Quantifying solid volume of stacked eucalypt timber using detection-segmentation and diameter distribution models by Gianmarco Goycochea Casas, Zool Hilmi Ismail, Mathaus Messias Coimbra Limeira, Carlos Pedro Boechat Soares, José Marinaldo Gleriani, Daniel Henrique Brada Binoti, Carlos Alberto Araújo Júnior, Mohd Ibrahim Shapiai, Leonardo Ippolito Rodrigues, Tassius Menezes Araújo, Helio Garcia Leite

“…Traditional methods for estimating the volume of stacked timber, often reliant on manual measurements, are time-consuming and prone to error. This research aims to develop an accurate procedure for estimating the volume of stacked eucalypt timber in yards. …”
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An Enhanced Tree Ensemble for Classification in the Presence of Extreme Class Imbalance by Samir K. Safi, Sheema Gul

“…Performance metrics such as classification error rate and precision are used for evaluation purposes. …”
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HIV-phyloTSI: subtype-independent estimation of time since HIV-1 infection for cross-sectional measures of population incidence using deep sequence data by Tanya Golubchik, Lucie Abeler-Dörner, Matthew Hall, Chris Wymant, David Bonsall, George Macintyre-Cockett, Laura Thomson, Jared M. Baeten, Connie L. Celum, Ronald M. Galiwango, Barry Kosloff, Mohammed Limbada, Andrew Mujugira, Nelly R. Mugo, Astrid Gall, François Blanquart, Margreet Bakker, Daniela Bezemer, Swee Hoe Ong, Jan Albert, Norbert Bannert, Jacques Fellay, Barbara Gunsenheimer-Bartmeyer, Huldrych F. Günthard, Pia Kivelä, Roger D. Kouyos, Laurence Meyer, Kholoud Porter, Ard van Sighem, Mark van der Valk, Ben Berkhout, Paul Kellam, Marion Cornelissen, Peter Reiss, Helen Ayles, David N. Burns, Sarah Fidler, Mary Kate Grabowski, Richard Hayes, Joshua T. Herbeck, Joseph Kagaayi, Pontiano Kaleebu, Jairam R. Lingappa, Deogratius Ssemwanga, Susan H. Eshleman, Myron S. Cohen, Oliver Ratmann, Oliver Laeyendecker, Christophe Fraser, the HPTN 071 (PopART) Phylogenetics protocol team, the BEEHIVE consortium and the PANGEA consortium

“…HIV-phyloTSI provides a continuous measure of TSI up to 9 years, with a mean absolute error of less than 12 months overall and less than 5 months for infections with a TSI of up to a year. …”
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Studies comparing the effectiveness of models for drying bitter gourd slices by Dinh Anh Tuan Tran, Tuan Nguyen Van, Thi Khanh Phuong Ho

“…Model performance was assessed using the coefficient of determination (R²), root mean square error (RMSE) and mean absolute percentage error (MAPE). …”
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Towards enhancing field‐based vegetation monitoring: A deep learning approach for species coverage estimation from ground‐level imagery by Pauline Müller, Stefano Puliti, Johannes Breidenbach

“…We evaluated our method against an independent test set of 156 images and found a root mean squared error (RMSE) of 8.82% for blueberry and 3.49% for lingonberry and no substantial systematic errors. …”
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Leveraging Artificial Intelligence in Public Health: A Comparative Evaluation of Machine-Learning Algorithms in Predicting COVID-19 Mortality by Eric B. Weiser

“…The four ML models were trained and tested on this dataset, with performance assessed using R2, Mean Absolute Error (MAE), and Root Mean Squared Error (RMSE). …”
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Leveraging Satellite Data for Predicting PM10 Concentration with Machine Learning Models: A Study in the Plains of North Bengal, India by Ayan Das, Manoranjan Sahu

“…Five different machine learning regression models, namely linear regression (LR), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting (GB), and Extreme Gradient Boosting (XGB), were employed and evaluated using Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) along with R2 for predicting the daily ground-level PM10 concentration using AOD, land cover data, and meteorological parameters. …”
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Apply Ridge Regression Model to Predict the Lateral Velocity Difference of Tight Reservoirs by HAN Longfei, ZHANG Yongfei, WANG Miaomiao, LI Yu

“…This error cannot meet the subsequent construction requirements. …”
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Machine learning algorithms to predict the tensile strength of novel composite materials by S. Sathees Kumar, P. Shyamala, Pravat Ranjan Pati

“…Five regression algorithms such as polynomial regression, bagging regression, random forest, XGBoost, and gradient boosting were trained and evaluated using five-fold cross-validation and standard error metrics. …”
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Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models by Mohammed Hilal Mukhsaf, Weiqin Li, Ghassan Husham Jani

