Mr. Md. Humayun Kabir | Transfer Learning Awards | Best Researcher Award

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Mr. Md. Humayun Kabir | Transfer Learning Awards | Best Researcher Award

Mr. Md. Humayun Kabir, International Islamic University Chittagong, Bangladesh

MD. Humayun Kabir is a dedicated educator and professional in the field of computer and communication engineering, currently serving as a Lecturer at the International Islamic University Chittagong, Bangladesh. With a strong foundation in Electronic and Telecommunication Engineering, he is pursuing his M.Sc. at Chittagong University of Engineering and Technology. Humayun’s professional experience includes roles as a Course Instructor at CSL Academy, Senior Technical Trainer at New Vision Information Technology Limited, and Course Instructor at ERevo Technologies Limited, where he specializes in Cisco Certified Network Associate (CCNA) training and various IT certifications. His career is marked by a commitment to enhancing students’ practical skills and preparing them for the rapidly evolving technology landscape. Humayun’s research interests encompass a broad range of topics, including computer networking, cybersecurity, artificial intelligence, machine learning, and the Internet of Things (IoT). With a passion for teaching and mentoring, he aims to inspire the next generation of technology professionals.

Professional Profile:

ORCID

Summary of Suitability for the Research for Best Researcher Award: 

MD. Humayun Kabir emerges as an outstanding candidate for the Research for Best Researcher Award due to his impressive combination of academic achievements, professional experience, and dedicated research interests. Here are the key reasons that support his suitability

📚 Education

  • M.Sc. (Engineering)
    Chittagong University of Engineering and Technology
    Studying since 2020
    GPA: 3.5/4.0
    Field: Electronic and Telecommunication Engineering
  • B.Sc. (Engineering)
    International Islamic University Chittagong
    Graduated in 2018
    GPA: 3.863/4.0
    Field: Electronic and Telecommunication Engineering
  • Higher Secondary Certificate (H.S.C)
    Chittagong Model School & College
    Graduated in 2013
    GPA: 4.90/5.0
    Field: Science
  • Secondary School Certificate (S.S.C)
    Agrasar Bouddya Anathalay High School
    Graduated in 2011
    GPA: 4.44/5.0
    Field: Science

💼 Professional Experience

  • Lecturer
    Department of Computer & Communication Engineering (CCE)
    International Islamic University Chittagong, Bangladesh
    January 2023 – Present
  • Course Instructor
    CSL Training (CSL Academy)
    Cisco Certified Network Associate Routing & Switching (CCNA)
    August 2024 – Present
  • Senior Technical Trainer
    New Vision Information Technology Limited (NVIT / New Horizons)
    Cisco Certified Network Associate Routing & Switching (CCNA)
    May 2023 – Present
  • Course Instructor
    ERevo Technologies Limited
    CCNA, MikroTik Certified Network Associate (MTCNA), RedHat Certified System Administrator (RHCSA), CompTIA A+, IT Essential Certification and Training, Microsoft Office Professionals
    February 2019 – Present
  • Assistant Proctor
    International Islamic University Chittagong, Bangladesh
    March 2023 – August 2024
  • Assistant Lecturer
    Department of Computer & Communication Engineering (CCE)
    International Islamic University Chittagong, Bangladesh
    January 2022 – December 2022
  • Adjunct Lecturer
    Department of Electronic & Telecommunication Engineering (ETE)
    International Islamic University Chittagong, Bangladesh
    November 2018 – December 2021
  • Teaching Assistant
    Department of Electronic & Telecommunication Engineering
    International Islamic University Chittagong, Bangladesh
    May 2018 – September 2018

🏆 Achievements and Awards

  • Cisco Certified Network Associate (CCNA) certification
  • MikroTik Certified Network Associate (MTCNA) certification
  • RedHat Certified System Administrator (RHCSA) certification
  • CompTIA A+ certification
  • IT Essentials Certification and Training
  • Microsoft Office Professionals certification

Publication Top Notes:

Design and Simulation of AI-Enabled Digital Twin Model for Smart Industry 4.0

Enhancing Insider Malware Detection Accuracy with Machine Learning Algorithms

Transfer Learning-Based Anomaly Detection System for Autonomous Vehicle

Design and Implement IoT-Based Intelligent Manageable Smart Street Lighting Systems for Future Smart City

Design and Analysis of Multiband Microstrip Patch Antenna Array for 5G Communications

 

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

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

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

 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.

🎓 Education

  • B.Sc. in Computer Science (2007)
    📍 Qassim University, Saudi Arabia
  • M.Sc. in Computer Science (2014)
    📍 Qassim University, Saudi Arabia
  • Ph.D. in Computer Science (2020)
    📍 Durham University, United Kingdom
    🧑‍💻 Specialization: Bioimage Informatics, Machine Learning, and Image Processing

💼 Work Experience

  • Teaching Assistant (2008-2016)
    📍 Qassim University – College of Computer Science
  • Lecturer (2016-2020)
    📍 Qassim University – College of Computer Science
  • Assistant Professor (2020–Present)
    📍 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

🏆 Achievements, Awards, and Honors

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

🌟 Areas of Interest

  • Machine Learning & Deep Learning 🤖
  • Medical Image Analysis 🏥
  • Computer Graphics and Signal Processing 🎨

Publication 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