Lien Orcid: https://orcid.org/0000-0002-6296-3414

Research Gate: https://www.researchgate.net/profile/Mfenjou-Martin-Luther

Google Citations: https://scholar.google.com/citations?user=aAdFG18AAAAJ

 

Courses Taught at the School of Geology and Mining Engineering:  Algorithms, Programming, Introduction to Data Analysis and Data Science, Graph Theory, Introduction to forecasting models, Operational Research, Data Science, Optimization, Machine Learning for Inversion Problem.

Martin Luther MFENJOU is Senior Lecturer at The Department of Applied Matheùatics and Computer Science, at The School of Geology and Mining Engineering of the University of Ngaoundere (Cameroon).

MFENJOU focuses on the global challenge on:

  • Models for Monitoring Road Networks using Intelligent Transportation Systems in Developing Countries: Accidents on road networks in developing countries cause so much material and human damage. Our work aims to propose mathematical models that can minimise accidents and many other disturbances that occur on road networks. Our current research is geared more towards involving machine learning in this work. In particular, for a recommendation system that would predict distrubances across inter-urban road networks. Another example is an ontology that detects and disseminates distrubances across inter-urban road networks.
  • Contribution of Metaheuristics and Methods based on Artificial Intelligence in the Mining, Oil and Gas sectors: Improvements in the estimation of reserves (as well as mineral resources), hydrocarbon wells, prediction models using metaheuristics and machine learning are all well established. As a result, our work is improving the accuracy of estimates of mining environments, the optimum placement of hydrocarbon wells, gravimetric mapping, the transport of pollutants in soils and sediments, and even the minimisation of water ingress into hydrocarbon reservoirs.
    MFENJOU is Member of Laboratory of Computer Science at The Université of Maroua                

    Engineering students:      Students in Master’s Degree 

    • 2021-2022: 20       
    •     2022-2023 25   
    •                 2023-2024: 20         2022-2023 : 02
    • 2023-2024: 15 
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