Enhancing Forest Biomass Estimation with Synthetic Airborne Laser Scanning via Voxel-based Forest Reconstruction Airborne laser scanning (ALS) data synthesized from voxelized virtual forests effectively complement real data, helping to improve the accuracy of forest a

【🎉Latest accepted article】
Enhancing Forest Biomass Estimation with Synthetic #AirborneLaserScanning via Voxel-based Forest Reconstruction

#AbovegroundBiomass | #LiDARSimulation | #VirtualForest | #RandomForest

@mapjournals.bsky.social

doi.org/10.1093/jpe/...

Predicting HIV testing status in pregnant women using balanced machine learning models: Insights from Sierra Leone's demographic health survey

#MachineLearning models enhanced with SMOTE improve identification of #PregnantWomen at risk of not receiving #HIV testing in Sierra Leone, with #RandomForest achieving optimal performance, enabling more accurate targeting of interventions.
#OpenAccess: doi.org/10.1016/j.dc...

Mapping coastal wetland biodiversity and ecosystem services using hyperspectral data and machine learning Coastal wetlands are vital ecosystems that support high biodiversity and multiple ecosystem services but are increasingly vulnerable to urbanization a…

New research from the #KochiCoast shows how combining hyperspectral imaging with #RandomForest models creates a "transferable framework" for protecting these vital #ecosystems 🌍

#BlueCarbon #Sustainability #EcoInformatics #RemoteSensing #MachineLearning #GIS

www.sciencedirect.com/science/arti...

Predicting HIV testing status in pregnant women using balanced machine learning models: Insights from Sierra Leone's demographic health survey

Researchers show #SMOTE-enhanced #MLModels improve prediction of #HIV testing gaps in #PregnantWomen in Sierra Leone, with #RandomForest achieving best performance, supporting targeted strategies to prevent vertical transmission.
#IDSky #EpiSky

#OpenAccess: doi.org/10.1016/j.dc...

RDAforest: Identifying Environmental Drivers of Polygenic Adaptation Identifying environmental gradients driving genetic adaptation is one of the major goals of ecological genomics. We present RDAforest, a methodology that leverages the predominantly polygenic nature ....

🧪 We show this problem with turnover-based predictions in simulations described in our paper on #RDAforest. Solution: use boundaries from turnover-based predictions, but rely on direct predictions from #randomforest to infer states. doi.org/10.1111/1755...

The Statistics Globe Hub starts in 3 days, and I would like to give you a short preview of the first module "Feature Selection Using Random Forest."

Interested in joining the Hub? You can find more information here: statisticsglobe.com/hub

#statistics #datascience #ai #rstats #python #randomforest

🎯 Can gastroenterologists predict who will die in-hospital based on day-of-admission values? Yes, using the CLEARED Liver consortium w internal/external validation, VA research, and a #randomforest ML model.
Read more at https://ow.ly/KjzX50XS38P
🖊️ Jasmohan Bajaj

Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 www.ombulabs.ai/blog/introdu...  

#AI #ML #RandomForest #Python

🌦️ main predictors of presence were meteorological factors (temp, atmo pressure, rainfall)

🌱 low (<3m) vegetation drives abundances,

🌳 more mosquitoes in urban parks and residential (with gardens) areas than in densely built areas.

#rstats #randomforest #openscience

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images Background: Nonalcoholic Fatty Liver Disease (NAFLD) as a prevalent condition can significantly have health implications. Early detection and accurate grading of NAFLD are essential for effective mana...

Classification of Nonalcoholic Fatty Liver Grades using Pre-Trained Convolutional Neural Networks and a Random Forest Classifier on B-Mode Ultrasound Images

#IBPE #FattyLiver #UltrasoundImages #LiverDiseases #Convolutional #NeuralNetworks #RandomForest

DOI: doi.org/10.31661/jbp...

ABC-SMC-(D)RF graph

@khanhndinh.bsky.social’s group developed a faster, more efficient #RandomForest based #ApproximateBayesianComputation to make mathematical modeling more reliable across disciplines. Read more: bit.ly/4oIvOsB
cc: @columbiacancer.bsky.social @columbiauniversity.bsky.social

Interpretable Machine Learning Boosts Life Expectancy Forecast Accuracy

Random Forest predicts life expectancy with R² = 0.9423, outperforming Linear Regression and decision trees, while vaccination rates emerge as top predictors. Read more: getnews.me/interpretable-machine-le... #randomforest #lifeexpectancy

Localized Uncertainty Quantification in Random Forests via Proximities

A new method uses random forest proximity scores to create uncertainty estimates, offering prediction intervals for regression and trust scores; size can be tuned. Read more: getnews.me/localized-uncertainty-qu... #randomforest #uncertainty

