High-isolation dual-band MIMO antenna for next-generation 5G wireless networks at 28/38 GHz with machine learning-based gain prediction by Md Ashraful Haque, Redwan A. Ananta, Md. Sharif Ahammed, Jamal Hossain Nirob, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Reem Ibrahim Alkanhel, Ahmed A. Abd El-Latif, May Almousa, Abdelhamied A. Ateya

“…Among the five different regression machine learning models considered, it was discovered that the Random Forest Regression (RFR) model performed the best in accuracy and achieved the lowest error when predicting gain. …”
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Combining UAV Remote Sensing with Ensemble Learning to Monitor Leaf Nitrogen Content in Custard Apple (<i>Annona squamosa</i> L.) by Xiangtai Jiang, Lutao Gao, Xingang Xu, Wenbiao Wu, Guijun Yang, Yang Meng, Haikuan Feng, Yafeng Li, Hanyu Xue, Tianen Chen

“…With a root mean square error (RMSE) of 0.059 and a mean absolute error (MAE) of 0.193, the coefficient of determination (R²) came to 0. 661. …”
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Targeted genotyping‐by‐sequencing of potato and data analysis with R/polyBreedR by Jeffrey B. Endelman, Moctar Kante, Hannele Lindqvist‐Kreuze, Andrzej Kilian, Laura M. Shannon, Maria V. Caraza‐Harter, Brieanne Vaillancourt, Kathrine Mailloux, John P. Hamilton, C. Robin Buell

“…The DArTag and SNP array platforms produced equivalent dendrograms in a test set of 298 tetraploid samples, and 83% of the common markers showed good quantitative agreement, with RMSE (root mean squared error) <0.5. DArTag is suited for genomic selection candidates in the clonal evaluation trial, coupled with imputation to a higher density platform for the training population. …”
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Machine learning optimization of environmental factors influencing biomass and nutritional composition in local algal species by Aisha Khan, Saleem Ullah, Rifat Ali, Mahwish Rehman, Said Moshawih, Khang Wen Goh, Long Chiau Ming, Lai Ti Gew

“…A novel metric, W_new, combining performance and error metrics, facilitated robust model evaluation and hyperparameter tuning. …”
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Modeling saturation exponent of underground hydrocarbon reservoirs using robust machine learning methods by Abhinav Kumar, Paul Rodrigues, A. K. Kareem, Tingneyuc Sekac, Sherzod Abdullaev, Jasgurpreet Singh Chohan, R. Manjunatha, Kumar Rethik, Shivakrishna Dasi, Mahmood Kiani

“…In addition, the graphical-based and statistical-based evaluations illustrate that AdaBoost and ensemble learning models outperforms all other developed data-driven intelligent models as these two models are associated with lowest values of mean square error (adaptive boosting: 0.017 and ensemble learning: 0.021 based on unseen test data) and largest values of coefficient of determination (adaptive boosting: 0.986 and ensemble learning: 0.983 based on unseen test data).…”
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Optimizing imputation strategies for mass spectrometry-based proteomics considering intensity and missing value rates by Yuming Shi, Huan Zhong, Jason C. Rogalski, Leonard J. Foster

“…Assuming the causes of MVs could be different in different regions, we then investigated the optimal imputation method in each bin, using normalized root mean square error (NRMSE), and found that the optimal imputation method varies across bins. …”
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A Machine Learning-Based Real-Time Remaining Useful Life Estimation and Fair Pricing Strategy for Electric Vehicle Battery Swapping Stations by Seyit Alperen Celtek, Seda Kul, A. Ozgur Polat, Hamed Zeinoddini-Meymand, Farhad Shahnia

“…Comparative analysis shows that the XGBoost model outperforms the second-best method (Random Forest) with a lower error (3.50 vs 3.79) while maintaining competitive computational efficiency (9.75 vs 8.52 seconds) and memory usage (2.12 vs 2.32 MB) when solving a typical numerical case study problem. …”
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Accounting for alternation in temporal quality analysis in MapBiomas Brazil by Ana Paula Matos, Maria Hunter, Robert Gilmore Pontius, Luis Rodrigo Baumann, Leandro Leal Parente, Laerte Guimarães Ferreira

“…Alternation, a newly defined error component, captures the number of land use transitions a location experiences throughout time. …”
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Spatial Accuracy Evaluation for Mobile Phone Location Data With Consideration of Geographical Context by Xiaoqing Song, Yi Long, Ling Zhang, David G. Rossiter, Fengyuan Liu, Wei Jiang

“…The RF model can estimate the spatial accuracy of the MPL data within narrow margins of error. The importance ranking of geographical variables shows that the population density, elevation and building density are the three most important factors, while the normalised difference water index (NDWI) and distance to the nearest cell tower (DNCT) are the least important variables. …”
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Machine Learning for Identifying Damage and Predicting Properties in 3D-Printed PLA/Lygeum Spartum Biocomposites by Khalil Benabderazag, Moussa Guebailia, Zouheyr Belouadah, Lotfi Toubal, Salah Eddine Tachi

