Machine Learning  Modelling of the Relationship between Weather and Paddy Yield in Sri Lanka by Piyal Ekanayake, Windhya Rankothge, Rukmal Weliwatta, Jeevani W. Jayasinghe

“…The significance of the weather indices on the paddy yield was explored by employing Random Forest (RF) and the variable importance of each of them was determined. …”
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An Improved Deep Learning-Based Technique for Driver Detection and Driver Assistance in Electric Vehicles with Better Performance by Gunapriya Balan, Singaravelan Arumugam, Suresh Muthusamy, Hitesh Panchal, Hossam Kotb, Mohit Bajaj, Sherif S. M. Ghoneim, null Kitmo

“…As a part of this research, a driver identification system based on a deep driver classification model (deep neural network as DNN) with feature reduction techniques (random forest as RF and principal component analysis as PCA) is implemented to help automate and aid in crucial jobs such as the brake system in an efficient manner. …”
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Assessing Urban Landscape Variables’ Contributions to Microclimates by Tammy E. Parece, Jie Li, James B. Campbell, David Carroll

“…Using this temperature data, together with six landscape variables, we interpolated (using Kriging and Random Forest) air temperatures across the city for each collection period. …”
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An Ecolevel Estimation Method of Individual Driver Performance Based on Driving Simulator Experiment by Yiping Wu, Xiaohua Zhao, Ying Yao, Jian Rong

“…In addition, comparative analysis displayed that the performance of backpropagation neural network based model was better than linear regression based model and random forest based model, from the aspects of elapsed time and prediction accuracy in estimating the ecolevel of driver performance. …”
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Long term study on blood glucose levels in wintering great tits Parus major in sites differing in artificial food availability by Adam Kaliński, Michał Glądalski, Marcin Markowski, Joanna Skwarska, Jarosław Wawrzyniak, Jerzy Bańbura, Piotr Zieliński

“…We showed that both females and males were characterised by significantly higher glucose levels at the study site, which was characterised by the high accessibility to human-provided food sources (forest clearing) than at the site with low and irregular artificial feeding. …”
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ENHANCING TOMATO LEAF DISEASE DETECTION THROUGH MULTIMODAL FEATURE FUSION by Puja SARAF, Jayantrao PATIL, Rajnikant WAGH

“…We have performed a comparison of different classifiers like Support Vector Machine (SVM), XGBoost, Random Forest (RF), Naive Bayes (NB), Convolutional Neural Network (CNN) and proposed Ensemble method used in the classification task. …”
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Performance evaluation of brain state discrimination using near-infrared spectroscopy for brain-computer interface: an exploratory case study by Akira Masuo, Takuto Sakuma, Shohei Kato

“…Seventeen trials of the NIRS signal were acquired for each task, and 52 samples with 24-dimensional features per trial data were extracted. Random forest was used as the classifier, and the number of correct responses in the binary discrimination of the brain states were calculated by cross-validation. …”
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Identification of key genes affecting intramuscular fat deposition in pigs using machine learning models by Yumei Shi, Yumei Shi, Xini Wang, Shaokang Chen, Yanhui Zhao, Yan Wang, Xihui Sheng, Xiaolong Qi, Lei Zhou, Yu Feng, Jianfeng Liu, Chuduan Wang, Kai Xing

“…A total of 155 DEGs were identified using a limma package between the two groups. 100 and 11 significant genes were identified by support vector machine recursive feature elimination (SVM-RFE) and random forest (RF) models, respectively. A total of six intersecting genes were in both models. …”
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Machine learning assisted composition design of high-entropy Pb-free relaxors with giant energy-storage by Xingcheng Wang, Ji Zhang, Xingshuai Ma, Huajie Luo, Laijun Liu, Hui Liu, Jun Chen

“…Herein, with the assistance of machine learning screening, we demonstrated a high energy-storage density of 20.7 J cm-3 with a high efficiency of 86% in a high-entropy Pb-free relaxor ceramic. A random forest regression model with key descriptors based on limited reported experimental data were developed to predict and screen the elements and chemical compositions of high-entropy systems. …”
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Physico-chemical changes and maturity evaluation of composts from wood residue mixed with sewage sludge and chicken manure by Mohammad Hossein Saghi, Payam Ghorbannezhad, Abotaleb Bay, Farangis Saeidi

