Volume 12, Number 2, December 2016 - DOI: http://dx.doi.org/10.21700/ijcis.2016.274

IJCIS

Computing and Information Sciences is a peer reviewed journal that is committed to timely publication of original research, surveying and tutorial contributions on the analysis and development of computing and information science. The journal is designed mainly to serve researchers and developers, dealing with information and computing. Papers that can provide both theoretical analysis, along with carefully designed computational experiments, are particularly welcome. The journal is published 2-3 times per year with distribution to librarians, universities, research centers, researchers in computing, mathematics, and information science. The journal maintains strict refereeing procedures through its editorial policies in order to publish papers of only the highest quality. The refereeing is done by anonymous Reviewers. Often, reviews take four months to six months to obtain, occasionally longer, and it takes an additional several months for the publication process.

DOI: http://dx.doi.org/10.21700/ijcis.2016.124

Firefighter Advisory System

Amal Dandashi* email: amal.dandashi@qu.edu.qa
Jihad Mohamad AlJa'am
Sebti Foufou

Department of Computer Science, Qatar University, Doha - Qatar

*Corresponding author

Received: 30 July 2016
Revised: 10 August 2016
Accepted: 25 August 2016
Published: 25 December 2016

Abstract: The large scale release of raw Arabic news related videos from many sources over the internet has only been increasing. The videos released are uncategorized, and unused. The majority of work aimed at classficiation of Arabic videos is based on textual annotation or closed caption text extraction and processing. We propose a system design that implements multimodal video classification such that annotations and caption processing is excluded. The domain targetted is the news domain. The system consists of audio features extraction and classification, combined with speech-to-text conversion and processing, by utilizing Arabic Named Entity Recognition tools. We also propose to develop a new Arabic dataset based on news channel videos as well as raw videos from various online sources for testing and evaluation. Results are to be documented and graphed.

Keywords: Multimodal Video Classification; Audio Feature Extraction; Named Entity Recognition; News Videos.


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