AI-enhanced automation of building energy optimization using a hybrid stacked model and genetic algorithms: Experiments with seven machine learning techniques and a deep neural net... by Mohammad H. Mehraban, Samad ME Sepasgozar, Alireza Ghomimoghadam, Behrouz Zafari

“…Seven machine learning (ML) models, including Linear Regression (LR), Decision Trees (DT), Random Forest Regressor (RFR), Gradient Boosting Machines (GBM), Support Vector Regressor (SVR), K-Nearest Neighbors (KNN), and Extreme Gradient Boosting (XGB), and a deep Feedforward Neural Network (FNN) are developed and assessed in predicting three key performance metrics: Energy Use Intensity (EUI), Predicted Percentage Dissatisfied (PPD), and Heating Load.A hybrid stacked model, combining FNN with XGB, using GBM meta learner, emerged as the top performer, achieving an impressive Coefficient of Determination (R²) of 0.99 and Mean Absolute Percentage Error (MAPE) of 0.02 across all targets. …”
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Effects of poultry by-product meal and complete replacement of fish oil with alternative oils on growth performance and gut health of rainbow trout (Oncorhynchus mykiss): a FEEDNET... by Imam Hasan, Simona Rimoldi, Biagina Chiofalo, Marianna Oteri, Micaela Antonini, Rosangela Armone, Violeta Kalemi, Laura Gasco, Genciana Terova

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UAV-Based Multispectral Winter Wheat Growth Monitoring with Adaptive Weight Allocation by Lulu Zhang, Xiaowen Wang, Huanhuan Zhang, Bo Zhang, Jin Zhang, Xinkang Hu, Xintong Du, Jianrong Cai, Weidong Jia, Chundu Wu

“…The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). …”
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Color-Sensitive Sensor Array Combined with Machine Learning for Non-Destructive Detection of AFB₁ in Corn Silage by Daqian Wan, Haiqing Tian, Lina Guo, Kai Zhao, Yang Yu, Xinglu Zheng, Haijun Li, Jianying Sun

“…Key variables were selected using five feature selection algorithms: Competitive Adaptive Reweighted Sampling (CARS), Principal Component Analysis (PCA), Random Forest (RF), Uninformative Variable Elimination (UVE), and eXtreme Gradient Boosting (XGBoost). …”
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MHRA-MS-3D-ResNet-BiLSTM: A Multi-Head-Residual Attention-Based Multi-Stream Deep Learning Model for Soybean Yield Prediction in the U.S. Using Multi-Source Remote Sensing Data by Mahdiyeh Fathi, Reza Shah-Hosseini, Armin Moghimi, Hossein Arefi

“…The results demonstrated the model’s robustness and adaptability to unseen data, achieving an R² of 0.82 and a Mean Absolute Percentage Error (MAPE) of 9% in 2021, and an R² of 0.72 and MAPE of 12% in 2022. …”
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Prediction of Snacking Behavior Involving Snacks Having High Levels of Saturated Fats, Salt, or Sugar Using Only Information on Previous Instances of Snacking: Survey- and App-Base... by Shaima Dammas, Tillman Weyde, Katy Tapper, Gerasimos Spanakis, Anne Roefs, Emmanuel M Pothos

“…Both datasets were analyzed using machine learning methods, including random forest regressor, Extreme Gradient Boosting regressor, feed forward neural network, and long short-term memory. …”
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Design and parameter optimization of portable tree transplanting machine by Zhang Jingping, Zhu Jianxi, Sun Teng

“…Seedling transplanting plays an important role in urban greening and forest plantation. However, this job is still relying on manual labor in China. …”
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Establishment of Hyperspectral Prediction Model of Water Content in Anshan-Type Magnetite by Xiaoxiao XIE, Yang BAI, Jiuling ZHANG, Yuna JIA

“…The prediction set determination coefficients (R2) of the models are 0.778 and 0.789, and the root mean square error (RMSE) were 5.45% and 5.41%, respectively. …”
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Transfer Learning Estimation and Transferability of LNC and LMA Across Different Datasets by Yingbo Wang, Mengzhu He, Lin Sun, Yong He, Zengwei Zheng

“…The LNC and LMA estimation performance in transfer models established by partial least squares regression (PLS), support vector regression (SVR), extreme gradient boosting (XGB), and random forest regression (RFR) algorithms across different datasets were employed, in which the RFR transfer models performed good prediction results. …”
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Petrological controls on the engineering properties of carbonate aggregates through a machine learning approach by Javid Hussain, Tehseen Zafar, Xiaodong Fu, Nafees Ali, Jian Chen, Fabrizio Frontalini, Jabir Hussain, Xiao Lina, George Kontakiotis, Olga Koumoutsakou

“…To enhance predictive accuracy, advanced machine learning models, including Random Forest, Gradient Boosting, Multi-Layer Perceptron, and Categorical Boosting, were applied. …”
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Data-driven thrust prediction in applied-field magnetoplasmadynamic thrusters for space missions using artificial intelligence-based models by Tarik Pinaffo Almeida, Shahin Alipour Bonab, Mohammad Yazdani-Asrami

