Chaotic Vibration Prediction of a Free-Floating Flexible Redundant Space Manipulator by Congqing Wang, Linfeng Wu

“…It has been proved that the SVR prediction model has a good performance of prediction. …”
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Attribute Selection Impact on Linear and Nonlinear Regression Models for Crop Yield Prediction by Alberto Gonzalez-Sanchez, Juan Frausto-Solis, Waldo Ojeda-Bustamante

“…This paper evaluates the most common data-driven modeling techniques applied to yield prediction, using a complete method to define the best attribute subset for each model. …”
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Machine learning algorithms for predicting PTSD: a systematic review and meta-analysis by Masoumeh Vali, Hossein Motahari Nezhad, Levente Kovacs, Amir H Gandomi

“…Abstract This study aimed to compare and evaluate the prediction accuracy and risk of bias (ROB) of post-traumatic stress disorder (PTSD) predictive models. …”
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Burnout among nurses practicing in critical care units: Predicting the contributing factors by Sahar Yaqoob Yousif, Sadeq AL-Fayyadh

“…Age was the only variable that predicted burnout inversely.…”
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An Algorithm That Predicts CSI to Allocate Bandwidth for Healthcare Monitoring in Hospital's Waiting Rooms by Di Lin, Fabrice Labeau

“…In this algorithm, the central unit can predict the current CSI by previous CSI when the current CSI is not available. …”
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Skillful prediction of Indian Ocean Dipole index using machine learning models by J.V. Ratnam, Swadhin K. Behera, Masami Nonaka, Kalpesh R. Patil

“…In this study, we evaluated six machine learning models for their skill in predicting the Indian Ocean Dipole (IOD). The results based on the IOD index predictions at 1–8 month lead time indicate that the AdaBoost model with Multi-Layer Perceptron as the base estimator, AdaBoost(MLP), to perform better than the other five models in predicting the IOD index at all lead times. …”
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Quantitative Complexity Theory Used in the Prediction of Head-Up Tilt Testing Outcome by Paweł Krzesiński, Jacek Marczyk, Bartosz Wolszczak, Grzegorz Gielerak

“…Complexity has been shown to be a sensitive marker of cardiovascular haemodynamic response to orthostatic stress and proved to be superior over HR and BP in predicting HUTT outcomes.…”
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A Novel Ferroptosis-Related Gene Signature for Prognosis Prediction in Ewing Sarcoma by Runhan Zhao, Zefang Li, Yanran Huang, Chuang Xiong, Chao Zhang, Hao Liang, Jingtao Xu, Xiaoji Luo

“…The validation results demonstrated that the gene signature has not only excellent prediction performance and generalization ability but is also good at predicting the response of immunotherapy and chemotherapy. …”
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A Stock Closing Price Prediction Model Based on CNN-BiSLSTM by Haiyao Wang, Jianxuan Wang, Lihui Cao, Yifan Li, Qiuhong Sun, Jingyang Wang

“…This paper proposes a composite model CNN-BiSLSTM to predict the closing price of the stock. Bidirectional special long short-term memory (BiSLSTM) improved on bidirectional long short-term memory (BiLSTM) adds 1 − tanh(x) function in the output gate which makes the model better predict the stock price. …”
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Predictive Probability Models of Road Traffic Human Deaths with Demographic Factors in Ghana by Christian Akrong Hesse, Dominic Buer Boyetey, Albert Ayi Ashiagbor

“…We found that gender and age groups have significant effects in predicting the probability of road traffic fatality for all three models. …”
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A Novel Metabolic Connectome Method to Predict Progression to Mild Cognitive Impairment by Min Wang, Zhuangzhi Yan, Shu-yun Xiao, Chuantao Zuo, Jiehui Jiang

“…Therefore, we present a novel metabolic connectome-based predictive modeling to predict progression from MCI to AD accurately. …”
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Prediction of elastic modulus, yield strength, and tensile strength in biocompatible titanium alloys by Marković G., Ružić J., Sokić M., Milojkov D., Manojlović V.D.

“…The aim of this study is to develop accurate machine learning models for predicting mechanical properties: modulus of elasticity, tensile strength, and yield strength, specifically using the Extra Trees Regressor model. …”
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Retracted: Prediction and Application of Computer Simulation in Time-Lagged Financial Risk Systems by null Complexity

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The Effect of Spatial Scale on the Prediction of Tropical Forest Attributes from Image Texture by J. Alberto Gallardo-Cruz, Jonathan V. Solórzano, Edgar J. González, Jorge A. Meave

“…The second best-predicted attribute was HSD, which peaked at the intermediate scale. …”
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Real-time prediction of port water levels based on EMD-PSO-RBFNN by Lijun Wang, Shenghao Liao, Sisi Wang, Jianchuan Yin, Ronghui Li, Jingyu Guan

Subjects: “…port water level prediction…”
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Predictive modelling and identification of critical variables of mortality risk in COVID-19 patients by Olawande Daramola, Tatenda Duncan Kavu, Maritha J. Kotze, Jeanine L. Marnewick, Oluwafemi A. Sarumi, Boniface Kabaso, Thomas Moser, Karl Stroetmann, Isaac Fwemba, Fisayo Daramola, Martha Nyirenda, Susan J. van Rensburg, Peter S. Nyasulu

“…By using the Shapley Additive exPlanations (SHAP) model to interpret the mortality risk predictions, we identified the Length of stay (LOS) in the hospital, LOS in the ICU, Time to ICU from admission, days discharged alive or death, D-dimer (blood clotting factor), and blood pH as the six most critical variables for mortality risk prediction. …”
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Prediction of Type 2 Diabetes Mellitus using a Logistic Regression Model by Olivia Altamirano Guerrero, Ronelsys Martínez Martínez, Jhonny Alejandro Rodríguez Gutiérrez

“…The statistical procedure was based on the binary logistic regression application. To evaluate the predictive capacity of the model, Cohen's Kappa test was used. …”
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Combining Region-Guided Attention and Attribute Prediction for Thangka Image Captioning Method by Fujun Zhang, Wendong Kang, Wenjin Hu

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Evaluation of parameters predicting in-hospital mortality and septic embolisms in patients with infective endocarditis by Tuba Tatlı Kış, Mehmet Kış, Tuncay Güzel, Çiğdem Mermutluoğlu

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Compressive Strength Prediction of Stabilized Dredged Sediments Using Artificial Neural Network by Van Quan Tran

“…The results show that the ANN model can accurately predict the compressive strength of stabilized dredged sediments with low water content. …”
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