Explainable classification of astronomical uncertain time series

Autor: Mbouopda, Michael Franklin, Ishida, Emille E O, Nguifo, Engelbert Mephu, Gangler, Emmanuel

Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transie

Externí odkaz: http://arxiv.org/abs/2210.00869

Uncertain Time Series Classification With Shapelet Transform

Autor: Mbouopda, Michael Franklin, Nguifo, Engelbert Mephu

Publikováno v: 2020 IEEE International Conference on Data Mining Workshops (ICDMW)

Time series classification is a task that aims at classifying chronological data. It is used in a diverse range of domains such as meteorology, medicine and physics. In the last decade, many algorithms have been built to perform this task with very a

Externí odkaz: http://arxiv.org/abs/2102.02090

Neurals Networks for Projecting Named Entities from English to Ewondo

Autor: Mbouopda, Michael Franklin, Yonta, Paulin Melatagia, Lombo, Guy Stephane B. Fedim

Publikováno v: International Conference on Learning Representations, AfricaNLP Workshop 2020

Named entity recognition is an important task in natural language processing. It is very well studied for rich language, but still under explored for low-resource languages. The main reason is that the existing techniques required a lot of annotated

Externí odkaz: http://arxiv.org/abs/2004.13841

Experimental Study of Time Series Forecasting Methods for Groundwater Level Prediction

Autor: Mbouopda, Michael Franklin, Guyet, Thomas, Labroche, Nicolas, Henriot, Abel

Publikováno v: Advanced Analytics and Learning on Temporal Data ISBN: 9783031243776
Advanced Analytics and Learning on Temporal Data. AALTD 2022. Lecture Notes in Computer Science
International Workshop on Advanced Analytics and Learning on Temporal Data
International Workshop on Advanced Analytics and Learning on Temporal Data, Sep 2022, Grenoble, France. pp.34-49, ⟨10.1007/978-3-031-24378-3_3⟩

International audience; Groundwater level prediction is an applied time series forecasting task with important social impacts to optimize water management as well as preventing some natural disasters: for instance, floods or severe droughts. Machine

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::af870500163ab447d19f94c1235ac767
https://doi.org/10.1007/978-3-031-24378-3_3

Explainable classification of astronomical uncertain time series

Autor: Mbouopda, Michael, Ishida, Emille, Mephu Nguifo, Engelbert, Gangler, Emmanuel

Exploring the expansion history of the universe, understanding its evolutionary stages, and predicting its future evolution are important goals in astrophysics. Today, machine learning tools are used to help achieving these goals by analyzing transie

Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f5df801175d9310f7dfab3922ebac5de
https://hal.uca.fr/hal-03790123