Predictive model for assessing the prognosis of rhabdomyolysis patients in the intensive care unit by Yaxin Xiong, Hongyu Shi, Jianpeng Wang, Quankuan Gu, Yu Song, Weilan Kong, Jun Lyu, Mingyan Zhao, Mingyan Zhao, Xianglin Meng, Xianglin Meng, Xianglin Meng

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An easily acquirable predictive model for strangulated bowel obstruction: the BAR-N by Cuifeng Zheng, BaoWei Xu, Pingxia Lu, Weixuan Xu, Shenhui Lin, Xianqiang Chen, Junrong Zhang, Zhengyuan Huang

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Exploration of Predictive Models for Lean Services in Government Lean Banking Industry by Fazel Saydi, Morteza Musa Khani, Sedigheh Tootian, Shahrazad Tayaran

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A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis by Rui Huang, Rui Huang, Dan Wu, Dan Wu, Ling Wang, Ling Wang, Ping Liu, Ping Liu, Xiaoru Zhu, Xiaoru Zhu, Leqiu Huang, Leqiu Huang, Mengmeng Chen, Mengmeng Chen, Xin Lv, Xin Lv

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Multidimensional predictive model for assessing clinical activity in thyroid eye disease by Yang Li, Yang Li, Yang Li, Guang-Hong Zhang, Guang-Hong Zhang, Guang-Hong Zhang, Man Tian, Man Tian, Man Tian, Chuan Hua, Chuan Hua, Chuan Hua, Chuan Hua, Jian-Ping Zhai, Jian-Ping Zhai, Jian-Ping Zhai, Yan-Qiong He, Yan-Qiong He, Yan-Qiong He, Xin-He Zuo, Xin-He Zuo, Xin-He Zuo, Xin-He Zuo

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Development of a predictive model for metachronous liver metastasis in gastric cancer by Siyuan Wang, Siyuan Wang, Gaozan Zheng, Fengsu Wu, Ye Tian, Xinyu Qiao, Xinyu Dou, Hanjun Dan, Hanjun Dan, Guangming Ren, Liaoran Niu, Pengfei Wang, Lili Duan, Yumao Yang, Jianyong Zheng, Fan Feng

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Chronic lung lesions in COVID-19 survivors: predictive clinical model by Paulo A Lotufo, Juliana C Ferreira, Eloisa Bonfa, Anna S Levin, Rodrigo Caruso Chate, Marta Imamura, Esper G Kallas, Roger Chammas, Thais Mauad, Izabel Marcilio, Nelson Gouveia, Ricardo Nitrini, José Eduardo Krieger, Marcio Valente Yamada Sawamura, Michelle Louvaes Garcia, Cristiano Gomes, Guilherme Fonseca, Jorge Hallak, Luis Yu, Marcio Mancini, Maria Elizabeth Rossi, Thiago Avelino-Silva, Edivaldo M Utiyama, Aluisio C Segurado, Beatriz Perondi, Anna Miethke-Morais, Amanda C Montal, Leila Harima, Marjorie F Silva, Marcelo C Rocha, Maria Amélia de Jesus, Carolina Carmo, Clarice Tanaka, Julio F M Marchini, Thaís Guimarães, Ester Sabino, Carlos Roberto Ribeiro Carvalho, Celina Almeida Lamas, Diego Armando Cardona Cardenas, Daniel Mario Lima, Paula Gobi Scudeller, João Marcos Salge, Cesar Higa Nomura, Marco Antonio Gutierrez, Adriana L Araújo, Bruno F Guedes, Carolina S Lázari, Cassiano C Antonio, Claudia C Leite, Emmanuel A Burdmann, Euripedes C Miguel, Fabio R Pinna, Fabiane Y O Kawano, Geraldo F Busatto, Giovanni G Cerri, Heraldo P Souza, Izabel C Rios, Larissa S Oliveira, Linamara R Batisttella, Luiz Henrique M Castro, Marcello M C Magri, Maria Cassia J M Corrêa, Maria Cristina P B Francisco, Maura S Oliveira, Orestes V Forlenza, Ricardo F Bento, Rodolfo F Damiano, Rossana P Francisco, Solange R G Fusco, Tarcisio E P Barros-Filho, Wilson J Filho

