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

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

 

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award

Assoc. Prof. Dr. Mohammed Farag | Machine Learning Awards | Best Researcher Award 

Assoc. Prof. Dr. Mohammed Farag, Alexandria University, Egypt

Dr. Mohammed M. Farag is an accomplished Associate Professor of Electrical Engineering with extensive academic experience spanning over two decades. Currently affiliated with King Faisal University, Saudi Arabia, and Alexandria University, Egypt, he specializes in the fields of machine learning, signal processing, and cybersecurity. His research is particularly focused on the development of innovative solutions for edge computing and cyber-physical systems. Dr. Farag holds a Ph.D. in Computer Engineering from Virginia Tech, where he conducted groundbreaking research on enhancing trust in cyber-physical systems. His academic journey also includes a Master’s and Bachelor’s degree in Electrical Engineering from Alexandria University, both achieved with distinction. A prolific researcher, he has an impressive publication record in high-impact journals and has secured numerous research grants. Beyond his research contributions, Dr. Farag is dedicated to advancing the field through excellence in teaching, mentorship, and quality assurance, actively contributing to program development and accreditation processes.

Professional Profile:

SCOPUS

ORCID

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Summary of Suitability for Best Researcher Award: Dr. Mohammed M. Farag

Dr. Mohammed M. Farag’s academic and professional profile reflects significant accomplishments in research, teaching, and academic leadership. Based on his qualifications and achievements, he is a strong candidate for the Best Researcher Award for the following reasons.

🧑‍🎓 Education

🎓 Ph.D. in Computer Engineering (GPA: 4.00/4.00)Virginia Tech, USA (2009-2012)
Dissertation: “Architectural Enhancements to Increase Trust in Cyber-Physical Systems Containing Untrusted Software and Hardware”

🎓 M.Sc. in Electrical Engineering (GPA: 4.00/4.00)Alexandria University, Egypt (2003-2007)
Thesis: “Hardware Implementation of The Advanced Encryption Standard on Field Programmable Gate Arrays”

🎓 B.Sc. in Electrical Engineering, Distinction with Honor (GPA: 3.89/4.00)Alexandria University, Egypt (1998-2003)
Project: “VLSI Design of Cryptographic Algorithms”

📚 Research Interests

🔍 Machine Learning for Signal Processing & Edge Computing
🔐 Cybersecurity and Hardware Security
💾 VLSI Design and Embedded Systems
🤖 AI Applications in Electrical Engineering
🌐 Cyber-Physical Systems

🏆 Key Achievements

📝 Citations: 411 | h-index: 11 | i10-index: 11 (As of October 2024)
📖 Published in IEEE Access, Sensors, and top-tier journals.
💰 Secured multiple research grants from King Faisal University, totaling over 100,000 SAR.

💻 Technical Expertise

💡 Programming: Python, C++, MATLAB
🖥️ Hardware Design: VHDL, Verilog
📊 Machine Learning: TensorFlow, PyTorch, Keras
🔧 CAD Tools: Synopsys, Cadence, Xilinx

🎓 Teaching Experience

🎓 Electrical Circuits, Signal Processing, Digital Logic, VLSI Design, Embedded Systems, and more!
🎯 Special focus on fostering practical skills in Semiconductor Devices and Cybersecurity.

Publication Top Notes

Wearable sensors based on artificial intelligence models for human activity recognition

A Tiny Matched Filter-Based CNN for Inter-Patient ECG Classification and Arrhythmia Detection at the Edge

Design and Analysis of Convolutional Neural Layers: A Signal Processing Perspective

Matched Filter Interpretation of CNN Classifiers with Application to HAR

A Self-Contained STFT CNN for ECG Classification and Arrhythmia Detection at the Edge

Aggregated CDMA Crossbar With Hybrid ARQ for NoCs

Overloaded CDMA crossbar for network-on-chip

Dr. Zhe Yuan | Deep Learning Awards | Best Researcher Award

Dr. Zhe Yuan | Deep Learning Awards | Best Researcher Award 

Dr. Zhe Yuan, xidian University, China

Zhe Yuan is a Ph.D. student at Xidian University, Xi’an, Shaanxi, specializing in cutting-edge research in image processing, small object detection using deep learning, and unmanned aerial vehicle (UAV) technology. He earned his Master’s degree from Shaanxi University of Technology (2019-2022) and has industry experience as a Testing Engineer at TPRI (2022-2023). His research contributions include pioneering techniques for small target detection in UAV remote sensing images, emphasizing advanced multi-scale fusion attention mechanisms and adaptive weighted feature fusion. Zhe has published multiple influential works in renowned journals, such as Remote Sensing, and collaborated on projects addressing dynamic electromagnetic forces in water-lubricated bearings, showcasing his interdisciplinary expertise. His innovative research has been cited and recognized internationally, reinforcing his position as a promising researcher in his field.

Professional Profile:

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Suitability for the Research for Best Researcher Award

Zhe Yuan has demonstrated exceptional contributions to fields such as image processing, small object detection using deep learning, and UAV technology. His research showcases a clear focus on impactful and innovative solutions, aligning well with the criteria for the Research for Best Researcher Award. Below is a summary of his suitability.

Education 🎓

  • Ph.D. Student (2023/09–Present): Xidian University
  • Testing Engineer (2022/06–2023/07): TPRI
  • Master’s Degree (2019/09–2022/06): Shaanxi University of Technology

Research Directions 🔬

  • Image Processing 🖼️
  • Small Object Detection Using Deep Learning 🤖
  • Unmanned Aerial Vehicle (UAV) Technology 🚁

Publication top Notes:

Dynamic variation mechanism of electromagnetic force for loading device of water⁃lubricated bearing

Small Object Detection in UAV Remote Sensing Images Based on Intra-Group Multi-Scale Fusion Attention and Adaptive Weighted Feature Fusion Mechanism

YuanZ,NWang,Wang P,et al. Research on Non- contact Electromagnetic Loading Device for Water- lubricated Bear ng[J]. Journal of Physics: Conference Series, 2020, 1624(6):062020 (7pp).

Dynamic electromagnetic force variation mechanism and energy loss of a non-contact loading device for a water-lubricated bearing

Research on Non-contact Electromagnetic Loading Device for Water-lubricated Bearing