A Model Predictive Algorithm for Active Control of Nonlinear Noise Processes by Qi-Zhi Zhang, Woon-Seng Gan, Ya-li Zhou

“…For ANC system to be successfully implemented, the nonlinearity of the primary path and time delay of the secondary path must be overcome. A nonlinear Model Predictive Control (MPC) strategy is introduced to deal with the time delay in the secondary path and the nonlinearity in the primary path of the ANC system. …”
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Genomic and algorithm-based predictive risk assessment models for benzene exposure by Minyun Jiang, Minyun Jiang, Na Cai, Na Cai, Juan Hu, Lei Han, Lei Han, Fanwei Xu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Baoli Zhu, Boshen Wang

“…AimIn this research, we leveraged bioinformatics and machine learning to pinpoint key risk genes associated with occupational benzene exposure and to construct genomic and algorithm-based predictive risk assessment models.Subject and methodsWe sourced GSE9569 and GSE21862 microarray data from the Gene Expression Omnibus. …”
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Application Of Machine Learning Models To Predict Warping Of Plastic Automotive Parts by Evandro Menezes de Souza Amarante, João Paulo Rios Brandão de Oliveira, Pedro Guilherme Carvalho de Souza Marconi, Armando Sá Ribeiro Júnior

“…The results indicated that the regression models developed for predicting warpage performed better with the ABS data. …”
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Neural network-based model predictive control for unmanned aerial vehicles by Yiwei Zhou

Subjects: “…model predictive control…”
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A Deep Learning Model for Stroke Patients’ Motor Function Prediction by Abeer Abdulaziz AlArfaj, Hanan A. Hosni Mahmoud, Alaaeldin M. Hafez

“…The experiments depict that the best performance is attained with the incorporation of the proposed EEG-DenseNet and the transfer model. The prediction accuracy of the model reached 96.5% with reduced time computational cost. …”
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Colorectal cancer risk prediction using a simple multivariable model. by Gillian S Dite, Chi Kuen Wong, Aviv Gafni, Erika Spaeth

“…For women and men separately, we developed (i) a multivariable risk prediction model using family history, a polygenic risk score (PRS) and clinical risk factors, and (ii) a simple model comprising family history and a PRS. …”
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Machine learning-based predictive modelling of mental health in Rwandan Youth by Fauste Ndikumana, Josias Izabayo, Joseph Kalisa, Mathieu Nemerimana, Emmanuel Christian Nyabyenda, Sylivain Hirwa Muzungu, Isaac Komezusenge, Melissa Uwase, Similien Ndagijimana, Celestin Twizere, Vincent Sezibera

“…The research findings indicate that the random forest model is the most effective with an accuracy of 88.8% in modeling and predicting factors contributing to mental health vulnerability and 75 % in predicting mental disorders comorbidity. …”
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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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Predictability of water resources with global climate models. Case of Northern Tunisia by Besbes, Mustapha, Chahed, Jamel

“…The results show that the use of raw GCMs predictions on large basins is possible and provides precisions comparable to what is produced when using Regional Climate Models in medium size basins.…”
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Predictive Models of Current, Voltage, and Power Losses on Electric Transmission Lines by O. M. Bamigbola, M. M. Ali, K. O. Awodele

“…The predictive models evolved as explicit expressions of the space variable and they are in close agreement with empirical data and reality.…”
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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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Performance Comparison of Multiple Discriminant Analysis and Logit Models in Bankruptcy Prediction by Matúš Mihalovič

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Offline Model Predictive Control Based on Weighted Projection over Polytopes by Fabio Andrade de Almeida

“…This work presents a novel offline model predictive control technique for tracking of constrained systems. …”
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Understanding the flowering process of litchi through machine learning predictive models by SU Zuanxian, NING Zhenchen, WANG Qing, CHEN Houbin

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High-risk factors and predictive models for hemorrhagic chronic radiation proctitis by Zhongli Liao, Xiaogang Hu, Liuling Hu, Jian Yang

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Predictive models for chemotherapy-induced oral mucositis: a systematic review by Yan Tao, Xiang Zeng, Hua Mao

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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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Systematic review of 99 extremity bone malignancy survival prediction models by Cheng-Yo Lai, Hung-Kuan Yen, Hao-Chen Lin, Olivier Quinten Groot, Wei-Hsin Lin, Hao-Ping Hsu

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