AIMC Topic: Brazil

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Using machine learning techniques for predicting the dropout of undergraduate students in Brazilian courses of statistics.

Anais da Academia Brasileira de Ciencias
This research aims to propose a machine learning approach to classify dropout outcomes among students in Statistics undergraduate programs in Brazil, identifying the most important factors associated with this phenomenon. This study uses microdata fr...

From root to result: Portable NIRS-based non-destructive prediction of cassava quality traits.

PloS one
Cassava (Manihot esculenta Crantz) is a staple food and a key industrial crop across tropical regions, but traditional phenotyping for critical quality traits like dry matter content (DMC) and starch content (StC) is a laborious and low-throughput pr...

Ecology-informed symbolic machine learning: a methodological framework for classification of forest succession.

Environmental monitoring and assessment
Accurately classifying forest successional stages remains a major challenge in applied ecology due to the continuum of succession, ecological heterogeneity, and limited interpretability of many machine learning (ML) approaches. Prevailing models typi...

Stable isotope profiling and machine learning for determining cocoa origin, ingredient composition, and cocoa content in Brazilian chocolates.

Food chemistry
Chocolate is a versatile product with flavours ranging from sweet to bitter. Cocoa, from a C3-photosynthetic plant, is its main raw material, while sugar from sugarcane (C4-metabolism) is commonly used. We analyzed the δ13C and δ15N composition of Br...

Decoding climate-induced phytoplankton dynamics in a tropical macrotidal estuary using explainable machine learning.

The Science of the total environment
Phytoplankton communities in tropical macrotidal estuaries are highly sensitive to hydroclimatic variability, particularly during extreme El Niño-Southern Oscillation (ENSO) events. This study assessed ENSO effects on phytoplankton dynamics in the Sã...

Enhancing enviromics based predictions in common bean multi-environment trials.

Scientific reports
Enviromic approaches enhance predictive models by incorporating environmental data into selection frameworks. By integrating factor analytic (FA) models, enviromics, and Geographic Information Systems (GIS), the GIS-FA method was proposed to improve ...

Reducing inequalities using an unbiased machine learning approach to identify births with the highest risk of preventable neonatal deaths.

Population health metrics
BACKGROUND: Despite contemporaneous declines in neonatal mortality, recent studies show the existence of left-behind populations that continue to have higher mortality rates than the national averages. Additionally, many of these deaths are from prev...

Invasive meningococcal disease in adolescents in Europe and select geographies: Disease burden, unmet medical need, and optimizing prevention.

Human vaccines & immunotherapeutics
Invasive meningococcal disease (IMD) is uncommon but serious; the case fatality rate is 8-15% and up to 20-40% of survivors experience disabling sequelae, with a substantial socioeconomic impact. Although incidence is highest in infants and young chi...

Combining wood traits as a promising timber origin verification and its application in the Brazilian trade chain.

The Science of the total environment
Tracing the geographic origin of wood remains a major challenge in the fight against illegal timber in tropical countries. Methods using anatomical, chemical, isotopic and DNA markers have been successfully tested, but methods are either expensive, u...

Populational influence on cephalometric landmark identification: performance of two AI-driven software programs in Brazilian and Korean images.

BMC oral health
OBJECTIVE: To assess the performance of cephalometric landmark identification performed by two AI-driven software programs in images from different populations (Brazilian and Korean).