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Prediction of the Quality of Life of Menopausal Women Based on Health Literacy and Self-Efficacy by Sara Jafarigiv, Nooshin Peyman, Habibollah Esmaily, Mohammad Tajfard

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Prediction of Hydrogen-Bonding Interaction Free Energies with Two New Molecular Descriptors by William E. Acree, Costas Panayiotou

“…The new predictive scheme is validated against corresponding estimations of the widely used Abraham’s LSER model. …”
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AISMPred: A Machine Learning Approach for Predicting Anti-Inflammatory Small Molecules by Subathra Selvam, Priya Dharshini Balaji, Honglae Sohn, Thirumurthy Madhavan

“…<b>Results:</b> A total of 15 ML models were developed using 2D, FP, and Hybrid feature sets, with the ET model with hybrid features achieving the highest accuracy of 92% and an AUC of 0.97 on the independent test dataset. …”
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Perception of operational environment conditions and situations as a prediction of adaptive potential in servicemen by V. V. Kalnysh, A. V. Shvets, O. V. Maltsev, I. K. Sereda, A. Yu. Kikh, K. Yu. Marushchenko

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Neural Network-Based Model for Predicting Preliminary Construction Cost as Part of Cost Predicting System by Diana Car-Pusic, Silvana Petruseva, Valentina Zileska Pancovska, Zlatko Zafirovski

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Functional Prediction of Hypothetical Proteins from and Validation of the Predicted Models by Using ROC Curve Analysis by Md. Amran Gazi, Sultan Mahmud, Shah Mohammad Fahim, Mohammad Golam Kibria, Parag Palit, Md. Rezaul Islam, Humaira Rashid, Subhasish Das, Mustafa Mahfuz, Tahmeed Ahmeed

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Fault Prediction of Hydropower Station Based on CNN-LSTM-GAN with Biased Data by Bei Liu, Xiao Wang, Zhaoxin Zhang, Zhenjie Zhao, Xiaoming Wang, Ting Liu

“…Finally, a dynamic multi-task training algorithm is proposed to ensure the convergence and training efficiency of the deep models. Experimental results show that compared with RNN, GRU, SVM, and threshold detection algorithms, the proposed fault prediction method improves the accuracy performance by <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>5.5</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>4.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>7.8</mn><mo>%</mo></mrow></semantics></math></inline-formula>, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>9.3</mn><mo>%</mo></mrow></semantics></math></inline-formula>, with at least a <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>160</mn><mo>%</mo></mrow></semantics></math></inline-formula> improvement in the fault recall rate.…”
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From Monitoring to Prediction: Velocity-Based Strength Training in Female Floorball Athletes by Basil B. Achermann, Naire Regazzi, Rahel Heynen, Dennis Lüdin, Julia Suter, Anna Drewek, Silvio R. Lorenzetti

“…Seventeen participants completed 12 training sessions, a strength block set (Sessions 1–6) and a power block set (Sessions 7–12). The predictive models explained 54% to 79% (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula> = 0.54–0.79) of the performance improvement in the strength-related tests. …”
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Predictive Model to Analyse Real and Synthetic Data for Learners' Performance Prediction Using Regression Techniques by SHABNAM ARA S.J, Tanuja R, Manjula S.H

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Seasonal Prediction of Atmospheric Rivers in the Western North Pacific Using a Seasonal Prediction Model by Yuya Baba

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Advanced predictive modeling for enhanced mortality prediction in ICU stroke patients using clinical data. by Armin Abdollahi, Negin Ashrafi, Xinghong Ma, Jiahao Zhang, Daijia Wu, Tongshou Wu, Zizheng Ye, Maryam Pishgar

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A Framework for User Traffic Prediction and Resource Allocation in 5G Networks by Ioannis Konstantoulas, Iliana Loi, Dimosthenis Tsimas, Kyriakos Sgarbas, Apostolos Gkamas, Christos Bouras

“…The models show high accuracy in the tasks performed, especially in the user traffic prediction task, where the models show an accuracy of over <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99</mn><mo>%</mo></mrow></semantics></math></inline-formula>. …”
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An Analytical Cost Function Design and Implementation for Predictive Control of Induction Machine Drives by Wei Wei, Liming Yan, Shun Tian, Xisheng Xu, Keke Sun

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