Identifying heterogeneous air pollution exposure using machine learning models with dynamic traffic and population data by Ruoxi Wu, Yifan Wen, Xiaomeng Wu, Cheng Huang, Qingyan Fu, Qingyao Hu, Hongli Wang, Ye Wu, Shaojun Zhang

“…Crucially, dynamic population-weighted exposure assessments show 4.1%–10.9% higher NO _2 and PM _2.5 exposure versus conventional static estimates on weekdays, with weekend O _3 exposure 7.1% lower, which highlight how data-driven traffic patterns and mobility data reshape social risk distributions. …”
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Machine learning models for improving the diagnosing efficiency of skeletal class I and III in German orthodontic patients by Eva Paddenberg-Schubert, Kareem Midlej, Sebastian Krohn, Agnes Schröder, Obaida Awadi, Samir Masarwa, Iqbal M. Lone, Osayd Zohud, Christian Kirschneck, Nezar Watted, Peter Proff, Fuad A. Iraqi

“…Within the same skeletal class, age influenced cephalometric parameters: in skeletal class I, adolescents presented a more horizontal pattern (PFH/AFH, Gonial angle, NL-ML) and prominent mandible (SNB, SN-Pg) than children. …”
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A Comprehensive Framework for Parkinson's Disease Detection Using Spiral Drawings and Advanced Machine Learning Techniques by Mohamed J. Saadh, Waleed K. Abdulsahib, Hardik Doshi, Anupam Yadav, J. Gowrishankar, Mayank Kundlas, Nargiza Mansurova, Kamal Kant Joshi, Fadhil Feez Sead, Bagher Farhood

“…It integrates advanced machine learning techniques to improve diagnostic accuracy and practical application in clinical settings. …”
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A data-driven approach to forest health assessment through multivariate analysis and machine learning techniques by Raja Waqar Ahmed Khan, Hamayun Shaheen, Muhammad Ejaz Ul Islam Dar, Tariq Habib, Muhammad Manzoor, Syed Waseem Gillani, Abeer Al-Andal, John Oluwafemi Ayoola, Muhammad Waheed

“…K-means clustering was used to group forests into three distinct classes based on ecological characteristics, due to its efficiency in identifying natural patterns within multivariate data. ML models, including Decision Tree (DT), Random Forest (RF), and Support Vector Machine (SVM) were trained and validated using an 80:20 train-test split and 5-fold cross-validation. …”
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The identification and validation of histone acetylation-related biomarkers in depression disorder based on bioinformatics and machine learning approaches by Lu Zhang, Lu Zhang, YuJing Lv, Mengqing Ma, Jile Lv, Jie Chen, Shang Lei, Yi Man, Guimei Xing, Yu Wang

“…Three hub genes (JDP2, ALOX5, and KPNB1) were gained by two machine learning algorithms. The nomogram constructed based on these three hub genes showed high predictive accuracy. …”
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Computational analysis of reproducibility and image quality of 2011 and 2017 models of Heidelberg Spectralis SD-OCT machines by Kyoung A Viola Lee, Corey Tesdahl, Keith Zimmerman, Kimberly Jun, Sabrina Khalil, Alexander Shahin, Abdullah Abou-Samra, Ramesh Ayyala, Radouil Tzekov

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Leveraging machine learning to uncover multi-pathogen infection dynamics across co-distributed frog families by Daniele L. F. Wiley, Kadie N. Omlor, Ariadna S. Torres López, Celina M. Eberle, Anna E. Savage, Matthew S. Atkinson, Lisa N. Barrow

“…Efforts to understand what drives patterns of pathogen prevalence and differential responses among species are challenging because numerous factors related to the host, pathogen, and their shared environment can influence infection dynamics. …”
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Agricultural GDP exposure to drought and its machine learning-based prediction in the Jialing River Basin, China by Xinzhi Wang, Qingxia Lin, Zhiyong Wu, Yuliang Zhang, Changwen Li, Ji Liu, Shinan Zhang, Songyu Li

“…Subsequently, the standardized precipitation evapotranspiration index (SPEI) and standardized soil moisture index (SSMI) were applied to analyze the spatial-temporal patterns of droughts. Additionally, cropland exposure to drought was evaluated using gridded agricultural GDP data derived from pixel interpolation. …”
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Predicting Cardiovascular Aging Risk Based on Clinical Data Through the Integration of Mathematical Modeling and Machine Learning by Kuat Abzaliyev, Madina Suleimenova, Siming Chen, Madina Mansurova, Symbat Abzaliyeva, Ainur Manapova, Almagul Kurmanova, Akbota Bugibayeva, Diana Sundetova, Raushan Bitemirova, Nazipa Baizhigitova, Merey Abdykassymova, Ulzhas Sagalbayeva

