PEMFC RUL Prediction for Non-Stationary Time Series Based on Crossformer Model by Ning Zhou, He Zeng, Zefei Zheng, Ke Wang, Jianxin Zhou

“…In this paper, we propose a PEMFC RUL prediction model based on the Crossformer for non-stationary time series (De-stationary-Crossformer). …”
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Hybrid Intelligence Model Based on Image Features for the Prediction of Flotation Concentrate Grade by YaLin Wang, XiaoFang Chen, XiaoLing Zhou, WeiHua Gui, Louis Caccetta, Honglei Xu

“…In flotation processes, concentrate grade is the key production index but is difficult to be measured online. The mechanism models reflect the basic tendency of concentrate grade changes but cannot provide adequate prediction precision. …”
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NECOP Propagation Experiment: Rain-Rate Distributions Observations and Prediction Model Comparisons by J. S. Ojo, S. E. Falodun

“…The empirical distribution functions were compared with cumulative distribution functions generated using four different rain-rate distribution models. It is found that although each of the models shows similar qualitative features at lower exceedance of time, the characteristic at higher time percentages shows quantitative difference from the experimental data except the improved version of Moupfouma model. …”
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A Dendritic Neural Network-Based Model for Residential Electricity Consumption Prediction by Ting Jin, Rui Xu, Kunqi Su, Jinrui Gao

“…In this study, a dendritic neural network-based model (DNM), combined with the AdaMax optimization algorithm, is used to predict residential electricity consumption. …”
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Deep Learning Shield Attitude Prediction Model Based on Grey Correlation Analysis by Ke MAN, Zongxu LIU, Yan SHANG, Zhifei SONG, Xiaoli LIU, Bao SU

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Urban Wastewater utilization Potential Prediction Modeling Based on Blind-number Theory by HE Xinchun, SHAO Donggua, LI Jie

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Prediction of loess collapsibility coefficient using bayesian optimized random forest model by Wan Zhang, Jiangtao Guo, Zhaopeng Li, Ruifang Cheng, Cuiping Ning, Hongfeng Niu, Ze Liu

“…These findings indicate that the proposed model is capable of reliably predicting the collapsibility coefficient of loess.…”
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Safety Prejudging Method for Power Transformer Based on Multi-Prediction Model Fusion by Dianyang LI, Yujie ZHANG, Shanyuan WANG, Jian FENG, Hongzhe WANG, Ling QIN

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Variational autoencoder-based model improves polygenic prediction in blood cell traits by Xiaoqi Li, Elena Kharitonova, Minxing Pang, Jia Wen, Laura Y. Zhou, Laura Raffield, Haibo Zhou, Huaxiu Yao, Can Chen, Yun Li, Quan Sun

“…Polygenic risk scores (PRSs) offer a way to aggregate information across the genome, enabling personalized risk prediction for complex traits and diseases. However, conventional PRS calculation methods that rely on linear models are limited in their ability to capture complex patterns and interaction effects in high-dimensional genomic data. …”
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Distinct creep regimes of methane hydrates predicted by a monatomic water model by Henrik Andersen Sveinsson, Pinqiang Cao

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Daily Automated Prediction of Delirium Risk in Hospitalized Patients: Model Development and Validation by Kendrick Matthew Shaw, Yu-Ping Shao, Manohar Ghanta, Valdery Moura Junior, Eyal Y Kimchi, Timothy T Houle, Oluwaseun Akeju, Michael Brandon Westover

“…ResultsThe boosted tree model achieved the greatest predictive power, with an area under the receiver operator characteristic curve (AUROC) of 0.92 (95% CI 0.913-9.22), followed by the random forest (AUROC 0.91, 95% CI 0.909-0.918), multilayer perceptron (AUROC 0.86, 95% CI 0.850-0.861), and logistic regression (AUROC 0.85, 95% CI 0.841-0.852). …”
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IGWO-MSVR model for predicting stress in coal seam during drilling process by Jian Tan, Yanfeng Geng, Liangke Xu

“…Furthermore, Back Propagation Neural network model (BP), Spatial Autoregressive model (SAR) and MSVR model were adopted to perform the stress prediction, and the stress prediction accuracy from these models was analyzed. …”
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Mammalian piRNA target prediction using a hierarchical attention model by Tianjiao Zhang, Liang Chen, Haibin Zhu, Garry Wong

“…Finally, this model was utilized to predict the targets of all mouse and human piRNAs available in the piRNA database. …”
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Research on Urban Rainfall Runoff Pollution Prediction Model Based on Feature Fusion by Junping Yao, Tianle Sun

“…The neural network algorithm is optimized and trained according to the sample data to obtain the sample features; the sample data are predicted according to the extracted sample features, and the prediction model is generated by using the feature fusion technology for two groups of prediction results to generate the prediction model and realize the water drop prediction. …”
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Validation of a Chemotherapy Toxicity Prediction Model in Older Adults With Cancer in Taiwan by Chieh-Ying Chang MD, Yu-Shin Hung MD, Ming-Chung Kuo MD, Wen-Chi Chou MD, PhD

“…Introduction The Cancer and Aging Research Group (CARG) model predicts chemotherapy-related toxicities in older patients; however, its applicability has not been validated in Taiwanese patients. …”
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Comprehensive Performance Assessment of Multi-Neural Ensemble Model for Mortality Prediction in ICU by M. Fathima Begum, Subhashini Narayan

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A Prediction Model Based on the Long Electrode Source for Fault Anomaly in Tunnel by Daiming Hu, Hao Liu, Xiaodong Yang, Mingxin Yue

“…The accuracy of the algorithm is verified by using the long electrode source model. By a lot of numerical simulations, a prediction model of a long electrode source for tunnel detection is firstly proposed. …”
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Cycle-efficient modeling for degradation staging and early life prediction of lithium batteries by Can Wang, Renjie Wang, Jianming Li, Zhuangzhuang Li, Quanqing Yu

“…An effective and time-saving early life prediction model is crucial for rapid battery assessment. …”
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Retracted: Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model by null Complexity

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Application of the RFA-XGBoost model in predicting potential complaint users in mobile network by ZHANG Peng, GAO Yuan

“…At the same time, the recursive feature augmented XGBoost (RFA-XGBoost) prediction model was proposed for the prediction of potential complaint users. …”
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