Mr. Koagne Silas | Neural Networks | Pioneer Researcher Award

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Mr. Koagne Silas | Neural Networks | Pioneer Researcher Award 

Mr. Koagne Silas, University of Dschang, Cameroon

KOAGNE LONGPA TAMO Silas is a Cameroonian researcher and Ph.D. student in Physics at Dschang State University, specializing in medical physics with a strong focus on automation and applied computer science. His academic background spans both physics and electrical engineering, with degrees from the University of Dschang and the University of Bamenda, where he developed expertise in embedded systems, analog artificial neural networks, and electronics. Silas has extensive experience in microcontroller programming, analog and digital circuit simulation, and tools such as MATLAB, Arduino, Proteus, and Cadence Virtuoso. In addition to his research, he has served as an electronics teacher at various technical colleges and as a junior lecturer in computer science. His hands-on experience includes internships in electronics maintenance and electrical network installation. A bilingual communicator in English and French, Silas is known for his leadership, creativity, and commitment to advancing applied technologies in medical physics.

Professional Profile:

SCOPUS

🏅 Summary of Suitability Pioneer Researcher Award 

KOAGNE LONGPA TAMO Silas is an emerging research talent in the field of medical physics and electronics, demonstrating a rare combination of early innovation, technical depth, and applied problem-solving across interdisciplinary domains. As a Ph.D. candidate with an M.Sc. specialization in analog artificial neural networks for medical applications, Silas is pioneering research at the intersection of electronics, embedded systems, and health technologies, aligning closely with the spirit of the Pioneer Researcher Award.

🎓 Education Background

  • Ph.D. in Physics (Medical Physics) – Dschang State University, Cameroon (📅 Dec 2022 – Present)

    • 🧠 Research Focus: Analog Artificial Neural Networks

    • 👨‍🏫 Supervisor: Prof. Geh Wilson Ejuh

  • M.Sc. in Physics, Electronics Speciality – Dschang State University, Cameroon (📅 July 2022)

    • 📘 Thesis: Specification and implementation of multilayer perceptron analog artificial neural networks

    • 👨‍🏫 Supervisor: Dr. Djimeli Tsajio Alain B.

  • B.Sc. in Physics – Dschang State University, Cameroon (📅 Aug 2021)

  • DIPET 2 in Electronics – University of Bamenda (📅 July 2020)

    • 🛰 Dissertation: Design and implementation of a digital breath alcohol detection system with SMS alert and vehicle tracking

  • DIPET 1 in Electronics – University of Bamenda (📅 Aug 2018)

    • 🚪 Project: RFID-based electronic attendance system with automatic door unit

  • GCE A/L – Government Bilingual High School, Mbouda (📅 July 2015)

  • GCE O/L – Government Bilingual High School, Mbouda (📅 June 2013)

  • FSLC – Ecole Primaire Bilingue de la Promotion, Mbouda (📅 June 2008)

💼 Work Experience

  • Electronics Teacher – Government Technical College Ngombo-ku, Cameroon (📅 Jan 2021 – Present)

  • Junior Lecturer in Computer Science – Higher Technical Teacher Training College Bambili (📅 2019–2020)

  • Electronics Teacher – Government Technical High School Bambui (📅 2017–2018)

  • Internship – Electronics & Maintenance

    • 📍 HYTECHS, Yaoundé (📅 2019)

    • 🔧 Worked on printer maintenance & installation

  • Internship – Electrical Network Installation

    • 📍 MEECH CAM Sarl, Yaoundé (📅 2016)

    • ⚡ Focus on underground cable installation and high voltage network

🏆 Achievements & Awards

  • ✅ Successfully designed and implemented:

    • 🤖 An analog artificial neural network (M.Sc. Thesis)

    • 🚘 A breath alcohol detection system with GPS and SMS alerts

    • 🛂 An RFID-based attendance system with automated doors

  • 📚 Published and presented academic work in medical physics and embedded systems

  • 👨‍🏫 Contributed to higher education through teaching and mentoring roles across several institutions

  • 🎓 Admitted to Ph.D. program based on excellent academic performance

  • 💻 Advanced skills in MATLAB, Arduino, MikroC, Cadence Virtuoso, PSPICE & Proteus

  • 🗣️ Bilingual in English and French – great asset for teaching and collaboration

Publication Top Notes:

Breast cancer detection and classification: A study on the specification and implementation of multilayer perceptron analog artificial neural networks

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

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:

Google Scholar

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