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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Reducing the Environmental Impact of Sewer Network Overflows Using Model Predictive Control Strategy by I. Vasiliev, L. Luca, M. Barbu, R. Vilanova, S. Caraman

“…The main objective of the paper is to minimize the wastewater quantity and the pollutant loads that overflow from the SN. The proposed algorithm to achieve this goal is Model Predictive Control using Particle Swarm Optimization as optimization method. …”
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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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Bitcoin price direction prediction using on-chain data and feature selection by Ritwik Dubey, David Enke

“…Results from the research show that the Boruta feature selection algorithm combined with the CNN-LSTM model performs best compared to other combinations with a prediction accuracy of 82.03 % over the testing period. …”
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DLTM: a deep learning method for tearing mode simulation and prediction by Zhipeng Chang, Baofeng Gao, Ruijie Yin, Xiaofei Zhao

“…Machine learning, particularly deep learning algorithms, has shown significant potential in various plasma applications, including disruption prediction and tokamak control. …”
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Gravity Predictions in Data-Missing Areas Using Machine Learning Methods by Yubin Liu, Yi Zhang, Qipei Pang, Sulan Liu, Shaobo Li, Xuguo Shi, Shaofeng Bian, Yunlong Wu

“…In recent years, the rapid development of artificial intelligence has opened up a new opportunity for data prediction. In this study, utilizing the EGM2008 satellite gravity model, we conducted a comprehensive analysis of three machine learning algorithms—random forest, support vector machine, and recurrent neural network—and compared their performances against the traditional Kriging interpolation method. …”
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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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Soft voting ensemble model to improve Parkinson’s disease prediction with SMOTE by Jumanto Unjung, Rofik Rofik, Endang Sugiharti, Alamsyah Alamsyah, Riza Arifudin, Budi Prasetiyo, Much Aziz Muslim

“…However, a major issue in predictive analysis is the imbalance in data distribution and the low performance of classification algorithms. …”
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Conjugate gradient neural network in prediction of clay behavior and parameters sensitivities by Hamed Memarian fard

“…The paper also intends to present how much the input memory may cover the accuracy of predicted behavior of standard triaxial drained and undrained tests. …”
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Predicting Drivers’ Eyes-Off-Road Duration in Different Driving Scenarios by Wei Yuan, Zhuofan Liu, Rui Fu

“…Moreover, HMM-based algorithms that fed up with both glance duration and glance location sequences resulted in a highest accuracy of 92.7% in driver’s eyes-off-road durations prediction. …”
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