Results 231 to 240 of about 1,054,443 (267)

A spatiotemporal hypergraph self-attention neural networks framework for the identification and pharmacological efficacy assessment of Parkinson's disease motor symptoms. [PDF]

NPJ Parkinsons Dis
An X, Su L, Yang Q, Shen B, Gan L, Jun Ji J, Wang J, Su H.   +7 more
europepmc   +1 more source

pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network. [PDF]

Sci Rep
Almusallam N, Shahid, Hayat M, Alarfaj FK.   +3 more
europepmc   +1 more source

AI-optimized GRU-based self-attention model for predictive diabetes staging in IoT healthcare 5.0.

Sci Rep
Zhou L, Gupta BB, Gaurav A, Attar RW, Alhomoud A, Arya V, Hsu CH.   +6 more
europepmc   +1 more source

Corrigendum to "A Novel Network-Level Fused Self-Attention Deep Neural Network for Cervical Cancer Classification from Cervicography Images". [PDF]

Technol Cancer Res Treat
europepmc   +1 more source

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Self-Attention for Cyberbullying Detection

2020 International Conference on Cyber Situational Awareness, Data Analytics and Assessment (CyberSA), 2020
In recent years, cyberbullying has grown out of proportion due to the increasing usage of social media platforms along with the benefit of user anonymization over the Internet. Affecting people across all demographics, the effect of cyberbullying has been more pronounced over adolescents and insecure individuals. Victims suffer from societal isolation,
Ankit Pradhan, Venu Madhav Yatam, Padmalochan Bera   +2 more
openaire   +1 more source

Self-Attention Agreement Among Capsules

2021 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2021
At the state of the art, Capsule Networks (CapsNets) have shown to be a promising alternative to Convolutional Neural Networks (CNNs) in many computer vision tasks, due to their ability to encode object viewpoint variations. Network capsules provide maps of votes that focus on entities presence in the image and their pose. Each map is the point of view
Pucci R., Micheloni C., Martinel N.
openaire   +2 more sources

SAED: self-attentive energy disaggregation

Machine Learning, 2021
The field of energy disaggregation deals with the approximation of appliance electric consumption using only the aggregate consumption measurement of a mains meter. Recent research developments have used deep neural networks and outperformed previous methods based on Hidden Markov Models.
Nikolaos Virtsionis Gkalinikis, Christoforos Nalmpantis, Dimitris Vrakas   +2 more
openaire   +1 more source