Robust EEG Characteristics for Predicting Neurological Recovery from Coma After Cardiac Arrest by Meitong Zhu, Meng Xu, Meng Gao, Rui Yu, Guangyu Bin

“…Significance: Our research identifies key electroencephalographic (EEG) biomarkers, including low-frequency connectivity and burst suppression thresholds, to improve early and objective prognosis assessments. By integrating machine learning (ML) algorithms, such as Gradient Boosting Models and Support Vector Machines, with SHAP-based feature visualization, robust screening methods were applied to ensure the reliability of predictions. …”
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Machine Learning–Based Calibration and Performance Evaluation of Low-Cost Internet of Things Air Quality Sensors by Mehmet Taştan

“…Future studies should focus on long-term data collection, testing under diverse environmental conditions, and integrating additional sensor types to further advance this field.…”
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Smart deep learning model for enhanced IoT intrusion detection by Faisal S. Alsubaei

“…This paper addresses these limitations with large preprocessing steps followed by hyperparameter tuning of machine learning XGBoost and deep learning Sequential Neural Network (OSNN) algorithms through Grid Search for their best values to improve multiclass intrusion detection across varied datasets. …”
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Explaining neural networks for detection of tropical cyclones and atmospheric rivers in gridded atmospheric simulation data by T. Radke, S. Fuchs, C. Wilms, I. Polkova, I. Polkova, I. Polkova, M. Rautenhaus, M. Rautenhaus

“…<p>Detection of atmospheric features in gridded datasets from numerical simulation models is typically done by means of rule-based algorithms. Recently, the feasibility of learning feature detection tasks using supervised learning with convolutional neural networks (CNNs) has been demonstrated. …”
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Effects of missing data imputation methods on univariate blood pressure time series data analysis and forecasting with ARIMA and LSTM by Nicholas Niako, Jesus D. Melgarejo, Gladys E. Maestre, Kristina P. Vatcheva

“…Imputation of missing values is an inevitable step in every incomplete univariate time series. …”
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A Deep Learning-Based Echo Extrapolation Method by Fusing Radar Mosaic and RMAPS-NOW Data by Shanhao Wang, Zhiqun Hu, Fuzeng Wang, Ruiting Liu, Lirong Wang, Jiexin Chen

“…Furthermore, as the extrapolation time increases, the smoothing effect inherent to convolution operations leads to increasingly blurred predictions. To address the algorithmic limitations of deep learning-based echo extrapolation models, this study introduces three major improvements: (1) A Deep Convolutional Generative Adversarial Network (DCGAN) is integrated into the ConvLSTM-based extrapolation model to construct a DCGAN-enhanced architecture, significantly improving the quality of radar echo extrapolation; (2) Considering that the evolution of radar echoes is closely related to the surrounding meteorological environment, the study incorporates specific physical variable products from the initial zero-hour field of RMAPS-NOW (the Rapid-update Multiscale Analysis and Prediction System—NOWcasting subsystem), developed by the Institute of Urban Meteorology, China. …”
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A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models by Yawei Yang, Junjie Tang, Liping Ma, Feng Wu, Xiaoqing Guan

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Spatio-temporal distribution, prediction and relationship of three major acute cardiovascular events: Out-of-hospital cardiac arrest, ST-elevation myocardial infarction and stroke by Angelo Auricchio, Tommaso Scquizzato, Federico Ravenda, Ruggero Cresta, Stefano Peluso, Maria Luce Caputo, Stefano Tonazzi, Claudio Benvenuti, Antonietta Mira

“…Widespread implementation in clinical practice of prediction algorithms may allow to improve resource allocation, reduce treatment delays, and improve outcomes.…”
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scAMZI: attention-based deep autoencoder with zero-inflated layer for clustering scRNA-seq data by Lin Yuan, Zhijie Xu, Boyuan Meng, Lan Ye

“…Meanwhile, ZI layer is used to handle zero values in the data. We compare the performance of scAMZI with nine methods (three shallow learning algorithms and six state-of-the-art DL-based methods) on fourteen benchmark scRNA-seq datasets of various sizes (from hundreds to tens of thousands of cells) with known cell types. …”
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Dynamic time-varying transfer function for cancer gene expression data feature selection problem by Hao-Ming Song, Yu-Cai Wang, Jie-Sheng Wang, Yu-Wei Song, Shi Li, Yu-Liang Qi, Jia-Ning Hou

“…Subsequently, to assess the generalizability of our proposed approach across 12 cancer gene expression datasets for testing purposes five algorithms (AOA, COA, PSO, WOA and ZOA) are employed. …”
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The superiority of feasible solutions-moth flame optimizer using valve point loading by Mohammad Khurshed Alam, Herwan Sulaiman, Asma Ferdowsi, Md Shaoran Sayem, Md Mahfuzer Akter Ringku, Md. Foysal