“…With a Goodness of Fit ( R ^2 ) of 98.55%, root mean square error of 1.421 N, and mean absolute error of 0.453 N, XGBoost specifically, and AI in general, has demonstrated its superiority, by significantly improving on the accuracy of previously published empirical models for AF-MPDT thrust prediction. …”
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Uncertainty-aware approach for multiple imputation using conventional and machine learning models: a real-world data study by Romen Samuel Wabina, Panu Looareesuwan, Suphachoke Sonsilphong, Htun Teza, Wanchana Ponthongmak, Gareth McKay, John Attia, Anuchate Pattanateepapon, Anupol Panitchote, Ammarin Thakkinstian

“…In the CKD cohort, uncertainty-aware models significantly improved performance (evaluated by root mean squared error (RMSE) and mean absolute error (MAE)) over standard MICE, except for XGBoost. …”
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Genomic Prediction in a Self-Fertilized Progenies of <i>Eucalyptus</i> spp. by Guilherme Ferreira Melchert, Filipe Manoel Ferreira, Fabiana Rezende Muniz, Jose Wilacildo de Matos, Thiago Romanos Benatti, Itaraju Junior Baracuhy Brum, Leandro de Siqueira, Evandro Vagner Tambarussi

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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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Enhancing soil organic carbon prediction by unraveling the role of crop residue coverage using interpretable machine learning by Yi Dong, Xinting Wang, Sheng Wang, Baoguo Li, Junming Liu, Jianxi Huang, Xuecao Li, Yelu Zeng, Wei Su

“…Given these issues, we used the Shapley Additive exPlanations (SHAP) approach to interpret the influence of natural and anthropogenic factors on SOC estimation using the random forest model. Our results show the high SHAP values of air temperature, CRC, and clay content due to their significant influence on SOC estimation. …”
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Skin hyperspectral imaging and machine learning to accurately predict the muscular poly-unsaturated fatty acids contents in fish by Yi-Ming Cao, Yan Zhang, Qi Wang, Ran Zhao, Mingxi Hou, Shuang-Ting Yu, Kai-Kuo Wang, Ying-Jie Chen, Xiao-Qing Sun, Shijing Liu, Jiong-Tang Li

“…With the spectral data processed with the SG, the RBF model achieved outstanding performance in predicting the EPA + DHA and PUFAs contents, yielding coefficients of determination (R2P) of 0.9914 and 0.9914, root mean square error (RMSE) of 0.3352 and 0.3346, and mean absolute error (MAE) of 0.2659 and 0.2660, respectively. …”
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MRI Application in Quantification of Epiphyseal Development in the Wrist and Bone Age Estimation of Han Male Adolescents in East China by ZHOU Zhi-lu, ZHANG Dong-fei, CHEN Jie-min, WANG Ya-hui, HAO Hong-xia, LIU Tai-ang, HE Yu-heng, LONG Ding-nian, LIU Rui-jue, WAN Lei

“…Quantifying the maximum width of the epiphysis and corresponding metaphysis of bone and combining it with MRI image classification can effectively reduce the estimation error.…”
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Machine learning-based modelling and analysis of carbonation depth of recycled aggregate concrete by Xuyong Chen, Xuan Liu, Shukai Cheng, Xiaoya Bian, Xixuan Bai, Xin Zheng, Xiong Xu, Zhifeng Xu

“…On this basis, six machine learning models were employed to predict RAC carbonation depth: Artificial Neural Network, Decision Tree, Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Light Gradient Boosting. …”
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Spatiotemporal monitoring of water storage in the North China Plain from 2002 to 2022 based on an improved GRACE downscaling method by Jinze Tian, Yu Chen, Shuai Wang, Xinlong Chen, Huibin Cheng, Xiaolong Tian, Xue Wang, Kun Tan

“…Study focus: The coupling effects of key drivers on water storage dynamics were quantitatively analyzed, integrating frequency-domain correlation analysis to identify lag effects, which were incorporated into a Random Forest (RF) downscaling method. GRACE data were refined through this approach, enhancing spatial resolution while maintaining accuracy, with the aim of precisely characterizing water storage dynamics and examining its interactions with climatic and anthropogenic factors, particularly the long-term impact of groundwater fluctuations on surface deformation. …”
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Multispectral Sensors and Machine Learning as Modern Tools for Nutrient Content Prediction in Soil by Rafael Felippe Ratke, Paulo Roberto Nunes Viana, Larissa Pereira Ribeiro Teodoro, Fábio Henrique Rojo Baio, Paulo Eduardo Teodoro, Dthenifer Cordeiro Santana, Carlos Eduardo da Silva Santos, Alan Mario Zuffo, Jorge González Aguilera

“…The models tested were linear regression, random forest (RF), reptree M5P, multilayer preference neural network, and decision tree algorithms, with the correlation coefficient (r) and mean absolute error (MAE) used as accuracy parameters. …”
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