Feature Forest Models for Probabilistic HPSG Parsing by Yusuke Miyao, Jun'ichi Tsujii

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Applications of Probabilistic Forecasting in Demand Response by María Carmen Ruiz-Abellón, Luis Alfredo Fernández-Jiménez, Antonio Guillamón, Antonio Gabaldón

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Mixing carrots and sticks to conserve forests in the Brazilian Amazon: a spatial probabilistic modeling approach. by Jan Börner, Eduardo Marinho, Sven Wunder

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Machine learning-based source apportionment and source-oriented probabilistic ecological risk assessment of heavy metals in urban green spaces by Jun Li, Jia-Yi Lu, Xin-Ying Tuo, Chao Wang, Jun-Zhuo Liu, Zhan-Dong Gao, Cun-Hao Yu, Fei Zang

“…Multiple pollution indices, including geo-accumulation index (Igeo), enrichment factor (EF), and Nemerow integrated enrichment factor (NIEF), were combined with Monte Carlo simulations (MCS) to assess probabilistic contamination levels. Machine learning methods, including SOM super-clustering and random forest (RF), were integrated with positive matrix factorization (PMF) to quantify the sources of soil HMs. …”
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SPRC: A novel Sentinel-1/-2 Phenology-enhanced framework for automated paddy rice mapping by Jian Li, Fengchang Xue, Guicai Li, Mingwei Zhang, Jinyan Tian, Hanchao Zhang

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Probabilistic Ensemble Framework for Injury Narrative Classification by Srushti Vichare, Gaurav Nanda, Raji Sundararajan

“…Four ensemble models were evaluated: Random Forest (RF) combined with Logistic Regression (LR), K-Nearest Neighbor (KNN) paired with RF, LR combined with KNN, and a model integrating LR, RF, and KNN, all utilizing a probabilistic likelihood-based approach to improve decision-making across different classifiers. …”
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Comprehensive data aleatory uncertainty propagation in regression random forest using a Monte Carlo approach: a struggle with incomplete data provision using a case study on probabilistic soil moisture regionalization by Hendrik Paasche, Ségolène Dega, Martin Schrön, Peter Dietrich, Peter Dietrich

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Three‐Dimensional Probabilistic Hydrofacies Modeling Using Machine Learning by Nafyad Serre Kawo, Jesse Korus, Yaser Kishawi, Erin Marie King Haacker, Aaron R. Mittelstet

“…This study uses colocated airborne electromagnetic resistivity and borehole lithology data to train multiple machine learning models and predict the 3D distribution of hydrofacies in glacial deposits of eastern Nebraska, USA. Random Forest, Gradient Boosting Classifier, Extreme Gradient Boosting, Multilayer Perceptron, and Stacking Classifier were used to model 3D probabilistic distributions of hydrofacies (sand and clay) at a grid size of 200 m × 200 m × 3 m. …”
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Long-term monitoring of plant diversity data in the “Montagna di Torricchio” Strict Nature Reserve, Italyb2share - EUDAT by Marco Cervellini, Leonardo Salvatori, Stefano Chelli, Federico Maria Tardella, Alessandro Chiarucci, James L. Tsakalos, Giandiego Campetella, Maura Francioni, Luciano Ludovico Maria De Benedictis, Roberto Canullo

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External drivers of heavy metal bioavailability and probabilistic ecological risk in water-level-fluctuating wetlands by Huan Zeng, Mingjun Ding, Hua Zhang, Yuping Wu, Xiang Xu, Honmei Chen, Liyao Chen, Yinhui Jiang, Peng Wang, Gaoxiang Huang

“…The total Fe content was identified as the primary mutual factor influencing heavy metal enrichment, whereas the total contents of Cu and Cd significantly affected the bioavailability of six heavy metals. The random forest results indicated that total Cu and Cd contents were also the primary contributors to their own bioavailability. …”
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Kidney Ensemble-Net: Enhancing Renal Carcinoma Detection Through Probabilistic Feature Selection and Ensemble Learning by Zaib Akram, Kashif Munir, Muhammad Usama Tanveer, Atiq Ur Rehman, Amine Bermak

“…These extracted features are then transferred into a refined probabilistic feature set, upon which we construct an ensemble model leveraging the strengths of Logistic Regression (LR), Random Forest (RF), and Gaussian Naive Bayes (GNB) classifiers. …”
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Probabilistic analysis of future drought propagation, persistence, and spatial concurrence in monsoon-dominant Asian regions under climate change by D. Muthuvel, X. Qin

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Transforming Building Energy Management: Sparse, Interpretable, and Transparent Hybrid Machine Learning for Probabilistic Classification and Predictive Energy Modelling by Yiping Meng, Yiming Sun, Sergio Rodriguez, Binxia Xue

“…A Random Forest classifier is employed to pinpoint key appliances influencing occupancy, with a drop-in accuracy analysis verifying their predictive power. …”
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A novel framework for assessing shrublines and their geophysical constraints in alpine regions through probabilistic vegetation mapping and seed-filling algorithm by Zexi Ren, Lin Zhang, Qianlong Wang, Wanjun Hu, Zhou Shi

“…In this study, we proposed a novel framework to map alpine shrublines in Xizang Rezhen National Forest Park in 2020 using multi-source spatial data, probabilistic vegetation mapping, and seed-filling algorithm. …”
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Bayesian weighted random forest for classification of high-dimensional genomics data by Oyebayo Ridwan Olaniran, Mohd Asrul A. Abdullah

“…The new model Bayesian Weighted Random Classification Forest (BWRCF) arises from the modification of the existing random classification forest in two ways. …”
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Smart Forest Navigation System Using LoRa and Dynamic Pathfinding by Isha Nevatia, Vraj Chaudhary, M. Thurai Pandian

“…Navigating dense forest environments presents significant challenges due to obstructed GPS signals, dynamic terrain, and limited communication infrastructure. …”
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Terrain or climate factor dominates vegetation resilience? Evidence from three national parks across different climatic zones in China by Shuang Liu, Lingxin Wu, Shiyong Zhen, Qinxian Lin, Xisheng Hu, Jian Li

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A New Semisupervised-Entropy Framework of Hyperspectral Image Classification Based on Random Forest by Mengmeng Sun, Chunyang Wang, Shuangting Wang, Zongze Zhao, Xiao Li

“…The framework is composed of five parts: (1) random samples selection with (2) probabilistic output initial random forest classification processing based on the number of votes; (3) semisupervised classification, which is an improvement of the supervision classification of random forest based on the weighted entropy algorithm; (4) precision evaluation; and (5) a comparison with the traditional minimum distance classification and the support vector machine (SVM) classification. …”
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Detecting Symptoms and Dispersal of Pine Tortoise Scale Pest in an Urban Forest by Remote Sensing by Marco Bascietto, Gherardo Chirici, Emma Mastrogregori, Loredana Oreti, Adriano Palma, Antonio Tiberini, Sabrina Bertin

“…Since its introduction to Italy in 2014, this pest has severely impacted <i>Pinus pinea</i> forests, with a major outbreak in 2019 affecting an urban forest in the Rome municipality area. …”
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Application and Analysis of Random Forest and Support Vector Classification in Risk Prediction of Childhood Obesity and Hyperuricemia by Wang Y, Shi S, Wei X, Wu Y, Shi Y, Cai J

“…Future studies will explore the integration of metabolomic data and ensemble approaches to further enhance model performance and clinical applicability.Keywords: childhood obesity, hyperuricemia, machine learning, random forest, support vector classification, SHAP…”
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