicine

  • ๐Ÿฅ‡ Honor degree in B.Sc.

  • ๐Ÿ‘ฉโ€โš– STEM Judge: INTEL ISEF & Graduation Projects

  • ๐Ÿ’ผ Speaker and participant in events by DAAD, UNESCO, Microsoft, SAS, Oracle

  • ๐ŸŒฑ Founder of IGreen (Intelligent Adaptive Environmental Farm)

  • ๐Ÿš€ Participated in entrepreneurship programs (Start Egypt, Flat6Labs)

  • ๐Ÿงญ Bridging analytics and IT knowledge for social development

Publicationย Top Notes:

An Optimized Hierarchal Cluster Formation Approach for Management of Smart Cities

ODCS: On-Demand Hierarchical Consistent Synchronization Approach for the IoT

A General Cyber Hygiene Approach for Financial Analytical Environment

Feedforward Deep Learning Optimizer-based RNA-Seq Women’s cancers Detection with a hybrid Classification Models for Biomarker Discovery

Semantic Sentiment Classification for COVID-19 Tweets Using Universal Sentence Encoder

Automated Prediction of Employee Attrition Using Ensemble Model Based on Machine Learning Algorithms

Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

Mr. Fangzhou Lin | Deep Learning | Best Scholar Awardย 

Mr. Fangzhou Lin, Hong Kong University of Science and Technology, Hong Kong

Fangzhou Lin is a Ph.D. researcher in Civil Engineering at the Hong Kong University of Science and Technology (HKUST), specializing in deep learning, machine vision, construction robots, and multimodal data fusion. He holds a Bachelorโ€™s degree in Civil Engineering from Fuzhou University (2015-2019) and a Masterโ€™s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Linโ€™s research focuses on the integration of artificial intelligence and robotics in construction automation, with applications in fire safety inspection, resource management, visual measurement, and quality assessment. His work has been published in leading journals such as Automation in Construction, Computer-Aided Civil and Infrastructure Engineering, and Advanced Engineering Informatics. He has contributed to multiple cutting-edge studies on robotic systems for construction site management, vision-based measurement techniques, and reinforcement learning-based scheduling for electric concrete vehicles. As an emerging scholar in construction automation and AI-driven inspection technologies, Fangzhou Lin actively collaborates on multi-disciplinary research projects to enhance efficiency, safety, and sustainability in the built environment. His contributions to automated reality capture, rebar positioning, and construction robotics are shaping the future of intelligent construction and infrastructure development.

Professional Profile:

SCOPUS

Suitability of Fangzhou Lin for the Best Scholar Award

Fangzhou Lin is an outstanding early-career scholar with a strong background in deep learning, machine vision, construction robotics, and multimodal data fusion within the field of civil engineering. His academic trajectory, research productivity, and innovative contributions make him a compelling candidate for the Best Scholar Award. Below is a detailed assessment of his suitability based on key criteria.

๐ŸŽ“ Education

  • 2015.09 – 2019.06 | Fuzhou University โ€“ Bachelor’s Degree in Civil Engineering
  • 2019.09 – 2022.06 | Southeast University โ€“ Master’s Degree in Structural Engineering
  • 2022.09 – Present | Hong Kong University of Science and Technology โ€“ Ph.D. in Civil Engineering

๐Ÿ—๏ธ Work & Research Experience

  • Expertise in: Deep learning, machine vision, construction robots, multimodal data fusion
  • Published in top journals such as Automation in Construction and Computer-Aided Civil and Infrastructure Engineering
  • Conducting research on:
    • ๐Ÿ”ฅ Fire Safety Inspection using AI-driven visual inspection
    • ๐Ÿค– Robotics for Construction Management with multi-task planning and automatic grasping
    • ๐Ÿ—๏ธ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
    • ๐ŸŽฏ Vision-based Monitoring for assembly alignment of precast concrete bridge members

๐Ÿ† Achievements & Awards

  • Published multiple high-impact journal papers ๐Ÿ“š
  • Lead researcher on innovative construction technology projects ๐Ÿ”
  • Contributed to advanced AI-driven automation for civil engineering ๐Ÿค–
  • Research works under review in prestigious engineering journals ๐Ÿ…
  • Collaborated with leading experts in civil engineering and robotics ๐Ÿค

Publicationย Top Notes:

Efficient visual inspection of fire safety equipment in buildings

 

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

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 Masterโ€™s degree in Electrical Engineering from the University of Tabriz and a Bachelor’s degree from Isfahan University of Technology.

Education & Training ๐ŸŽ“

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

Awards & Honors ๐Ÿ†

  • 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 ๐Ÿ› ๏ธ

  • Machine Learning ๐Ÿค–
  • Data Science ๐Ÿ“Š
  • Programming Languages: MATLAB, Python ๐Ÿ’ป

Peer Review Activities ๐Ÿง

Seyed has reviewed articles for prestigious journals, such as:

  • IEEE Access
  • Artificial Intelligence Review
  • BMC Public Health
  • Environmental Monitoring and Assessment ๐ŸŒฑ

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