Youtube Videos Detection And Classification Of Content
Keywords:
EfficientNet, CNN, bidirectional LSTM, video classification, social media analysis, deep learning.Abstract
The bulk of YouTube's viewers are young,
and the site has attracted billions of them as a result of
the site's videos' exponential growth. Now-a-days
YouTube content are access by all age group of peoples
as this provide digital entertainment content on various
topics such as Sports, Religious, Movies and many
more. This channel provides vast amount cartoon
entertainment for KIDS. Some malicious users are
taking advantage of this digital content to spread
inappropriate content for kids the in the form of
cartoons. Such inappropriate content may put bad
influence on growing kids and need a technique to
prevent such content before showing to kids.
Additionally, malicious up-loaders use this network as a
distribution channel for disturbing visual information,
such as sharing inappropriate material with kids
through animated cartoon videos. This paper proposes
a unique deep learning-based architecture for
identifying and categorising objectionable information
in videos. . Overall, cutting-edge performance was
obtained by the Efficient-Net and BiLSTM design with
128 hidden units (f1 score = 0.9267).
