Research on Adaptive Model Predictive Control Algorithm for Remotely Operated Vehicle by ZHU Yinggu, ZHANG Dinghua, TU Shaoping, SONG Junhui

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Development and validation of a predictive model for the risk of endocervical curettage positivity by Fang Feng, Hui-hui Tuo, Jin-meng Yao, Wei-hong Wang, Feng-lan Guo, Rui-fang An

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Event-triggered boost converter model predictive control with Kalman filter by Ranya Badawi, Jun Chen

Subjects: “…Model predictive control…”
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Predictive modeling of telemedicine implementation in central Asia using neural networks by Zhannur ABDRAKHMANOVA, Talgat DEMESSINOV, Kadisha JAPAROVA, Monika KULISZ, Gulzhan BAYTIKENOVA, Ainur KARIPOVA, Zhansaya ERSAINOVA

“…The predictive model showed excellent accuracy, as evidenced by a very strong correlation between predicted and observed values (R = 0.99245). …”
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A predictive model for Epstein-Barr virus-associated hemophagocytic lymphohistiocytosis by Rui Huang, Rui Huang, Dan Wu, Dan Wu, Ling Wang, Ling Wang, Ping Liu, Ping Liu, Xiaoru Zhu, Xiaoru Zhu, Leqiu Huang, Leqiu Huang, Mengmeng Chen, Mengmeng Chen, Xin Lv, Xin Lv

“…These results suggested that the model was highly stable and accurate. Furthermore, calibration curves and clinical decision curves indicated that the model possessed good calibration and offered significant clinical benefits.ConclusionsThe nomogram, which was based on these five predictors, exhibited robust predictive value and stability, thereby can be used to aid clinicians in the early detection of EBV-HLH.…”
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The relationship between anxiety disorders and alcohol use: predictors and predictive modeling by Dmitriy S. Radionov, Tatiana A. Karavaeva

“…Using CHAID analysis and logistic regression, researchers developed a predictive model with high accuracy (AuROC=0.937), which takes into account both clinical and individual psychological characteristics. …”
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Predictive Analytics in Agriculture: Machine Learning Models for Coconut Tree Health by Goswami Anjali, Kirit Dhablia Dharmesh

“…Active machine learning (ML) models have started replacing the conventional methods of crops health management and farm productivity with predictive analytics. …”
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Practitioner perspectives on informing decisions in One Health sectors with predictive models by Kim M. Pepin, Keith Carlisle, Richard B. Chipman, Dana Cole, Dean P. Anderson, Michael G. Baker, Jackie Benschop, Michael Bunce, Rachelle N. Binny, Nigel French, Suzie Greenhalgh, Dion RJ O’Neale, Scott McDougall, Fraser J. Morgan, Petra Muellner, Emil Murphy, Michael J. Plank, Daniel M. Tompkins, David TS Hayman

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Predictive model of malignancy probability in pulmonary nodules based on multicenter data by Yuyan Huang, Yong Chen, Fang He, Li Jiang

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Unplanned cesarean sections in advanced maternal age: A predictive model by Joyce Veenstra, Zoë Cohen, Fleurisca J. Korteweg, David P. van derHam, Simone M. Kuppens, Janna A. Kroese, Brenda B. Hermsen, Marije M. Kamphuis, Joost W. Vanhommerig, Maria G. vanPampus

“…Subsequently, a predictive model and risk scores were constructed to predict unplanned cesarean section. …”
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Predictive Modeling of Tool Life in Turning Using ANN-Taguchi Hybridization by Shrishail Sollapur B., Shubham R. Suryawanshi, Mitali Mhatre S., Dipak K. Dond, Ganesh Chate, Abhijit Bhowmik

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Predictive modeling of 3D reconstruction trajectories for potential programmable materials by Yechun Jin, Jie Li, Qi Yu, Panghua Tian

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Application of machine learning models in predictive maintenance of Francis hydraulic turbines by Júlio César Silva de Souza, Oswaldo Honorato Júnior, Geraldo Lúcio Tiago Filho, Otávio Augusto Salgado Carpinteiro, Hailton Silveira Domingues Biancardine Júnior, Ivan Felipe Silva dos Santos

Subjects: “…Machine Learning Model, Multilayer Perceptron (MLP)…”
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Development and evaluation of predictive models for pregnancy risk in UK dairy cows by Matthew Barden, Robert Hyde, Martin Green, Andrew Bradley, Edna Can, Rachel Clifton, Katharine Lewis, Al Manning, Luke O'Grady

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Predictive model of ulcerative colitis syndrome with ensemble learning and interpretability methods by Ling Zhu, Shan He, Wanting Zheng, Yuanyuan Tong, Feng Yang

“…To bridge this gap, we propose an ensemble prediction model enhanced with SHAP (SHapley Additive exPlanations) and LIME (Local Interpretable Model-agnostic Explanations) to improve interpretability and clinical utility. …”
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Deep Learning-Driven Predictive Modelling for Optimizing Stingless Beekeeping Yields by Noor Hafizah Khairul Anuar, Mohd Amri Md Yunus, Muhammad Ariff Baharudin, Sallehuddin Ibrahim, Shafishuhaza Sahlan

“…The dataset extracted from the 6th of January 2024 to the 5th of February 2024, at a 15-minute time interval comprising a total of 2577 data points was analyzed using various deep learning approaches for best RMSE performance. A single-layer LSTM model with 50 units produced the best RMSE performance of 0.039, representing that the beehive weight was accurately predicted. …”
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Offset-Free Strategy by Double-Layered Linear Model Predictive Control by Tao Zou

“…In the real applications, the model predictive control (MPC) technology is separated into two layers, that is, a layer of conventional dynamic controller, based on which is an added layer of steady-state target calculation. …”
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Predictive modeling of flexible EHD pumps using Kolmogorov–Arnold Networks by Yanhong Peng, Yuxin Wang, Fangchao Hu, Miao He, Zebing Mao, Xia Huang, Jun Ding

“…We evaluated KAN on a dataset of flexible EHD pump parameters and compared its performance against RF, and MLP models. KAN achieved superior predictive accuracy, with Mean Squared Errors of 12.186 and 0.012 for pressure and flow rate predictions, respectively. …”
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Risk-Predictive Models for Adverse Events in Cardiac Surgery: A Review by Huan Luo

“…Risk prediction models are an important part of assessing operative mortality and postoperative complication rates in current cardiac surgery practice. …”
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Application predictive modelling of Penicillium roqueforti germination in environmental conditions in cake by Hassan Nakhchian, Farideh Tabatabaee Yazdi, Seyed Ali Mortazavi, Mohebat Mohebi

“…The results of analysis of variance (ANOVA) proved that environmental conditions affect germination significantly (P < 0.05). Predictive modelling illustrated that the temperature did not affect germination significantly, while no germination was seen at aw = 0.65. …”
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