MNPM: research on metabolic neural network prediction model for predicting carbon emission accuracy by Entao Luo, Li Shi, Jiyan Liu, Zheng Wu, Guoyun Duan, Lingxuan Zeng, Tangsen Huang

Subjects: “…gray model…”
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Dynamic selection of optimal tunnel convergence prediction model for a probabilistic deformation prediction by Peng ZENG, Zhiqiang ZHANG, Tianbin LI, Hao Tang, Zulong YAN, Lubo MENG

“…Traditional single prediction models struggle to adapt to the dynamic nature of tunnel convergence, limiting their predictive accuracy. …”
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Enhanced Prediction and Uncertainty Modeling of Pavement Roughness Using Machine Learning and Conformal Prediction by Sadegh Ghavami, Hamed Naseri, Farzad Safi Jahanshahi

“…Pavement performance models are considered a key element in pavement management systems since they can predict the future condition of pavements using historical data. …”
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Vehicle Trajectory Prediction Algorithm Based on Hybrid Prediction Model with Multiple Influencing Factors by Tao Wang, Yiming Fu, Xing Cheng, Lin Li, Zhenxue He, Yuchi Xiao

“…In light of this limitation, we propose a vehicle trajectory prediction algorithm predicated on a hybrid prediction model. …”
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Predicting climate-related compositional shifts in nut-producing species that are important for bears during hyperphagia by José Carlos Pérez-Girón, José Vicente López-Bao, Emilio Díaz-Varela, Pedro Álvarez-Álvarez

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Predicting biking preferences in Kigali city: A comparative study of traditional statistical models and ensemble machine learning models by Jean Marie Vianney Ntamwiza, Hannibal Bwire

“…This research used a dataset of 6386 observations incorporated weather and air quality variables and applied correlation-based and iterative model-based feature selection techniques to improve predictive accuracy. …”
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Development and validation of hybrid machine learning approach for predicting survival in patients with cervical cancer: a SEER-based population study by Anjana Eledath Kolasseri, Venkataramana B.

“…The study aims to create a hybrid survival model that integrates Cox Proportional Hazards (CoxPH) with Elastic Net regularization and Random Survival Forest (RSF) to improve prediction accuracy and interpretability.MethodsData from the SEER database (2013–2015) were pre-processed through normalization and encoding. …”
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Prediction of Solar Radiation Data for Garlic Production in Magelang Regency Using Long Short-Term Memory by Muhammad Safrul Safrudin, Imas Sukaesih Sitanggang, Hari Agung Adrianto, Syarifah Aini

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Predictive modeling for step II therapy response in periodontitis - model development and validation by Elias Walter, Tobias Brock, Pierre Lahoud, Nils Werner, Felix Czaja, Antonin Tichy, Caspar Bumm, Andreas Bender, Ana Castro, Wim Teughels, Falk Schwendicke, Matthias Folwaczny

“…Models accurately predicted that healthy sites stay healthy, but performed suboptimally for diseased sites. …”
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Editorial: Tropical cyclone modeling and prediction: advances in model development and its applications by Xuejin Zhang, Vijay Tallapragada, Zhan Zhang, Avichal Mehra

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Topside Electron Density Modeling Using Neural Network and Empirical Model Predictions by S. Dutta, M. B. Cohen

“…In prior work, an artificial neural network (NN) was developed and trained on two solar cycles worth of Defense Meteorological Satellite Program data (113 satellite‐years), along with global drivers and indices to predict topside electron density. In this paper, we highlight improvements made to this NN, and present a detailed comparison of the new model to E‐CHAIM and IRI as a function of location, geomagnetic condition, time of year, and solar local time. …”
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A Distributed Model Predictive Control for Multiple Mobile Robots with the Model Uncertainty by Wei Shang, Hanzong Zhu, Yurong Pan, Xiuhong Li, Daode Zhang

“…In this paper, a distributed predictive control with the model uncertainty which uses the data-driven strategy and robust theory (data-driven RDMPC) is proposed for the formation control of multiple mobile robots. …”
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Model Reduction and Chattering Mitigation in Multi-Model Predictive Control for Quadrotor UAVs by Ghulam E. Mustafa Abro, Sufyan Ali Memon, Saddaf Rubab, Kaznah Alshammari, Faheem Khan

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Predictive performance of risk prediction models for lung cancer incidence in Western and Asian countries: a systematic review and meta-analysis by Yah Ru Juang, Lina Ang, Wei Jie Seow

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Application of Metaheuristics for Optimizing Predictive Models in iHealth: A Case Study on Hypotension Prediction in Dialysis Patients by Felipe Cisternas-Caneo, María Santamera-Lastras, José Barrera-Garcia, Broderick Crawford, Ricardo Soto, Cristóbal Brante-Aguilera, Alberto Garcés-Jiménez, Diego Rodriguez-Puyol, José Manuel Gómez-Pulido

“…This study examines the application of advanced machine learning techniques, combined with metaheuristic optimization methods, to improve predictive models for intradialytic hypotension (IDH) in hemodialysis patients. …”
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Improving appendix cancer prediction with SHAP-based feature engineering for machine learning models: a prediction study by Ji Yoon Kim

“…Purpose This study aimed to leverage Shapley additive explanation (SHAP)-based feature engineering to predict appendix cancer. Traditional models often lack transparency, hindering clinical adoption. …”
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Analysis of the dynamics of changes in factor load models for predicting failures of a spacecraft by Kraev Konstantin, Zaitsev Pavel, Kazakovtsev Vladimir

“…The structure of the methodology for preparing data and creating factor load models is described. A methodology for analyzing the dynamics of changes in factor load models is proposed. …”
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Performance of machine learning models for predicting high-severity symptoms in multiple sclerosis by Subhrajit Roy, Diana Mincu, Lev Proleev, Chintan Ghate, Jennifer S. Graves, David F. Steiner, Fletcher Lee Hartsell, Katherine Heller

“…Finally, we retrospectively developed three classical ML methods and two deep learning models to accurately and continually predict the incidence of five high-severity symptoms (fatigue, sensory disturbance, walking instability, depression or anxiety and cramps/spasms) three months in advance.…”
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Predicting customer subscription in bank telemarketing campaigns using ensemble learning models by Michael Peter, Hawa Mofi, Said Likoko, Julius Sabas, Ramadhani Mbura, Neema Mduma

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Proposing Optimized Random Forest Models for Predicting Compressive Strength of Geopolymer Composites by Feng Bin, Shahab Hosseini, Jie Chen, Pijush Samui, Hadi Fattahi, Danial Jahed Armaghani

“…Both hybrid models significantly outperformed traditional methods, demonstrating their higher accuracy and reliability in predicting the compressive strength of GePC. …”
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