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arXiv research

A locally-built, LLM-digested index of recent arXiv papers in quant finance, geometry/topology, and statistical ML — keyword search served straight from SQLite on this machine.

169,341 papers · 148 categories

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3 results for “anopheles”

Regression Trees and Random forest based feature selection for malaria risk exposure predictionstat.ML

The paper uses machine learning to predict malaria risk using environmental and climate variables.

problem Predicting anopheles number for malaria risk using environmental and climate variables.

method Regression trees and random forests combined with stratified two levels cross validation for feature selection.

result The selected subset of variables improves prediction quality compared to GLM-Lasso method.

malaria feature selection machine learning anopheles environmental variables

2016-06-24abs ↗pdf ↗

Sélection de variables par le GLM-Lasso pour la prédiction du risque palustrestat.ML

In this study, we propose an automatic learning method for variables selection based on Lasso in epidemiology context. One of the aim of this approach is to overcome the pretreatment of experts in medicine and epidemiology on collected data. These pretreatment consist in recoding some variables and to choose some inter…

2015-09-09abs ↗pdf ↗

Improving Malaria Parasite Detection from Red Blood Cell using Deep Convolutional Neural Networkseess.IV

Deep neural network improves malaria detection from red blood cells.

problem Improving malaria detection from red blood cell images.

method End-to-end deep learning approach using convolutional neural networks.

result Best model achieves 97.77% accuracy.

malaria deep learning red blood cells neural networks image segmentation

2019-07-23abs ↗pdf ↗