Artificial Neural Network Individualised Prediction of Time to Colorectal Cancer Surgery by N. J. Curtis, G. Dennison, E. Salib, D. A. Hashimoto, N. K. Francis

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Improving Cardiovascular Disease Prediction With Deep Learning and Correlation-Aware SMOTE by Maria Trigka, Elias Dritsas

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Risk Prediction Score for Thermal Mapping of Pharmaceutical Transport Routes in Brazil by Clayton Gerber Mangini, Nilsa Duarte da Silva Lima, Irenilza de Alencar Nääs

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Multibranch Adaptive Fusion Graph Convolutional Network for Traffic Flow Prediction by Xin Zan, Jasmine Siu Lee Lam

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Climate change impacts on the predicted geographic distribution of Betula tianschanica Rupr by Hang Zhou, Hang Zhou, Ao Li, Ao Li, Xuequn Luo, Xuequn Luo, Jiafeng Wang, Jiafeng Wang, Yihong Xie, Zhongping Lin, Donglai Hua

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Enhanced slope stability prediction using ensemble machine learning techniques by Devendra Kumar Yadav, Swarup Chattopadhyay, Debi Prasad Tripathy, Pragyan Mishra, Pritiranjan Singh

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Research on Efficient Prediction and Suppression of Electromagnetic Interference in Electric Drive Systems by Ruoxi Tan, Shangbin Ye, Qianlei Peng, Changhong Du, Zeyan Zhou

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Customer Churn Prediction Approach Based on LLM Embeddings and Logistic Regression by Meryem Chajia, El Habib Nfaoui

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Integrated analyses and a novel nomogram for the prediction of significant fibrosis in patients by Mengxin Lu, Shuai Tao, Xinyan Li, Qunling Yang, Cong Du, Weijia Lin, Shuangshuang Sun, Conglin Zhao, Neng Wang, Qiankun Hu, Yuxian Huang, Qiang Li, Yi Zhang, Liang Chen

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An adaptive dual distillation framework for efficient remaining useful life prediction by Xiang Cheng, Jun Kit Chaw, Shafrida Sahrani, Mei Choo Ang, Saraswathy Shamini Gunasekaran, Moamin A. Mahmoud, Halimah Badioze Zaman, Yanfeng Zhao, Fuchen Ren

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Prediction of Human Papillomavirus-Host Oncoprotein Interactions Using Deep Learning by Sheila Santa, Samuel Kojo Kwofie, Kwasi Agyenkwa-Mawuli, Osbourne Quaye, Charles A Brown, Emmanuel A Tagoe

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Retinal thickness predicts the risk of cognitive decline over five years by Leila Sara Eppenberger, Chi Li, Damon Wong, Bingyao Tan, Gerhard Garhöfer, Saima Hilal, Eddie Chong, An Qi Toh, Narayanaswamy Venketasubramanian, Christopher Li-Hsian Chen, Leopold Schmetterer, Jacqueline Chua

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Prediction of adolescent subjective well-being: A machine learning approach by Lin He, Fei Xu, Yifeng Xu, Naixin Zhang, Chuanxin Liu, Zhixuan Chen, Lin An, Decheng Ren, Fan Yuan, Ruixue Yuan, Lei Ji, Yan Bi, Zhenming Guo, Gaini Ma, Fengping Yang, Liping Zhu, Gabirel Robert, Bo Bai, Tao Yu, Guang He

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An engineering method of aerodynamic heating prediction for hypersonic blunt body vehicles by Jimin Chen, Guoyi He

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A general approach for predicting the behavior of the Supreme Court of the United States. by Daniel Martin Katz, Michael J Bommarito, Josh Blackman

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On the estimation of inverse-probability-of-censoring weights for the evaluation of survival prediction error. by Thomas Prince, Andrea Bommert, Jörg Rahnenführer, Matthias Schmid

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Sensibilización hacia la discapacidad a través de un programa integrado de Educación Deportiva y Aprendizaje-Servicio (Disability awareness through an integrated program of sport... by Jorge Abellán, Yessica Segovia, David Gutiérrez, Luis Miguel García López

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Scanning Micromirror Calibration Method Based on PSO-LSSVM Algorithm Prediction by Yan Liu, Xiang Cheng, Tingting Zhang, Yu Xu, Weijia Cai, Fengtian Han

“…The objective is to establish a correspondence between the actual deflection angle of the micromirror and the output of the measurement system employing a regression algorithm, thereby enabling the prediction of the tilt angle of the micromirror. The decision factor (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><msup><mi>R</mi><mn>2</mn></msup></mrow></semantics></math></inline-formula>) for this model at the <i>x</i>-axis reaches a value of 0.9947.…”
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Application of machine learning in depression risk prediction for connective tissue diseases by Leilei Yang, Yuzhan Jin, Wei Lu, Xiaoqin Wang, Yuqing Yan, Yulan Tong, Dinglei Su, Kaizong Huang, Jianjun Zou

“…Additionally, the study provided an interpretation of the best-performing model using SHAP and developed a user-friendly R Shiny application ( https://macnomogram.shinyapps.io/Catboost/ ) to facilitate clinical use. …”
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PREDIKSI PRODUKTIVITAS JAGUNG DI INDONESIA SEBAGAI UPAYA ANTISIPASI IMPOR MENGGUNAKAN JARINGAN SARAF TIRUAN BACKPROPAGATION by Anjar Wanto

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