Monitoring Canopy Height in the Hainan Tropical Rainforest Using Machine Learning and Multi-Modal Data Fusion by Qingping Ling, Yingtan Chen, Zhongke Feng, Huiqing Pei, Cai Wang, Zhaode Yin, Zixuan Qiu

“…A total of 140 field survey plots and 315 unmanned aerial vehicle photogrammetry plots, along with multi-modal remote sensing datasets (including GEDI and ICESat-2 satellite-carried LiDAR data, Landsat images, and environmental information) were used to validate forest canopy height from 2003 to 2023. The results showed that RH80 was the optimal choice for the prediction model regarding percentile selection, and the RF algorithm exhibited the optimal performance in terms of accuracy and stability, with R² values of 0.71 and 0.60 for the training and testing sets, respectively, and a relative root mean square error of 21.36%. …”
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SPA-Net: An Offset-Free Proposal Network for Individual Tree Segmentation from TLS Data by Yunjie Zhu, Zhihao Wang, Qiaolin Ye, Lifeng Pang, Qian Wang, Xiaolong Zheng, Chunhua Hu

“…Conventional ITS algorithms often struggle in complex forest stands due to reliance on heuristic rules and manual feature engineering. …”
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Ecofriendly Extraction of Polyphenols from <i>Ampelopsis grossedentata</i> Leaves Coupled with Response Surface Methodology and Artificial Neural Network–Genetic Algorithm by Xubo Huang, Chen Li, Yanbin Wang, Jinrong Jiang, Weizhi Wu, Shifeng Wang, Ming Lin, Liang He

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Retrieval of Chinese fir tree parameters under different understory conditions with the integration of handheld and airborne Lidar data by Yunhe Li, Guiying Li, Sirong Wang, Dengsheng Lu

“…Accurate extraction of tree parameters is vital for the calculation of high-quality forest volume or biomass. The Light Detection and Ranging (Lidar) technology with its ability to acquire three-dimensional forest stand structures is an important data source for extracting tree parameters. …”
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Predicting Nonlinear Behavior of Cellular Cross-Laminated Timber Under Bending and Rolling Shear by Suman Pradhan, Mostafa Mohammadabadi

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A natural language processing approach to support biomedical data harmonization: Leveraging large language models. by Zexu Li, Suraj P Prabhu, Zachary T Popp, Shubhi S Jain, Vijetha Balakundi, Ting Fang Alvin Ang, Rhoda Au, Jinying Chen

“…In addition, we developed an ensemble-learning method, using the Random Forest (RF) model, to integrate individual NLP methods. …”
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Estimation of Leaf, Spike, Stem and Total Biomass of Winter Wheat Under Water-Deficit Conditions Using UAV Multimodal Data and Machine Learning by Jinhang Liu, Wenying Zhang, Yongfeng Wu, Juncheng Ma, Yulin Zhang, Binhui Liu

“…Correlation analysis and Variance Inflation Factor (VIF) were employed for feature selection, and estimation models for leaf, spike, stem, and total AGB were constructed using Random Forest (RF), Support Vector Machine (SVM), and Neural Network (NN) models. …”
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Prediction of barite scale formation and inhibition in hydrocarbon reservoirs using AI modeling: Focus on different optimization algorithms by Ouafa Belkacem, Ahmed Rezrazi, Kamel Aizi, Lokmane Abdelouahed, Maamar Laidi, Abdelhafid Touil, Leila Cherifi, Salah Hanini

“…Innovative intelligent models, including Random Forest (RF), k-nearest Neighbors (KNN), Extreme Learning Machine (ELM), Support Vector Regression (SVR), Decision Trees (DT), and Multilayer Perceptron (MLP), were developed and optimized for this purpose. …”
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Field scale wheat yield prediction using ensemble machine learning techniques by Sandeep Gawdiya, Dinesh Kumar, Bulbul Ahmed, Ramandeep Kumar Sharma, Pankaj Das, Manoj Choudhary, Mohamed A. Mattar

“…The ensemble model, which combines random forest (RF) and artificial neural networks (ANN), demonstrated better performance by achieving a mean absolute percentage error of 4.65 %, and R2 value of 98.48 % and 98.18 % accuracy on test data.Our results demonstrate that ensemble models combining RF with support vector regression (SVR) outperformed individual models such as RF, SVR, ANN, multivariate adaptive regression splines (MARS). …”
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Detection of Banana Diseases Based on Landsat-8 Data and Machine Learning by Renata Retkute, Kathleen S. Crew, John E. Thomas, Christopher A. Gilligan

