LC oscillator frequency prediction using machine learning linear regression algorithm by Mandar Jatkar, Vasudeva G, Tripti R. Kulkarni, Roopa R. Kulkarni, Aneesh Pandurangi

“…It was found that Linear Regression reaches close to zero RMSE, but SVR and Random Forest show higher errors. The study proves that log-transformed linear regression works as a basic but efficient method for accurate frequency estimation in resonant LC circuits.…”
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Simulation of snow accumulation and melting in the Kama river basin using data from global prognostic models by S. V. Pyankov, A. N. Shikhov, P. G. Mikhaylyukova

“…The most important result is that under conditions of 2017/18 the mean square error of calculating the maximum snow storage by the GFS, GEM and PL-AB models was less than 25% of its measured values. …”
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Multi-Output Regression for the Prediction of World-Class Performances in Women’s Handball by Rayane Elimam, Nicolas NICOLAS, Jacques Prioux, Jacky Montmain, Stephane Perrey

“…We compared 4 single-output models (kNN, regression tree, random forest and(NN) Predictive models inspired by the human brain, used in this study for multi-output prediction in sports performance analysis (neural networks)), their multi-output counterparts and aA baseline model predicting future performance as the average of each player’s past performance, serving as a simple reference for comparison with more complex models (dummy baseline) (predicting the average performance of each player over the last month) in terms of average(Root Mean Squared Error) A measure of the quadratic difference between predicted and actual values in regression models (RMSE) (aRMSE) during aAn evaluation method where past training and game data are used sequentially to predict performance of the next game (chronological evaluation) where previous trainings and games data are used to train models to predict the next game performances. …”
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Transformers deep learning models for missing data imputation: an application of the ReMasker model on a psychometric scale by Monica Casella, Nicola Milano, Pasquale Dolce, Davide Marocco

“…Various factors contribute to this issue, including participant non-response, dropout, or technical errors during data collection. Traditional methods like mean imputation or regression, commonly used to handle missing data, rely upon assumptions that may not hold on psychological data and can lead to distorted results.MethodsThis study aims to evaluate the effectiveness of transformer-based deep learning for missing data imputation, comparing ReMasker, a masking autoencoding transformer model, with conventional imputation techniques (mean and median imputation, Expectation–Maximization algorithm) and machine learning approaches (K-nearest neighbors, MissForest, and an Artificial Neural Network). …”
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Comparison of Machine-Learning Algorithms for Near-Surface Air-Temperature Estimation from FY-4A AGRI Data by Ke Zhou, Hailei Liu, Xiaobo Deng, Hao Wang, Shenglan Zhang

“…The performance of each model and the temporal and spatial distribution of the estimated Tair errors were analyzed. The results showed that the XGB model had better overall performance, with R2 of 0.902, bias of −0.087°C, and root-mean-square error of 1.946°C. …”
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Artificial intelligence for herbicide recommendation: Case study for the use of clomazone in Brazilian soils by Hamurábi Anizio Lins, Matheus de Freitas Souza, Lucrecia Pacheco Batista, Luma Lorena Loureiro da Silva Rodrigues, Francisca Daniele da Silva, Bruno Caio Chaves Fernandes, Stefeson Bezerra de Melo, Paulo Sergio Fernandes das Chagas, Daniel Valadão Silva

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The Role of Landscape Metrics and Spatial Processes in Performance Evaluation of GEOMOD (Case Study: Neka River Basin) by Shrif Joorabian Shooshtari, Kamran Shayesteh, Mehdi Gholamalifard, Mahmood Azari, Juan Ignacio López-Moreno

“…According to the modeling results, a decrease 4225 ha was revealed in this class of land cover. The area under forest showed a decreasing trend from 2001 to 2010, and the model showed a good consistency between the forest areas of reference and simulated maps with a relative error value of zero. …”
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Estimating Maize Leaf Water Content Using Machine Learning with Diverse Multispectral Image Features by Yuchen Wang, Jianliang Wang, Jiayue Li, Jiacheng Wang, Hanzeyu Xu, Tao Liu, Juan Wang

“…The results indicate that the RFR model performs optimally during the seedling stage, with a root relative mean square error (RRMSE) of 2.99%, whereas estimation errors are larger during the tasseling stage, with an RRMSE of 4.13%. …”
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Reconstructing Evapotranspiration in British Columbia Since 1850 Using Publicly Available Tree-Ring Plots and Climate Data by Hang Li, John Rex

