Maximizing oil recovery in sandstone reservoirs through optimized ASP injection using the super learner algorithm by Dike Fitriansyah Putra, Mohd Zaidi Jaafar, Ku Muhd Na’im Khalif, Apri Siswanto, Ichsan Lukman, Ahmad Kurniawan

“…This study introduces a novel application of the Super Learner (SL) ensemble, a stacking-based machine learning algorithm integrating multiple base models (XGBoost, SVR, BRR, and Decision Tree), to systematically predict and optimize ASP injection parameters. …”
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Numerical and artificial neural network inspired study on step-like-plenum battery thermal management system by Olanrewaju M. Oyewola, Emmanuel T. Idowu

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Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images by Jeremiah W. Johnson, Doğacan Su Öztürk, Donald Hampton, Hyunju K. Connor, Matthew Blandin, Amy Keesee

“…Abstract We report a novel machine‐learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. …”
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Detection of serum composition in pediatric inflammatory multisystem syndrome associated with SARS-CoV-2 and the response for the treatment by FTIR by Joanna Depciuch, Izabela Sieminska, Pawel Jakubczyk, Bartosz Klebowski, Katarzyna Ptak, Izabela Szymońska, Przemko Kwinta, Maciej Siedlar, Jan Jakub Kęsik, Magdalena Parlinska-Wojtan, Jarek Baran

“…Importantly, FTIR data correlates well with medical parameters, however the correlation differs with respect to the groups before and after the treatment.…”
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Resting-state EEG microstate analysis reveals potential biomarkers for subclinical insomnia by Yujie Shi, Mengqi Ji, Fan Zhong, Rui Jiang, Zhuhong Chen, Chi Zhang, Yuting Li, Junpeng Zhang, Wen Wang

“…The electroencephalogram(EEG) microstates, reflecting brain network dynamics, may provide potential biomarkers by comparing resting-state EEG parameters between sINSO patients and healthy controls.Methods Resting-state EEG data from 20 sINSO subjects and 20 healthy controls, under both open and closed eye conditions, were analyzed using microstate clustering (labeled A, B, C, and D) and machine learning to evaluate their discriminative power.Results The microstate global explained variance of the eyes-closed data was better than that of the eyes-open data. …”
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Deciphering the impact of sepsis phenotypes on improving clinical outcome predictions: a multicenter retrospective analysis based on critical care in China by Luyao Zhou, Weimin Zhang, Min Shao, Cui Wang, Yu Wang

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Enhanced convolutional neural networks for defect detection in fiber-reinforced composites: a hyperparameter optimization approach by Pankaj Beldar, Rasika Chandramore, Sarika Jagtap, Dipali Bendale

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A Method for Monitoring the Reliability of Technical Systems by Identifying the Entropy of the Causes of their Failures by A. T. Rybak, S. V. Teplyakova, A. V. Olshevskaya, A. S. Prutskov

“…In modern literature, the topic of assessing the reliability of machines, considered as complex probabilistic systems that take into account not only the dynamic parameters under operation, but also the processes of manufacturing the components of the system, is not sufficiently covered. …”
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Reconstruction of reservoir rock using attention-based convolutional recurrent neural network by Indrajeet Kumar, Anugrah Singh

“…While x-ray micro-computed tomography gives us three-dimensional images of the porous media, it is often impossible to quantify the variability of the pore, grains, structure, and orientation experimentally. Recently, machine learning has successfully demonstrated the reconstruction ability of reservoir rock images or any porous media. …”
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Optimized Ensemble Methods for Classifying Imbalanced Water Quality Index Data by Zaharaddeen Karami Lawal, Ali Aldrees, Hayati Yassin, Salisu Dan'azumi, Sujay Raghavendra Naganna, Sani I. Abba, Saad Sh. Sammen

“…The dataset of this study comprises 301 records collected from eight monitoring stations along the Kinta River, encompassing 31 pollution indicators, including hydrological, chemical, physical, and microbiological parameters. Six algorithms used include decision tree, logistic regression, random forest, support vector machine, AdaBoost, and XGBoost. …”
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A manganese metabolism-related gene signature stratifies prognosis and immunotherapy efficacy in kidney cancer by Yang Liu, Hao Ye, Ruoxuan Zhang, Xiaolong Liu, Ranlu Liu

