Emerging strontium isoscapes of Anatolia (Türkiye): new datasets and perspectives in bioavailable 87Sr/86Sr baseline studies by G. Bike Yazıcıoğlu, David C. Meiggs, Maxwell Davis, Suzanne E. Pilaar Birch, Suzanne E. Pilaar Birch

“…We combine all published baseline 87Sr/86Sr data from Türkiye with our unpublished 87Sr/86Sr data from proxy samples (plants and snail shells) from central Anatolia, and by incorporating this data (n = 688) into the global database (where data from Türkiye is currently lacking), we create a modeled 87Sr/86Sr isoscape of Türkiye utilizing the R-script and we calculate the predicted standard error for this isoscape.Results and discussionThis study demonstrates how additional empirical data serves to improve the Türkiye section of the global model using kriging and random forest regression (RFR) techniques and it discusses how the uneven distribution of data impacts the resultant isoscape map. …”
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Machine learning survival models for Non-alcoholic fatty liver disease based on a health checkup cohort by Hongyu Zhang, Li Zhang, Na Li, Yongsheng Zhang, Xiaowen Zhang, Dawei Wang

“…Abstract Objectives This study aimed to develop an accurate prediction model for the risk of Non-alcoholic fatty liver disease (NAFLD) using the random survival forests (RSF), and to investigate the distribution of NAFLD risk with time. …”
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Groundwater fluoride modeling using an artificial neural network: a review by Neeta Kumari, Gaurav Kumar, Saahil Hembrom

“…The prediction accuracy of the network can be assessed using root mean square error (RMSE) analysis and the coefficient of determination (R2). …”
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Mitigating Algorithmic Bias Through Probability Calibration: A Case Study on Lead Generation Data by Miroslav Nikolić, Danilo Nikolić, Miroslav Stefanović, Sara Koprivica, Darko Stefanović

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Downscaling of Urban Land Surface Temperatures Using Geospatial Machine Learning with Landsat 8/9 and Sentinel-2 Imagery by Ratovoson Robert Andriambololonaharisoamalala, Petra Helmholz, Dimitri Bulatov, Ivana Ivanova, Yongze Song, Susannah Soon, Eriita Jones

“…Tested in Perth, Australia, GeoML generated an enhanced LST with good agreement with ground-based measurements, with a Pearson’s correlation coefficient of 0.85, a root mean square error (RMSE) of 2.7 °C, and a mean absolute error (MAE) of less than 2.2 °C. …”
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APPLICATION AND PERFORMANCE COMPARISON OF MULTI-OUTPUT MACHINE LEARNING FOR NUMERICAL-NUMERICAL AND NUMERICAL-CATEGORICAL OUTPUTS by Karin Joan, Robyn Irawan, Benny Yong

“…The prediction results indicated a trade-off in error between two output variables when comparing the Multivariate Regression Tree and Multivariate Random Forest with their single output counterparts. …”
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Predicting the Multiphotonic Absorption in Graphene by Machine Learning by José Zahid García-Córdova, Jose Alberto Arano-Martinez, Cecilia Mercado-Zúñiga, Claudia Lizbeth Martínez-González, Carlos Torres-Torres

“…Decision tree-based models, such as random forests and gradient boosting regression, demonstrated superior performance compared to linear regression, especially in terms of mean squared error. …”
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Intelligent islanding detection framework for smart grids using wavelet scalograms and HOG feature fusion by Kumaresh Pal, Kumari Namrata, Ashok Kumar Akella, Akshit Samadhiya, Ahmad Taher Azar, Mohamed Tounsi, Naglaa F. Soliman, Walid El-Shafai

“…The HOG descriptors effectively capture the intricate patterns and subtle signal changes associated with islanding conditions. A Random Forest classifier, selected for its robustness against noise and minimal parameter tuning, is trained and validated extensively using these descriptors. …”
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Comparative study on RF-BP model prediction of mining water-conducting fracture zone height in Binchang Coal Mine by Yadong JI, Xuan LIU, Kaipeng ZHU, Chunhu ZHAO, Kai LI, Chenhan YUAN, Panpan LI, Pengzhen YAN

