Proof-of-concept evaluation at Cox’s Bazar of the Safe Water Optimization Tool: water quality modelling for safe water supply in humanitarian emergencies by Tarra L Penney, James Orbinski, Syed Imran Ali, Michael De Santi, Matt Arnold, Usman T Khan, Syed Saad Ali, Jean-François Fesselet

“…The SWOT’s machine-learning model predicted a 1%–9% probability of household FRC<0.2 mg/L at 15 hours, close to the observed 12% and in line with the observed 7% risk during baseline and endline, respectively. …”
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Machine learning-driven design of wide-angle impedance matching structures for wide-angle scanning arrays by Sina Hasibi Taheri, Javad Mohammadpour, Ali Lalbakhsh, Slawomir Koziel, Stanislaw Szczepanski

“…Decision Tree-based models are chosen to provide accurate prediction while minimizing the dataset preparation time. …”
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Predicting Endpoint Temperature of Molten Steel in VD Furnace Refining Process Using Metallurgical Mechanism and Bayesian Optimization XGBoost by Ji XU, Zicheng XIN, Mo LAN, Wenhui LIN, Bo ZHANG, Qing LIU

“…Results and Discussions This study employs grid search (GS) and random search (RS) for hyperparameter optimization of the model to compare the results to BO hyperparameter optimization. …”
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A Hybrid Artificial Neural Network and Particle Swarm Optimization algorithm for Detecting COVID-19 Patients by Alla Ahmad Hassan, Tarik A Rashid

“…The purpose of this study is to contribute to resolving this issue by presenting the implementation and assessment of Machine Learning models. Using Neural Networks and Particle Swarm Optimization to help in the detection of COVID-19 in its early stages. …”
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Non-Destructive Detection of Current Internal Disorders and Prediction of Future Appearance in Mango Fruit Using Portable Vis-NIR Spectroscopy by Jasciane da Silva Alves, Bruna Parente de Carvalho Pires, Luana Ferreira dos Santos, Tiffany da Silva Ribeiro, Kerry Brian Walsh, Ederson Akio Kido, Sergio Tonetto de Freitas

“…A method based on Vis-NIR spectroscopy and machine learning-based modeling for non-destructive detection of the internal disorders of black flesh, spongy tissue, jelly seed, and soft nose in mango fruit was developed using the vis-NIR spectra of intact mango fruit of three cultivars sourced from three orchards in each of the two seasons, with spectra collected both at harvest and after storage. …”
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Explainable artificial intelligence for sustainable urban water systems engineering by Shofia Saghya Infant, Sundaram Vickram, A Saravanan, C M Mathan Muthu, Devarajan Yuarajan

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Artificial intelligence prediction models for acute respiratory distress syndrome：progress and challenges by MENG Xianglin*，XIONG Yaxin，HAN Ci，GE Xin，ZHAO Mingyan

“…Current artificial intelligence（AI）technology，especially machine learning（ML）models，have shown significant potential in the early diagnosis，risk stratification and personalized management of ARDS. …”
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Hyperparameter Optimization EM Algorithm via Bayesian Optimization and Relative Entropy by Dawei Zou, Chunhua Ma, Peng Wang, Yanqiu Geng

“…Hyperparameter optimization (HPO), which is also called hyperparameter tuning, is a vital component of developing machine learning models. …”
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Exploring Predictive Modeling for Food Quality Enhancement: A Case Study on Wine by Cemil Emre Yavas, Jongyeop Kim, Lei Chen, Christopher Kadlec, Yiming Ji

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Driver Takeover Performance Prediction Based on LSTM-BiLSTM-ATTENTION Model by Lijie Chen, Daofei Li, Tao Wang, Jun Chen, Quan Yuan

“…By building a takeover scenario and conducting experiments in the driving simulation experimental platform under the human–machine co-driving environment, the relevant state indicators in the 15 s per second before the takeover request is sent are extracted from three perspectives, namely, driver state, traffic environment, and personal attributes, as model inputs, and the level of takeover performance was labeled; the hybrid LSTM-BiLSTM-ATTENTION algorithm is used to construct a driver takeover performance prediction model and compare it with other five algorithms. …”
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Electric vehicle charging station demand prediction model deploying data slotting by A.V. Sreekumar, R.R. Lekshmi

“…One major challenge associated with development of machine learning models is the inherent uncertainty in electric vehicle charging behaviour that includes variations in charging patterns, user preferences, and vehicle types. …”
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Unveiling diabetes onset: Optimized XGBoost with Bayesian optimization for enhanced prediction. by Muhammad Rizwan Khurshid, Sadaf Manzoor, Touseef Sadiq, Lal Hussain, Mohammed Shahbaz Khan, Ashit Kumar Dutta

“…This study focused on optimizing the hyperparameters of an XGBoost ensemble machine learning model using Bayesian optimization. …”
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Novel Data-Driven PDF Modeling in FGM Method Based on Sparse Turbulent Flame Data by Guihua Zhang, Jiayue Liu, Yuxin Wu, Guangxi Yue

“…To expand the model’s applicable range, a data fusion strategy was applied in different machine learning methods. …”
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Perceptual-Preference-Based Touring Routes in Xishu Gardens Using Panoramic Digital-Twin Modeling by Xueqian Gong, Zhanyuan Zhu, Li Guo, Yong Zhong, Deshun Zhang, Jing Li, Manqin Yao, Wei Yong, Mengjia Li, Yujie Huang

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Development and validation of a risk prediction model for acute kidney injury in coronary artery disease by Ming Ye, Chang Liu, Duo Yang, Hai Gao

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Enhancing EEG-Based Emotion Detection with Hybrid Models: Insights from DEAP Dataset Applications by Badr Mouazen, Ayoub Benali, Nouh Taha Chebchoub, El Hassan Abdelwahed, Giovanni De Marco

“…Our findings highlight the effectiveness of hybrid deep learning models in improving accuracy, interpretability, and real-time processing capabilities. …”
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Stroke Prediction Based on Machine Learning by Zhang Yuhan

“…To improve performance, further model optimization, such as adjusting class weights or employing ensemble methods, is necessary to reduce these false-negative rates and enhance diagnostic accuracy. …”
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The role of hybrid models in financial decision-making: Forecasting stock prices with advanced algorithms by Xiaoyi Zhu

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Toward an Accurate Liver Disease Prediction Based on Two-Level Ensemble Stacking Model by Marghany Hassan Mohamed, Botheina Hussein Ali, Ahmed Ibrahim Taloba, Ahmad O. Aseeri, Mohamed Abd Elaziz, Shaker El-Sappagah, Nora Mahmoud El-Rashidy

“…Also, a two-level ensemble stacking model is applied based on several meta-ensemble classifiers and the feature selection technique to optimize the accuracy of the ensemble classifiers. …”
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Advancing the accuracy of clathrin protein prediction through multi-source protein language models by Watshara Shoombuatong, Nalini Schaduangrat, Pakpoom Mookdarsanit, Jaru Nikom, Lawankorn Mookdarsanit

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