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              <text>An Efficient Multi-Modal Classification Approach for Disaster-related Tweets</text>
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              <text>Convolution Neural Network; Deep Learning; Disaster Tweet; Gated Recurrent Unit; Late Fusion; VGG-16</text>
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              <text>Owing to the unanticipated and thereby treacherous nature of disasters, it is essential to gather necessary information and data regarding the same on an urgent basis; this helps to get a detailed overview of the situation and helps humanitarian organizations prioritize their tasks. In this paper, "An Efficient Multi-Modal Classification Approach for Disaster-related Tweets,"the proposed framework based on Deep Learning to classify disaster-related tweets by analyzing text and image contents. The approach is based on Gated Recurrent Unit (GRU) and GloVe Embedding for text classification and VGG-16 network for image classification. Finally, a combined model is proposed using both text and image modules by the Late Fusion Technique. This portrays that the proposed multi-modal system performs significantly well in classifying disaster-related content.   2022 IEEE.</text>
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              <text>Mondal A.; Kesan A.; Rodrigues A.; George J.</text>
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              <text>IEEE International Conference on Distributed Computing and Electrical Circuits and Electronics, ICDCECE 2022</text>
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              <text>Institute of Electrical and Electronics Engineers Inc.</text>
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              <text>&lt;a href="https://doi.org/10.1109/ICDCECE53908.2022.9792951" target="_blank" rel="noreferrer noopener"&gt;https://doi.org/10.1109/ICDCECE53908.2022.9792951&lt;/a&gt;
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              <text>ISBN: 978-166548316-2</text>
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              <text>Mondal A., Deemed to Be University, Department of Data Science Christ, India; Kesan A., Deemed to Be University, Department of Data Science Christ, India; Rodrigues A., Deemed to Be University, Department of Data Science Christ, India; George J., Deemed to Be University, Department of Data Science Christ, India</text>
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