Development and validation of a risk score to predict adverse birth outcomes using maternal characteristics in northwest Ethiopia: a retrospective follow-up study by Rahel Mulatie Anteneh, Getayeneh Antehunegn Tesema, Ayenew Molla Lakew, Sefineh Fenta Feleke

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Development and Validation of a Nomogram for Predicting the Survival of Patients with Ovarian Yolk Sac Tumor: A Surveillance, Epidemiology, and End Results-Based Retrospective Stud... by Haiyan Chen, Dan Zhong, Dake Li

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Development and external validation of a machine learning-based model to predict postoperative recurrence in patients with duodenal adenocarcinoma: a multicenter, retrospective coh... by Xu Liu, Qifeng Xiao, Zongting Gu, Xin Wu, Chunhui Yuan, Xiaolong Tang, Fanbin Meng, Dong Wang, Ren Lang, Kaiqing Guo, Xiaodong Tian, Yu Zhang, Enhong Zhao, Zhenzhou Wu, Jingyong Xu, Ying Xing, Feng Cao, Chengfeng Wang, Jianwei Zhang

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Development of Mathematical and Computational Models for Predicting Agricultural Soil–Water Management Properties (ASWMPs) to Optimize Intelligent Irrigation Systems and Enhance Cr... by Brigitta Tóth, Oswaldo Guerrero-Bustamante, Michel Murillo, Jose Duque, Rodrigo Polo-Mendoza

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Cooperative maximum adhesion tracking control for multi-motor electric locomotives by Leiting Zhao, Yongxiang Wang, Kan Liu, Liran Li, Jingyuan Zhan, Qingliang Liu

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Bayesian inference-based wear prediction method for plain bearings under stationary mixed-friction conditions by Florian König, Florian Wirsing, Georg Jacobs, Rui He, Zhigang Tian, Ming J. Zuo

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Improving ENSO Prediction at Longer Lead Times: Role of Off‐Equatorial South Pacific Heat Content by Guang Yang, Yishuai Jin, Yingying Zhao, Emanuele Di Lorenzo, Michael J. McPhaden, Xiaopei Lin

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Deciphering organic substrate impacts in Anammox systems: A machine learning driven framework for predictive classification and process mechanism analysis by Zemin Li, Yulun Wu, Tao Chen, Bo Yan, Chaohai Wei

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Comparison of Deep Learning and Gradient Boosting: ANN Versus XGBoost for Climate‐Based Dengue Prediction in Bangladesh by Arman Hossain Chowdhury

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Machine Learning-Based Software for Predicting <i>Pseudomonas</i> spp. Growth Dynamics in Culture Media by Fatih Tarlak

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Harnessing Electronic Health Records and Artificial Intelligence for Enhanced Cardiovascular Risk Prediction: A Comprehensive Review by Ming‐Lung Tsai, Kuan‐Fu Chen, Pei‐Chun Chen

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Phylogenetically informed predictions outperform predictive equations in real and simulated data by Jacob D. Gardner, Joanna Baker, Chris Venditti, Chris L. Organ

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Comparative analysis of machine learning algorithms for predicting tibial intramedullary nail length from patient characteristics by Yujian Hui, Hengda Hu, Jinghua Xiang, Xingye Du

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Computational predictions of magnetic resonance acoustic radiation force imaging for breast cancer focused ultrasound therapy by Chloe K. Nelson, Michelle Kline, Allison Payne, Christopher R. Dillon

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A multimodal deep learning architecture for predicting interstitial glucose for effective type 2 diabetes management by Muhammad Salman Haleem, Daphne Katsarou, Eleni I. Georga, George E. Dafoulas, Alexandra Bargiota, Laura Lopez-Perez, Miguel Rujas, Giuseppe Fico, Leandro Pecchia, Dimitrios Fotiadis, Gatekeeper Consortium

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Methodological Aspects of Predictive Mineragenic Studies Using Earth Remote Sensing Data by Petrov Vladislav, Ustinov Stepan, Minaev Vasilii

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Machine learning in modeling, analysis and control of electrochemical reactors: A tutorial review by Wenlong Wang, Zhe Wu, Dominic Peters, Berkay Citmaci, Carlos G. Morales-Guio, Panagiotis D. Christofides

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Integrated Analysis of Permeability Reduction Caused by Polymer Retention for Better Understanding Polymer Transport by ByungIn Choi, Jinsuk Choi, Kun Sang Lee

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Machine Learning-Based Surface Roughness Prediction in Turning of Hardened AISI 4340 Steels: Incorporating Tool Wear via Cutting Length by A. Ginting, M.S. Kasim, B.T. Hang Tuah Baharudin

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Adversarial Sparse Teacher: Defense Against Distillation-Based Model Stealing Attacks Using Adversarial Examples by Eda Yilmaz, Hacer Yalim Keles

“…Typically, a model generates a peak in its output corresponding to its prediction. …”
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