“…In addition, we applied k-means clustering to identify hidden patterns and risk profiles within the dataset. A Random Forest classifier was trained to distinguish between high-risk and low-risk individuals using the same feature set. …”
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Utilizing machine learning to predict MRI signal outputs from iron oxide nanoparticles through the PSLG algorithm by Fatemeh Hataminia, Anahita Azinfar

“…Consequently, the new pattern, PSLG, was selected for predicting MRI behavior.…”
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The Dynamics Affecting the Export-Import Ratio in Turkey: A Hybrid Model Proposal with Econometrics and Machine Learning Approach by Erdemalp Özden

“…In addition, a 1% increase in consumer price index will increase ratio of exports to imports by 1.9 points, while a 1% increase in producer price index will cause a -0.8 point decrease on the ratio of exports to imports. Then, the pattern between the variables was analyzed with quadratic support vector machine, a machine learning method. …”
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Uncovering glycolysis-driven molecular subtypes in diabetic nephropathy: a WGCNA and machine learning approach for diagnostic precision by Chenglong Fan, Guanglin Yang, Cheng Li, Jiwen Cheng, Shaohua Chen, Hua Mi

“…The hub genes associated with DN and glycolysis-related clusters were identified via weighted gene co-expression network analysis (WGCNA) and machine learning algorithms. Finally, the expression patterns of these hub genes were validated using single-cell sequencing data and quantitative real-time polymerase chain reaction (qRT-PCR). …”
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Synergizing human insight and machine learning: A dual-lens approach to uncovering healthcare research and innovation outcomes by Stijn Horck, Sanne Steens, Jermain Kaminski

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Influence of Ocean Current Features on the Performance of Machine Learning and Dynamic Tracking Methods in Predicting Marine Drifter Trajectories by Huan Lin, Weiye Yu, Zhan Lian

“…In general, LSTM provides a more accurate geometric pattern of trajectories at the initial stages of forecasting, while DT offers superior accuracy in predicting specific trajectory positions. …”
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Fault Diagnosis of Train Bogie Bearing Based on Multi-scale Sample Entropy Improved Extreme Learning Machine by JIN Zhenzhen, HE Deqiang, MIAO Jian, XU Weichang

“…Finally, the feature vector set is divided into test set and training set, and the improved extreme learning machine is used as a pattern recognition algorithm for fault pattern recognition. …”
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The role of senescence-related genes in major depressive disorder: insights from machine learning and single cell analysis by Kun Lian, Wei Yang, Jing Ye, Yilan Chen, Lei Zhang, Xiufeng Xu

“…Consensus cluster analysis, based on SRGs expression patterns, identified subclusters of MDD patients. Weighted Gene Co-expression Network Analysis (WGCNA) identified gene modules strongly linked to each cluster. …”
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Cascaded Machine Learning of Soil Moisture and Salinity Prediction in Estuarine Wetlands Based on In Situ Internet of Things Monitoring by Jie Song, Yujun Yi

“…The elucidation of transport pattern and prediction of water and salt in estuarine wetland soils remain significant challenges. …”
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Computer Aided Diagnostic System for Blood Cells in Smear Images Using Texture Features and Supervised Machine Learning by Shakhawan Hares Wady

“…The framework combines the features extracted by Center Symmetric Local Binary Pattern (CSLBP), Gabor Wavelet Transform (GWT), and Local Gradient Increasing Pattern (LGIP), the data was then fed into machine learning classifiers including Decision Tree (DT), Ensemble, K-Nearest Neighbor (KNN), Naïve Bayes (NB), and Random Forest (RF)).  …”
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Calculating the High‐Latitude Ionospheric Electrodynamics Using a Machine Learning‐Based Field‐Aligned Current Model by V. Sai Gowtam, Hyunju Connor, Bharat S. R. Kunduri, Joachim Raeder, Karl M. Laundal, S. Tulasi Ram, Dogacan S. Ozturk, Donald Hampton, Shibaji Chakraborty, Charles Owolabi, Amy Keesee

“…ML‐AIM produces physically accurate ionospheric potential patterns such as the two‐cell convection pattern and the enhancement of electric potentials during active times. …”
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Machine Learning-Driven Parametric Analysis of Eco-Friendly Ultrasonic Welding for AL6061-CU Alloy Joints by A. Karan, S. Arungalai Vendan, M. R. Nagaraj, M. Chaturvedi, S. Sivadharmaraj

“…The primary focus is on the metallurgical transformations to evaluate the pattern of molecular diffusion and spread within the weld, the consistency of diffusion, and the resulting alterations in strength caused by ultrasonic vibrational heat. …”
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