MATHEMATICAL MODEL TO PREDICT OF THE SINGLE SCREW EXTRUDER DESIGN SPECIFICATIONS FOR FISH OIL EXTRACTION by Tarek FOUDA

“…Mathematical and prediction models provide solutions for designing machines and help determine the design factors for many models of different capacities and sizes. …”
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A Hybrid Network Analysis and Machine Learning Model for Enhanced Financial Distress Prediction by Saba Taheri Kadkhoda, Babak Amiri

“…The proposed model showcases superior predictive capabilities and offers a holistic understanding of the dynamic interactions among financial entities. …”
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Optimizing treatment sequencing in multiple myeloma: a novel model to predict survival outcomes by Maria Teresa Petrucci, Sara Bringhen, Cristina Entrala Cerezo, João Mendes, Patrizio Armeni

“…Furthermore, there is a lack of direct comparative clinical trial data to guide effective treatment sequencing. A novel model is described comparing alternative MM treatment sequences to optimize patient outcomes.Methods The model compares treatment sequences and outcomes for adults with newly diagnosed transplant-eligible (TE) or transplant-ineligible (TIE) MM across four treatment lines (first-line [FL] to fourth-line [4L]). …”
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Fungi-Kcr: a language model for predicting lysine crotonylation in pathogenic fungal proteins by Yong-Zi Chen, Yong-Zi Chen, Xiaofeng Wang, Zhuo-Zhi Wang, Haixin Li, Haixin Li

“…Moreover, our results indicate that a general predictive model performs better than species-specific models. …”
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Prediction of Landslide Displacement Based on GreyRelational Analysis and VMD-SES-BP Model by AN Bei, JIANG Yanan, ZENG Qifei

“…Aiming at the “stepped” landslide displacement in the Three Gorges area,this paper proposes a new landslide displacement time series prediction model,namely VMD-SES-BP prediction model by combining variational mode decomposition (VMD),second exponential smoothing (SES),and BP neural network (BPNN);conducts the VMD of the GPS monitoring displacement data of landslide at Baishuihe River of the Three Gorges through this model to obtain the trend component and other sub-sequence components;makes rolling predictions of trend components by the SES,determines the influencing factors of other displacement components of the landslide through gray relational analysis (GRA),and learns and predicts by considering it as the training sample of BPNN.Comparing the prediction results of each component with the true value,the average relative error of prediction is 0.78%,the mean square error is 3.14 cm,and the correlation coefficient is 0.986.The experimental results show that the model is well applicable to the prediction of “stepped” landslide displacement,with high prediction accuracy,which provides a certain reference value for landslide displacement prediction.…”
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A Model for Predicting IoT User Behavior Based on Bayesian Learning and Neural Networks by Xin Xu, Chengning Huang, Yuquan Zhu

“…To facilitate the allocation of energy and resources in the Internet of Things system, this paper presents a model for predicting user behavior in Internet of Things environments. …”
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A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin by Hugo J. M. Miniere, Ernesto A. B. F. Lima, Guillermo Lorenzo, David A. Hormuth II, Sophia Ty, Amy Brock, Thomas E. Yankeelov

“…Thus, our model can capture and predict the spatiotemporal dynamics of MCF7 cells treated with doxorubicin in an in-vitro setting.…”
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AMFGNN: an adaptive multi-view fusion graph neural network model for drug prediction by Fang He, Fang He, Fang He, Fang He, Lian Duan, Lian Duan, Lian Duan, Lian Duan, Guodong Xing, Guodong Xing, Guodong Xing, Guodong Xing, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Xiaojing Chang, Huixia Zhou, Huixia Zhou, Huixia Zhou, Huixia Zhou, Mengnan Yu, Mengnan Yu, Mengnan Yu, Mengnan Yu

“…However, existing methods for drug-disease association prediction still face limitations in feature representation, feature integration, and generalization capabilities.MethodsTo address these challenges, we propose a novel model named AMFGNN (Adaptive Multi-View Fusion Graph Neural Network). …”
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A web-based surveillance model of eosinophilic meningitis: future prediction and distribution patterns by Noppadol Aekphachaisawat, Kittisak Sawanyawisuth, Chalongchai Phitsanuwong, Sittichai Khamsai, Paiboon Chattakul, Verajit Chomtmongkol, Somsak Tiamkao, Panita Limpawattana, Vichai Senthong, Jarin Chindaprasirt, Chetta Ngamjarus