“…ET satellite images from 1982 to 2010 formed our dataset to train models for each vegetated pixel. The random forest regression outperformed the other approaches with lower errors and better robustness (adjusted R² value = 0.69; root mean square error = 10.72 mm/month). …”
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Advanced sentiment analysis in online shopping: Implementing LSTM models analyzing E-commerce user sentiments by Lu Liyuan

“…Sarcasm and irony accounted for 22% of the classification errors, while mixed sentiment accounted for 18%, and implicit accounted for 15%. …”
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Predictive Modeling of River Water Temperatures in Catu River: A Neural Network-Based Approach by Carmen Goncalves de Macedo e Silva, José Roberto de Araújo Fontoura, Alarcon Matos de Oliveira, Thais de Souza Neri, Roberto Luiz Souza Monteiro, Thiago Barros Murari, Alexandre do Nascimento Silva, Leandro Brito Santos, Marcos Batista Figueredo

“…The results show that the BiLSTM model achieved the best performance, with a root mean square error (RMSE) of 0.12°C and R2 = 0.98, followed by BPNN with an RMSE of 0.18°C and R2 = 0.91, and the Random Forest model, which obtained an NSE of 0.95. …”
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Recensement d'éléphants dans la Réserve Communautaire du Lac Télé, République du Congo by Fortuné Iyenguet, Guy-Aimé Malanda, Bola Madzoke, Hugo Rainey, Catherine Schloeder, Michael Jacobs

“…However, there was a large error in calculating estimates because of the low number of dung piles. …”
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Spatially-informed interpolation for reconstructing lake area time series using semantic neighborhood correlation by Chen Liu

“…Compared with polynomial fitting, Random Forest, and Long Short-Term Memory, SNCI consistently achieves lower interpolation errors. …”
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Study on the temperature prediction model of residual coal in goaf based on ACO-KELM by ZHAI Xiaowei, WANG Chen, HAO Le, LI Xintian, HOU Qinyuan, MA Teng

“…Compared to the prediction models based on extreme learning machine (ELM) and random forest (RF) algorithms, the ACO-KELM model achieved an average absolute error of 0.0701 ℃ and a root mean square error (RMSE) of 0.0748 ℃ on the test set, reducing these errors by 65% and 195%, respectively, compared to the ELM-based model, and by 53% and 156%, respectively, compared to the RF-based model. …”
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Biomass and Volume Models Based on Stump Diameter for Assessing Degradation of Miombo Woodlands in Tanzania by Bernardol John Manyanda, Wilson Ancelm Mugasha, Emannuel F. Nzunda, Rogers Ernest Malimbwi

“…Models to estimate forest degradation in terms of removed volume and biomass from the extraction of wood fuel and logging using stump diameter (SD) are lacking. …”
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Development of a Short-Range Multispectral Camera Calibration Method for Geometric Image Correction and Health Assessment of Baby Crops in Greenhouses by Sabina Laveglia, Giuseppe Altieri, Francesco Genovese, Attilio Matera, Luciano Scarano, Giovanni Carlo Di Renzo

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Improved estimation of two-phase capillary pressure with nuclear magnetic resonance measurements via machine learning by Oriyomi Raheem, Misael M. Morales, Wen Pan, Carlos Torres-Verdín

“…Extreme Gradient Boosting and Random Forest models performed the best, with average estimation errors of 5 % and 10 %, respectively, for capillary pressure and pore throat size distribution. …”
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Maximizing multi-source data integration and minimizing the parameters for greenhouse tomato crop water requirement prediction by Xinyue Lv, Youli Li, Lili Zhangzhong, Chaoyang Tong, Yibo Wei, Guangwei Li, Yingru Yang

“…The results show that the stacking model has the best prediction effect, and the error is lower than that of RandomForest, LightGBM, CatBoost, Average fusion model and Weighted fusion model. …”
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Investigating Tree Family Machine Learning Techniques for a Predictive System to Unveil Software Defects by Rashid Naseem, Bilal Khan, Arshad Ahmad, Ahmad Almogren, Saima Jabeen, Bashir Hayat, Muhammad Arif Shah

“…Performance of each technique is evaluated using different measures, i.e., mean absolute error (MAE), relative absolute error (RAE), root mean squared error (RMSE), root relative squared error (RRSE), specificity, precision, recall, F-measure (FM), G-measure (GM), Matthew’s correlation coefficient (MCC), and accuracy. …”
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Correction to: Ophiostomatoid fungi associated with Ips subelongatus, including eight new species from northeastern China by Zheng Wang, Ya Liu, Huimin Wang, Xianjing Meng, Xuewei Liu, Cony Decock, Xingyao Zhang, Quan Lu

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