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

“…A machine learning approach (Random Forest) was employed to generate digital distribution maps using climatic, topographic, vegetation, geological, and soil variables. …”
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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 current study evaluates the performance of three machine learning models—Decision Trees, Random Forest, and Linear Regression—applied to aquaculture data to mitigate risks in aquaculture management. …”
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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. …”

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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Automated Computer Vision System for Urine Color Detection by Ban Shamil Abdulwahed, Ali Al-Naji, Izzat Al-Rayahi, Ammar Yahya, Asanka G. Perera

“…The proposed system uses a web camera to capture an image in real-time, analyze it, and then classify the color of urine by using the random forest (RF) algorithm and show the result via the Graphical User Interface (GUI). …”
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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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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

“…Study region: South-east Australia Study focus: This study develops two ensemble-based regional flood frequency analysis (RFFA) techniques, Random Forest Regression (RFR) and Gradient Boosting Regression (GBR)) with a standalone method (Artificial Neural Network (ANN), for south-east Australia. …”
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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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Predictive modelling of sustainable concrete compressive strength using advanced machine learning algorithms by Tejas Joshi, Pulkit Mathur, Parita Oza, Smita Agrawal

“…The results show that ML algorithms are highly effective in predicting CS, with the random forest algorithm achieving the highest accuracy (R² = 0,95; error = 3,74). …”
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How to map soil organic carbon stocks at field scale in South Africa? by Willie Herman Cloete, Gerhard du Preez, George Munnik Van Zijl

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Estimation of the yield and green water footprint of rainfed wheat based on remote sensing and machine learning by Mojgan Ahmadi, Hadi Ramezani Etedali, Abbass Kaviani, Alireza Tavakoli

“…Abstract In this research, the relationship between remote sensing drought indices NDVI, EVI, SAVI, and LAI with the yield and green water footprint (WF) of rainfed wheat in 5 fields in Saqqez City (2001–2020) was investigated using multivariate regression (MR), random forest (RF), and support vector regression (SVR) methods. …”
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Ensemble machine learning prediction accuracy: local vs. global precision and recall for multiclass grade performance of engineering students by Yagyanath Rimal, Yagyanath Rimal, Navneet Sharma

“…These findings are further corroborated by precision-recall error plots. The grid search for random forest algorithms achieved a score of 79% when optimally tuned; however, the training accuracy was 99%. …”
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Estimating timber assortment reduction and sawlog proportions with the application of harvester measurements and open big geodata by Ville Vähä-Konka, Lauri Korhonen, Kalle Kärhä, Matti Maltamo

“…Absolute sawlog volumes were derived by multiplying the model-derived sawlog proportions by volumes in the Metsään.fi forest data repository. Our results showed that the root mean square error (RMSE) values associated with sawlog volumes of Norway spruce (Picea abies (L.) …”
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Unraveling C-to-U RNA editing events from direct RNA sequencing by Adriano Fonzino, Caterina Manzari, Paola Spadavecchia, Uday Munagala, Serena Torrini, Silvestro Conticello, Graziano Pesole, Ernesto Picardi

“…To overcome this issue in direct RNA reads, here we introduce a novel machine learning strategy based on the isolation Forest (iForest) algorithm in which C-to-U editing events are considered as sequencing anomalies. …”
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Obtaining the Highest Quality from a Low-Cost Mobile Scanner: A Comparison of Several Pipelines with a New Scanning Device by Marek Hrdina, Juan Alberto Molina-Valero, Karel Kuželka, Shinichi Tatsumi, Keiji Yamaguchi, Zlatica Melichová, Martin Mokroš, Peter Surový

“…The accurate measurement of the tree diameter is vital for forest inventories, urban tree quality assessments, the management of roadside and railway vegetation, and various other applications. …”
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The Aboveground Biomass Estimation of the Grain for Green Program Stands Using UAV-LiDAR and Sentinel-2 Data by Gaoke Yueliang, Gentana Ge, Xiaosong Li, Cuicui Ji, Tiancan Wang, Tong Shen, Yubo Zhi, Chaochao Chen, Licheng Zhao

“…Aboveground biomass (AGB) serves as a crucial indicator of the effectiveness of the Grain for Green Program (GGP), and its accurate estimation is essential for evaluating forest health and carbon sink capacity. However, due to the dominance of sparse forests in GGP stands, research in this area remains significantly limited. …”
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Generation of 1 km high resolution Standardized precipitation evapotranspiration Index for drought monitoring over China using Google Earth Engine by Yile He, Youping Xie, Junchen Liu, Zengyun Hu, Jun Liu, Yuhua Cheng, Lei Zhang, Zhihui Wang, Man Li

“…After preprocessing such as gridding and sample balancing, a random forest regression model was constructed to achieve high spatial resolution prediction of SPEI. …”
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Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

“…The purpose of this study is to investigate the effects of including spatial information into the Random Forest (RF) and eXtreme Gradient Boosting (XGBoost) models for forecasting LST. …”
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PREDICTION OF SOFTWARE ANOMALIES METHODS BASED ON ENSEMBLE LEARNING METHODS by Raghda Azad Hasan, Ibrahim Ahmed Saleh

“…In order to design high-quality and reliable software and avoid risks resulting from software errors, including physical and human errors, this is considered a major challenge due to the limited time and budget specified. …”
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A Hybrid Intelligent Model for Olympic Medal Prediction Based on Data-Intelligence Fusion by Ning Li, Junhao Li, Hejia Fang, Jian Wang, Qiao Yu, Yafei Shi

“…The model demonstrates strong accuracy with root mean square errors of 3.21 (gold) and 4.32 (total medals), and mean-relative errors of 17.6% and 8.04%. …”
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