Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triang... by Bankole Olatosi, Jiajia Zhang, Xiaoming Li, Chen Liang, Jihong Liu, Peiyin Hung, Shan Qiao, Berry A Campbell, Myriam E Torres, Neset Hikmet

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A lightweight knowledge graph-driven question answering system for field-based mineral resource survey by Mingguo Wang, Chengbin Wang, Jianguo Chen, Bo Wang, Wei Wang, Xiaogang Ma, Jiangtao Ren, Zichen Li, Yicai Ye, Jiakai Zhang, Yue Wang

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Targeted urinary metabolomics combined with machine learning to identify biomarkers related to central carbon metabolism for IBD by Miao-Lin Lei, Guan-Wei Bi, Xiao-Lin Yin, Xiao-Lin Yin, Yue Wang, Yue Wang, Yue Wang, Zi-Ru Sun, Zi-Ru Sun, Zi-Ru Sun, Xin-rui Guo, Hui-peng Zhang, Xiao-han Zhao, Feng Li, Feng Li, Yan-Bo Yu

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Comparative study on inversion of the unsaturated hydraulic parameters using optimization and Bayesian estimation methods by KE Fengqiao, MAN Jun, ZENG Lingzao, WU Laosheng

“…However, this method is sensitive to the initial guess of parameters, and the obtained predictions occasionally deviate from the measurements. 2) The MCMC algorithm can provide state predictions which better fit measurements. …”
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Homogenization of Heterogeneous Tissue Scaffold: A Comparison of Mechanics, Asymptotic Homogenization, and Finite Element Approach by Z. Fang, C. Yan, W. Sun, A. Shokoufandeh, W. Regli

“…Actual prediction of the effective mechanical properties of tissue scaffolds is very important for tissue engineering applications. …”
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Modeling of the Power Station Boiler Combustion Efficiency Considering Multiple Work Condition with Feature Selection by TANG Zhenhao, WU Xiaoyan, CAO Shengxian

“…It is difficult for power station boiler efficiency to measure precisely A datadriven modeling method is proposed to establish the boiler combustion efficiency model, according to the machine learning theories A classification and regression trees (CART) algorithm provides correlated variables which have significant relation with the boiler combustion efficiency by data analysis Then, a KNearest Neighbor (KNN) classifies the samples to distinguish the data from different work conditions Based on the classified data, a least square support vector machine (LSSVM) optimized by differential evolution (DE) algorithm is proposed to establish a datadriven model (DDMMF) The parameters of LSSVM are optimized dynamically by DE to improve the model accuracy Finally, the prediction model is corrected dynamically for further improvement of the prediction accuracy The experimental results based on actual production data illustrate that the proposed approach can predict the boiler combustion efficiency accurately, which meets the requirements of boiler control and optimization…”
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A monitoring method of semiconductor manufacturing processes using Internet of Things–based big data analysis by Seok-Woo Jang, Gye-Young Kim

“…We modify the Line, Buzo, and Gray algorithm for classifying the time-series patterns. …”
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Modelling and Output Power Estimation of a Combined Gas Plant and a Combined Cycle Plant Using an Artificial Neural Network Approach by Vasileios Xezonakis, Olusegun David Samuel, Christopher Chintua Enweremadu

“…Researchers, academicians, and stakeholders have been unable to predict, ensure effective operation, and prevent power outages in COGAS due to the nonlinearity. …”
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Effectiveness of machine learning models in diagnosis of heart disease: a comparative study by Waleed Alsabhan, Abdullah Alfadhly

“…An extensive array of preprocessing techniques is thoroughly examined in order to optimize the predictive models’ quality and performance. Our study employs a wide range of ML algorithms, such as Logistic Regression (LR), Naive Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neibors (KNN), AdaBoost (AB), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), CatBoost (CB), Linear Discriminant Analysis (LDA), and Artificial Neural Network (ANN) to assess the predictive performance of these algorithms in the context of heart disease detection. …”
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Network archaeology: uncovering ancient networks from present-day interactions. by Saket Navlakha, Carl Kingsford

“…To overcome this limitation, we propose several novel algorithms to reconstruct the growth history of a present-day network. …”
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Analysing learning behaviour: A data-driven approach to improve time management and active listening skills in students by Vinayak Hegde, Vishrutha M, Pallavi M. Shanthappa, Rekha Bhat, Nisha Raveendran, Roshin C

“…Active listening is an indispensable skill in both educational and interpersonal contexts. Methodologically, the study began with comprehensive data collection through a survey, data preprocessing tasks and feature selection, followed by training and evaluating predictive models using various ML algorithms. …”
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Analyzing Patient Experience on Weibo: Machine Learning Approach to Topic Modeling and Sentiment Analysis by Xiao Chen, Zhiyun Shen, Tingyu Guan, Yuchen Tao, Yichen Kang, Yuxia Zhang

“…The majority of the posts described the interpersonal aspects of care (2947/4008, 73.53%); the five most frequently discussed topics were “health care professionals’ attitude,” “access to care,” “communication, information, and education,” “technical competence,” and “efficacy of treatment.” …”
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Securing IoT devices with zero day intrusion detection system using binary snake optimization and attention based bidirectional gated recurrent classifier by Ali Saeed Almuflih, Ilyos Abdullayev, Sergey Bakhvalov, Rustem Shichiyakh, Bibhuti Bhusan Dash, K. B. V. Brahma Rao, Kritika Bansal

“…DL-based models, particularly convolutional neural networks (CNN) with regularization techniques, direct this problem, offer a superior prediction value with unidentified data, and prevent over-fitting. …”
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QoS Routing in Telecommunications Networks by N. I. Listopad, O. A. Lavshuk

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An Adaptive Robust Event-Triggered Variational Bayesian Filtering Method with Heavy-Tailed Noise by Di Deng, Peng Yi, Junlin Xiong

“…The one-step state prediction probability density function and the measurement likelihood function are modeled as Student’s t-distributions. …”
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Efficient Data Collection Method in Sensor Networks by Keyan Cao, Haoli Liu, Yefan Liu, Gongjie Meng, Si Ji, Gui Li

“…An efficient data collection algorithm based on the position prediction of extreme learning machines is proposed. …”
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Empirical Reduced-Order Modeling for Boundary Feedback Flow Control by Seddik M. Djouadi, R. Chris Camphouse, James H. Myatt

“…This paper deals with the practical and theoretical implications of model reduction for aerodynamic flow-based control problems. …”
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Innovation of Urban Circular Economy Growth Path Based on Neural Network by Weifeng Qiu, Yi Yang

“…Moreover, it has obvious advantages over the traditional algorithm in terms of error and recall rate. Compared with the actual economic data, the economic data predicted by the model is quite consistent, and the prediction of future data by the model basically accords with the development goal of the regional master plan. …”
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Rapid and Accurate Acquisition of Equivalent Economic Scale in Flood Protection Area Based on Multi-source Big Data by ZHENG Yong, XU Zhangfan, ZHOU Xiaoxin, CAI Jihong, JI Hongxiang, QIN Yan

“…The results show that the accuracy of the population prediction model is 82%, and the accuracy of the GDP prediction model is 76%. …”
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A New State Estimation Method with Radar Measurement Missing by Hongjian Wang, Cun Li, Ying Wang, Qing Li, Xicheng Ban

“…The state transition model based on the historical observation data fit prediction is designed because the existing state estimation method can only use the system model prediction while the observation is missing. …”
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