Application of machine learning algorithms in predicting new onset hypertension: a study based on the China Health and Nutrition Survey by Manhui Zhang, Xian Xia, Qiqi Wang, Yue Pan, Guanyi Zhang, Zhigang Wang

Subjects: “…machine learning algorithms…”
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Enhanced LSTM-based AI model for accurate dissolved oxygen prediction in aquaculture systems by Ala Saleh Alluhaidan, Prabu P, Romana Aziz, Shakila Basheer

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Model order reduction of boiler system using nature-inspired metaheuristic optimization of PID controller by Anurag Singh, Shekhar Yadav, Nitesh Tiwari, Dinesh Kumar Nishad, Saifullah Khalid

“…This study proposes a dual-stage optimization framework that integrates balanced truncation-based model order reduction with nature-inspired metaheuristic algorithms for PID controller tuning to address these issues. …”
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Using Prediction Confidence Factors to Enhance Collaborative Filtering Recommendation Quality by Dionisis Margaris, Dimitris Spiliotopoulos, Kiriakos Sgardelis, Costas Vassilakis

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Prediction of Electric Vehicle Mileage According to Optimal Energy Consumption Criterion by Oleksii Chkalov, Roman Dropa

“…Within this context, a novel model-based predictive approach is introduced for estimating electric vehicle energy consumption. …”
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Sensor-validated simulations predict fracture healing outcomes in an ovine model by Alicia Feist, Carla Hetreau, Manuela Ernst, Peter Varga, Peter Schwarzenberg

“…Overall, this study demonstrated successful application of the healing algorithm with validation of the simulation results to healing outcomes. …”
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Comparative Study on Prediction Models for Crack Opening Degree in Concrete Dam by HUANG Song, WU Jie, FANG Zhanchao, CHU Huaping, WU Yan'gang, XUE Zilong, HE Linbo

“…However, there are some deficiencies in the predictive power of the former and the theoretical explanation of the latter. …”
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Large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring by Zhenyu ZHU, Xiaorong ZHU, Yan CAI, Hongbo ZHU

“…In order to solve the problem of high collision rate and low timeliness of large-scale terminals access in the Internet of things, a large-scale terminal access algorithm based on slot ALOHA and adaptive access class barring (ACB) was proposed.Firstly, the services were classified based on the data from each terminal by the volume of the services processed and the requirements for delay.For the services that were not time-sensitive and whose effective data portion was less than 1 000 bit, a slot-based ALOHA-based competitive access method was used.ACB-based random access was used for the services that were time-sensitive or whose data portion was greater than 1 000 bit.On this basis, a method was proposed for predicting the access application volume based on the quantitative estimation, and dynamically adjusting the ACB control parameters based on this predicted value.Simulation results show that compared with other existing access algorithms, the proposed algorithm reduces the collision rate and improves the system access success rate under the premise of ensuring the high priority service delay requirements.…”
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Impact of surrogate model accuracy on performance and model management strategy in surrogate-assisted evolutionary algorithms by Yuki Hanawa, Tomohiro Harada, Yukiya Miura

Subjects: “…Surrogate-assisted evolutionary algorithms…”
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Application of Machine Learning Algorithms for Predicting the Dynamic Stiffness of Rail Pads Based on Static Stiffness and Operating Conditions by Isaac Rivas, Jose A. Sainz-Aja, Diego Ferreño, Víctor Calzada, Isidro Carrascal, Jose Casado, Soraya Diego

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Post-Anesthesia Care Unit (PACU) readiness predictions using machine learning: a comparative study of algorithms by Shahnam Sedigh Maroufi, Maryam Soleimani Movahed, Azar Ejmalian, Maryam Sarkhosh, Ali Behmanesh

“…Results he RF algorithm showed high performance in both prediction approaches. …”
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Modeling and prediction of tribological properties of copper/aluminum-graphite self-lubricating composites using machine learning algorithms by Huifeng Ning, Faqiang Chen, Yunfeng Su, Hongbin Li, Hengzhong Fan, Junjie Song, Yongsheng Zhang, Litian Hu

“…Herein, the LSBoost model based on the integrated learning algorithm presented the best prediction performance for friction coefficients and wear rates, with R 2 of 0.9219 and 0.9243, respectively. …”
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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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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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Prediction of Coiled Tubing Erosion Rate Based on Sparrow Search Algorithm Back-Propagation Neural Network Model by Yinping Cao, Fengying Fang, Guowei Wang, Wenyu Zhu, Yijie Hu

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Machine learning algorithms in constructing prediction models for assisted reproductive technology (ART) related live birth outcomes by Junwei Peng, Xiaoyujie Geng, Yiyue Zhao, Zhijin Hou, Xin Tian, Xinyi Liu, Yuanyuan Xiao, Yang Liu

“…Four machine learning (ML) algorithms including random forest, extreme gradient boosting, light gradient boosting machine and binary logistic regression were used to construct prediction models. …”
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Application of interpretable machine learning algorithms to predict macroangiopathy risk in Chinese patients with type 2 diabetes mellitus by Ningjie Zhang, Yan Wang, Hui Zhang, Huilong Fang, Xinyi Li, Zhifen Li, Zhenghang Huan, Zugui Zhang, Yongjun Wang, Wei Li, Zheng Gong

“…This study establish an approach based on machine learning algorithm in features selection and the development of prediction tools for diabetic macroangiopathy.…”
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Parking Demand Prediction Method of Urban Commercial-Office Complex Buildings Based on the MRA-BAS-BP Algorithm by Xiang Tang, Jianxiao Ma, Shun Zhou, Tianci Shan

“…Hence, in this paper, a combined algorithm based on the MRA model, beetle antennae search (BAS) algorithm, and BP neural network is proposed for demand prediction. …”
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Prediction of outpatient rehabilitation patient preferences and optimization of graded diagnosis and treatment based on XGBoost machine learning algorithm by Xuehui Fan, Ruixue Ye, Yan Gao, Kaiwen Xue, Zeyu Zhang, Jing Xu, Jingpu Zhao, Jun Feng, Yulong Wang

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