379 citations · 866 across the 40 of their papers we have counts for
7 papers · 1 filter
Explainable Event Recognition
Imran Khan, Kashif Ahmad, Namra Gul +3
The literature shows outstanding capabilities for CNNs in event recognition in images. However, fewer attempts are made to analyze the potential causes behind the decisions of the…
Flood Detection via Twitter Streams using Textual and Visual Features
Firoj Alam, Zohaib Hassan, Kashif Ahmad +4
The paper presents our proposed solutions for the MediaEval 2020 Flood-Related Multimedia Task, which aims to analyze and detect flooding events in multimedia content shared over T…
Floods Detection in Twitter Text and Images
Naina Said, Kashif Ahmad, Asma Gul +2
In this paper, we present our methods for the MediaEval 2020 Flood Related Multimedia task, which aims to analyze and combine textual and visual content from social media for the d…
Visual Sentiment Analysis from Disaster Images in Social Media
Syed Zohaib Hassan, Kashif Ahmad, Steven Hicks +4
The increasing popularity of social networks and users' tendency towards sharing their feelings, expressions, and opinions in text, visual, and audio content, have opened new oppor…
Sentiment Analysis from Images of Natural Disasters
Syed Zohaib, Kashif Ahmad, Nicola Conci +1
Social media have been widely exploited to detect and gather relevant information about opinions and events. However, the relevance of the information is very subjective and rather…
Multi-Modal Machine Learning for Flood Detection in News, Social Media and Satellite Sequences
Kashif Ahmad, Konstantin Pogorelov, Mohib Ullah +4
In this paper we present our methods for the MediaEval 2019 Mul-timedia Satellite Task, which is aiming to extract complementaryinformation associated with adverse events from Soci…
Active Learning for Event Detection in Support of Disaster Analysis Applications
Naina Said, Kashif Ahmad, Nicola Conci +1
Disaster analysis in social media content is one of the interesting research domains having abundance of data. However, there is a lack of labeled data that can be used to train ma…