Research on Robust Adaptive Model Predictive Control Based on Vehicle State Uncertainty by Yinping Li, Li Liu

“…The results show that the improved algorithm achieves a 13% faster response time (5.2 s vs. 6 s control cycles), a 15% higher minimum speed during cornering (2.98 m/s vs. 2.51 m/s), a 32% narrower lateral velocity fluctuation range ([−0.11, 0.22] m/s vs. [−0.19, 0.22] m/s), and reduced yaw rate oscillations ([−1.8, 2.8] rad/s vs. [−2.8, 2.5] rad/s) compared with a traditional fixed-weight MPC algorithm. …”
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SSMM: Semi-supervised manifold method with spatial-spectral self-training and regularized metric constraints for hyperspectral image dimensionality reduction by Bei Zhu, Yao Jin, Xuehua Guan, Yanni Dong

“…In the dimensionality reduction experiments, the overall accuracy of SSMM in any dimension is higher than that of other algorithms. …”
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Pumping up your predictive power for cognitive state detection with the proper GAINS by Victoria Ribeiro Rodrigues, Jeremy R. Prieto, Szilard L. Beres, Chad Stephens, Christopher Myers, Nicholas J. Napoli

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Model Predictive Control of Piecewise Affine System with Constrained Input and Time Delay by Zhilin Liu, Lutao Liu, Jun Zhang, Xin Yuan

“…A model predictive control (MPC) is proposed for the piecewise affine (PWA) systems with constrained input and time delay. …”
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Design and Application of Offset-Free Model Predictive Control Disturbance Observation Method by Xue Wang, Baocang Ding, Xin Yang, Zhaohong Ye

“…Model predictive control (MPC) with its lower request to the mathematical model, excellent control performance, and convenience online calculation has developed into a very important subdiscipline with rich theory foundation and practical application. …”
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K-Nearest Neighbors for Anomaly Detection and Predictive Maintenance in Water Pumping Systems by João Pablo Santos da Silva, André Laurindo Maitelli

“…The input parameters used for a model are the sensor parameters, and the model analyzes the correlation between the input parameters (sensors) and determines which parameters are the most important, deciding on the output of the model, and thereby building the simplest model, which requires the least input parameters and gives the most accurate prediction results. In this project, using on the input signal from the sensors, the k-nearest neighbors machine learning algorithm was used to correlate/predict whether the pump was shut down (broken) for a certain period of time.…”
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Multi-objective Predictive Control of Gas Turbine System Based on T-S Fuzzy Model by Guolian HOU, Xiaoyan DAI, Linjuan GONG, Haixin XU, Jianhua ZHANG

“…In this paper, a T-S fuzzy model-based multi-objective predictive controller is proposed to enhance both the tracking performance and economic performance together. …”
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Disturbance Rejection and Uncertainty Analysis in Wind Turbines Using Model Predictive Control by Alok Kumar, Atul Kelkar

“…We have tailored the algorithm to the practical parameters of the National Renewable Energy Laboratory’s (NREL) Controls Advanced Research Turbine (CART) model. …”
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Efficacy and predictive biomarkers of immunotherapy in Epstein-Barr virus-associated gastric cancer by Lin Shen, Xiaochen Zhao, Zhenghang Wang, Yuezong Bai, Feilong Zhao, Zhi Peng, Jinping Cai, Tong Xie, Shuang Tong, Xiaofan Wei

“…The molecular predictive biomarkers for ICB efficacy were identified to improve the prediction accuracy of ICB efficacy in 22 patients with EBVaGC.Results Compared with orthogonal assay (EBER-ISH) results, the NGS-based algorithm achieved high performance with a sensitivity of 95.7% (22/23) and a specificity of 100% (53/53). …”
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Fractional-Order Predictive Functional Control of Industrial Processes with Partial Actuator Failures by Min-Ying Li, Kang-Di Lu, Yu-Xing Dai, Guo-Qiang Zeng

“…The comprehensive simulation results demonstrate the performance of the proposed control method by comparing with a recently developed predictive functional control, genetic algorithm, and particle swarm optimization-based versions in terms of four performance indices.…”
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Enhanced Inverse Model Predictive Control for EV Chargers: Solution for Rectifier-Side by Ali Sharida, Abdullah Berkay Bayindir, Sertac Bayhan, Haitham Abu-Rub

