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

“…Results showed that in the simulation of the yield of rainfed wheat, the RF method had a high correlation, NSE was close to one, and root mean square error (RMSE) was less than 0.2 (ton/ha) and had good accuracy. …”
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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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Exploring spatial machine learning techniques for improving land surface temperature prediction by Arunab K.S., Mathew A.

“…The significance and impact of each input parameter on the models' predictive capabilities are assessed using the SHAP (SHapley Additive exPlanations) approach and the model intercomparisons were done using the error evaluation metrices. The predictions were further validated using the Pearson correlation, independent samples t-test and potential geographic anomalies in the predictions are examined by spatial comparison of predicted errors using classification maps and error envelopes. …”
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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

“…The accuracy of 1 km resolution SPEI was evaluated through metrics such as root mean square error (RMSE), Pearson correlation coefficient (PCC) and determination coefficient (R2). …”
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Scalable machine learning framework for predicting critical links in urban networks by Nourhan Bachir, Chamseddine Zaki, Hassan Harb, Roland Billen

“…Validated on two diverse datasets, namely, Luxembourg (LuST) and Monaco (MoST), the framework achieves high precision (∼72% and ∼73% in single-city scenarios) and robust cross-city performance (∼70% for LuST → MoST and ∼66% for MoST → LuST). Random Forest and Gradient Boosting emerged as the top-performing models, consistently delivering the best precisions and lowest number of errors. …”
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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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Activity prediction of anti-cancer drug candidate ERα inhibitor by XIA Yulan, XIE Jiming, WANG Yajing, LU Mengyuan, WANG Jinrui, QIN Yaqin

“…The results show that compared with the GBDT integrated learning method, the prediction effect of Mul-BHO-Bi-LSTM integrated machine learning prediction model is better, and the model error indexes MSE, NRMSE, error mean, and error std are less than 0.15, and the correlated indicators R2 and r are above 0.99, indicating that the integrated machine learning predictionmodel of Mul-BHO-Bi-LSTM has the good robustness and generalization, and the model can provide a method for the screening and design of anti-breast cancer drugs.…”
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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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Exploring PM2.5 and PM10 ML forecasting models: a comparative study in the UAE by Waad Abuouelezz, Nazar Ali, Zeyar Aung, Ahmed Altunaiji, Shaik Basheeruddin Shah, Derek Gliddon

“…Performance metrics including Mean Absolute Percentage Error (MAPE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Percent Bias (PBIAS) were applied. …”
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Study on infrasonic leakage monitoring and signal processing for product oil pipeline by Yuanbo YIN, Yuxing LI, Wen YANG, Shu LU, Chen ZHANG, Cuiwei LIU, Kai YANG, Wuchang WANG

“…At a 91 km monitoring interval along the product oil pipeline, the positioning error was about 800 m, facilitating reliable monitoring up to a leak rate of 0.001 6 m3/s, with the minimum detectable leak rate recorded at 0.000 46 m3/s. …”
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Influence of artificial intelligence on higher education reform and talent cultivation in the digital intelligence era by Limin Qian, Weiran Cao, Lifeng Chen

“…The results show that SEOM has high accuracy and generalization ability in three different teaching scenes: online mixed teaching, personalized teaching and project-based teaching. The Root Mean Square Error (RMSE) value in cross-validation is between 0.2 and 0.5, and the Mean Absolute Error (MAE) value is between 0.1 and 0.5. …”
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Research on Oil Well Production Prediction Based on GRU-KAN Model Optimized by PSO by Bo Qiu, Jian Zhang, Yun Yang, Guangyuan Qin, Zhongyi Zhou, Cunrui Ying

“…The validity of the model was verified on two oil wells and the results on well F14 show that the proposed GRU-KAN model achieves a Root Mean Square Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and Coefficient of Determination (<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>) values of 11.90, 9.18, 6.0% and 0.95, respectively. …”
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Precision Estimation of Rice Nitrogen Fertilizer Topdressing According to the Nitrogen Nutrition Index Using UAV Multi-Spectral Remote Sensing: A Case Study in Southwest China by Lijuan Wang, Qihan Ling, Zhan Liu, Mingzhu Dai, Yu Zhou, Xiaojun Shi, Jie Wang

“…The RF surpassed BPNN/PLSR by 6.14–10.10% in R² and 13.71–33.65% in error reduction across the critical rice growth stages. …”
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Bayesian optimization of hybrid quantum LSTM in a mixed model for precipitation forecasting by Yumin Dong, Huanxin Ding

“…Experiments are conducted on meteorological datasets from Seattle and Ukraine, and the results are verified using mean absolute error (MAE), root mean square error (RMSE), and bias evaluation indicators. …”
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