maximum entropy algorithm maxent (MAXNET Co Ltd)

About

News

Press Release

Team

Advisors

Partners

Contact

Bioz Stars

Bioz vStars

Buy from Supplier

Structured Review

MAXNET Co Ltd maximum entropy algorithm maxent
Potential distribution <t>of</t> <t>tungiasis</t> in Latin America (1896-2021) based on a <t>Maxent</t> model with LQH FC's and a RM of 1.5 (delta. AICc = 0).

Potential distribution <t>of</t> <t>tungiasis</t> in Latin America (1896-2021) based on a <t>Maxent</t> model with LQH FC's and a RM of 1.5 (delta. AICc = 0).

Maximum Entropy Algorithm Maxent, supplied by MAXNET Co Ltd, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
https://www.bioz.com/product/maximum+entropy+algorithm+maxent/maxent+over+glmnet/pmc09903635-95-14-24
Average 90 stars, based on 1 article reviews

maximum entropy algorithm maxent - by Bioz Stars, 2026-11

90/100 stars

Images

1) Product Images from "Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model"

Article Title: Distribution of tungiasis in latin America: Identification of areas for potential disease transmission using an ecological niche model

Journal: Lancet Regional Health - Americas

doi: 10.1016/j.lana.2021.100080

Potential distribution of tungiasis in Latin America (1896-2021) based on a Maxent model with LQH FC's and a RM of 1.5 (delta. AICc = 0).

Figure Legend Snippet: Potential distribution of tungiasis in Latin America (1896-2021) based on a Maxent model with LQH FC's and a RM of 1.5 (delta. AICc = 0).

Techniques Used:

Functional Assay:

Article Title: Identifying the habitat suitability of Pteris vittata in China and associated key drivers using machine learning models.
Article Snippet: • 10 machine learning models were compared to analyze the habitat of P. vittata.. • XGBOOST model had better performance compared with other machine learning models.. • The P. vittata predicted by XGBOOST was mainly distributed in southern China.

Generated:

Article Title: Turning the Tide: A 2°C Increase in Heat Tolerance Can Halve Climate Change‐Induced Losses in Four Cold‐Adapted Kelp Species
Article Snippet: .. The SDMs for S. latissima , A. esculenta , L. digitata , and L. hyperborea were built using the MaxEnt algorithm via the SDMtune package MaxNet function (MaxEnt without Java) for R. Maxent is an SDM method using presence‐only data (no absences or generated pseudoabsences are required) to estimate the probability distribution of maximum entropy and is well‐suited to the abundance of presence‐only occurrence data for the study species (Phillips and Dudík ). ..

Construct:

Article Title: Endemic cushions of the Khorassan-Kopet Dagh floristic province show differential responses to future climate change.
Article Snippet: .. Twelve modeling algorithms, including the Generalized Linear Model (GLM), Generalized Additive Model (GAM), Generalized Boosting Model (GBM), Classification Tree Analysis (CTA), Artificial Neural Network (ANN), Surface Range Envelop (SRE), Multiple Adaptive Regression Splines (MARS), Random Forest (RF), Maximum Entropy (MAXENT), Maximum Entropy (MAXNET), Flexible Discriminant Analysis (FDA) and eXtreme Gradient Boosting Training (XGBOOST) were used to construct the models in the current study. ..

other:

Article Title: Comparative analysis of modeling methods and prediction accuracy for Japanese sardine habitat under three climate scenarios with differing greenhouse emission pathways.
Article Snippet: Climate change threatens to marine ecosystems, making it essential to understand habitat shifts for ecologically and economically valuable species like the Japanese sardine (Sardinops melanostictus).. This study employs Biomod2 ensemble algorithm and MaxEnt model, integrating different spatial resolutions and varying numbers of species presence data points at the same resolution, to select the most accurate method for assessing the habitat suitability of the Japanese sardine under both current and future climate scenarios.. Biomod2 outperforms MaxEnt across all conditions, particularly with high-resolution data and increased presence data points, achieving its highest accuracy (AUC = 0.965) with the EMwmean at 0.083 resolution and 10,462 presence points.

Article Title: EcoNicheS: enhancing ecological niche modeling, niche overlap and connectivity analysis using the shiny dashboard and R package
Article Snippet: The plataform employs the biomod2 R package, a widely used ecological niche modeling framework that supports 12 correlative algorithms: generalized linear models (GLMs), generalized additive models (GAMs), generalized boosting models (often called boosted regression trees; GBM), classification tree analysis (CTA), artificial neural networks (ANNs), surface range envelopes or BIOCLIM (SRE), flexible discriminant analysis (FDA), multiple adaptive regression splines (MARS), random forest (RF), and maximum entropy (MAXENT) using the MIAmaxent ( ) R package MAXNET and extreme gradient boosting (XGBOOST) ( ).

Article Title: Modeling the Distribution of the Rare and Red-Listed Halophytic Moss Species Entosthodon hungaricus Under Various Climate Change Scenarios in Serbia.
Article Snippet: Five algorithms were used: Generalized Linear Model (GLM); Generalized Boosted Model, i.e., boosted regression trees (GBM); Maximum Entropy (MAXENT and MAXNET); and Random Forest (RF).

Article Title: First records of Oxychilus alliarius and O. cellarius ( Gastropoda , Stylommatophora , Oxychilidae ) in Mexico: mtDNA identification and potential distributions
Article Snippet: The model was created utilizing an R implementation ( ) of Maxent from the Maxnet library ( ) and SDMtune ( ) for the training and evaluation of distribution models.

Review