Enhanced data-driven shear strength predictive modeling framework for RCDBs using explainable boosting-based ensemble learning algorithms coupled with Bayesian optimization by Imad Shakir Abbood, Noorhazlinda Abd Rahman, B.H. Abu Bakar

“…Despite over 70 years of investigation into the behavior of reinforced concrete deep beams (RCDBs), it remains challenging to accurately predict their shear strength (SS) due to the underlying intricate mechanism. …”
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Practical Application of Complementary Regulation Strategy of Run-of-River Small Hydropower and Distributed Photovoltaic Based on Multi-Scale Copula-MPC Algorithm by Xianpin Zhu, Weibo Li, Shuai Cao, Wei Xu

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Design of a Machine Learning-Based Platform for Currency Market Prediction: A Fundamental Design Model by Kevin Gordillo-Orjuela, Paulo Alonso Gaona-García, Carlos Enrique Montenegro-Marín

“…This growth has made it possible to train several techniques that increasingly allow us to improve predictions according to the criteria that each algorithm supports and can cover. …”
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Interpretable prognostic modeling for long-term survival of Type A aortic dissection patients using support vector machine algorithm by Hao Cai, Yue Shao, Xuan-yu Liu, Chang-ying Li, Hao-yu Ran, Hao-ming Shi, Cheng Zhang, Qing-chen Wu

“…Abstract Objectives This study aims to develop a reliable and interpretable predictive model for long-term survival in Type A aortic dissection (TAAD) patients, utilizing machine learning (ML) algorithms. …”
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Model Data Mining sebagai Prediksi Penyakit Hipertensi Kehamilan dengan Teknik Decision Tree by Ari Muzakir, Rika Anisa Wulandari

Subjects: “…Data mining, Decision tree, C4.5 algorithms, Prediction, Pregnancy…”
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Diversity is key: fantasy football dream teams under budget constraints by Josef Gullholm, Jil Klünder, Julie Rowlett, Jonathan Stålberg

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Research on the Time-varying Meshing Stiffness Algorithm of Spur Gears with Large Tooth Number Cracks by Jiao Wei, Xu Kejun, Qin Haiqin

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Research on Ginger Price Prediction Model Based on Deep Learning by Fengyu Li, Xianyong Meng, Ke Zhu, Jun Yan, Lining Liu, Pingzeng Liu

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Time Prediction in Ship Block Manufacturing Based on Transfer Learning by Jinghua Li, Pengfei Lin, Dening Song, Zhe Yan, Boxin Yang, Lei Zhou

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Multiple machine learning algorithms identify 13 types of cell death-critical genes in large and multiple non-alcoholic steatohepatitis cohorts by Renao Jiang, Longfei Dai, Xinjian Xu, Zhen Zhang

“…Consensus clustering analysis was then used to stratify patients with NASH into distinct phenotypic subgroups based on expression levels of these genes. Results A NASH prediction model, developed using the random forest (RF) algorithm, demonstrated high diagnostic accuracy across multiple cohorts. …”
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Integration of epigenomic and genomic data to predict residual feed intake and the feed conversion ratio in dairy sheep via machine learning algorithms by Pablo Augusto de Souza Fonseca, Aroa Suarez-Vega, Cristina Esteban-Blanco, Héctor Marina, Rocío Pelayo, Beatriz Gutiérrez-Gil, Juan-José Arranz

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Integrative machine learning approach for forecasting lung cancer chemosensitivity: From algorithm to cell line validation by Jinghong Chen, Yonglin Yi, Chunqian Yang, Haoxuan Ying, Jian Zhang, Anqi Lin, Ting Wei, Peng Luo

“…Methods: This study developed a model to predict chemotherapy response in lung cancer patients by integrating multi-omics and clinical data from the Genomics of Drug Sensitivity in Cancer database, employing 45 machine learning algorithms. …”
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Investigation of Micro-Scale Damage and Weakening Mechanisms in Rocks Induced by Microwave Radiation and Their Associated Strength Reduction Patterns: Employing Meta-Heuristic Optimization Algorithms and Extreme Gradient Boosting Models by Zhongyuan Gu, Xin Xiong, Chengye Yang, Miaocong Cao

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Influence of Modal Decomposition Algorithms on Nonlinear Time Series Machine Learning Prediction Models in Engineering: A Case Study of Subway Tunnel Settlement by Qingmeng Shen, Yuming Wu, Limin Wan, Qian Chen, Yue Li, Zichao Liao, Wenbo Wang, Feng Li, Tao Li, Jiajun Shu

“…The results show that the prediction model with the integrated decomposition algorithm reduces the RMSE and MAE by 33% and 37%, respectively, which significantly improves the prediction accuracy and generalization ability of the neural network to meet the demand of practical engineering prediction and simultaneously enhances the risk warning ability of the model.…”
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Frequency Limited & Weighted Model Reduction Algorithm With Error Bound: Application to Discrete-Time Doubly Fed Induction Generator Based Wind Turbines for Power System by Sajid Bashir, Sammana Batool, Muhammad Imran, Mian Ilyas Ahmad, Fahad Mumtaz Malik, Muhammad Salman, Abdul Wakeel, Usman Ali

“…The proposed work produces steady and precise outcomes in contrast to conventional reduction methods, which shows the efficacy of the proposed algorithm.…”
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Aplicación de un algoritmo de reducción de grafos al Método de los Grafos Dicromáticos//Applying a graph reduction algorithm to Dichromatic Graphs Method by Rafael Rodríguez-Puente, Sergio Marrero-Osorio, Manuel Lazo-Cortés

“…For this, we define the equivalence relations and partitions necessary for the application of a graph reduction algorithm to a graph obtained from the application of Dichromatic Graph Method, this method has used in terms of rational design and computational problem solving in mechanical engineering. …”
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An optimal control method considering degradation and economy based on mutual learn salp swarm algorithm of an islanded zero‐carbon DC microgrid by Ying Han, Yujing Hou, Luoyi Li, Weifeng Meng, Qi Li, Weirong Chen

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Spatial-temporal evolution characteristics and driving factors of carbon emission prediction in China-research on ARIMA-BP neural network algorithm by Sanglin Zhao, Zhetong Li, Hao Deng, Xing You, Jiaang Tong, Bingkun Yuan, Zihao Zeng

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Robust Parameter Inversion and Subsidence Prediction for Probabilistic Integral Methods in Mining Areas by Xinjian Fang, Rui Yang, Mingfei Zhu, Jinling Duan, Shenshen Chi

“…Validation on the Huainan mining case study showed that the IGGIII-BFGS method achieved a 25.8% reduction in subsidence RMSE compared to standard BFGS, with predicted curves exhibiting strong agreement with field measurements. …”
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Hybrid Feature Selection and Classifying Stages through Electrocardiogram (ECG) Signal for Heart Disease Prediction by Babu Kumar, Radhakrishnan Soundararajan, Kanimozhi Natesan, Roobini Maridhas Santhi

“…Clinical data analysis must predict cardiovascular disease. Machine learning (ML) may aid decision making and prediction using the healthcare field’s massive data set. …”
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