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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A machine learning-based model for predicting survival in patients with Rectosigmoid Cancer. by Yifei Wang, Bingbing Chen, Jinhai Yu

“…After evaluating each model, the prediction model based on XGBoost was determined to be the optimal model, with AUC of 0.7856, 0.8484, and 0.796 at 1, 3, and 5 years. …”
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Monthly Water Level Prediction Based on ESMD-VMD-ESN Hybrid Model by LI Ang, ZHANG Kun, SANG Yuting, BI Wan

“…Water level sequence contain complex features of multiple frequency information.To improve the prediction accuracy of the water level sequences,a combined model was developed based on Extreme-point Symmetric Mode Decomposition (ESMD),Variational Mode Decomposition (VMD) and Echo State Network (ESN),namely ESMD-VMD-ESN.And it was applied to forecast water level of the Taipuzha station in the upper reaches of Taipu River.The predictive effect of the “first decomposition-second decomposition-prediction-reconstruction” model was explored by comparing it with a single model ESN and the combination model ESMD-ESN.The results show that ESMD-VMD-ESN has the highest accuracy,followed by ESMD-ESN,and the lowest ESN accuracy.Compared with the ESN,the Willmott's Index of Agreement (WIA) and Pearson Correlation Coefficient (PCC) of ESMD-ESN respectively increased by 51% and 11%,the Mean Absolute Error (MAE) and Root Mean Squard Error (RMSE) of ESMD-ESN respectively decreased by 14% and 45%.ESMD can effectively simplify the water level sequence and reduce the prediction error.Compared with the ESMD-ESN,the WIA and PCC of ESMD-VMD-ESN respectively increased by 5% and 10%,the MAE and RMSE of ESMD-ESN respectively decreased by 52% and 50%.VMD can further simplify the highest frequency component of ESMD and improving the model prediction accuracy.In conclusion,the combined model ESMD-VMD-ESN has well applicability and stability in the monthly water level prediction.…”
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Failure prediction of T-peel adhesive joints by different cohesive laws and modelling approaches by Alessandro Pirondi

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Navigating tenses in Bengali sentences: A stacked ensemble model for enhanced prediction by Umme Ayman, Md. Nahid Hasan, Ms. Nusrat Khan, Ms. Chayti Saha, Md. Fayejullah

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Integrated clinical and proteomic-based model for diagnostic and prognostic prediction in pRCC by Zeya Xu, Linhui Zhang, Jiacheng Lyu, Maoping Cai, Tao Ji, Lin Bai, Liqing Li, Yao Zhu, Huashan Xu, Subei Tan, Hualei Gan, Shujuan Ni, Wenhao Xu, Xi Tian, Aihetaimujiang Anwaier, Beiyan Liu, Qinqin Hou, Guohai Shi, Hailiang Zhang, Jianyuan Zhao, Dingwei Ye, Yuanyuan Qu, Chen Ding

“…In summary, this study provides a comprehensive plasma proteomic analysis and establishes diagnostic and prognostic predictive models for pRCC.…”
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Prediction and Analysis of Ship Engine Vibration Signals Based on Prompted Language Models by Yunzhou Zhang, Yanghui Tan, Shuai Hao, Hong Zeng, Peisheng Sang, Ya Gao

“…The proposed approach was compared with traditional models, including LSTM, RNN, and SVR, in vibration signal prediction tasks. …”
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Machine learning prediction model for lateral lymph node metastasis in rectal cancer by Longchun Dong, Shiyong Du, Hongjie Yang, Hongjie Yang, Hongjie Yang, Hongjie Yang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Xipeng Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Zhichun Zhang, Shuan Geng, Shuan Geng, Shuan Geng, Shuan Geng, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Yuanda Zhou, Peng Li, Peng Li, Peng Li, Peng Li, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Qingsheng Zeng, Yi Sun, Yi Sun, Yi Sun, Yi Sun, Peishi Jiang

“…Extramural vascular invasion (EMVI), MRI clinical N stage (MRI cN stage), and the number of enlarged lateral lymph nodes (NoELLN) were used to construct the logistic prediction model. The model achieved an accuracy of 0.62, sensitivity of 0.80, specificity of 0.43, and area under the curve (AUC) of 0.80 in predicting the pathological characteristics of lateral lymph nodes using the test dataset.ConclusionEMVI, MRI cN stage, and NoELLN are significant predictive factors for predicting lateral lymph node pathology in patients with rectal cancer. …”
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Reconstruction and Prediction of Regional Population Migration Neural Network Model with Age Structure by Cuiying Li, Yulin Wu, Yi Cheng, Yandong Guo, Kun Wei, Jie Zhao

