Results 231 to 240 of about 37,859 (302)

A Bibliometric Analysis of Process Mining

WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.
Process mining studies with numbers. ABSTRACT Process mining (PM) has emerged as a pivotal discipline in data science, bridging traditional process analysis with data‐driven techniques to extract actionable insights from event logs. This study conducts a comprehensive bibliometric analysis of 1764 peer‐reviewed articles from the Web of Science database
Seyfullah Tokumaci, Onur Dogan, Oguzhan Ozturk   +2 more
wiley   +1 more source

A Machine Learning-Based Model for Individualized Prediction of Vancomycin Concentration-Time Curves in ICU Patients. [PDF]

Pharmacol Res Perspect
Wang JY, Hu J, Chen Y, Li S, Peng JM, Dong R, Hu XY, Jiang W, Wang CY, Weng L, Du B, China Critical Care Clinical Trials Group (CCCCTG).   +11 more
europepmc   +1 more source

Predicting Daily Cardiovascular Emergencies Using Weather and Air Quality Data: A 23‐Year Machine‐Learning Analysis in Taiwan

GeoHealth, Volume 10, Issue 6, June 2026.
Abstract Short‐term variability in weather and air quality is known to influence cardiovascular emergencies, yet its day‐to‐day predictive value at the population level remains insufficiently understood. Using 23 years of nationwide data from Taiwan (2000–2022), we evaluated how weather and air quality conditions shape daily cardiovascular disease (CVD)
Hsiang‐Han Chen, Pei‐Shan Tsai, Yu‐Chia Chen, Cheng‐Yu Li, Yu‐Kai Lin, Wan‐Ru Huang, Kate Huihsuan Chen   +6 more
wiley   +1 more source

Fuel-driven hybrid deep learning model for forecasting finger millet prices. [PDF]

Sci Rep
Chaitra B, Meena K.
europepmc   +1 more source

Multidimensional Distributional Neural Network Output Demonstrated in Super‐Resolution of Surface Wind Speed

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Accurate quantification of uncertainty in neural network predictions remains a central challenge for scientific applications involving high‐dimensional, correlated data. While existing methods capture either aleatoric or epistemic uncertainty, few offer closed‐form, multidimensional distributions that preserve spatial correlation while ...
Harrison J. Goldwyn, Mitchell Krock, Johann Rudi, Daniel Getter, Julie Bessac   +4 more
wiley   +1 more source

Mini-batch size sensitivity in deep residual networks for short-term load forecasting: an empirical study. [PDF]

Sci Rep
Liu J, Ahmad FA, Samsudin K, Hashim F, Ab Kadir MZA.   +4 more
europepmc   +1 more source

Reconstructing Digital Elevation Models From Single Synthetic Aperture Radar Images

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.
Abstract Traditional radar‐based methods of generating topography involve complex dual‐antenna or formation‐flying satellite configurations, interferometric processing, and specialized expertise. We present an approach for reconstructing digital elevation models (DEMs) from single Synthetic Aperture Radar (SAR) images using deep learning.
W. Hamish Mitchell, Miriam Cha, Michael Newey, J. Taylor Perron   +3 more
wiley   +1 more source

When Does Directional Reflectance Matter? Evaluating BRDF Effects in Plant Canopy Light Simulations. [PDF]

Plants (Basel)
Balasus J, Wirth F, Herzog A, Khanh TQ.
europepmc   +1 more source

Spatiotemporal and Economic Impacts of Media on Water Conservation During Drought: An Explainable AI Approach

Water Resources Research, Volume 62, Issue 6, June 2026.
Abstract Reduced public engagement in water conservation during droughts can exacerbate water scarcity and its associated impacts. However, capturing these coupled human‐natural dynamics in hydrologic models remains a persistent challenge. Here, we develop an AI model framework to estimate household water‐saving patterns during the 2022–2023 drought in
Eunmi Lee, Seunghui Choi, Sangeun Lee, Newsha K. Ajami, Hyeoncheol Yoon, Xiaogang He, Jonghun Kam   +6 more
wiley   +1 more source

A fuzzy time-series driven ensemble approach for accurate forecasting of higher education rankings. [PDF]

Sci Rep
Agarwal N, Tayal DK, Rajeena PPF, Rahoof PP, Alkhaldi N, Ali MAS, Sunder R, Ali HS.   +7 more
europepmc   +1 more source