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

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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

GOOGLE SCHOLAR

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

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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:

SCOPUS

ORCID

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

Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award

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Assoc Prof Dr. Izabela Rojek | Artificial Intelligence | Best Researcher Award 

Assoc Prof Dr. Izabela Rojek, Kazimierz Wielki University, Poland

Dr. Izabela Rojek is a prominent academic and researcher serving as the Head of the Department of Data Processing Methods and Tools and the Dean of the Faculty of Computer Science at Kazimierz Wielki University in Bydgoszcz, Poland. She holds the qualifications of Ph.D., D.Sc.Eng., and Associate Professor. Dr. Rojek’s research is centered on engineering sciences, specifically in Technical Informatics, Telecommunications, and Mechanical Engineering. Her extensive scientific output includes five books, 190 articles and chapters in monographs, and over 6000 points in the Ministry of Science and Higher Education (MNiSW) ranking, with a Hirsch index of 18 (Web of Science and Scopus) and 20 (Google Scholar). She has been recognized with 15 national and international awards, including four UKW Rector’s Awards and three foreign medals for outstanding inventions. Dr. Rojek’s contributions extend to 20 grants and innovation projects, and she actively participates in the Manufacturing Engineering Committee of the Polish Academy of Sciences, where she chairs the Manufacturing Digitisation Section.

Professional Profile:

 

Suitability for Best Researcher Award:

Izabela Rojek is an exemplary candidate for the Best Researcher Award due to her outstanding contributions to the field of engineering sciences, particularly in Technical Informatics and Telecommunications. Her extensive publication record, high citation metrics, and significant involvement in national and international research projects highlight her impact on the field. Her leadership roles and innovative research further demonstrate her exceptional qualifications for this award.

Education:

  • Ph.D. in Engineering Sciences from Kazimierz Wielki University
  • D.Sc.Eng. (Doctor of Science in Engineering)
  • Associate Professor (Assoc. Prof.)

Work Experience:

  • Kazimierz Wielki University, Bydgoszcz
    • Head of the Department of Data Processing Methods and Tools
    • Dean of the Faculty of Computer Science

Additional Roles and Experience:

  • Member of the Manufacturing Engineering Committee of the Polish Academy of Sciences
  • Chair of the Manufacturing Digitisation Section of this Committee
  • Participation in the implementation of the IFS Applications IT system, including solution design, data migration, and training material preparation

Research and Contributions:

  • Authored 5 books and over 190 articles and chapters in monographs
  • Achieved over 6000 points in MNiSW (Polish Ministry of Science and Higher Education) evaluation
  • Total Impact Factor (IF) above 120
  • Hirsch index: h=18 (573 citations, Web of Science), h=18 (717 citations, Scopus), h=20 (1097 citations, Google Scholar)
  • Involved in 20 grants and innovation projects and 10 research topics
  • Recipient of 15 national and international awards, including 4 UKW Rector’s Awards and 3 foreign medals for outstanding inventions

Publication top Notes:

 

Enhancing 3D Printing with Procedural Generation and STL Formatting Using Python

Green Energy Management in Manufacturing Based on Demand Prediction by Artificial Intelligence—A Review

Use of Machine Learning to Improve Additive Manufacturing Processes

Review of the 6G-Based Supply Chain Management within Industry 4.0/5.0 Paradigm

Utilizing Selected Machine Learning Methods for Conicity Prediction in the Process of Producing Radial Tires for Passenger Cars