Predicting Excavation-Induced Tunnel Response by Process-Based Modelling by Linlong Mu, Jianhong Lin, Zhenhao Shi, Xingyu Kang

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An Improved Artificial Neural Network Model for Effective Diabetes Prediction by Muhammad Mazhar Bukhari, Bader Fahad Alkhamees, Saddam Hussain, Abdu Gumaei, Adel Assiri, Syed Sajid Ullah

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Ada-GCNLSTM: An adaptive urban crime spatiotemporal prediction model by Miaoxuan Shan, Chunlin Ye, Peng Chen, Shufan Peng

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Core-Level Modeling and Frequency Prediction for DSP Applications on FPGAs by Gongyu Wang, Greg Stitt, Herman Lam, Alan George

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Machine learning models for predicting spatiotemporal dynamics of groundwater recharge by Azeddine Elhassouny

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Prediction of air pollutant emission in Xi’an based on LEAP model by TAN Zhihai, YUAN Yubo, WANG Xuemei, LEI Qiujing, MIAO Jihong, GU Maolin, TAN Tantan

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An Ensemble Learning Model for Short-Term Passenger Flow Prediction by Xiangping Wang, Lei Huang, Haifeng Huang, Baoyu Li, Ziyang Xia, Jing Li

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Unidirectional and Bidirectional LSTM Models for Short-Term Traffic Prediction by Rusul L. Abduljabbar, Hussein Dia, Pei-Wei Tsai

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Macroeconomic models for predicting indirect impacts of disasters: A review by Tinger Zhu, Charalampos Avraam, Jack W. Baker

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Sand Production Prediction Model for Tight Sandstone Oil Reservoirs by Zhan-dong Li, Hong Pang, Zhong Li, Hai-xiang Zhang, Dian-ju Wang, Ji Li

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Diagnostic prediction model for levodopa-induced dyskinesia in Parkinson’s disease by Bruno Lopes SANTOS-LOBATO, Artur F. SCHUMACHER-SCHUH, Carlos R. M. RIEDER, Mara H. HUTZ, Vanderci BORGES, Henrique Ballalai FERRAZ, Ignacio F. MATA, Cyrus P. ZABETIAN, Vitor TUMAS

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Ensemble prediction modeling of flotation recovery based on machine learning by Guichun He, Mengfei Liu, Hongyu Zhao, Kaiqi Huang

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Hyperparameter optimization of machine learning models for predicting actual evapotranspiration by Chalachew Muluken Liyew, Elvira Di Nardo, Stefano Ferraris, Rosa Meo

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Multi-model assessment and thermodynamic prediction for oxalate-tungstate complexes by Yong Liang, Ting Pu, Zanhong Chen, Yinliang Liu, Congyu Zhang

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Predicting lung nodules malignancy by M. Jacob, J. Romano, D. Araújo, J.M. Pereira, I. Ramos, V. Hespanhol

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Machine learning proteochemometric models for Cereblon glue activity predictions by Francis J. Prael, III, Jiayi Cox, Noé Sturm, Peter Kutchukian, William C. Forrester, Gregory Michaud, Jutta Blank, Lingling Shen, Raquel Rodríguez-Pérez

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A Combined PSO-LSTM Prediction Model for Dam Deformation by HAO Ze-jia, SHI Yu-qun, CHENG Bo-chao, HE Jin-ping

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Prediction of coalbed methane productivity based on neural network models by JIN Yi, ZHENG Chenhui, SONG Huibo, MA Jiaheng, YANG Yunhang, LIU Shunxi, ZHANG Kun, NI Xiaoming

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Theoretical Modeling of 4.3 μm Mid-Infrared Lasing in Dy³⁺-Doped Chalcogenide Fiber Lasers by Xusheng Xiao, Yantao Xu, Haitao Guo, Pengfei Wang, Xiaoxia Cui, Min Lu, Yishan Wang, Bo Peng

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Development and Validation of an Interpretable Machine Learning Model for Prediction of the Risk of Clinically Ineffective Reperfusion in Patients Following Thrombectomy for Ischem... by Hu X, Qi D, Li S, Ye S, Chen Y, Cao W, Du M, Zheng T, Li P, Fang Y

“…The SHAP algorithm identified the number of endovascular thrombectomy (EVT) attempts as the key factor influencing CIR. Based on the RF model, a web-based calculator for CIR prediction is available at https://ineffectivereperfusion.shinyapps.io/calculate/. …”
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