Prediction of Gear Life based on Unbiased Grey Markov Combined Model by Duan Mingde, Wei Baolei, You Yanwei

“…For the difficulty of predicting the life of gear,an unbiased Grey GM（1,1）model is established based on the original data,and it is modified by using residual error model.The development trend of gear life is analyzed,the predicting accuracy is improved by introducing Markov optimization based on unbiased Grey GM（1,1）modification model.Comparing with the predicted value and the actual value,the predicting accuracy of gear life is improved from 87.06%to 90.09%.The result shows that the combined model has a better predicting precision and practicality within a period in the case of few data and poor information,it provides an available method for predicting the life of gear.…”
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Modelling and Prediction of Random Delays in NCSs Using Double-Chain HMMs by Yuan Ge, Yan Zhang, Wengen Gao, Fanyong Cheng, Nuo Yu, Jincenzi Wu

“…This paper is concerned with the modelling and prediction of random delays in networked control systems. …”
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Establishment of model predicting early patency rate of forearm arteriovenous fistula by CHEN Yong-ping, LIANG Yu-feng, LIANG Xing-lan, QIU Mei-lan, HUANG Bin-san, LIAO Wei-zeng, ZHANG Gui-rong

“…Using the ROC,the predictive efficacy of the model was evaluated to increase the positive predictive value of the operation. …”
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Urinary metabolite model to predict the dying process in lung cancer patients by Séamus Coyle, Elinor Chapman, David M. Hughes, James Baker, Rachael Slater, Andrew S. Davison, Brendan P. Norman, Ivayla Roberts, Amara C. Nwosu, James A. Gallagher, Lakshminarayan R. Ranganath, Mark T. Boyd, Catriona R. Mayland, Douglas B. Kell, Stephen Mason, John Ellershaw, Chris Probert

“…Methods We performed urinary metabolomics analysis on patients with lung cancer to create a metabolite model to predict dying over the last 30 days of life. …”
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Rainfall prediction based on CNN-LSTM model under sliding window by Rui Yuan

“…Due to the new division of data through the sliding window method, the input data dimension to the CNN-LSTM model was relatively small. Therefore, data augmentation was employed to enhance the overall predictive ability of the model. …”
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A Lightweight Transformer Edge Intelligence Model for RUL Prediction Classification by Lilu Wang, Yongqi Li, Haiyuan Liu, Taihui Liu

“…Currently, gated recurrent networks, hybrid models, and attention-enhanced models used for predictive maintenance face the challenge of balancing prediction accuracy and model lightweighting when extracting complex degradation features. …”
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Prediction model for chemical explosion consequences via multimodal feature fusion by Yilin Wang, Beibei Wang, Yichen Zhang, Jiquan Zhang, Yijie Song, Shuang-Hua Yang

“…Scientific Contributions This study constructed a Bayes-Transformer-SVM model for predicting the consequences of hazardous chemical explosions. …”
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Sensor-validated simulations predict fracture healing outcomes in an ovine model by Alicia Feist, Carla Hetreau, Manuela Ernst, Peter Varga, Peter Schwarzenberg

“…The potential of the simulation to predict healing patterns and to be used as a tool for non-union risk assessment was illustrated. …”
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A machine learning model for the prediction of hail-affected area in Germany by Siyu Li, Peter Knippertz, Michael Kunz, Jannik Wilhelm, Julian Quinting

“…Sensitivity analysis highlights CAPESHEAR as the dominant predictor influencing model decisions. These findings demonstrate the potential of ML-based hail prediction using only convective environmental parameters. …”
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Construction and validation of a presenteeism prediction model for ICU nurses in China by Jijun Wu, Yuxin Li, Yuxin Li, Xiaoli Liu, Yuting Fan, Ping Dai, Baixia Chen, Zhenfan Liu, Xian Rong, Xiaoli Zhong

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Prediction of gastrointestinal hemorrhage in cardiology inpatients using an interpretable XGBoost model by Yahui Li, Xujie Wang, Xuhui Liu

“…We developed an interpretable and practical machine learning model to predict the risk of GIB in cardiology inpatients. …”
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Machine learning model to predict sepsis in ICU patients with intracerebral hemorrhage by Lei Tang, Ye Li, Ji Zhang, Feng Zhang, Qiaoling Tang, Xiangbin Zhang, Sai Wang, Yupeng Zhang, Siyuan Ma, Ran Liu, Lei Chen, Junyi Ma, Xuelun Zou, Tianxing Yao, Rongmei Tang, Huifang Zhou, Lianxu Wu, Yexiang Yi, Yi Zeng, Duolao Wang, Le Zhang

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Revolutionizing pharmacology: AI-powered approaches in molecular modeling and ADMET prediction by Irfan Pathan, Arif Raza, Adarsh Sahu, Mohit Joshi, Yamini Sahu, Yash Patil, Mohammad Adnan Raza, Ajazuddin

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Point Transformer Network-Based Surrogate Model for Spatial Prediction in Bridges by Javier Grandío, Brais Barros, Manuel Cabaleiro, Belén Riveiro

“…To overcome this computational limitation, this paper presents an innovative deep learning-based surrogate model for predicting local displacements in bridge structures. …”
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Poisonous Plant Prediction Using Explainable Deep Inherent Learning Model by Ahmed S. Maklad, Ashraf Alyanbaawi, Mohammed Farsi, Hani M. Ibrahim, Mahmoud Elmezain

“…While computer vision techniques show promise for classifying plant species and predicting toxicity, the lack of comprehensive datasets including images, scientific names, descriptions, local names, and poisonous status complicates these predictions. …”
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A prediction model for traffic conditions based on an improved Markov chain by Zhou Mingsheng, Liu Shuyang

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Development of an AI-Based Suicide Ideation Prediction Model for People with Disabilities by Jimin Han

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Micromechanical model to predict the effect of temperature on the behavior of unsaturated porous media by Bao Viet Tran, Xavier Chateau

“…The result show that taking into account the coupling between the deformation of the porous space and the capillary effects can radically modify the prediction of the temperature influence on the capillary curve. …”
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An Explainable Machine Learning Model for Predicting Macroseismic Intensity for Emergency Management by Federico Mori, Giuseppe Naso

“…Key input features include Arias Intensity, spectral accelerations at four representative periods (0.15 s, 0.4 s, 0.6 s, and 2 s), and site condition proxies, such as slope and Vs30. The model achieves strong predictive performance (RMSE = 0.73, R² = 0.76), corresponding to a 33% reduction in residual standard deviation compared to traditional GMICE-based regression methods. …”
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Development of a Hybridized Model for Predicting the Life Span of Power Transformers by O.O. Olaluwoye, A.A. Okubanjo, O.K. Oyetola, P.O. Alao, O.G. Olasunkanmi

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