International Conference on Smart and Sustainable Agriculture (SSA'2021)
Tutorial: Christophe Maudoux, CNAM, France
Title: Machine-Learning Methods and Tools
Abstract
This tutorial focuses on Machine-Learning based approaches and how accurate or timely detection can be achieved by using supervised ML algorithms. The aim of this survey is to identify which algorithm presents the best detection rate and is the most suitable for smart and sustainable agriculture.
Biography
Christophe Maudoux graduated from CNAM Network and System Engineering track in 2019, where he is now a part-time Professor. He works on WebSSO engineering, also known as Identity and Access Management (IAM), and is part of the WebSSO open source project LemonLDAP::NG core team as maintainer and advanced Perl programmer.
Since 2020, Christophe Maudoux has developed a research activity on the detection of security anomalies by means of machine learning algorithms.