“…The prediction models were developed using an 80/20 train–test split and further validated by 5-fold cross-validation, with performance evaluated by R-squared (<i>R</i>²) and Mean Squared Error (<i>MSE</i>) metrics. Our results demonstrate robust prediction capabilities, with the RFR model achieving the highest accuracy (<i>R</i>² > 0.98 and <i>MSE</i> as low as 0.013 for tensile stress prediction). …”
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An Empirical Analysis of Above-Ground Biomass and Carbon Sequestration Using UAV Photogrammetry and Machine Learning Techniques by Thinnakon Angkahad, Teerawong Laosuwan, Satith Sangpradid, Narueset Prasertsri, Yannawut Uttaruk, Titipong Phoophathong, Joe Nuchthapho

“…This research aims to analyze above-ground biomass and carbon sequestration using unmanned aerial vehicle (UAV) photogrammetry and machine learning methods, focusing on a case study of the dry dipterocarp forest in the Ban Hin Lat and Hin Lat Phatthana Community Forests. …”
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A Hierarchical RF-XGBoost Model for Short-Cycle Agricultural Product Sales Forecasting by Jiawen Li, Binfan Lin, Peixian Wang, Yanmei Chen, Xianxian Zeng, Xin Liu, Rongjun Chen

“…It adopts the Random Forest (RF) in the first layer to extract residuals and achieve initial prediction results based on correlation features from Grey Relation Analysis (GRA). …”
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In-Season Potato Nitrogen Prediction Using Multispectral Drone Data and Machine Learning by Ehsan Chatraei Azizabadi, Mohamed El-Shetehy, Xiaodong Cheng, Ali Youssef, Nasem Badreldin

“…The results indicate that RF outperformed SVM and GBR, achieving the highest coefficient of determination (R² = 0.571) and the lowest mean absolute error (MAE = 0.365%) using the RFE feature selection method. …”
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Spatiotemporal Dynamics and Future Projections of Carbon Use Efficiency on the Mongolian Plateau: A Remote Sensing and Machine Learning Approach by Xinyu Yang, Qiang Yu, Buyanbaatar Avirmed, Yu Wang, Jikai Zhao, Weijie Sun, Huanjia Cui, Bowen Chi, Ji Long

“…The results indicate the following: (1) significant spatial variation exists, with high-value CUE areas (≥0.7) in the northwest due to favorable climatic conditions, while low-value areas (<0.6) in the east are affected by decreasing precipitation and overgrazing; (2) CUE increased at an annual rate of 1.03%, with a 43% acceleration after the 2005 climate shift, highlighting the synergistic effects of ecological engineering; (3) our findings reveal that the interaction of evapotranspiration and temperature dominates CUE spatial differentiation, with the random forest model accurately predicting CUE dynamics (root mean square error (RMSE) = 0.0819); (4) scenario simulations show the SSP3-7.0 pathway will peak CUE at 0.6103 by 2050, while the SSP5-8.5 scenario will significantly reduce spatial heterogeneity. …”
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Integration of UAV-sensed features using machine learning methods to assess species richness in wet grassland ecosystems by Clara Oliva Gonçalves Bazzo, Bahareh Kamali, Murilo dos Santos Vianna, Dominik Behrend, Hubert Hueging, Inga Schleip, Paul Mosebach, Almut Haub, Axel Behrendt, Thomas Gaiser

“…Models combining VI and GLCM features demonstrated the highest predictive accuracy, particularly in frequently cut grasslands, as indicated by higher R2 values (up to 0.52) and lower root mean square errors (rRMSE as low as 34.9 %). RF models generally outperformed PLS models across different feature sets, with the CH + VI + GLCM combination yielding the best results. …”
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Large variability of nitrate load estimated from sparse measurements by typical methods in Atlantic Canada by Kang Liang, Kang Liang, Yefang Jiang, Keith Fuller, Marcos Cordeiro, Xuesong Zhang, Junyu Qi, Xiaoyuan Geng, Tao Liu, Qian Zhang, Mohammad Amir Azimi, Fan-Rui Meng

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Research on Time Series Interpolation and Reconstruction of Multi-Source Remote Sensing AOD Product Data Using Machine Learning Methods by Huifang Wang, Min Wang, Pan Jiang, Fanshu Ma, Yanhu Gao, Xinchen Gu, Qingzu Luan

“…A comparison of five machine learning models showed that the random forest model performed optimally in AOD inversion, achieving a root mean square error (RMSE) of 0.11 and a coefficient of determination (R²) of 0.93. …”
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Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting by Ubaid Ahmed, Anzar Mahmood, Ahsan Raza Khan, Levin Kuhlmann, Khurram Saleem Alimgeer, Sohail Razzaq, Imran Aziz, Amin Hammad

“…Results demonstrate that when trained with the selected features, the mean absolute percentage error (MAPE) of PBNN is improved by $$46.9\%$$ , and $$73.9\%$$ for Islamabad and San Diego city datasets, respectively. …”
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CFS-MOES Ensemble Model on Metaheuristic Search-Based Feature Selection by Santosini Bhutia, Bichitrananda Patra, Mitrabinda Ray

“…Three classifiers, namely, K-nearest neighbour (KNN), multilayer perceptron (MLP), and random forest (RF), were chosen as the base classifiers based on their F-measure score. …”
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A machine learning-based recommendation framework for material extrusion fabricated triply periodic minimal surface lattice structures by Sajjad Hussain, Carman Ka Man Lee, Yung Po Tsang, Saad Waqar

“…ML algorithms included Bayesian regression (BR), K-nearest neighbors (KNN), Random Forest (RF), Decision Tree (DT), and DL algorithm convolutional neural network (CNN). …”
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