Logo Lanfrica
  • Home
  • Atlas
  • Insights
  • Docs
  • Sign in

© 2026 Lanfrica. All rights reserved. All copyrights of the resources shown on the Lanfrica website belong to the original copyright holders, unless explicitly stated otherwise.

Six types of dust events in Eastern Mediterranean identified using unsupervised machine-learning classification

Record type:

paper
Creator:
DorRonYinShi
Publisher:
Elsevier BV
Host:

Visit

doi.org

Licenses

https://www.elsevier.com/tdm/userlicense/1.0/https://www.elsevier.com/legal/tdmrep-licensehttp://creativecommons.org/licenses/by-nc-nd/4.0/

Similar

Developing Classification Model for Chickpea Types using Machine Learning AlgorithmsImage Segmentation for Dust Detection Using Semi-supervised Machine LearningCattle Pinkeye Disease Classification using Machine Learningjonas-72/-Classification-of-Legislation-in-Zambia-using-Machine-LearningA Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine LearningModeling random events using a mixed approach Machine Learning techniques

Developing Classification Model for Chickpea Types using Machine Learning Algorithms

Ethiopia is the leading producer of chickpea in Africa and among the top ten most important

Image Segmentation for Dust Detection Using Semi-supervised Machine Learning

Dust plumes originating from the Earth’s major arid and semi-arid areas can significantly affect the

Cattle Pinkeye Disease Classification using Machine Learning

Pinkeye (infectious bovine keratoconjunctivitis, or IBK) is a bacterial infection of the cattle eye

jonas-72/-Classification-of-Legislation-in-Zambia-using-Machine-Learning

Classification of legislation in Zambia; given a bill/act/Statutory Instrument, classify the type of

A Contributor-Focused Intrinsic Quality Assessment of OpenStreetMap in Mozambique Using Unsupervised Machine Learning

Anyone can contribute geographic information to OpenStreetMap (OSM), regardless of their level of ex

Modeling random events using a mixed approach Machine Learning techniques

Modeling random events using a mixed approach Machine Learning techniques 

Poster presented at the Deep Learning Indaba 2023 by KABEYA MWEPU Simon Isaac