“…We constructed a clinical nomogram incorporating the MMCG risk score and other clinical parameters, which demonstrated highly accurate predictive capabilities. …”
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LASSO logistic regression reveals a mixed MiRNA and serum-marker classifier for prediction of immunotherapy response in liquid biopsies of melanoma patients by Marc Bender, I.-Peng Chen, Leonie Bluhm, Peter Mohr, Beate Volkmer, Rüdiger Greinert

“…Among six machine learning models tested, a relaxed LASSO approach on the entire dataset performed best (AUC = 0.851). …”
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Selection of features for modeling the risk of fatal outcomes in patients after myocardial infarction or unstable angina by D. A. Shvets, S. V. Povetkin

“…There were 8 following most significant parameters for predicting a fatal outcome according to machine selection results: age, LVEF, BSA, creatinine level, systolic blood pressure, HF, comorbidity, nosological unit.…”
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Assessment of using transfer learning with different classifiers in hypodontia diagnosis by Tansel Uyar, Didem Sakaryalı Uyar

“…Pretrained convolutional neural network models (AlexNet, DarkNet-19, DarkNet-53, DenseNet-201, EfficientNet, GoogLeNet, InceptionV3, IncResV2, MobileNetV2, NasNet-Mobile, Places365, ResNet-18, ResNet-50, ResNet-101, ShuffleNet, SqueezeNet, VGG-16, VGG-19, and Xception) were used for training with the fine-tuning method and different machine learning classifiers (decision trees, discriminant analysis, logistic regression, naive Bayes, support vector machines, nearest neighbor, ensemble method, and artificial neural network). …”
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Determination of similar material proportions based on orthogonal experiments and neural network optimization in the goaf area by Shengdi Wang, Junzhi Chen, Yonggang Zhang, Xiaowei Qiao, Zhiping Zhang, Xin Wang

“…Furthermore, this study proposed a novel machine learning-based prediction model that utilizes a PSO-BP neural network to regress and predict experimental data. …”
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Imaging‐Based Prediction of Ki‐67 Expression in Hepatocellular Carcinoma: A Retrospective Study by Chiyu Cai, Liancai Wang, Lianyuan Tao, Hengli Zhu, Yongnian Ren, Deyu Li, Dongxiao Li

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Time in range prediction using the experimental mobile application in type 1 diabetes by A. N. Rusanov, T. I. Rodionova

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Primary Controlling Factors of Apatite Trace Element Composition and Implications for Exploration in Orogenic Gold Deposits by Genshen Cao, Huayong Chen, Yu Zhang, Weipin Sun, Junfeng Zhao, Hongtao Zhao, Hao Wang

“…Feature importance analysis (Gini decrease and hidden layer weights) suggest that Pb, As, U, Sr, Eu, Mn, and Fe are the important parameters. Arsenic, U, Eu, Mn, and Fe are redox‐sensitive elements, with their concentrations responding to changes in fluid redox conditions. …”
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Framingham Risk Score Prediction at 12 Months in the STANDFIRM Randomized Control Trial by Thanh G. Phan, Velandai K. Srikanth, Dominique A. Cadilhac, Mark Nelson, Joosup Kim, Muideen T. Olaiya, Sharyn M. Fitzgerald, Christopher Bladin, Richard Gerraty, Henry Ma, Amanda G. Thrift

“…We determine the optimal machine learning and associated tuning parameters from the following: random forest, extreme gradient boosting, category boosting, support vector regression, multilayer perceptron neural network, and K‐nearest neighbor. …”
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EEG microstate analysis in children with prolonged disorders of consciousness by Yi Zhang, Zhichong Hui, Yuwei Su, Weihang Qi, Guangyu Zhang, Liang Zhou, Jiamei Zhang, Kaili Shi, Yonghui Yang, Lei Yang, Gongxun Chen, Sansong Li, Mingmei Wang, Dengna Zhu

“…Correlation analysis examined relationships between microstate parameters and Coma Recovery Scale-Revised (CRS-R) scores in children with pDoC. …”
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