Results 201 to 210 of about 95,777 (258)

Edge-Based Autonomous Fire and Smoke Detection Using MobileNetV2. [PDF]

Sensors (Basel)
Sharobiddinov D, Siddiqui HUR, Saleem AA, Mezquita GM, Vargas DLR, Díez IT.   +5 more
europepmc   +1 more source

Segmentation and Tracking of Eruptive Solar Phenomena With Convolutional Neural Networks

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Solar eruptive events are complex phenomena, which most often include coronal mass ejections (CME), CME‐driven compressive and shock waves, flares, and filament eruptions. CMEs are large eruptions of magnetized plasma from the Sun's outer atmosphere or corona, that propagate outward into the interplanetary space.
Oleg Stepanyuk, Kamen Kozarev
wiley   +1 more source

Advancements in Parkinson's Disease Prediction Using Machine Learning: A Neurological Perspective. [PDF]

Healthc Inform Res
Chaithanya AS, Kumar NK, Prasad GV, Keerthana B.   +3 more
europepmc   +1 more source

Machine Learning Approximations for Fast and Accurate Prediction of Nonlinear Four‐Wave Interactions in Spectral Wave Models

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Operational wave forecasting requires a delicate balance between the accuracy and computational speed of the spectral wave model used, in which the nonlinear wave–wave interaction “source” term plays an important role. The exact formulation of these nonlinear four‐wave interactions requires solving the six‐dimensional Boltzmann integral, an ...
Jialun Chen, Thomas A. A. Adcock, Qingxiang Liu, Ronald Clark, Tianning Tang   +4 more
wiley   +1 more source

Experimental Verification of a Two‐Dimensional Inverse Method for Turbidity Currents Using a Deep Neural Network

Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 1, February 2026.
Abstract Turbidites have been widely studied as indicators of the occurrences and magnitudes of paleo‐tsunamis and paleo‐earthquakes. Inversion to estimate flow conditions from turbidites offers valuable insights into the magnitudes of paleo‐seismic and tsunami events.
Seiya Fujishima, Hajime Naruse
wiley   +1 more source

JAX‐LaB: A High‐Performance, Differentiable Lattice Boltzmann Library for Modeling Multiphase Fluid Dynamics in Geosciences and Engineering

Journal of Advances in Modeling Earth Systems, Volume 18, Issue 2, February 2026.
Abstract We introduce JAX‐LaB, a differentiable, Python‐based Lattice Boltzmann simulation library designed for modeling multiphase and multiphysics fluid dynamics problems in hydrologic, geologic, and engineered porous media settings. The library is designed as an extension to XLB (Ataei & Salehipour, 2024, https://doi.org/10.1016/j.cpc.2024.109187 ...
Piyush Pradhan, Pierre Gentine, Shaina Kelly   +2 more
wiley   +1 more source

Harnessing artificial intelligence for sustainable rice leaf disease classification. [PDF]

Front Plant Sci
Rana ME, Hameed VA, Eng IKY, Tripathy HK, Mallik S.   +4 more
europepmc   +1 more source

Forecasting Total Electron Content During Geomagnetic Storms Using Convolutional Long Short‐Term Memory (ConvLSTM): Performance and Limitations

Space Weather, Volume 24, Issue 2, February 2026.
Abstract This study investigates the effects of quiet time ionospheric conditions and the number of storm events used for training on the prediction of ionospheric total electron content (TEC) during geomagnetic storms using a deep learning method.
Se‐Heon Jeong, Hyosub Kil, Woo Kyoung Lee, Jeong‐Heon Kim, Young‐Sil Kwak   +4 more
wiley   +1 more source

Automated grading using natural language processing and semantic analysis. [PDF]

MethodsX
Ayaan A, Ng KW.
europepmc   +1 more source

Physics‐Informed, Differentiable Hydrologic Models for Capturing Unseen Extreme Events

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Recently, a hybrid framework combining machine learning (ML) and process‐based equations, termed differentiable modeling, has shown comparable accuracy to pure ML models while offering enhanced interpretability and spatial generalizability. However, it remained unclear how well hybrid models generalize to extreme floods outside of the range of
Yalan Song, Kamlesh Sawadekar, Jonathan M. Frame, Ming Pan, Martyn P. Clark, Wouter J. M. Knoben, Andrew W. Wood, Kathryn E. Lawson, Trupesh Patel, Chaopeng Shen   +9 more
wiley   +1 more source