Robust Invariant Set Design for Reliable Stopping in Adaptive Cruise Control via Model Predictive Control by Shila Alizadehghobadi, Reza Ehsani

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USING ARTIFICIAL NEURAL NETWORKS FOR PREDICTION MODELING IN THE PATIENTS WITH CONCURRENT HIV INFECTION AND TUBERCULOSIS IN THE PENITENTIARY SYSTEM by D. S. Ponomarev, S. A. Sterlikov, S. B. Ponomarev, E. L. Аveryanova

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Correction: Development and validation of a cardiovascular risk prediction model for Sri Lankans using machine learning. by Chamila Mettananda, Isuru Sanjeewa, Tinul Benthota Arachchi, Avishka Wijesooriya, Chiranjaya Chandrasena, Tolani Weerasinghe, Maheeka Solangaarachchige, Achila Ranasinghe, Isuru Elpitiya, Rashmi Sammandapperuma, Sujeewani Kurukulasooriya, Udaya Ranawaka, Arunasalam Pathmeswaran, Anuradhini Kasturiratne, Norihiro Kato, Rajitha Wickramasinghe, Prasanna Haddela, Janaka de Silva

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Development and validation of a predictive model for critical illness in adult patients requiring hospitalization for COVID-19. by Neha Paranjape, Lauren L Staples, Christina Y Stradwick, Herman Gene Ray, Ian J Saldanha

“…We developed a free, web-based calculator to facilitate use of the prediction model (https://icucovid19.shinyapps.io/ICUCOVID19/).…”
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Comparative and Interpretative Analysis of CNN and Transformer Models in Predicting Wildfire Spread Using Remote Sensing Data by Yihang Zhou, Ruige Kong, Zhengsen Xu, Linlin Xu, Sibo Cheng

“…Employing a real‐world data set that includes nearly a decade of remote sensing data from California, U.S., these models predict the spread of wildfires for the following day. …”
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Modeling for the Prediction and Evaluation of the Crimp Percentage of Plain Woven Fabric Based on Yarn Count and Thread Density by Kura Alemayehu Beyene, Chirato Godana Korra

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DeepMethyGene: a deep-learning model to predict gene expression using DNA methylations by Yuyao Yan, Xinyi Chai, Jiajun Liu, Sijia Wang, Wenran Li, Tao Huang

“…Here we propose DeepMethyGene, an adaptive recursive convolutional neural network model based on ResNet that predicts gene expression using DNA methylation information. …”
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StatModPredict: A user-friendly R-Shiny interface for fitting and forecasting with statistical models. by Amanda Bleichrodt, Amelia Phan, Ruiyan Luo, Alexander Kirpich, Gerardo Chowell

“…<h4>Methods</h4>StatModPredict supports robust real-time forecasting and retrospective model analysis, including fitting, forecasting, evaluation, visualization, and comparison of results from four popular models. …”
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Study on the predictive value of preoperative CT features for the mitotic index of GIST based on the nomogram by Ren Yingzheng, Jiang Linlin, Yang Yang, An Junjie, Dong Yonghong

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Climate Forecasting in Sulaymaniyah City Using Deep Learning Techniques by Rebin Abdulkareem Hamaamin, Omar Mohammed Amin Ali, Shahab Wahhab Kareemc

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Integrating transcriptomic data with a novel drug efficacy prediction model for TCM active compound discovery by Yingcan Li, Yu Shen, Yezi Cai, Yulin zhang, Jiahui Gao, Lei Huang, Weinuo Si, Kai Zhou, Shan Gao, Qichao Luo

“…Code and data are available at https://github.com/t9lex/Meta-DEP .…”
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Construction of a clinically significant prostate cancer risk prediction model based on traditional diagnostic methods by Wen-Tong Ji, Yong-Kun Wang, Zhan-Yang Han, Si-Qi Wang, Yao Wang, Yao Wang

“…Subsequently, we developed an online calculator (https://jiwentong0.shinyapps.io/dynnomapp/) with six variables for biopsy optimization.ConclusionThis study incorporated the results of three traditional diagnostic methods to establish a cost-effective and highly accurate model for predicting csPCa before biopsy. …”
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MoLPre: A Machine Learning Model to Predict Metastasis of cT1 Solid Lung Cancer by Jie Lan, Heng Wang, Jing Huang, Weiyi Li, Min Ao, Wanfeng Zhang, Junhao Mu, Li Yang, Longke Ran

“…Furthermore, we embedded this model in a web application called MoLPre (https://molpre.cqmu.edu.cn/), a user‐friendly tool assisting in the metastasis prediction of cT1 solid lung cancer.…”
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Development and validation of an explainable machine learning model for predicting osteoporosis in patients with type 2 diabetes mellitus by Qipeng Wei, Zihao Liu, Xiaofeng Chen, Hao Li, Weijun Guo, Qingyan Huang, Jinxiang Zhan, Shiji Chen, Dongling Cai, Dongling Cai

“…Eight supervised ML algorithms were applied to construct predictive models. Model performance was evaluated based on the area under the receiver operating characteristic curve (AUC), calibration plots, decision curve analysis (DCA), accuracy, sensitivity, specificity, and F1 score. …”
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Development of a predictive model for systemic lupus erythematosus incidence risk based on environmental exposure factors by Qianjin Lu, Ying Zhang, Cheng Zhao, Yu Lei, Hui Jin, Qilin Li

“…Leave-one-out cross-validation confirmed that the ForestMDG model had the best accuracy (0.8338). Finally, we developed a dynamic nomogram for practical use, which is accessible via the following link: https://yingzhang99321.shinyapps.io/dynnomapp/.Conclusion We created a user-friendly dynamic nomogram for predicting the relative risk of SLE onset based on occupational and living environmental exposures.Trial registration number ChiCTR2000038187.…”
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A neural network based computational model to predict the output power of different types of photovoltaic cells. by WenBo Xiao, Gina Nazario, HuaMing Wu, HuaMing Zhang, Feng Cheng

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Predictive modelling and ranking: Azadirachta indica compounds through indices and multi-criteria decision-making techniques by D. S. Anuradha, B. Jaganathan

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A spatial predictive model for malaria resurgence in central Greece integrating entomological, environmental and social data. by Panagiotis Pergantas, Andreas Tsatsaris, Chrisovalantis Malesios, Georgia Kriparakou, Nikolaos Demiris, Yiannis Tselentis

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Development of a Predictive Model for Hepatitis B Virus (HBV) Status Using Gender and Serum Biomarkers by Stanley Obinna Ezeadichie, Joy E. Ikekpeazu, Amechi Uchenna Katchy, Mima Wariso, Chioma Cordelia Ugwu, Osah Martins Onwuka

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Application of multimodal large language models for safety indicator calculation and contraindication prediction in laser vision correction by Joon Yul Choi, Doo Eun Kim, Sung Jin Kim, Hannuy Choi, Tae Keun Yoo

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