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

Aurélie Cools | Deep Neural Networks | Best Researcher Award

Ms. Aurélie Cools, University of Mons, Belgium.

Aurélie Cools is a Ph.D. candidate in Engineering Sciences at the University of Mons (UMons), specializing in deep neural networks and dimensionality reduction for CBIR search engines. She holds dual Master’s degrees: Civil Engineering in Computer Science and Management (Summa Cum Laude) and Management Engineering (Magna Cum Laude), showcasing her expertise in software engineering, business analytics, and optimization. Alongside her research, she contributes as a teaching assistant at UMons. With a strong foundation in Python, SQL, and PyTorch, Aurélie is multilingual and adept at problem-solving, team management, and communication. 👩‍💻

Education and Experience

Education

Ph.D. in Engineering Sciences

Institution: University of Mons (UMons), Polytechnic Faculty
Thesis Topic: CBIR search engine with deep neural networks and dimensionality reduction methods
Duration: 2021 – Present

Master’s in Civil Engineering (Summa Cum Laude)

Institution: UMons, Polytechnic Faculty
Specialization: Software Engineering and Business Intelligence
Duration: 2018 – 2021

Master’s in Management Engineering (Magna Cum Laude)

Institution: UCL Mons
Specialization: Business Analytics – Logistics and Transportation
Duration: 2015 – 2017

Bachelor’s in Management Engineering (Cum Laude)

Institution: UCL Mons
Duration: 2012 – 2015

Experience

Teaching Assistant & Ph.D. Student

Institution: UMons
Duration: September 2021 – Present

Credit Analyst

Institution: CPH Bank, La Louvière
Duration: July 2017 – August 2021

Student Worker

Institution: Colruyt Group, Mons
Duration: March 2013 – December 2016

Suitability For The Award

Ms. Aurélie Cools is an outstanding candidate for the Best Researcher Award, combining academic excellence with impactful research. Currently pursuing a Ph.D. in Engineering Sciences at the University of Mons, her work on CBIR systems using deep neural networks and dimensionality reduction demonstrates innovation and technical expertise. With dual Master’s degrees in Civil and Management Engineering earned with high honors, Aurélie excels in both research and practical applications. Her proficiency in programming, data analysis, and problem-solving, coupled with strong communication skills, makes her a deserving nominee.

Professional Development

Aurélie excels in the realms of engineering and management, leveraging cutting-edge techniques like deep neural networks and dimensionality reduction.

Her research bridges technical and analytical fields, emphasizing CBIR technologies for efficient image retrieval. With years of experience as a teaching assistant, she fosters innovation and critical thinking among students. Aurélie’s blend of programming skills in Python, SQL, and PyTorch, coupled with proficiency in tools like MongoDB and Excel, enhances her adaptability in diverse challenges. A polyglot and skilled communicator, she thrives in team management, problem-solving, and delivering impactful solutions.

Research Focus

Aurélie’s research focuses on developing advanced Content-Based Image Retrieval (CBIR) systems, leveraging deep neural networks and cutting-edge dimensionality reduction techniques to enhance image search and analysis efficiency. Her interdisciplinary approach combines software engineering, artificial intelligence, and data science for innovative solutions.

With a keen interest in the practical applications of CBIR, such as medical imaging or multimedia management, Aurélie contributes to expanding the potential of machine learning in real-world scenarios. Her expertise lies at the intersection of engineering precision and computational intelligence, making her a significant contributor to AI-driven image processing.