Mr. Fangzhou Lin | Deep Learning | Best Scholar Award

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Mr. Fangzhou Lin | Deep Learning | Best Scholar Award 

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 Bachelor’s degree in Civil Engineering from Fuzhou University (2015-2019) and a Master’s degree in Structural Engineering from Southeast University (2019-2022). Fangzhou Lin’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.

🎓 Education

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

🏗️ 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:
    • 🔥 Fire Safety Inspection using AI-driven visual inspection
    • 🤖 Robotics for Construction Management with multi-task planning and automatic grasping
    • 🏗️ BIM-integrated Reality Capture for indoor inspection using multi-sensor quadruped robots
    • 🎯 Vision-based Monitoring for assembly alignment of precast concrete bridge members

🏆 Achievements & Awards

  • Published multiple high-impact journal papers 📚
  • Lead researcher on innovative construction technology projects 🔍
  • Contributed to advanced AI-driven automation for civil engineering 🤖
  • Research works under review in prestigious engineering journals 🏅
  • Collaborated with leading experts in civil engineering and robotics 🤝

Publication Top Notes:

Efficient visual inspection of fire safety equipment in buildings

 

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

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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