Masoud DANESHTALAB | deep learning | Best Researcher Award

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Prof. Masoud DANESHTALAB | deep learning | Best Researcher Award 

Prof. Masoud DANESHTALAB, Mälardalen University, Sweden.

Masoud Daneshtalab, Ph.D., Docent, Full Professor
Masoud Daneshtalab is a globally recognized scholar and Full Professor at Mälardalen University (MDU), Sweden. With over two decades of academic and professional excellence, he has made significant contributions to computer science and engineering, specializing in dependable systems, AI, and hardware/software co-design. A prolific researcher with an H-index of 35 and over 5,100 citations, Dr. Daneshtalab is included in the prestigious World’s Top 2% Scientists Ranking. He serves as the Scientific Director of Fundamental AI at MDU and collaborates internationally, holding adjunct professorships and contributing to cutting-edge research initiatives.

Professional Profile:

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Suitability of Masoud Daneshtalab for the Best Researcher Award

Dr. Masoud Daneshtalab is a highly suitable candidate for the “Research for Best Researcher Award,” based on his exceptional academic achievements and professional contributions. Here are the key reasons

Education

🎓 Academic Journey

  • Docent (2018): Qualified in Computer Science and Electronics, Mälardalen University, Sweden.
  • Ph.D. (2008–2011): Information and Communication Technology, University of Turku, Finland. Dissertation: Adaptive Implementation of On-Chip Networks under Prof. Hannu Tenhunen.
  • M.Sc. (2004–2006): Computer Engineering, University of Tehran, Iran. Thesis: Low Power Methods in Network-on-Chips under Prof. Ali Afzali-Kusha.
  • B.Sc. (1998–2002): Computer Engineering, Shahid Bahonar University of Kerman, Iran.

Experience

💼 Professional Contributions

  • Scientific Director (2024–Present): Fundamental AI, Mälardalen University, Sweden.
  • Full Professor (2020–Present): Innovation, Design & Engineering, MDU.
  • Adjunct Professor (2019–Present): Computer Systems, Tallinn University of Technology, Estonia.
  • Previous Roles: Associate Professor at MDU (2016–2020), EU Marie Curie Fellow at KTH Royal Institute of Technology (2014–2016), Lecturer at the University of Turku (2011–2014), and Researcher at the University of Tehran (2006–2008).

Research Interests

🔬 Key Areas

  • Optimization and robustness in deep learning models.
  • HW/SW co-design and heterogeneous computing.
  • Dependable systems, memory architectures, and interconnection networks.
  • Cutting-edge projects include sustainable AI, federated learning, and reliable autonomous systems.

Awards

🏆 Recognitions

  • Best Paper Awards: IEEE ECBS (2019), IEEE MCSoC (2018), and multiple HiPEAC Paper Awards (2013–2017).
  • Research Grants: Marie Skłodowska-Curie Fellowship (2014), Nokia Foundation (2009), and others.
  • Top Reviewer: IEEE Transactions on Computers (2013).
  • Fellowships: GETA, Helsinki University of Technology (2008–2011).

Publications

A review on deep learning methods for ECG arrhythmia classification

CITIED: 490

Time-Sensitive Networking in automotive embedded systems: State of the art and research opportunities

CITIED: 147

Routing algorithms in networks-on-chip

CITIED: 136

Smart hill climbing for agile dynamic mapping in many-core systems

CITIED: 125

EDXY–A low cost congestion-aware routing algorithm for network-on-chips

CITIED: 124

Deep Maker: A multi-objective optimization framework for deep neural networks in embedded systems

CITIED: 122

 

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

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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. Tara P Banjade | Artificial Intelligence Awards | Best Researcher Award

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Dr. Tara P Banjade | Artificial Intelligence Awards | Best Researcher Award 

Dr. Tara P Banjade, East China University of Technology, Nanchang, China

Dr. Tara P. Banjade is an Associate Professor at the East China University of Technology, Nanchang, China, specializing in applied mathematics, seismic signal processing, and artificial intelligence applications for seismic data processing. He completed his Ph.D. in Applied Mathematics at Harbin Institute of Technology in China in 2020, following a Master’s and Bachelor’s in Mathematics from Tribhuvan University, Nepal. Dr. Banjade’s research focuses on developing mathematical algorithms for denoising seismic data, including 1D earthquake signals and 2D geophysical data like oil, gas, and ground-penetrating radar (GPR) data. His innovative approaches employ techniques such as variational mode decomposition, wavelet transforms, and artificial intelligence, including DARE U-Net for seismic noise attenuation and self-guided singular value decomposition for data edge detection.