“…We used a pixel-level random forest (RF) model to predict 11 key vegetation indices (VIs) as a function of historical meteorological conditions, specifically daytime and nighttime temperature from MODIS and precipitation from NASA GES DISC. …”
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Forecasting Electricity Production in a Small Hydropower Plant (SHP) Using Artificial Intelligence (AI) by Dawid Maciejewski, Krzysztof Mudryk, Maciej Sporysz

“…Finally, after applying forecast quality measures of Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Coefficient of Determination (R²), the most effective model was indicated. …”
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Predicting the compressive strength of concrete incorporating waste powders exposed to elevated temperatures utilizing machine learning by Islam N. Fathy, Hany A. Dahish, Mohammed K. Alkharisi, Alaa A. Mahmoud, Hala Emad Elden Fouad

“…The XGB model demonstrated the highest R2 of 0.9989, alongside the lowest prediction errors: MAE of 0.1351 MPa, RMSE of 0.1842 MPa, and MAPE of 0.48%. …”
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Ultra Wideband radar-based gait analysis for gender classification using artificial intelligence by Adil Ali Saleem, Hafeez Ur Rehman Siddiqui, Muhammad Amjad Raza, Sandra Dudley, Julio César Martínez Espinosa, Luis Alonso Dzul López, Isabel de la Torre Díez

“…Spectral features extracted from these cycles were transformed using a novel integration of Feedforward Artificial Neural Networks and Random Forests , enhancing discriminative power. Among the models evaluated, the Random Forest classifier demonstrated superior performance, achieving 94.68% accuracy and a cross-validation score of 0.93. …”
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Estimation of Cation Exchange Capacity for Low-Activity Clay Soil Fractions Using Experimental Data from South China by Jun Zhu, Zhong-Xiu Sun

“…Four covariate datasets were combined based on available soil data and environmental variables and various parameters for machine learning techniques including an artificial neural network, a deep belief network, support vector regression and random forest were optimized. The results, based on 10-fold cross-validation, showed that the simple division of CEC_soil by clay content led to significant overestimation of CEC_clay, with a mean error of 14.42 cmol(+) kg⁻¹. …”
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Retrieval of non-optical active water quality parameters in complex Lake environments using a novel zoning-based ensemble modeling strategy by Cheng Cai, Linlin Liu, Ziming Wang, Wei Pang, Congshuo Bai, Huanxue Zhang

“…Finally, the ZBEMS integrating four machine learning models (Random Forest Regression (RFR), Gradient Boosting Regressor (GBR), eXtreme Gradient Boosting (XGBoost), and Support Vector Regression (SVR)) was applied across different zones for NAWQPs retrieval. …”
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Developing supervised machine learning algorithms to classify lettuce foliar tissue samples into interpretation zones for 11 plant essential nutrients by Patrick Veazie, Hsuan Chen, Kristin Hicks, Jake Holley, Nathan Eylands, Neil Mattson, Jennifer Boldt, Devin Brewer, Roberto Lopez, Brian Whipker

“…Abstract Greenhouse crop nutrient management recommendations based on foliar tissue testing rely heavily on human interpretation, which can result in recommendation variations and errors. Critical nutrient ranges vary for each species, and the potential for error in interpretation increases due to this complexity. …”
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Machine learning techniques for predicting the peak response of reinforced concrete beam subjected to impact loading by Ali Husnain, Munir Iqbal, Hafiz Ahmed Waqas, Mohammed El-Meligy, Muhammad Faisal Javed, Rizwan Ullah

“…Except for KNN, all models showed satisfactory generalization capabilities with R2 values over 0.8. Statistical errors such as RMSE, a-10 index, MAE, and a-20 index are within acceptable limits. …”
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New categorized machine learning models for daily solar irradiation estimation in southern Morocco's, Zagora city by Zineb Bounoua, Laila Ouazzani Chahidi, Abdellah Mechaqrane

“…Inaccuracies or discontinuities in solar data can lead to errors in system assessments, potentially resulting in misguided conclusions about their economic feasibility. …”
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Exploration of machine learning approaches for automated crop disease detection by Annu Singla, Ashima Nehra, Kamaldeep Joshi, Ajit Kumar, Narendra Tuteja, Rajeev K. Varshney, Sarvajeet Singh Gill, Ritu Gill

“…We comprehensively analyze various ML techniques, including Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Support Vector Machines (SVMs), Random Forest (RF), and Deep Learning Architectures like ResNet and Inception, among others, highlighting their methodologies, datasets, performance metrics, and real-world applications. …”
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A Study on Canopy Volume Measurement Model for Fruit Tree Application Based on LiDAR Point Cloud by Na Guo, Ning Xu, Jianming Kang, Guohai Zhang, Qingshan Meng, Mengmeng Niu, Wenxuan Wu, Xingguo Zhang

“…For feature selection, a random forest-based recursive feature elimination method with cross-validation was employed to filter 10 features. …”
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