Effect of Hyperparameter Tuning on Performance on Classification model by Muhammad Sholeh, Uning Lestari, Dina Andayati

“…This research aims to analyze the effect of hyperparameter tuning on the performance of Logistic Regression, K-Nearest Neighbours, Support Vector Machine, Decision Tree, Random Forest, Random Forest Classifier, Naive Bayes algorithms.  …”
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Tit wit: environmental and genetic drivers of cognitive variation along an urbanization gradient by Megan J. Thompson, Laura Gervais, Dhanya Bharath, Samuel P. Caro, Alexis S. Chaine, Charles Perrier, Denis Réale, Anne Charmantier

“…We find that wild urban and forest tits do not clearly differ in inhibitory control performance (number of errors or the latency to escape) during a motor detour task; a result that was consistent in birds from urban and forest origins reared in a common garden (N = 73) despite average performance differing between wild and captive birds. …”
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Soil Mapping of Small Fields with Limited Number of Samples by Coupling EMI and NIR Spectroscopy by Leonardo Pace, Simone Priori, Monica Zanini, Valerio Cristofori

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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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Improving the evapotranspiration estimation by coupling soil moisture and atmospheric variables in the relative evapotranspiration parameterization by Elisabet Walker, Virginia Venturini

“…Accurate monthly evapotranspiration (ET) estimation is essential for many forest, climate, and hydrological applications, as well as for some agricultural uses. …”
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Validation of Cross-Calibration for the Time-Series of HJ-2A/B CCD Sensor Based on Baotou Sandy Site by Yidan Chen, Jie Han, Yutong Li, Yong Xie, Wen Shao, Aonan Hao

“…Besides, for the bands exhibiting larger individual errors relative to the OCCs, the validation results indicate that the average relative errors of these bands are smaller than those of the OCCs when referenced against the Baotou sandy site. …”
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Enhancing safety in surface mine blasting operations with IoT based ground vibration monitoring and prediction system integrated with machine learning by Mangalpady Aruna, Harsha Vardhan, Abhishek Kumar Tripathi, Satyajeet Parida, N. V. Raja Sekhar Reddy, Krishna Moorthy Sivalingam, Li Yingqiu, P. V. Elumalai

“…Among these, the Random Forest model outperformed the others, achieving an R2 score of 0.92, Mean Absolute Error (MAE) of 0.21, and Root Mean Squared Error (RMSE) of 0.31. …”
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Evaluating Spatio-Temporal Kriging with Machine Learning Considering the Sources of Spatio-Temporal Variation by Min Jeong, Hyeongmo Koo

“…Global trend estimates differ by the models, with polynomial regression producing smoother patterns but larger errors, while random forest and boosting yield more abrupt patterns with smaller errors. …”
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AI-Driven Dynamic Covariance for ROS 2 Mobile Robot Localization by Bogdan Felician Abaza

“…Aggressive dynamics posed challenges, with errors up to 0.9491 rad due to data distribution bias and Random Forest model constraints. …”
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Simulation and Experimental Study on the Shrub-Cutting Performance of Quasi-Planetary Cutter by Zikai Song, Xibin Dong, Chi Teng, Ben Guo, Jiawang Zhang, Yuchen Zhang

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Monitoring active fires in Borneo from Sentinel-2 MSI images by Xiaoxiao Guo, Yongxue Liu, Peng Liu, Huize Wang, Wei Wu

“…While moderate-resolution sensors offer unprecedented opportunities for detecting small and subtle fires, they face the dilemma of high commission errors (CE). To address this problem, we propose an object-oriented method to effectively detect AFs from Sentinel-2 MSI images, which focuses on suppressing the interference of various CEs through object-level inter-spectral criteria cloud filtering, seamline exclusion based on granule footprints, and false positive refinement based on random forest classification model. …”
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Burned area detection based on time-series analysis in a cloud computing environment by J.A. Anaya, W.F. Sione, A.M. Rodriguez-Montellano

“…There are large omission errors in the estimation of burned area in map products that are generated at a global scale. …”
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FORECASTING STOCK MARKET LIQUIDITY WITH MACHINE LEARNING: AN EMPIRICAL EVALUATION IN THE GERMAN MARKET by Bogdan Ionut ANGHEL

“…Empirical testing shows that the two gradient-boosting ensembles consistently outperform both Random Forest and the LSTM model, tracking sudden liquidity swings more accurately and delivering the tightest forecast errors. …”
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Erratum

“…The journal’s editors and the authors apologize to readers for the errors in two articles published in “Zeszyty Teoretyczne Rachunkowości” in 2024.In the article:Beata Sadowska, Relevant information from the perspective of sustainable forest management – auditing socio-environmental information and data,published in “Zeszyty Teoretyczne Rachunkowości” 2024, Vol. 48, No. 3, pp. 233–262, https://doi.org/10.5604/01.3001.0054.7265The error occurs on page 236:For example, Marshall acknowledged the problem of imperfect information, although it was not of great academic interest at the time.Reason for erratum:The original sentence omitted the necessary citation to Mielcarek (2011, p. 73).It should be:For example, A. …”
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The Development of Digital Twin Baby Incubators for Fault Detection and Performance Analysis by Hatice Kabaoğlu, Emine Uçar, Fecir Duran

“…The digital twin employs a hybrid model integrating Long Short-Term Memory (LSTM) and Random Forest (RF) algorithms to predict potential errors and alarms. …”
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Early Warning System for Debt Group Migration: The Case of One Commercial Bank in Vietnam by Nguyen Quoc Hung, Trinh Hoang Viet, Phuong Truong Viet, Ly Truong Thi Minh

“…This study utilizes machine learning models, including Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest, in the early warning system for debt group migration in a Vietnamese commercial bank. …”
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Evaluation of snow parameters at weather stations in small catchments in the south of Western Siberia by D. K. Pershin, L. F. Lubenets, D. V. Chernykh

“…The small snow depth error occurred due to the composition of the error distribution and large differences between open and forested areas.…”
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Analysis and prediction of storability and storage quality of pear fruit based on clustering and time series analysis by ZHOU Hui-juan, YE Zheng-wen, LI Rong-hua, WANG Xiao-qing, LUO Jun

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Modelling the Daily Concentration of Airborne Particles Using 1D Convolutional Neural Networks by Ivan Gudelj, Mario Lovrić, Emmanuel Karlo Nyarko

“…The results show that the 1D CNN model outperforms the other machine learning models (LSTM and Random Forest) in terms of the coefficients of determination and absolute errors.…”
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Research on memory failure prediction based on ensemble learning. by Peng Zhang, Jialiang Zhang, Yi Li

“…To address this, we propose a new ensemble model for predicting CE-driven memory failures, where failures occur due to a surge of correctable errors (CEs) in memory, causing server downtime. Our model combines several strong-performing classifiers, such as Random Forest, LightGBM, and XGBoost, and assigns different weights to each based on its performance. …”
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