Terrain-guided flatten memory network for deep spatial wind downscaling
High-resolution wind analysis plays an essential role in pollutant dispersion and renewable
energy utilization. This article focuses on spatial wind downscaling. Specifically, a novel …
energy utilization. This article focuses on spatial wind downscaling. Specifically, a novel …
Video quality adaptation using CNN and RNN models for cost-effective and scalable video streaming Services
M Darwich, M Bayoumi - Cluster Computing, 2024 - Springer
Video streaming services require adaptive bit rate strategies that optimize video quality
based on network conditions and user preferences to provide a cost-effective and scalable …
based on network conditions and user preferences to provide a cost-effective and scalable …
Accurate recognition of human abnormal behaviours using adaptive 3D residual attention network with gated recurrent units (GRU) in the video sequences
TS Balakrishnan, D Jayalakshmi, P Geetha… - Computer Methods in …, 2024 - Taylor & Francis
Abnormal or violent behaviour by individuals with mental disorders presents significant risks
to public safety, necessitating advanced systems capable of detecting such behaviours in …
to public safety, necessitating advanced systems capable of detecting such behaviours in …
DSTnet: Deformable Spatio-Temporal Convolutional Residual Network for Video Super-Resolution
A Khan, AB Sargano, Z Habib - Mathematics, 2021 - mdpi.com
Video super-resolution (VSR) aims at generating high-resolution (HR) video frames with
plausible and temporally consistent details using their low-resolution (LR) counterparts, and …
plausible and temporally consistent details using their low-resolution (LR) counterparts, and …
Video Super-Resolution using Deep Neural Networks
M Al-Shedivat, HE Yihui, M Hardy… - US Patent App. 17 …, 2022 - Google Patents
BACKGROUND [0002] This specification relates to neural networks.[0003] Neural networks
are machine learning models that employ one or more layers of nonlinear units to predict an …
are machine learning models that employ one or more layers of nonlinear units to predict an …