H∞ filtering of uncertain predictive models: Gain computation using LMI and performance evaluation by Eli G. Pale Ramon, Oscar G. Ibarra-Manzano, José A. Andrade-Lucio, Yuriy S. Shmaliy

“…The filter is designed for processes represented in discrete time using the forward Euler method, which allows for predictive modeling. Since the error covariance of a state estimator is a quadratic function of K, a new theorem is proved and a numerical algorithm is developed for computing K using linear matrix inequality (LMI). …”
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Machine Learning Methods for Predicting Cardiovascular Diseases: A Comparative Analysis by Aiym B. Temirbayeva, Arshyn Altybay

“…The primary research question is to identify which algorithm demonstrates the best predictive performance for heart disease diagnosis. …”
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Artificial Intelligence-Based Prediction Model for Maritime Vessel Type Identification by Hrvoje Karna, Maja Braović, Anita Gudelj, Kristian Buličić

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Development and validation of an interpretable risk prediction model for the early classification of thalassemia by Jin-Xin Lai, Jia-Wei Tang, Shan-Shan Gong, Ming-Xiong Qin, Yu-Lu Zhang, Quan-Fa Liang, Li-Yan Li, Zhen Cai, Liang Wang

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Machine learning for predicting resistance spot weld quality in automotive manufacturing by Nuttapong Chuenmee, Nattachai Phothi, Kontorn Chamniprasart, Sorada Khaengkarn, Jiraphon Srisertpol

“…The research highlights that the proposed methodology, particularly leveraging XGBoost, achieves a notable prediction accuracy of 97.1% when applied to unseen data.…”
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Status of research on prediction models for severity in confirmed Covid-19 patients by Maicel Eugenio Monzón-Pérez, Lizet Sánchez-Valdés, Elizabeth Rieche-Gómez, Agustín Lage-Dávila

“…Introduction: Previous knowledge in the scientific literature on clinical prediction models in patients with COVID-19 may be useful for the development of new research. …”
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Predicting cancer risk using machine learning on lifestyle and genetic data by Mohamed Abdelmoaty Ahmed, Ahmed AbdelMoety, Asmaa Mohamed Ahmed Soliman

“…This study investigates the application of Machine Learning (ML) techniques to predict cancer risk based on a combination of genetic and lifestyle factors. …”
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Reliable QoE Prediction in IMVCAs Using an LMM-Based Agent by Michael Sidorov, Tamir Berger, Jonathan Sterenson, Raz Birman, Ofer Hadar

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Machine learning to predict the source of campylobacteriosis using whole genome data. by Nicolas Arning, Samuel K Sheppard, Sion Bayliss, David A Clifton, Daniel J Wilson

“…To gain insight beyond the source model prediction, we use Bayesian inference to analyse the relative affinity of C. jejuni strains to infect humans and identified potential differences, in source-human transmission ability among clonally related isolates in the most common disease causing lineage (ST-21 clonal complex). …”
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Integration of Hash Encoding Technique with Machine Learning for Employee Turnover Prediction by Ahya Radiatul Kamila, Johanes Fernandes Andry, Francka Sakti Lee, Felliks F. Tampinongkol

“…After preprocessing is completed, the prediction model is trained using the Random Forest algorithm to predict employee turnover. …”
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Protein structural domain-disease association prediction based on heterogeneous networks by Jingpu Zhang, Lianping Deng, Lei Deng

“…Finally, we train a binary classifier based on the XGBOOST (eXtreme Gradient Boosting) algorithm to predict the potential associations between domains and diseases. …”
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On Feasibility and Asymptotically Stability of Switched Systems Using Adaptive Multi-Model Predictive Control by Mohammad Fathi, Hossein Bolandi, Bahman Ghobrani Vaghei, Saeed Ebadollahi

“…The present paper attempts to design an adaptive multi-model predictive control strategy for strongly nonlinear or switched systems with various operating points. …”
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A model predictive control method for directional borehole trajectories in underground coal mines by Hao LI, Ningping YAO, Chengda LU, Jinbao ZHANG, Hongchao WEI, Jiajun WU, Jinyu LI

“…Furthermore, this study designed a model predictive controller with functions of predictive modeling, rolling optimization, and feedback correction. …”
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Mixed Time/Event-Triggered Model Predictive Tracking Control for Networked Mobile Robots by Huixin Liu, Yonghua Lai, Hongsong Lian, Guobin Wang, Dongsheng Zheng

“…Focusing on the tracking control challenges in networked mobile robot systems, this research formulates a mixed time/event-triggered model predictive control (MPC) method. The method integrates time-triggered and event-triggered mechanisms, where the time-triggered module improves the performance of the MPC, and the event-triggered module reduces the resource consumption without sacrificing the control performance. …”
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Using nonlinear model predictive control to find optimal therapeutic strategies to modulate inflammation by Judy Day, Jonathan Rubin, Gilles Clermont

“…In this work, we pursue this goal by implementing a nonlinear model predictive control (NMPC) algorithm in the context of a reduced computational model of the acute inflammatory response to severe infection. …”
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A data-driven spatial-temporal model for prediction of tunnel deformation by Ziyi Zhang, Han Zhang, Cong Du, Mingzhao Wei, Xiaochao Wang, Jianqing Wu

“…The results show that the proposed model significantly outperforms other algorithms, including GRU, TCN, ChebNet, GCN, GAT, and GAT-TCN, in terms of prediction accuracy. …”
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Data-Driven Approaches for Predicting and Forecasting Air Quality in Urban Areas by Cosmina-Mihaela Rosca, Madalina Carbureanu, Adrian Stancu

“…For this purpose, 19 predictive models were developed and compared: 12 machine learning algorithms, 7 deep learning, and 1 forecasting model based on structural component analysis. …”
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Community evolution prediction based on feature change patterns in social networks by Jingyi Ding, Guojing Sun, Tiwen Wang, Licheng Jiao, Junzhao Du, Jianshe Wu, Hongfei Wang, Ruohui Cheng

“…Most existing algorithms for predicting community evolution rely on extracting community state features to forecast evolutionary events. …”
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A quantitative analysis of Koopman operator methods for system identification and predictions by Zhang, Christophe, Zuazua, Enrique

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A Financial Fraud Prediction Framework Based on Stacking Ensemble Learning by Shanshan Zhu, Haotian Wu, Eric W. T. Ngai, Jifan Ren, Daojing He, Tengyun Ma, Yubin Li

“…It uses the stacking ensemble technique to integrate numerous base models of machine learning algorithms for predicting financial fraud. Furthermore, the proposed framework has high versatility and is suitable for various tasks related to financial fraud prediction, addressing the problem of model selection difficulties in previous research due to different scenarios and data. …”
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