Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence by Gabriele De Carolis, Vincenzo Giannico, Leonardo Costanza, Francesca Ardito, Anna Maria Stellacci, Afwa Thameur, Sergio Ruggieri, Sabina Tangaro, Marcello Mastrorilli, Nicola Sanitate, Simone Pietro Garofalo

“…A SHAP analysis highlighted that GNDVI, Cl1, and NDRE were the most important VIs for predicting RCC, while yellow and red bands were the most important for DM prediction, and yellow and nir bands for RWC prediction. …”
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Deep learning-based prediction of mortality using brain midline shift and clinical information by An-Rong Wu, Sun-Yuan Hsieh, Hsin-Hung Chou, Cheng-Shih Lai, Jo-Ying Hung, Bow Wang, Yi-Shan Tsai

“…The model detected large brain MLS cases well in the prediction of outcomes in the prognosis-predicting cohort. …”
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Research on the Strength Prediction Model of Softened Mudstone Based on Triaxial Compressive Test of Rock by Kai Yun, Yongquan Zhu, Renyuan Wang, Zhichun Fang

“…Then, the applicability of five commonly used strength criteria to the strength prediction of softened mudstone is compared and analyzed. …”
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Models based on dietary nutrients predicting all-cause and cardiovascular mortality in people with diabetes by Fang Wang, Yukang Mao, Jinyu Sun, Jiaming Yang, Li Xiao, Qingxia Huang, Chenchen Wei, Zhongshan Gou, Kerui Zhang

“…The study aims to establish models predicting long-term mortality and explore dietary nutrients associated with reduced long-term events to guide daily dietary decisions in people with DM. …”
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Analysis and Prediction of the Interval Duration between the First and Second Accidents considering the Spatiotemporal Threshold by Fang Liu, Lanlan Zheng, Mingyang Li, Jinjun Tang

“…Bayesian method is applied to optimize the hyperparameters in the RF, while three evaluation indicators, including the Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), are used to estimate the prediction effect. The test results and comparative experiments confirm that RF is able to predict the interval well and has better prediction performance. …”
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Model Reduction Using Proper Orthogonal Decomposition and Predictive Control of Distributed Reactor System by Alejandro Marquez, Jairo José Espinosa Oviedo, Darci Odloak

“…This paper studies the application of proper orthogonal decomposition (POD) to reduce the order of distributed reactor models with axial and radial diffusion and the implementation of model predictive control (MPC) based on discrete-time linear time invariant (LTI) reduced-order models. …”
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Predictive Assessment of the Antiviral Properties of Imperata cylindrica against SARS-CoV-2 by Frank Eric Tatsing Foka, Hazel Tumelo Mufhandu

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CircRNA-Disease Associations Prediction Based on Metapath2vec++ and Matrix Factorization by Yuchen Zhang, Xiujuan Lei, Zengqiang Fang, Yi Pan

“…Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. …”
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Sign Prediction on Unlabeled Social Networks Using Branch and Bound Optimized Transfer Learning by Weiwei Yuan, Jiali Pang, Donghai Guan, Yuan Tian, Abdullah Al-Dhelaan, Mohammed Al-Dhelaan

“…Sign prediction problem aims to predict the signs of links for signed networks. …”
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Learning Coupled Meteorological Characteristics Aids Short-Term Photovoltaic Interval Prediction Methods by Yue Guo, Yu Song, Zilong Lai, Xuyang Wang, Licheng Wang, Hui Qin

“…The model leverages the strengths of the TCN, the LSTM, and the self-attention mechanism to enhance prediction accuracy and construct reliable prediction intervals. …”
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Prediction of Optimal Daily Step Count Achievement from Segmented School Physical Activity by Ryan D. Burns, Timothy A. Brusseau, James C. Hannon

“…The purpose of this study was to examine the predictive relationship between step counts during specific school segments and achieving optimal school (6,000 steps/day) and daily (12,000 steps/day) step counts in children. …”
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Application of Improved Naive Bayesian-CNN Classification Algorithm in Sandstorm Prediction in Inner Mongolia by Li Tiancheng, Ren Qing-dao-er-ji, Qiu Ying

“…Experimental results showed that the prediction accuracy of the sandstorm prediction model based on INB-CNN classification algorithm is higher than that of others and the model can better reflect the law of sandstorm occurrence. …”
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Prediction of Dissolved Oxygen Concentration in Sewage Treatment Process Based on Data Recognition Algorithm by Lili Ma, Jiangping Liu

“…In order to realize the real-time and accurate prediction of dissolved oxygen concentration in the sewage treatment process, a prediction model of dissolved oxygen concentration in the sewage treatment process based on a data identification algorithm was proposed. …”
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Spectroscopic analysis (UV-VIS-NIR) for predictive modeling of macro and micronutrients in grapevine leaves by J.I. Manzano, M. Rodríguez-Febereiro, M. Fandiño, M. Vilanova, J.J. Cancela

“…In contrast, reflectances in the near-infrared region (NIR) had a greater impact on macronutrient prediction, particularly for P and Mg, due to their stronger interaction with organic compounds. …”
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Application of machine learning in diabetes prediction based on electronic health record data analysis by Yang Zihan

“…This study introduces an improved machine learning model specifically designed to predict diabetes risk, with the aim of improving the accuracy of predictions. …”
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Comparative Analysis of Shear Strength Prediction Models for Reinforced Concrete Slab–Column Connections by Sarmed Wahab, Nasim Shakouri Mahmoudabadi, Sarmad Waqas, Nouman Herl, Muhammad Iqbal, Khurshid Alam, Afaq Ahmad

“…The findings of the study are validated through FEA of slabs to confirm experimental results and machine learning predictions that showed excellent agreement with PSOFNN predictions. …”
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Prediction of Compressive Strength Behavior of Ground Bottom Ash Concrete by an Artificial Neural Network by Kraiwut Tuntisukrarom, Raungrut Cheerarot

“…The proposed ANN-based explicit equation represented a highly accurate predictive model, for which the statistical values of R2 were higher than 0.996. …”
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Improving 10-year cardiovascular risk prediction in patients with type 2 diabetes with metabolomics by Ruijie Xie, Teresa Seum, Sha Sha, Kira Trares, Bernd Holleczek, Hermann Brenner, Ben Schöttker

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Predicting the Onset of Cavitation in Automotive Torque Converters—Part I: Designs with Geometric Similitude by D. L. Robinette, J. M. Schweitzer, D. G. Maddock, C. L. Anderson, J. R. Blough, M. A. Johnson

“…A power product model was fit on dimensionless stator torque data to create a model capable of predicting cavitation thresholds. Comparison of the model to test data taken over a range of operating points showed an error of 3.7%. …”
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One Step Ahead Prediction of Ozone Concentration for Determination of Outdoor Air Quality Level by Ercan Avşar, Waleed Mahmood

“…In this work, performances of six machine learning methods were analyzed for prediction of maximum ozone (O_3) concentration for the next-day. …”
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