English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

In the present work it is proposed to identify if a person is distracted or not, when he is driving a vehicle. This can be achieved by classifying images of drivers to determine if they are available or distracted using convolutional neural networks (CNN) and tools to improve the algorithm, which are Learning Transfer and Characteristics Engineering. Kaggle competition images are used to perform the training, in which you can obtain more results and obtain more results. Later the red extractor of characteristics VGG16 was used, which is a pre-trained model, from which it is lowered in its last layers to reduce the overfit and adapt it to our algorithm. The results obtained in the classifier gave us a training efficiency and validation of 99.30% and 99.46% respectively.

Transferencia de aprendizaje mediante redes neuronales convolucionales para el reconocimiento de conductores distraídos