“…This study aimed to compare the stability and maturity of the soil amendments produced by the compostation of forest industrial waste and sewage sludge on seed germination. …”
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Assessment of future urban flood risk of Thailand's bangkok metropolis using geoprocessing and machine learning algorithm by Duangporn Garshasbi, Jarunya Kitiphaisannon, Tanaphoom Wongbumru, Nawhath Thanwiset Thanvisitthpon

“…In the assessment of flood risk, the future values of six dynamic urban flood indicators are first projected using an integrative geoprocessing and random forest machine learning algorithm. The projected future indicator values are subsequently used to assess urban flood risk across Bangkok's 50 districts. …”
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Variation characteristics and influencing mechanisms of CO2 flux from grassland ecosystem in the Central Tianshan Mountains, China by Kun Zhang, Yu Wang, Ali Mamtimin, Yongqiang Liu

“…Multiple environmental factors were integrated for an attribution analysis of CO2 flux using advanced systems, including random forest model, hyperbolic tangent model, future scenario simulation, and stepwise multiple regression model. …”
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Evaluating machine learning models for supernova gravitational wave signal classification by Y Sultan Abylkairov, Matthew C Edwards, Daniil Orel, Ayan Mitra, Bekdaulet Shukirgaliyev, Ernazar Abdikamalov

“…We test convolutional and recurrent neural networks, as well as six classical algorithms: random forest, support vector machines, naïve Bayes(NB), logistic regression, k -nearest neighbors, and eXtreme gradient boosting. …”
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A Multi-Faceted Approach to Trending Topic Attack Detection Using Semantic Similarity and Large-Scale Datasets by Insaf Kraidia, Afifa Ghenai, Samir Brahim Belhaouari

“…Five machine learning models—Random Forest, Decision Tree, K-Neighbors, Gradient Boosting, and XGBoost—were tested, with results benchmarked against nine baseline methods across different linguistic datasets and learning scenarios. …”
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Modeling Terrestrial Net Ecosystem Exchange Based on Deep Learning in China by Zeqiang Chen, Lei Wu, Nengcheng Chen, Ke Wan

“…The model was also compared with the random forest, long short-term memory, deep neural network, and convolutional neural networks (1D) models to distinguish it from previous shallow machine learning models to estimate NEE, and the results show that deep learning models have great potential in NEE modeling. …”
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Development of data-driven algal bloom alert models with low temporal resolution data and application to Hong Kong rivers by Shujie Xu, Zhongnan Ye, Shu-Chien Hsu, Xiaoyi Liu, Chunmiao Zheng

“…New hydrological insights for the region: Models that integrate data discretization outperformed those using numerical normalization, showing higher recall scores and greater stability across selected algorithms (linear regression, support vector machine, random forest, and decision tree). Permutation importance analysis identified nitrogen compounds and rising temperatures as key triggers of algal blooms, while false negative analysis highlighted total phosphorus and flow as critical factors. …”
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A CNN-RF Hybrid Approach for Rice Paddy Fields Mapping in Indramayu Using Sentinel-1 and Sentinel-2 Data by Dodi Sudiana, Mia Rizkinia, Rahmat Arief, Tiara De Arifani, Anugrah Indah Lestari, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo

“…This study proposes the CNN-RF method, which combines a convolutional neural network (CNN) as a feature extractor and a random forest (RF) as a classifier. The experiment used combinations of input data, including variations of single and multisource data, to achieve optimal results. …”
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Research on the Parameter Prediction Model for Fully Mechanized Mining Equipment Selection Based on RF-WOA-XGBoost by Yue Wu, Wenlong Sang, Xiangang Cao, Longlong He

“…Feature selection is performed based on the feature importance ranking obtained through the Random Forest (RF) method, thereby reducing the model complexity. …”
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Bioâdigestion and postâtreatment of effluents by bioâfermentation, an opportunity for energy uses and generation of organic fertilizers from bovine manure... by Luis Antonio Barzallo-Bravo, David Carrera-Villacrés, Rafael Eduardo Vargas-Verdesoto, Lourdes Karina Ponce-Loaiza, Modesto Correoso, Ãlvaro Petronio Gavilanes-Quishpi

“…The project was located at the tropical zone into the Choco forest, at an average altitude of 1110 m above sea level and an average temperature annual of 21â22 °C.…”
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Machine learning algorithms to predict depression in older adults in China: a cross-sectional study by Yan Li Qing Song, Lin Chen, Haoqiang Liu, Yue Liu

“…Six ML algorithms, namely, logistic regression, k-nearest neighbors, support vector machine, decision tree, LightGBM, and random forest, were used in constructing a predictive model for depression among the older adult. …”
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