Social Networks for Disaster Management - Bouzidi, Zair
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The alert model based on neural networks is used to retrieve, in real time, content from social networks (Twitter and Facebook). Once cleaned of duplicate and replicated content, we want to learn from the first-hand content, through manually tagged information, relevant content to warn and alert people and claims handlers to make quick and effective decisions. that will save lives. It is enhanced as a Machine Learning Environment (ALE) based on Deep Learning to fetch content from all over the Web.It is also enhanced to be a hybrid of a deep CNN-LSTM based ALE using awareness, assessment and…mehr

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Produktbeschreibung
The alert model based on neural networks is used to retrieve, in real time, content from social networks (Twitter and Facebook). Once cleaned of duplicate and replicated content, we want to learn from the first-hand content, through manually tagged information, relevant content to warn and alert people and claims handlers to make quick and effective decisions. that will save lives. It is enhanced as a Machine Learning Environment (ALE) based on Deep Learning to fetch content from all over the Web.It is also enhanced to be a hybrid of a deep CNN-LSTM based ALE using awareness, assessment and education. Experiments have shown that it continues to give better results than previous work.
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Autorenporträt
Dr Zair Bouzidi is associate Professor in informatics at the University of Bejaia. Its Interests are Artificial Intelligence, Social Media, Big Data and Fake News. His research interests are in the field of Artificial Intelligence, Social Media and Disaster Management in the University of Bouira in Algeria. He works in managing disaster.