Mohamed Laib ~ $ 
      

          - There are lies, damned lies, and statistics -          
-popularised by Mark Twain-         

Research Topic

I am interested in data science and knowledge extraction from data. My current work is about developing new tools for mining space-time data using several statistical tools and machine learning algorithms. My research aims at exploring, understanding, and investigating challenging datasets. My work focuses on some important problems that can be faced when dealing with challenging data. Among the proposed methodologies:

Detected communities using a mutual information-based network on wind speed monitoring system in Switzerland.

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Some R libraries

I have a great interest in R programming, I propose some libraries that I developed in collaboration with my colleagues. Please do not hesitate to contact me if you face problems with these libraries.

MFDFA: an R package computes the multifractal detrended fluctuation analysis for a time series. Available on GitHub.

SFtools: an R package for the unsupervised feature selection based on a space filing measure. Available on CRAN and GitHub.

More Libraries

Deep Learning for Remote Sensing Scene Classification

Scene classification is an important and challenging problem in Earth observation remote sensing. This work, was partly presented at the EGU 2018 in the session Learning from spatial data: unveiling the geo-environment through quantitative approaches. It aims at exploring small-scale convolutional neural networks architecture to obtain good performance. For more information, please visit the website of this project, in which you find some Python codes and datasets used for this work:

UnilNet

Please do not hesitate to contact me for any question related to my work.

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