Professional Profile:

ORCID

Summary of Suitability for Best Researcher Award

Dr. Tara P. Banjade demonstrates an impressive academic and research profile, particularly within Applied Mathematics and Seismic Signal Processing, fields which align closely with the scope of the Best Researcher Award. His doctoral education from Harbin Institute of Technology and ongoing research position at East China University of Technology position him as a strong candidate.

Education

  1. Harbin Institute of Technology, Harbin, China
    • Ph.D. in Applied Mathematics
    • Duration: September 2015 – January 2020
  2. Tribhuvan University, Kathmandu, Nepal
    • Master’s in Mathematics
    • Duration: 2012 – 2014
  3. Tribhuvan University, Kathmandu, Nepal
    • Bachelor’s in Mathematics
    • Duration: 2006 – 2010

Work Experience

  1. Associate Professor
    • Institution: East China University of Technology, School of Geophysics and Measurement-Control Technology, Nanchang, Jiangxi, China
    • Duration: March 2023 – Present
  2. Founder/Chairperson
    • Organization: Intellisia Institute for Research and Development, Nepal
  3. Research Director
    • Organization: Girija Prasad Koirala Foundation
    • Duration: 2020 – Present
  4. Visiting Scientist
    • Institution: Research Centre for Applied Science and Technology (RECAST), Tribhuvan University, Nepal
  5. Founding Member and Mathematics Lecturer
    • Institution: Arunima College, Tribhuvan University, Nepal
    • Duration: 2020 – 2023
  6. Executive Member
    • Organization: Nepal Mathematical Society
    • Duration: 2021 – 2024
  7. Visiting Faculty
    • Institution: School of Mathematical Science, Tribhuvan University, Nepa.

Publication top Notes:

Seismic Random Noise Attenuation Using DARE U-Net

Enhancing seismic data by edge-preserving geometrical mode decomposition

Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award

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Ms. Hind MEZIANE | Artificial Intelligence | Best Scholar Award 

Ms. Hind MEZIANE, ACSA Lab, Faculty of Sciences, University Mohammed First, Oujda, Morocco

Hind Meziane is a dedicated researcher and Ph.D. candidate in Computer Science at the ACSA Laboratory, Department of Mathematics, Faculty of Sciences, Mohammed Premier University, Oujda, Morocco. Her academic journey began with a Baccalaureate in Science (Science Mathematics Option B) from Mehdi Ben Berka High School in Oujda in 2012. She then pursued higher education at Mohammed Premier University, obtaining a DEUG in Mathematics and Computer Science (2012-2014), a LICENSE in Mathematics and Computer Science (2014-2016), and a Specialized Master’s in Computer Engineering with Honors (2017-2019).

Professional Profile:

Summary of Suitability for Best Scholar Award

Hind Meziane is a highly accomplished researcher whose work primarily focuses on the security of Internet of Things (IoT) systems. She is currently pursuing a Ph.D. in Computer Science at Mohammed Premier University and has an impressive academic background, including a specialized master’s degree in Computer Engineering and a bachelor’s degree in Mathematics and Computer Science. Her research contributions are well-documented through various publications in reputable international journals and conference proceedings.

🎓 Education:

  • 2019-Present: Doctorate (PhD) in Computer Science at Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2017-2019: Specialized Master in Computer Engineering, with Honors, at Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2014-2016: LICENSE in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2012-2014: DEUG in Mathematics and Computer Science from Mohammed Premier University, Faculty of Sciences, Oujda.
  • 2011-2012: Baccalaureate in Science, Mathematics Option B from Mehdi Ben Berka High School, Oujda.

Publication top Notes:

A survey on performance evaluation of artificial intelligence algorithms for improving IoT security systems

A Comparative Study for Modeling IoT Security Systems

Modeling IoT based Forest Fire Detection System with IoTsec

A Study of Modelling IoT Security Systems with Unified Modelling Language (UML)

Classifying security attacks in iot using ctm method

Internet of Things: Classification of attacks using CTM method