Cultivating clean skies: unveiling the tapestry of air quality in Gujarat through innovative machine learning analysis by Gaddam Advitha, Allada Nagasai Varaprasad, Koti Vennela Khushi, Pullabhotla Vijay, Sukanta Nayak

“…Air pollution emerges as a formidable threat to both public health and environmental integrity, especially in regions undergoing rapid development. …”
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Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN by Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov

“…The study aims to apply unsupervised clustering algorithms to ECG data to detect latent risk profiles related to heart failure, based on distinctive ECG features. …”
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Enhanced gap-filling for satellite-derived crop monitoring using temperature-driven reconstruction techniques by Flavian Tschurr, Lukas Valentin Graf, Achim Walter, Helge Aasen

“…Moreover, our proposed method outperforms state-of-the-art approaches based on logistic functions in terms of physiological plausibility, fitting requirements, and representation of high GLAI values. Our approach requires fewer satellite observations compared to traditional remote sensing time series algorithms, making it suitable for agricultural areas with high cloud cover. …”
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Mechanism–Data Collaboration for Characterizing Sea Clutter Properties and Training Sample Selection by Wenhao Chen, Yong Zou, Zhengzhou Li, Shengrong Zhong, Haolin Gan, Aoran Li

“…Multi-feature-based maritime radar target detection algorithms often rely on statistical models to accurately characterize sea clutter variations. …”
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A Survey on Video Compression Optimization Techniques for Accuracy Enhancement in Video Analytics Applications (VAPs) by Kholidiyah Masykuroh, Hendrawan, Eueung Mulyana, Farhan Krishna

“…The results indicate higher QP values lead to noticeable quality degradation, particularly at lower bitrates. …”
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Precision Soil Moisture Monitoring Through Drone-Based Hyperspectral Imaging and PCA-Driven Machine Learning by Milad Vahidi, Sanaz Shafian, William Hunter Frame

“…This correlation corresponded to lower RMSE values, highlighting improved model accuracy.…”
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Research on Interval Probability Prediction and Optimization of Vegetation Productivity in Hetao Irrigation District Based on Improved TCLA Model by Jie Ren, Delong Tian, Hexiang Zheng, Guoshuai Wang, Zekun Li

“…Traditional machine learning algorithms frequently experience overfitting when processing high-dimensional time-series data or substantial numbers of outliers, impeding the accurate prediction of various vegetation metrics. …”
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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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