The Design of the 1D CNN–GRU Network Based on the RCS for Classification of Multiclass Missiles [PDF]
, 2023 A Ran Kim +3 more
openalex +1 more source
Quality and Reliability Engineering International, Volume 42, Issue 4, Page 1426-1442, June 2026.ABSTRACT Accurate state of health (SOH) estimation of Li‐ion batteries is essential for ensuring safety, reliability, and prolonging battery lifespan in energy storage systems and electric vehicles. This study proposes a hybrid temporal convolutional network (TCN)–transformer framework that effectively captures both short‐term temporal dynamics and ...
Fusen Guo +6 more
wiley +1 more source
PREDIKSI HARGA EMAS MENGGUNAKAN METODE LSTM DAN GRU
, 2023 Abu Tholib +2 more
openalex +2 more sources
Recommender Systems: Taxonomy, Applications and Current Research Trends
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.Integrating taxonomy, application developments, open‐source software, and publication trends, this paper identifies and outlines promising future directions for recommender systems research. ABSTRACT Recommender Systems play an essential role in assisting users to navigate the immense amount of information and services available online, aiding them in ...
Daniel Ranchal‐Parrado +2 more
wiley +1 more source
Prediction Model of Hydropower Generation and Its Economic Benefits Based on EEMD-ADAM-GRU Fusion Model [PDF]
, 2022 Jiechen Wang, Zhimei Gao, Yan Ma
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Deep Learning for Satellite‐Based Forest Disturbance Monitoring: Recent Advances and Challenges
WIREs Data Mining and Knowledge Discovery, Volume 16, Issue 2, June 2026.Overview of key research challenges in forest disturbance monitoring, including the detection of disturbances of varying severity, the attribution of disturbance agents, and the development of models capable of generalizing across regions. ABSTRACT Climate change and land use pressures are intensifying forest disturbances in many world regions, as ...
Carolina Natel +3 more
wiley +1 more source
SGT: Session-based Recommendation with GRU and Transformer
, 2023 Ling-Mei Wu +4 more
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Spatiotemporal Machine Learning Approaches for Atmospheric Composition Emulation in NASA GISS ModelE
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract Earth System Models (ESMs) rely on parameterizations to represent sub‐grid scale processes that cannot be explicitly resolved at typical model resolutions. However, maintaining full coupling between these parameterizations and other model components creates substantial computational demands. This challenge is particularly acute for atmospheric
Mohammad H. Erfani +5 more
wiley +1 more source
Animation video frame prediction based on ConvGRU fine-grained synthesis flow
Nonlinear EngineeringDue to the complexity and dynamism of animated scenes, frame prediction in animated videos is a challenging task. In order to improve the playback frame rate of animated videos, an innovative convolutional neural network combined with convolutional gated
Duan Xue
doaj +1 more source
A Deep Ensemble Transformer Model for Global Ionosphere Prediction and Uncertainty Quantification
Journal of Geophysical Research: Machine Learning and Computation, Volume 3, Issue 3, June 2026.Abstract Accurate global ionospheric forecasting is important for various purposes, from geophysical research to practical applications, including real‐time precise positioning and navigation. Existing studies primarily focus solely on deterministic predictions and often overlook uncertainty quantification.
Shuyin Mao, Junyang Gou, Benedikt Soja
wiley +1 more source