Two-step hybrid model for monthly runoff prediction utilizing integrated machine learning algorithms and dual signal decompositions by Shujun Wu, Zengchuan Dong, Sandra M. Guzmán, Gregory Conde, Wenzhuo Wang, Shengnan Zhu, Yiqing Shao, Jinyu Meng

“…Long Short-Term Memory (LSTM) and eXtreme Gradient Boosting (XGBoost) algorithms were employed to predict monthly runoff generation in sub-basins delineated by the Soil and Water Assessment Tool (SWAT), which were subsequently integrated using a Recurrent Neural Network (RNN) for monthly runoff concentration prediction. …”
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Prediction Analysis of College Students’ Physical Activity Behavior by Improving Gray Wolf Algorithm and Support Vector Machine by Minjian Wang

“…In order to overcome the problem of low accuracy of traditional algorithms in prediction, this paper uses the improved gray wolf algorithm (IGWO) and support vector machine (SVM) for predictive analysis of college students' physical exercise behavior. …”
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Machine learning and microfluidic integration for oocyte quality prediction by Hassan Saffari, Davood Fathi, Peyman Palay, Hamid Gourabi, Rouhollah Fathi

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Analytical Device and Prediction Method for Urine Component Concentrations by Zhe Wang, Jianbang Huang, Qimeng Chen, Yuanhua Yu, Xuan Yu, Yue Zhao, Yan Wang, Chunxiang Shi, Zizhao Zhao, Dachun Tang

“…To tackle the low-accuracy problem with analyzing urine component concentrations in real time, a fully automated dipstick analysis device of urine dry chemistry was designed, and a prediction method combining an image acquisition system with a whale optimization algorithm (WOA) for BP neural network optimization was proposed. …”
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Nematophagous Fungi Occurrence: Prediction Using Bioclimatic Variables by Ana Martha Cruz-Avalos, Montserrat Chagoya-Sánchez, César Andres Ángel-Sahagún, Ana Isabel Mireles-Arriaga, Griselda Maki-Díaz, René Loredo-Portales, Jesús Hernández-Ruíz

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Improving prediction accuracy of open shop scheduling problems using hybrid artificial neural network and genetic algorithm by Mohammad Reza Komari Alaei, Reza Rostamzadeh, Kadir Albayrak, Zenonas Turskis, Jonas Šaparauskas

“…Furthermore, an examination of the average values of standard error revealed that the neural network model outperformed in terms of predictive accuracy. The estimated minimum time necessary for task completion, as determined by the neural network, was calculated to be 0.96699, facilitating an optimal condition for meeting the established objectives. …”
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Leveraging Azure Automated Machine Learning and CatBoost Gradient Boosting Algorithm for Service Quality Prediction in Hospitality by Avisek Kundu, Seeboli Ghosh Kundu, Santosh Kumar Sahu, Nitesh Dhar Badgayan

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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

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Biometric monitoring system based on K-means ＆MTLS-SVM algorithm by Jingming XIA, Lingling TANG, Ling TAN, Han ZHENG

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AI-Driven Predictive Maintenance in Mining: A Systematic Literature Review on Fault Detection, Digital Twins, and Intelligent Asset Management by Luis Rojas, Álvaro Peña, José Garcia

Subjects: “…predictive maintenance…”
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Comparative analysis of machine learning models for predicting river water quality: a case study of the Zayandeh Rood River by Elham Fazel Najafabadi, Paria Shojaei, Mojgan Askarizadeh

Subjects: “…Water quality prediction…”
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Evaluating Machine Learning Models for Predicting Hardness of AlCoCrCuFeNi High-Entropy Alloys by Uma Maheshwera Reddy Paturi, Muhammad Ishtiaq, Pasupuleti Lakshmi Narayana, Anoop Kumar Maurya, Seong-Woo Choi, Nagireddy Gari Subba Reddy

“…This study evaluates the predictive capabilities of various machine learning (ML) algorithms for estimating the hardness of AlCoCrCuFeNi high-entropy alloys (HEAs) based on their compositional variables. …”
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Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges by Ebenezer Afrifa‐Yamoah, Eric Adua, Emmanuel Peprah‐Yamoah, Enoch O. Anto, Victor Opoku‐Yamoah, Emmanuel Acheampong, Michael J. Macartney, Rashid Hashmi

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Feature Selection Using a Genetic Algorithms and Fuzzy logic in Anti-Human Immunodeficiency Virus Prediction for Drug Discovery by Houda Labjar, Mohammad Al-Sarem, Mohamed Kissi

“…This paper presents an approach that uses both genetic algorithm (GA) and fuzzy inference system (FIS), for feature selection for descriptor in a quantitative structure activity relationships (QSAR) classification and prediction problem. …”
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Unleashing quantum algorithms with Qinterpreter: bridging the gap between theory and practice across leading quantum computing platforms by Wilmer Contreras-Sepúlveda, Braulio Misael Villegas-Martínez, Sandra Gesing, José Javier Sánchez-Mondragón, Juan Carlos Sánchez-Pérez, Claudia Andrea Vidales-Basurto, J. Jesús Escobedo-Alatorre, Angel David Torres-Palencia, Omar Palillero-Sandoval, Jacob Licea-Rodriguez, Néstor Lozano-Crisóstomo, Julio César García-Melgarejo, Eddie Nelson Palacios-Perez

“…Due to the inherent complexity and divergence from classical computing, several major quantum computing libraries have been developed to implement quantum algorithms, namely IBM Qiskit, Amazon Braket, Cirq, PyQuil, and PennyLane. …”
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Algorithm for selecting predictors and prognosis of atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting by B. I. Geltser, K. I. Shakhgeldyan, V. Yu. Rublev, B. O. Shcheglov, E. A. Kokarev

“…These values in best model based on multivariate LR were lower (0,75; 0,7; 0,68 and 0,7, respectively).Conclusion. The developed algorithm for selecting predictors made it possible to verify significant predictive ranges and weight coefficients characterizing their influence on PAF development. …”
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Long and short term fault prediction using the VToMe-BiGRU algorithm for electric drive systems by Lihui Zheng, Xu Fan, Zongshan Kang, Xinjun Jin, Wenchao Zheng, Xiaofen Fang

“…Specifically, the VToMe algorithm achieves stable detection of medium to long term system faults, while the BiGRU network achieves rapid fault prediction in the short term. …”
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Charging pile fault prediction method combining whale optimization algorithm and long short-term memory network by Yansheng Huang, Atthapol Ngaopitakkul, Suntiti Yoomak

“…., the model optimization process stays in the non-optimal regional minimum) in complex parameter space, the study innovatively proposes a hybrid prediction model that combines the whale optimization algorithm with the gated recurrent unit-long short-term memory neural network. …”
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Health State Prediction of Lithium-Ion Battery Based on Improved Sparrow Search Algorithm and Support Vector Regression by Deyang Yin, Xiao Zhu, Wanjie Zhang, Jianfeng Zheng

“…To enhance prediction performance, this paper introduces an SOH prediction model based on an improved sparrow algorithm and support vector regression (ISSA-SVR). …”
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An Ultra-Short-Term Wind Power Prediction Method Based on the Fusion of Multiple Technical Indicators and the XGBoost Algorithm by Xuehui Wang, Yongsheng Wang, Yongsheng Qi, Jiajing Gao, Fan Yang, Jiaxuan Lu

“…However, its inherent volatility and unpredictability pose challenges for accurate short-term prediction. This study proposes an ultra-short-term wind power prediction framework that integrates multiple technical indicators with the extreme gradient boosting (XGBoost) algorithm. …”
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