Evapotranspiration Disaggregation Using an Integrated Indicating Factor Based on Slope Units by Linjiang Wang, Bingfang Wu, Weiwei Zhu, Abdelrazek Elnashar, Nana Yan, Zonghan Ma

“…The validation in Huairou and Baotianman shows coefficients of determination of 0.9 and 0.91, respectively, and root mean square errors of 0.45 mm and 0.47 mm. Compared to the original 1 km resolution ET data, the disaggregated results show improved accuracy, with R² values increasing by 1% (Huairou) and 2% (Baotianman) and RMSE decreasing by 21% and 13%, respectively. …”
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Assessment of active fire detection in Serra da Canastra National Park using MODIS and VIIRS sensors by G. S. Pinto, H. Bernini, C. G. Messias, O. A. S. Silva, P. W. Cunha, P. S. Victorino, F. Morelli

“…The geographic data utilization for the occurrence of wildfires and forest fires for monitoring fire usage in vegetation has become increasingly important for generating information that aids in decision-making and policy development regarding climate change and its impacts. …”
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Improved DBO algorithm tunes fuzzy-PD controller for robot manipulator trajectory tracking by Ma Haohao, Azizan As’arry, Li Chaoqun, Mohd Idris Shah Ismail, Hafiz Rashidi Ramli, Aidin Delgoshaei, M.Y.M. Zuhri

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Contribution to the research of Anthropometric measurements and comparison of body proportions in the student population in Bangladesh by Md Eanamul Haque Nizam, Emeritus Darko Ujevic, Ayub Nabi Khan

“…The model demonstrated superior performance, achieving an R2 score of 0.647 where the mean squared error value is 18.62, surpassing both linear regression and random forest models. …”
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Detection of breast cancer using machine learning and explainable artificial intelligence by Tharunya Arravalli, Krishnaraj Chadaga, H Muralikrishna, Niranjana Sampathila, D. Cenitta, Rajagopala Chadaga, K. S. Swathi

“…Out of the algorithms, random forest showed the best result, an F1-score of 84%. …”
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A machine learning-based method for predicting the shear behaviors of rock joints by Liu He, Yu Tan, Timothy Copeland, Jiannan Chen, Qiang Tang

“…The results demonstrated that the MLPMs provided reliable predictions for shear stress, with the mean squared errors (MSEs) between their predicted and measured shear stress varying from 0.003 to 0.069 and the coefficients of determination (R2 values) varying from 0.964 to 0.998. …”
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Mutational signature analyses in multi-child families reveal sources of age-related increases in human germline mutations by Habiballah Shojaeisaadi, Andrew Schoenrock, Matthew J. Meier, Andrew Williams, Jill M. Norris, Nicholette D. Palmer, Carole L. Yauk, Francesco Marchetti

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From data to decision: Alleviating poverty and promoting development through measuring the unmeasurable economic numbers by Emmanuel A. Onsay, Jomar F. Rabajante

“…Research on indigenous peoples typically takes a qualitative approach, whereas studies on poverty tend to be broad, making them susceptible to significant sampling errors and primarily intended for national policy-making. …”
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Present-day vegetation helps quantifying past land cover in selected regions of the Czech Republic. by Vojtěch Abraham, Veronika Oušková, Petr Kuneš

“…Vegetation proportions of 17 taxa were obtained by combining the CORINE Land Cover map with forest inventories, agricultural statistics and habitat mapping data. …”
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A hybrid model for improving customer lifetime value prediction using stacking ensemble learning algorithm by Amir Mohammad Haddadi, Hodjat Hamidi

“…., Elastic Net, Random Forest, XGBoost, and SVM. The results demonstrate that integrating those features and using the Stacking Ensemble model substantially increases the prediction accuracy and decreases the errors. …”
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Predictive Machine Learning Approaches for Supply and Manufacturing Processes Planning in Mass-Customization Products by Shereen Alfayoumi, Amal Elgammal, Neamat El-Tazi