“…It is found that the four methods have better fitting effect on the original data, and random forest RF has higher fitting accuracy than the other models. …”
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Skip-based combined prediction method for distributed photovoltaic power generation by WU Minglang, PANG Zhenjiang, HONG Haimin, ZHAN Zhaowu, JIN Fei, TANG Yuanyang, YE Xuan

“…The results show that the proposed model can reduce the root mean square error, mean absolute error, mean squared error, and mean absolute percentage error by 0.109 7, 0.059 1, 0.050 7, and 0.036 8 respectively, and improve the goodness of fit by 0.080 4. …”
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Forecasting models for Quebec’s lumber demand and exports using multivariate regression technique by Mounia Ferguene, Nadia Lehoux, Camélia Dadouchi

“…The business environment of the forest products industry is impacted by a variety of factors that makes it hard to predict the market’s behavior. …”
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Predicting Sugar Yield From Sugarcane Using Machine Learning for Jaggery Production by Kathirvel Narayanasamy, Ilayaraja Venkatachalam

“…After rigorous evaluation and hyperparameter optimization, the Random Forest model demonstrated superior performance with a coefficient of determination (R<inline-formula> <tex-math notation="LaTeX">${^{{2}}} = 0.9503$ </tex-math></inline-formula>), Root Mean Squared Error (RMSE = 3.13), and Mean Absolute Error (MAE = 1.63). …”
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High-resolution soil temperature and soil moisture patterns in space, depth and time: An interpretable machine learning modelling approach by Maiken Baumberger, Bettina Haas, Sindhu Sivakumar, Marvin Ludwig, Nele Meyer, Hanna Meyer

“…We used multi-depth soil temperature and soil moisture measurements from 212 locations and linked them to 45 potential predictors, representing meteorological conditions, soil parameters, vegetation coverage, and landscape relief. We trained random forest models that were able to predict soil temperature with a mean absolute error of 0.93 °C and soil moisture with a mean absolute error of 4.64 % volumetric water content. …”
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Quinary Classification of Human Gait Phases Using Machine Learning: Investigating the Potential of Different Training Methods and Scaling Techniques by Amal Mekni, Jyotindra Narayan, Hassène Gritli

“…The models were rigorously evaluated using performance metrics like cross-validation score, Mean Squared Error (MSE), Root Mean Squared Error (RMSE), accuracy, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> score, offering a comprehensive assessment of their effectiveness in classifying gait phases. …”
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Analysis of machine learning approaches for the interpretation of acoustic fields obtained by well noise data modelling by N. V. Mutovkin

“…It is shown that in the absence of data distortion, it is possible to build models that provide an absolute error in the assessment of the phase composition about 1% after the zone of fluid inflow and about 5% in the zone before the inflow.…”
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Comparative Evaluation of Ensemble Machine Learning Models for Methane Production from Anaerobic Digestion by Dorijan Radočaj, Mladen Jurišić

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Current status and influencing factors of pelvic floor muscle training adherence in rectal cancer patients with prophylactic ostomy by LIU Na, PI Hongying, GAO Na

“…Results‍　‍The overall PFMT adherence score was 14.52±4.18 among the 247 patients. The random forest algorithm identified 7 key predictors when the minimum error was achieved at a λ value of 2.293. …”
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Characteristics and prediction methods of coal spontaneous combustion for deep coal mining in the Ximeng mining area by Li MA, Wenbo GAO, Longlong TUO, Pengyu ZHANG, Zhou ZHENG, Ruizhi GUO

“…Then, the hyperparameters of the random forest (RF) model were optimized using the crested porcupine optimizer (CPO) algorithm. …”
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Development of algorithms and software for classification of nucleotide sequences by V. R. Zakirava, D. A. Syrakvash, S. V. Hileuski, P. V. Nazarov, M. M. Yatskou

“…An error of the coding and non-coding sequences classification using the random forests method on a set of the 23 most informative features is 2,93 %.…”
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Crop choice advisory for the West African Sudan Savanna based on soil type and presowing rainfall forecasts: A machine learning residual model approach by Toshichika Iizumi, Kohtaro Iseki, Kenta Ikazaki, Toru Sakai, Shintaro Kobayashi, Benoit Joseph Batieno

“…Using the random forest (RF) algorithm, residual models were developed for cowpea, groundnut, soybean, maize, millet, and sorghum cultivated at three locations in Burkina Faso. …”
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