“…This article presents a methodology for determining the optimal energy transmission system configuration by integrating power producers. The MFO, Grey Wolf Optimizer (GWO), Success-history-based Parameter Adaptation Technique of Differential Evolution - Superiority of Feasible Solutions (SHADE-SF), and Superiority of Feasible Solutions-Moth Flame Optimizer (SF-MFO) algorithms are applied to address the OPF problem with two objective functions: (1) reducing energy production costs and (2) minimizing power losses. …”
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Supervised Machine Learning Models for Predicting SS304H Welding Properties Using TIG, Autogenous TIG, and A-TIG by Subhodwip Saha, Barun Haldar, Hillol Joardar, Santanu Das, Subrata Mondal, Srinivas Tadepalli

“…A total of 80% of the collected dataset was used for training the models, while the remaining 20% was reserved for testing their performance. Six ML algorithms—Artificial Neural Network (ANN), K-Nearest Neighbors (KNN), Support Vector Regression (SVR), Random Forest (RF), Gradient Boosting Regression (GBR), and Extreme Gradient Boosting (XGBoost)—were implemented to assess their predictive accuracy. …”
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Enhancing drinking water safety: Real-time prediction of trihalomethanes in a water distribution system using machine learning and multisensory technology by Antonio J. Aragón-Barroso, David Ribes, Francisco Osorio

“…In total, a total of two predictive models were built, based on data filtered by conductivity levels, with coefficients of determination (R2) of 0.64 and 0.47. The algorithms of these predictive models were integrated into the control center of the water company in the study area. …”
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Simulation of energy consumption processes at the metallurgical enterprises in the energy-saving projects implementation by Kiyko S. G., Druzhinin E. A., Prokhorov O. V., Haidabrus B. V.

“…The model likewise includes algorithms for transport equipment management that minimize disruptions in continuous casting machines’ operation and simulate emergencies. …”
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Multimodal MRI radiomics-based stacking ensemble learning model with automatic segmentation for prognostic prediction of HIFU ablation of uterine fibroids: a multicenter study by Bing Wen, Chengwei Li, Qiuyi Cai, Dan Shen, Xinyi Bu, Fuqiang Zhou

“…The stacking ensemble learning model, which integrated these five algorithms, demonstrated a notable enhancement in performance, with an AUC of 0.897 (95% CI: 0.818–0.977) in the internal test set and 0.854 (95% CI: 0.759–0.948) in the external test set.ConclusionThe DL based automatic segmentation MRI radiomics stacking ensemble learning model demonstrated high accuracy in predicting the prognosis of HIFU ablation of uterine fibroids.…”
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Machine learning in CTEPH: predicting the efficacy of BPA based on clinical and echocardiographic features by Qiumeng Xi, Juanni Gong, Jianfeng Wang, Xiaojuan Guo, Yuanhua Yang, Xiuzhang lv, Suqiao Yang, Yidan Li

“…By comparing the predictive performance of different algorithms, we aimed to establish a robust tool to identify patients most likely to benefit from BPA. …”
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A Novel Dataset for Early Cardiovascular Risk Detection in School Children Using Machine Learning by Rafael Alejandro Olivera Solís, Emilio Francisco González Rodríguez, Roberto Castañeda Sheissa, Juan Valentín Lorenzo-Ginori, José García

“…We conducted a rigorous performance evaluation of 10 machine learning (ML) algorithms to classify cardiovascular risk into two categories: at risk and not at risk. …”
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PlantGaussian: Exploring 3D Gaussian splatting for cross-time, cross-scene, and realistic 3D plant visualization and beyond by Peng Shen, Xueyao Jing, Wenzhe Deng, Hanyue Jia, Tingting Wu

“…It marks one of the first applications of 3D Gaussian splatting techniques in plant science, achieving high-quality visualization across species and growth stages. By integrating the Segment Anything Model (SAM) and tracking algorithms, PlantGaussian overcomes the limitations of classic Gaussian reconstruction techniques in complex planting environments. …”
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Risk Factors for Gout in Taiwan Biobank: A Machine Learning Approach by Liu YR, Nfor ON, Zhong JH, Lin CY, Liaw YP

“…GB also performed robustly, with AUC values around 0.987– 0.988 and maintaining high sensitivity (0.944– 0.950) and specificity (0.995– 0.999) across different model variations. …”
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Identification of novel molecular subtypes and construction of a prognostic signature via multi-omics analysis and machine learning in lung adenocarcinoma by Ke Ma, Jie Xu, Jie Xu, Congyue Wang, Congyue Wang, Xu Cao, Xu Cao, Wenjie Yu, Jingjing Xi, Xuan Zhang, Jiamin Zhan, Yang Liu, Aoyang Yu, Aoyang Yu, Shuhan Liu, Yanhua Liu, Yanhua Liu, Chong Chen, Chong Chen, Xiaoli Mai, Xiaoli Mai

“…This study aimed to identify novel molecular subtypes and construct a prognostic signature to enhance the stratification of LUAD prognosis.Materials and methodsNovel molecular subtypes of LUAD patients were identified by applying 10 distinct clustering algorithms on multi-omics data. Single-cell RNA-sequencing (scRNA-seq) data were integrated to characterize subtype-specific immune microenvironments. …”
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