Projects

Research Lab of Flair-Pricnac Group

2025

A web platform showcasing the activities, publications, and projects of the Flair-Pricnac Research Lab. Designed to improve visibility and collaboration for the lab's work in research and development.

• Developed an interactive and visually appealing platform using Vue.js.
• Showcased lab activities, team members, and ongoing projects with a structured and user-friendly interface.
• Ensured maintainability and extensibility for future updates to the platform.

Technologies: Vue.js, Web Development

EEPER:MD Project

2024

A web platform to showcase project activities and team for the Economie D'Energie et Procédés Ecoresponsables initiative in Africa.

Technologies: React.js, Node.js, Vercel

Design and Implementation of a Data Warehouse using Talend Open Studio

2023

Designed a robust data warehouse and ETL pipeline for efficient data integration and analytics. Developed a user-friendly dashboard for real-time monitoring and visualization.

• Built a data warehouse for structured storage and optimized querying of large datasets.
• Developed an ETL pipeline using Talend Open Studio for seamless data integration from multiple sources.
• Created an interactive dashboard using Node.js and EJS for real-time data monitoring and reporting.
• Ensured data consistency and quality throughout the ETL process.

Technologies: Talend Open Studio, Node.js, EJS, ETL Pipeline, Data Integration

End-to-End MLOps Network Security Project

2025

Developed a comprehensive MLOps pipeline for network security threat detection. Implemented an end-to-end machine learning workflow with automated data ingestion, validation, model training, and deployment.

• Built a complete ETL pipeline for processing and validating network security data from multiple sources.
• Implemented automated data ingestion, validation, and transformation workflows with DVC for version control.
• Designed and trained machine learning models for network intrusion detection with comprehensive evaluation metrics.
• Deployed the ML model with containerization using Docker and CI/CD pipelines via GitHub Actions.
• Integrated model versioning and artifact management for reproducible ML experiments.

Technologies: Python, MLOps, DVC, Docker, GitHub Actions, Machine Learning, MLflow, DagHub, Scikit-learn

Computer Vision Algorithms in C

2022

A project focused on developing computer vision algorithms using PGM and PNM image formats in the C programming language. The implementation showcases foundational image processing techniques and efficient manipulation of raw image data.

• Built custom algorithms for image enhancement, filtering, and transformation.
• Utilized PGM and PNM formats for precise control over image data representation.
• Optimized memory usage and computational performance in C for large-scale image processing.
• Designed the project with extensibility for various computer vision tasks.

Technologies: C, PGM/PNM, Image Processing

University Information Voice Bot

2022

A voice bot designed to improve information dissemination among students and staff at the University of Yaoundé 1. The bot addresses challenges like miscommunication, lack of internet access, and overwhelming messages in group chats, providing a centralized, voice-activated source of information.

• Implemented a voice-interactive system to provide essential information to students, such as exam schedules, course reprogramming, and availability of results.
• Developed an Interactive Voice Response (IVR) system using Asterisk for seamless phone interactions.
• Built a machine learning model using Naive Bayes and SVM for processing natural language queries.
• Provided access to information such as class schedules, exam planning, menus at the university restaurant, and location of classrooms.
• Adopted Scrum methodology for effective project management and iterative development.

Technologies: Vue.js, Django, Asterisk, Interactive Voice Response (IVR), Naive Bayes, Natural Language Processing (NLP), Scrum

Supermarket Management System

2020

A comprehensive supermarket management system designed to streamline inventory tracking, billing, and employee management. The system ensures efficiency in operations and reduces errors through automation.

• Developed a user-friendly interface using JavaFX for intuitive interaction.
• Implemented core functionalities including inventory management, customer billing, and employee records using Java.
• Integrated MySQL as the database backend for secure and efficient data storage.
• Utilized JDBC for seamless interaction between the Java application and the MySQL database.
• Designed the system following Object-Oriented Programming principles to ensure modularity and scalability.
• Included advanced features like generating sales reports and tracking low-stock items.

Technologies: Java, MySQL, JDBC, JavaFX, Object-Oriented Programming (OOP)

Acceleration of Gradual Pattern Generation

2025

This project focuses on improving the efficiency of gradual pattern extraction algorithms. The goal is to handle large datasets more effectively by implementing techniques to reduce computation time.

• Ongoing work aims at further optimization and exploring new computing techniques.

Technologies: Python, data mining, gradual pattern mining, acceleration

Explainable Artificial Neural Network (ANN) to Predict Compressive Strength of Concrete

2024

A project focused on using an explainable Artificial Neural Network (ANN) to predict the compressive strength of concrete based on various input features. The project employs SHAP for global and local model explanations, and Layer-wise Relevance Propagation (LRP) for local explanations to enhance transparency and interpretability of predictions.

• Implemented an ANN model for predicting the compressive strength of concrete.
• Utilized SHAP for global and local explanations of the model's predictions.
• Applied Layer-wise Relevance Propagation (LRP) for local model explanations.
• Evaluated the model's explanations using fidelity and stability scores.

Technologies: Python, TensorFlow, SHAP, LRP, ANN, Machine Learning