“…R²), mean absolute error (MAE), and root mean square error (RMSE), were KNN < DT < RF < XGBoost. …”
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Unraveling the Scale Dependency of SIF-Based Phenology: Amplified Trends and Climate Responses by Xiufeng Chen, Yanbin Yuan, Tao Xiong, Sicong He, Heng Dong

“…Our results showed that the 1-km resolution SIF had a significant advantage over the 5-km and 50-km resolution SIFs in terms of consistency with the extracted phenology results from the Gross Primary Productivity (GPP) sites, with mean absolute errors (MAEs) of 4.48 and 15.49 days for SOS and EOS, respectively. …”
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Machine learning-based mapping of Acidobacteriota and Planctomycetota using 16 S rRNA gene metabarcoding data across soils in Russia by E. A. Ivanova, A. R. Suleymanov, D. A. Nikitin, M. V. Semenov, E. V. Abakumov

“…Model interpration was performed using variable importance assessment and Shapley values. According to the error metrics, the Acidobacteriota model achieved a root mean squared error (RMSE) of 6.67% and an R2 of 0.41, while the Planctomycetota model achieved an RMSE of 2.04% and an R2 of 0.46. …”
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Application of Machine Learning for Predictive Analysis and Management of Mediterranean-Farmed Fish Mortalities: A Risk Management Case Study Using Apache Spark by Marios C. Gkikas, Dimitris C. Gkikas, Gerasimos Vonitsanos, John A. Theodorou, Spyros Sioutas

“…The performances of these models are analyzed and properly demonstrated using metrics including the Mean Squared Error (MSE), R-squared (R²), Root Mean Squared Error (RMSE), and Concordance Index (C-index). …”
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Distribution Ratio Prediction of Major Components in 30%TBP/kerosene-HNO3 System Based on Machine Learning by YU Ting1, ZHANG Yinyin2, ZHANG Ruizhi3, JIN Wenlei2, LUO Yingting2, ZHU Shengfeng3, HE Hui1, YE Guoan1, GONG Helin4

“…Since the traditional mathematical model of uranium distribution ratio leads to at least 15% prediction error, in this paper, three classical machine learning models (namely, random forest, support vector regression and K-nearest neighbor) were constructed to predict the distribution ratios of uranium, plutonium, and HNO3 in the 30%TBP/kerosene-HNO3 system. …”

BIM-Based Machine Learning Application for Parametric Assessment of Building Energy Performance by Panagiotis Tsikas, Athanasios Chassiakos, Vasileios Papadimitropoulos, Antonios Papamanolis

“…However, alternative designs should be checked individually, and this makes the process time-consuming and prone to errors. Machine learning techniques can provide valuable assistance in developing decision support tools. …”
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Exploring Machine Learning Models for Vault Safety in ICL Implantation: A Comparative Analysis of Regression and Classification Models by Qing Zhang, Qi Li, Zhilong Yu, Ruibo Yang, Emmanuel Eric Pazo, Yue Huang, Hui Liu, Chen Zhang, Salissou Moutari, Shaozhen Zhao

“…Model performance was evaluated using metrics including the mean absolute error (MAE) and root mean squared error (RMSE) for regression models, while accuracy, F1-score, and area under the curve (AUC) were used for classification models. …”
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Efficient Swell Risk Prediction for Building Design Using a Domain-Guided Machine Learning Model by Hani S. Alharbi

“…On an 80:20 stratified hold-out set, this simplified model reduced root mean square error (RMSE) from 9.0% to 6.8% and maximum residuals from 42% to 16%. …”
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Automated Computer Vision System for Urine Color Detection by Ban Shamil Abdulwahed, Ali Al-Naji, Izzat Al-Rayahi, Ammar Yahya, Asanka G. Perera

“…In the comparison with the current methods the proposed system has maximum accuracy and minimum error rate. This methodology can pave the way for an additional case study in medical applications, particularly in diagnosis, and patient health monitoring. …”
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Ensemble machine learning (EML) based regional flood frequency analysis model development and testing for south-east Australia by Nilufa Afrin, Ataur Rahman, Ahmad Sharafati, Farhad Ahamed, Khaled Haddad

“…The findings indicate that ensemble methods (RFR and GBR) provide better performance than the standalone ANN technique. The median relative error values are found to be in the range of 33–44 % for the RFR, 34–46 % for the GBR, and 35–53 % for the ANN. …”
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A Lightweight Received Signal Strength Indicator Estimation Model for Low-Power Internet of Things Devices in Constrained Indoor Networks by Samrah Arif, M. Arif Khan, Sabih ur Rehman

“…The experimental results show that the RFR(F) method shows approximately 39.62% improvement in Mean Squared Error (MSE) over the Feature-based ANN(F) model and 37.86% advancement over the state-of-the-art. …”
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