“…Results: we developed autoregressive integrated moving average (ARIMA) models and seasonal ARIMA (SARIMA) models. The best model was used for predicting numbers of future cases. …”
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Development of an early mortality risk prediction model for pediatric patients with secondary hemophagocytic lymphohistiocytosis by Nandu Luo, Guangli Yang, Pingping Zhang, Baoli Li, Yan Chen, Zuochen Du, Pei Huang

“…Abstract Background To establish a visual prediction model for the risk of early mortality in pediatric patients with secondary hemophagocytic lymphohistiocytosis (sHLH). …”
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Prediction of polar motion and UT1-UTC based on the hybrid EEMD_LSTM model by Chenxiang Wang, Pengfei Zhang, Wenbin Shen, Jiayao Wang

“…Therefore, the ERPs prediction with high accuracy is very significant. Conventional approaches to predicting ERPs are mainly based on the linear models. …”
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CNN–Patch–Transformer-Based Temperature Prediction Model for Battery Energy Storage Systems by Yafei Li, Kejun Qian, Qiuying Shen, Qianli Ma, Xiaoliang Wang, Zelin Wang

“…Experiments conducted on two real-world residential BESS datasets demonstrate that the proposed model achieved superior prediction accuracy compared to models such as Informer and iTransformer across temperature prediction tasks with various horizon lengths. …”
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Model error propagation in a compatible tree volume, biomass, and carbon prediction system by James A. Westfall, Philip J. Radtke, David M. Walker, John W. Coulston

“…As these attributes are typically predicted from statistical models, frameworks that provide compatible relationships among these attributes are usually preferred over approaches that provide independent predictions. …”
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An interpretable coupled model (SWAT-STFT) for multispatial-multistep evapotranspiration prediction in the river basin by Zhonghui Guo, Chang Feng, Liu Yang, Qing Liu

“…This integration of physics-based and data-driven modeling not only provides valuable insights into watershed ET modeling prediction and mechanistic understanding but also underscores the broader potential for application across global watersheds and related disciplines.…”
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Forecasting Civil Aviation Incident Rate in China Using a Combined Prediction Model by Yixiang Sun, Nana Geng

“…Then, it used the smallest sum of squared errors as the principle to use a combined prediction model based on the IOWA operator. It can be seen from the experimental results that the prediction accuracy of the combined model is better than single models. …”
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Prediction model for psychological disorders in ankylosing spondylitis patients based on multi-label classification by Kun Yang, Yifan Gong, Xiaohan Xu, Tiantian Sun, Xinning Qu, Xiaxiu He, Hongxiao Liu

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ARIMA-Kriging and GWO-BiLSTM Multi-Model Coupling in Greenhouse Temperature Prediction by Wei Zhou, Shuo Liu, Junxian Guo, Na Liu, Zhenglin Li, Chang Xie

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Climate change projections and hydrological modelling to predict the streamflow in Berach-Banas catchment, Rajasthan by Kuldeep Pareta, Yogita Dashora

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A Bayesian Prediction Spatial Model for Confirmed Dengue Cases in the State of Chiapas, Mexico by Manuel Solís-Navarro, Cruz Vargas-De-León, María Gúzman-Martínez, Josselin Corzo-Gómez

“…Therefore, this study aimed to develop and validate a simple Bayesian prediction spatial model for the state of Chiapas, Mexico. …”
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Numerical Prediction of Solid Particle Erosion in Jet Pumps Based on a Calibrated Model by Xuanchen Wan, Mengxue Dong, Maosen Xu, Chuanhao Fan, Jiegang Mou, Shuai Han

“…The CFD-DEM method was used to simulate the solid–liquid two-phase flow in the jet pump, comparing six erosion models for predicting erosion rates. The Grey Wolf Optimization algorithm was applied to calibrate model coefficients. …”
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