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Geography

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The effect of climate change on cholera disease: The road ahead using artificial neural network.

PloS one
Climate change has been described to raise outbreaks of water-born infectious diseases and increases public health concerns. This study aimed at finding out these impacts on cholera infections by using Artificial Neural Networks (ANNs) from 2021 to 2...

Delineation of high resolution climate regions over the Korean Peninsula using machine learning approaches.

PloS one
In this research, climate classification maps over the Korean Peninsula at 1 km resolution were generated using the satellite-based climatic variables of monthly temperature and precipitation based on machine learning approaches. Random forest (RF), ...

Quantifying the usage of small public spaces using deep convolutional neural network.

PloS one
Small public spaces are the key built environment elements that provide venues for various of activities. However, existing measurements or approaches could not efficiently and effectively quantify how small public spaces are being used. In this pape...

[The use of artificial neural networks to classify the social vulnerability of municipalities in Rio Grande do Norte State, Brazil].

Cadernos de saude publica
The objective was to apply artificial neural networks to classify municipalities (counties) in Rio Grande do Norte State, Brazil, according to their social vulnerability. This was an ecological study using 17 variables that reflected epidemiological,...

A new backpropagation neural network classification model for prediction of incidence of malaria.

Frontiers in bioscience (Landmark edition)
Malaria is an infectious disease caused by parasitic protozoans of the Plasmodium family. These parasites are transmitted by mosquitos which are common in certain parts of the world. Based on their specific climates, these regions have been classifie...

Hyperspectral imaging for accurate determination of rice variety using a deep learning network with multi-feature fusion.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy
The phenomena of rice adulteration and shoddy rice arise continuously in high-quality rice and reduce the interests of producers, consumers and traders. Hyperspectral imaging (HSI) was conducted to determine rice variety using a deep learning network...

A systematic machine learning and data type comparison yields metagenomic predictors of infant age, sex, breastfeeding, antibiotic usage, country of origin, and delivery type.

PLoS computational biology
The microbiome is a new frontier for building predictors of human phenotypes. However, machine learning in the microbiome is fraught with issues of reproducibility, driven in large part by the wide range of analytic models and metagenomic data types ...

Geographic origin discrimination of pork from different Chinese regions using mineral elements analysis assisted by machine learning techniques.

Food chemistry
Porkis thelargest-producedandmost-consumedmeat intheworld, and the food market globalization has increased public attention to food origin. Therefore, advanced techniques are required to determine the geographical origin of pork. This study investiga...

Authentication and Provenance of Walnut Combining Fourier Transform Mid-Infrared Spectroscopy with Machine Learning Algorithms.

Molecules (Basel, Switzerland)
Different varieties and geographical origins of walnut usually lead to different nutritional values, contributing to a big difference in the final price. The conventional analytical techniques have some unavoidable limitations, e.g., chemical analysi...

A machine learning framework to determine geolocations from metagenomic profiling.

Biology direct
BACKGROUND: Studies on metagenomic data of environmental microbial samples found that microbial communities seem to be geolocation-specific, and the microbiome abundance profile can be a differentiating feature to identify samples' geolocations. In t...