LSTM, Random Forest and XGBoost Compared for Solar & Wind Forecasting

A study compares LSTM, Random Forest and XGBoost for solar and wind forecasts, noting Random Forest offers faster inference while LSTM is the most complex model. Read more: getnews.me/lstm-random-forest-and-x... #lstm #randomforest #xgboost

Machine Learning Forecasts Monthly Cattle Weight Gain in Mob Farming

Random Forest predicted monthly cattle weight gain with R² = 0.973, RMSE = 0.040 and MAE = 0.033 from 756 records; the dataset is on GitHub. Read more: getnews.me/machine-learning-forecas... #machinelearning #cattle #randomforest

Just ran a random forest and one of the trees tried to unionize. Another predicted my coffee order with 92% accuracy. The rest are still arguing over feature importance. Democracy in the woods is wild. #DataScience #MLhumor #RandomForest

Next in our #MachineLearning series: We build our very own decision tree (a Random Forest!) using #Ruby and Scikit-learn 🐍

This is a very popular tree-based ensemble model. Check it out 👉 go.fastruby.io/l1y 

#AI #ML #RandomForest #Python

Variation analysis using random forests reveals domestication patterns and breeding trends in sugar beet Cultivated beets (Beta vulgaris), including sugar beet, are important crops, and several studies employed whole genome sequencing to explore genomic v…

Advancing crop genomics: COBI scientists
@bokuvienna.bsky.social applied bioinformatics and machine learning methods on hundreds of genomes, tackling open questions in domestication genetics and breeding history of beets.

www.sciencedirect.com/science/arti...

#BetaVulgaris #Genomics #RandomForest

Boosted trees 🌲 can sometimes outperform random forests, but in terms of academic influence Breiman’s paper is monumental — cited over 160,000 times. By comparison, the original gradient boosting paper has “only” around 30,000 citations

#randomforest

Sentinel-2, PlanetScope and drone data were combined with #RandomForest classification to track aquatic plant species, achieving up to 97% accuracy with analysis of seasonal reflectance changes. This supports freshwater ecosystem conservation and monitoring.

#RemoteSensing

doi.org/10.1080/0143...

Heading west: ecology of swift foxes in a novel landscape beyond their range vist.ly/3zdhu #Fox #CameraTraps #RandomForest

Question intonation in conversational speech: Chungcheong and Gyeongsang varieties of Korean This study analyzed unscripted, conversational speech to investigate differences in question intonation in Korean varieties spoken in Chungcheong (CC) and Gyeon

New #jasa paper using semi-automatic #intonation analysis to show distributional differences between language varieties

Question intonation in conversational speech: Chungcheong and Gyeongsang varieties of Korean. doi.org/10.1121/10.0...

#prosody #clustering #randomforest

📢 New paper!

Federated Random Forests for secure, interpretable ML across institutions—no data sharing needed.

🔗 DOI: doi.org/10.1109/CBMS...
📘 Kudos: link.growkudos.com/1f2ix2os6io
💻 Code: github.com/ieeta-pt/fed...
#FederatedLearning #AI #Privacy #RandomForest

NEW PAPER 📣 Estimating 3D individual tree crown growth using multi-temporal LiDAR

doi.org/10.1080/2150...

📖: Remote Sensing Letters

✍️: McGlade (jamesmcglade.bsky.social‬) & Coops

@forestry.ubc.ca

#remotesensing #ubcforestry #irsslab #LiDAR #ALS #randomforest #alphashape

📢 Daily-scale dataset of highly dynamic water of Poyang Lake
👉Article link: doi.org/10.1080/2096...
💌 #Remotelysensing #PoyangLake #randomforest #hydrology #landcover #opticalimagery #SAR #WaterIndex

✳️ #MachineLearning

🔹 Suele ofrecer mejores predicciones que la regresión
🔹 Técnicas como #RandomForest y #GradientBoosting captan las no linealidades y las interacciones entre elementos sin necesidad de tanto preprocesamiento

#BigData #CienciaDeDatos #predicciones #hoteles

To characterize ovarian aging rates, we also devised a non-invasive blood-based #ovarian aging clock 🕐, based on a #RandomForest algorithm, which we call #OvAge. OvAge is derived from serum #AMH, #FSH and #INHBA, and is easily calculated using a shiny app:
minhooki.shinyapps.io/OvAge_Predic.... 4/9

Please Repost:
Mapping discrete forest age classes of Mediterranean pinelands since the pre-satellite era using historical orthoimage mosaics and machine learning, #Spain
link.springer.com/article/10.1...

#ImageSegmentation #RandomForest #pinelands@forestryjfr.bsky.social

Key insights: 🌱

🔬 Methods:
- Tracked phenology with Sentinel imagery & field data.
- Mapped communities via #RandomForest (elevation + NIR/tasseled cap indices were top predictors).

Predicting global intraspecific trait variation of grasses vist.ly/3n5xhx2 #MachineLearning #RandomForest #Phylogeny