Rosas Behoundja

Rosas Behoundja

Hi, I'm Rosas Behoundja, an Artificial Intelligence enthusiast with a deep passion for solving real-world challenges using cutting-edge AI techniques. I specialize in AI research, machine learning, and optimization. My goal is to develop innovative solutions that improve the quality of life in Africa and beyond.

My areas of interest include:

  • Deep Learning – Building and training neural networks to solve complex problems.
  • Machine Learning – Exploring algorithms that help machines learn from data and make predictions.
  • Optimization Algorithms – Developing techniques to find the most efficient solutions to complex problems.
  • Ethical AI – Ensuring fairness, transparency, and accountability in AI applications.

Currently, I am deeply involved in exploring Genetic Algorithms and Deep Learning and their application in solving problems.

Feel free to contact me at my email: perrierosas@gmail.com or connect with me on LinkedIn.

Education

Bachelor degree in Artificial Intelligence

Institut de Formation et de Recherche en Informatique (UAC), Benin

2023–2026 (in progress)

Scientific Baccalaureate

Collège Catholique St Jean-Paul II de Djougou, Benin

2022

Recent Projects

Sokoban Game

Implementation of the Sokoban puzzle game using AI algorithms like DFS, BFS, and A* to automatically solve levels.

Python CLI Heuristics
View on GitHub

PIL1_2324_2

Social network site built with Django, allowing users to connect with recommandation system.

Python Django SQL HTML/CSS JavaScript
View on GitHub

MLScratch

Implementation of machine learning algorithms (regression, SVM, decision trees) built from scratch using Python.

Python NumPy ML
View on GitHub

Cryptojacking Detection

Network activity classification to detect cryptojacking attacks, developed during the IndabaX Benin 2024 hackathon.

Python XGBoost Scikit-learn
View on GitHub

PicTerminal Group 5

Command-line image processing application built in C++ as part of a university project.

C++ CLI
View on GitHub

Sentiment Analysis in Python

A web application using Streamlit to perform sentiment analysis on user-input text. Implements natural language processing techniques to classify emotions.

Python NLP Streamlit
View on GitHub

Titanic Classification

A machine learning model that predicts passenger survival on the Titanic using a Flask-based web interface and scikit-learn.

Python Flask Scikit-learn
View on GitHub

Skills

Python
Artificial Intelligence
Machine Learning
Deep Learning
Metaheuristics
C++
Numpy
Pandas
Data Analysis
Data Visualisation
Scikit-learn
Linux
Flask
FastAPI

Achievements

Winner – IndabaX Hackathon 2024

Cryptojacking detection

View Certificate

DNSathon 2024

AI for DNS anomaly detection

Volunteering

FRIARE

Fondation Ratheil pour une Intelligence Artificielle Responsable et Efficiente

February 2025 - Present

Supporting research, awareness and education in AI in Africa.

CMP

Classmate Mentoring Program

January 2025 - Present

An initiative to promote peer-to-peer learning and mentorship among AI students.

Contact

Feel free to reach out to me through any of the following channels:

Email

GitHub

LinkedIn