“…Based on artificial neural networks, this article proposes a class of population models with age structure described by partial differential equations to predict the future trends of regional population changes. …”
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Indirect modeling of derived outcomes: Are minor prediction discrepancies a cause for concern? by John P. Prybylski

“…Because these derivations are indirectly predicted from the model, they are valuable tests for misspecification when used in visual or numeric predictive checks (V/NPCs). …”
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An efficient method for predicting the morphology of proppant packs based on a surrogate model by Tao ZHANG, Hangyu ZHOU, Yifan ZHANG, Jianchun GUO, Haoran GOU, Tang TANG

“…Through correlation analysis, the primary factors influencing these characteristic parameters were identified. Intelligent proxy models for the prediction of proppant placement patterns were established on the basis of the cascade neural network, including a time-concentration model for predicting particle volume fraction and a displacement-height model for predicting particle placement height. …”
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IVIM-DWI-based radiomic model for preoperative prediction of hepatocellular carcinoma differentiation by ZHUANG Yuxiang, LI Xiaofeng, ZHOU Daiquan

“…Objective To construct a radiomic model based on intravoxel incoherent motion diffusion-weighted imaging (IVIM-DWI) for preoperative prediction of hepatocellular carcinoma (HCC) differentiation and validate its clinical value. …”
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Machine learning-based e-commerce platform repurchase customer prediction model. by Cheng-Ju Liu, Tien-Shou Huang, Ping-Tsan Ho, Ping-Tsan Ho, Jui-Chan Huang, Ching-Tang Hsieh

“…In this paper, we first combine the single model, and then use the model fusion algorithm to fuse the prediction results of the single model. …”
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ECG‐based epileptic seizure prediction: Challenges of current data‐driven models by Sotirios Kalousios, Jens Müller, Hongliu Yang, Matthias Eberlein, Ortrud Uckermann, Gabriele Schackert, Witold H. Polanski, Georg Leonhardt

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Prediction of Metastasis in Paragangliomas and Pheochromocytomas Using Machine Learning Models: Explainability Challenges by Carmen García-Barceló, David Gil, David Tomás, David Bernabeu

“…One of the main issues with paragangliomas and pheochromocytomas is that these tumors have up to a 20% rate of metastatic disease, which cannot be reliably predicted. While machine learning models hold great promise for enhancing predictive accuracy, their often opaque nature limits trust and adoption in critical fields such as healthcare. …”
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Adaptive machine learning framework: Predicting UHPC performance from data to modelling by Yinzhang He, Shaojie Gao, Yan Li, Yongsheng Guan, Jiupeng Zhang, Dongliang Hu

“…Ultra-High Performance Concrete (UHPC) is vital for next-generation infrastructure, necessitating complex interaction modeling beyond empirical methods. This study proposes an interpretable machine learning (ML) framework to predict the compressive strength (CS) of UHPC and analyze input variable influences. …”
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Structural Constraints in Current Stomatal Conductance Models Preclude Accurate Prediction of Evapotranspiration by Pushpendra Raghav, Mukesh Kumar, Yanlan Liu

“…In contrast, a ML approach, wherein the model structure is learned from the data, outperforms traditional models, thus highlighting that there still is significant room for improvement in the structure of traditional models for predicting ET. …”
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Thermal conduction behavior and prediction model of scrap tire rubber-sand mixtures by Tao Zhang, Yang Chen, Yu-Ling Yang, Cai-Jin Wang, Guo-Jun Cai

“…Several series of thermal probe tests were conducted on scrap rubber tire-sand mixtures with varied rubber contents, moisture contents, and dry densities. A predictive model was proposed by resorting to the artificial neural network technology to capture the thermal conductivity data. …”
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Development and validation of prediction models for diabetic retinopathy in type 2 diabetes patients. by Shadi Naderyan Feˈli, Mohammad Hassan Emamian, Mehdi Yaseri, Hamid Riazi-Esfahani, Hassan Hashemi, Akbar Fotouhi, Kamran Yazdani

“…<h4>Background and objective</h4>Prediction models enable healthcare providers to perform early risk stratification. …”
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