“…Then, an adaptive estimation strategy employing a recursive least square algorithm is proposed for online dynamic model estimation, which is then used by the IMPC for optimal switching states prediction. …”
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Beyond purchase patterns: harnessing predictive analytics to anticipate unarticulated consumer needs by Khaled Alshaketheep, Hind Al-Ahmed, Ahmad Mansour

“…The results underscore the importance of integrating human-centered design with algorithmic precision to translate predictive insights into actionable business value.…”
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AI based predictive acceptability model for effective vaccine delivery in healthcare systems by Muhammad Shuaib Qureshi, Muhammad Bilal Qureshi, Urooj Iqrar, Ali Raza, Yazeed Yasin Ghadi, Nisreen Innab, Masoud Alajmi, Ayman Qahmash

“…A sample dataset containing 7150 data records with 31 demographic and socioeconomic attributes from PDHS (2017–2018) is used in this paper. Using the LightGBM algorithm, the proposed model constructed on the basis of different machine-learning procedures achieved 98% accuracy to accurately predict the acceptability of vaccines included in the immunization program. …”
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Predictive analytics in gamified education: A hybrid model for identifying at-risk students by Devanshu Sawarkar, Latika Pinjarkar, Pratham Agrawal, Devansh Motghare

“…This research proposes a hybrid predictive model designed to identify at-risk students within a gamified education environment accurately. …”
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Optimizing public health management with predictive analytics: leveraging the power of random forest by Hongman Wang, Yifan Song, Yifan Song, Hua Bi

“…This study employs a Random Forest Algorithm (RFA) to address this limitation and enhance the predictive modeling of community health outcomes. …”
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Predictive density profile control with discrete pellets, applied to integrated simulations of ITER by C.A. Orrico, M. van Berkel, T.O.S.J. Bosman, L. Ceelen, W.P.M.H. Heemels, F. Koechl, D. Krishnamoorthy

“…The model predictive control (MPC) scheme we deploy combines the offset-free method to correct prediction model inaccuracies and our novel modified penalty term homotopy algorithm for real-time MPC (PTH-MPC). …”
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Analysis of the 50-mile ultramarathon distance using a predictive XGBoost model by Jonas Turnwald, David Valero, Pedro Forte, Katja Weiss, Elias Villiger, Mabliny Thuany, Volker Scheer, Matthias Wilhelm, Marilia Andrade, Ivan Cuk, Pantelis T. Nikolaidis, Beat Knechtle

“…Utilizing a dataset with ultramarathon races from 1863 to 2022, a machine learning model based on the XGBoost algorithm was developed to predict the race speed based on the aforementioned variables. …”
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Sialyltransferase-related genes as predictive factors for therapeutic response and prognosis in cervical cancer by Jia Shao, Can Zhang, Yaonan Tang, Aiqin He, Xiangyan Cheng

“…“MCPcounter” R package, ESTIMATE algorithm and TIMER online tools were used to depict the tumor immune microenvironment in CC. …”
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Risk Factors and Predictive Model for Ischemic Complications in Endovascular Treatment of Intracranial Aneurysms: Insights From a Large Patient Cohort by Jianwen Jia, Zeping Jin, Mirzat Turhon, Yixin Lin, Xinjian Yang, Yang Wang, Yunpeng Liu

“…Our study aimed to identify the risk factors for ischemic complications after the interventional treatment of IAs and to make an individualized prediction of the occurrence of ischemic complications, providing important reference guidance for clinicians. …”
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Improving T2D machine learning-based prediction accuracy with SNPs and younger age by Cynthia AL Hageh, Andreas Henschel, Hao Zhou, Jorge Zubelli, Moni Nader, Stephanie Chacar, Nantia Iakovidou, Haralampos Hatzikirou, Antoine Abchee, Siobhán O’Sullivan, Pierre A. Zalloua

“…Integration of a polygenic risk score (PRS) further supported risk prediction, particularly in younger individuals, though incremental gains were modest. …”
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