“…While soft computing techniques are widely used for optimizing mass-customization products, they face scalability issues when handling large datasets and rely heavily on manually defined rules, which are prone to errors. In contrast, machine learning techniques offer an opportunity to overcome these challenges by automating rule generation and improving scalability. …”
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Global Ionospheric TEC Forecasting for Geomagnetic Storm Time Using a Deep Learning‐Based Multi‐Model Ensemble Method by Xiaodong Ren, Pengxin Yang, Dengkui Mei, Hang Liu, Guozhen Xu, Yue Dong

“…The test set results showed that the DLMEM model can reduce the root mean square errors (RMSE) by an average of 43.6% in comparison to our previously presented model Ion‐LSTM, especially during the recovery period of geomagnetic storms. …”
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Ammonia and ethanol detection via an electronic nose utilizing a bionic chamber and a sparrow search algorithm-optimized backpropagation neural network. by Yeping Shi, Yunbo Shi, Haodong Niu, Jinzhou Liu, Pengjiao Sun

“…It also outperforms the other models at regression prediction, with smaller absolute, mean absolute, and root mean square errors. Its coefficient of determination (R2) is greater than 0.99, surpassing those of the SVM and RF models. …”
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The role of spectral vs spatial resolution of satellite data on the accuracy of mapping unburned vegetation within fire scar perimeters by Magdalini Pleniou, Nikos Koutsias

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Remaining Useful Life Estimation through Deep Learning Partial Differential Equation Models: A Framework for Degradation Dynamics Interpretation Using Latent Variables by Sergio Cofre-Martel, Enrique Lopez Droguett, Mohammad Modarres

“…The latest research efforts have focused on applying complex DL models to achieve low prediction errors rather than studying how they interpret the data’s behavior and the system itself. …”
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Robust Hybrid Data-Level Approach for Handling Skewed Fat-Tailed Distributed Datasets and Diverse Features in Financial Credit Risk by Musara Keith R, Ranganai Edmore, Chimedza Charles, Matarise Florence, Munyira Sheunesu

“…This approach was coupled with widely employed ensemble algorithms, namely the random forest (RF) and the extreme gradient boost (XGBoost). …”
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Understanding the environmental health implications of tourism on carbon emissions in China by Jinhua Shao, Sheng Fang, Meiling Zhao, Wanxin Qian, Cai Wang

“…Our findings demonstrate that sparrow search algorithm and random forest (SSA-RF) hybrid model can model the relationship between carbon emissions and tourism factors with low error. …”
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Pioneering machine learning techniques to estimate thermal conductivity of carbon-based phase change materials: A comprehensive modeling framework by Raouf Hassan, Alireza Baghban

“…Among them, CatBoost achieved the highest predictive performance with an R2 of 0.979 and the lowest mean squared error (MSE) of 0.006 on the test set. SHAP analysis revealed that nanoparticle concentration was the most influential feature. …”
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Visual–Inertial Autonomous UAV Navigation in Complex Illumination and Highly Cluttered Under-Canopy Environments by Leyang Zhao, Weixi Wang, Qizhi He, Li Yan, Xiaoming Li

“…Subsequently, employs a high-dimensional error-state optimizer coupled with a low-dimensional height filter to achieve high-precision localization of the UAV in under-canopy environments. …”
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Data-Driven and Mechanistic Soil Modeling for Precision Fertilization Management in Cotton by Miltiadis Iatrou, Panagiotis Tziachris, Fotis Bilias, Panagiotis Kekelis, Christos Pavlakis, Aphrodite Theofilidou, Ioannis Papadopoulos, Georgios Strouthopoulos, Georgios Giannopoulos, Dimitrios Arampatzis, Evangelos Vergos, Christos Karydas, Dimitris Beslemes, Vassilis Aschonitis

“…By comparing the Mean Absolute Error (MAE) between predicted and observed cotton yield values across three ML algorithms, i.e., Random Forest (RF), XGBoost, and LightGBM, the RF model achieved the lowest error (422.6 kg/ha), outperforming XGBoost (446 kg/ha) and LightGBM (449 kg/ha). …”
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