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Rapid Classification of Crisis-Related Data on Social Networks using Convolutional Neural Networks [article]

Dat Tien Nguyen, Kamela Ali Al Mannai, Shafiq Joty, Hassan Sajjad, Muhammad Imran, Prasenjit Mitra
2016 arXiv   pre-print
However, time-critical analysis of big crisis data on social media streams brings challenges to machine learning techniques, especially the ones that use supervised learning.  ...  In this work, we introduce neural network based classification methods for binary and multi-class tweet classification task.  ...  All of these modules can be built on top of our classification system and work in concert with it. Figure 1 : 1 Convolutional neural network on a tweet.  ... 
arXiv:1608.03902v1 fatcat:632knvdypnf5za6xi26kiavfnm

Applications of Online Deep Learning for Crisis Response Using Social Media Information [article]

Dat Tien Nguyen, Shafiq Joty, Muhammad Imran, Hassan Sajjad, Prasenjit Mitra
2016 arXiv   pre-print
We test our models using a crisis-related real-world Twitter dataset.  ...  In this paper, we propose to use Deep Neural Network (DNN) to address two types of information needs of response organizations: 1) identifying informative tweets and 2) classifying them into topical classes  ...  RELATED WORK Recent studies have shown the usefulness of crisis-related data on social media for disaster response and management [1, 22, 24] .  ... 
arXiv:1610.01030v2 fatcat:fekss7cixfarjg7lbajt3hzwpa

Identifying Informative Messages in Disasters using Convolutional Neural Networks

Cornelia Caragea, Adrian Silvescu, Andrea H. Tapia
2016 International Conference on Information Systems for Crisis Response and Management  
Our approach is based on Convolutional Neural Networks and shows significant improvement in performance over models that use the "bag of words" and n-grams as features on several datasets of messages from  ...  Data produced through social networking sites is seen as ubiquitous, rapid and accessible, and it is believed to empower average citizens to become more situationally aware during disasters and coordinate  ...  The views and conclusions contained in this document are those of the authors and should not be interpreted as necessarily representing the official policies, either expressed or implied, of the National  ... 
dblp:conf/iscram/CarageaST16 fatcat:fbcmlz4h7jhjfl3nplpf3f3un4

Sportsman's Mental State Evaluation and Early Warning Method Based on Intelligent CNN

Suxuan Xing, Rahman Ali
2022 Scientific Programming  
This approach uses the text of student forums within universities as the database and introduces the convolutional neural network (CNN) model in deep learning, which contains a convolutional layer, a pooling  ...  For data processing, behavioral features, attribute features, content features, and social relationship features are extracted from text information as the input of the CNN.  ...  to provide timely intervention. e mapping relationship m used in this paper is a convolutional neural network (CNN) in deep learning.  ... 
doi:10.1155/2022/4711490 fatcat:lgedhhamtvejhfxvgv7eizhske

Detection of Types of Mental Illness through the Social Network Using Ensembled Deep Learning Model

Syed Nasrullah, Asadullah Jalali, Deepika Koundal
2022 Computational Intelligence and Neuroscience  
The Reddit social networking platform is used for the analysis, and the ensembling deep learning model is implemented through convolutional neural network and the recurrent neural network.  ...  In today's era, social networking platforms are widely used to share emotions. These types of emotions are often analyzed to predict the user's behavior.  ...  So, this proposed work has used the limited number of convolution neural network for the extraction of the features and later on recurrent neural network is used for performing the classification.  ... 
doi:10.1155/2022/9404242 pmid:35378814 pmcid:PMC8976617 fatcat:cidrc2lnnvg7bcjvetcaum4qhy

Analysis of Social Media Data using Multimodal Deep Learning for Disaster Response [article]

Ferda Ofli, Firoj Alam, Muhammad Imran
2020 arXiv   pre-print
In this paper, we propose to use both text and image modalities of social media data to learn a joint representation using state-of-the-art deep learning techniques.  ...  Specifically, we utilize convolutional neural networks to define a multimodal deep learning architecture with a modality-agnostic shared representation.  ...  of multimodal analysis of the crisis-related social media data.  ... 
arXiv:2004.11838v1 fatcat:t46ww2jm2zauffn2g7fdkhef6u

Graph Based Semi-supervised Learning with Convolution Neural Networks to Classify Crisis Related Tweets [article]

Firoj Alam, Shafiq Joty, Muhammad Imran
2018 arXiv   pre-print
During time-critical situations such as natural disasters, rapid classification of data posted on social networks by affected people is useful for humanitarian organizations to gain situational awareness  ...  However, the scarcity of labeled data in the early hours of a crisis hinders machine learning tasks thus delays crisis response.  ...  (Johnson and Zhang 2015) use a Convolutional Neural Networks (CNN) via region embedding in which the CNN learns a small region from embedding. Miyato et al.  ... 
arXiv:1805.06289v1 fatcat:4iomqtnrknb2hbvdtt3khgx3va

Using Convolution Neural Network for Defective Image Classification of Industrial Components

Hao Wu, Zhi Zhou, Fazlullah Khan
2021 Mobile Information Systems  
For this purpose, a pretrained convolution neural network based on the PyTorch framework is employed to extract discriminating features from the dataset, which is then used for the classification task.  ...  Computer vision provides effective solutions in many imaging relation problems, including automatic image segmentation and classification.  ...  Introduction With the rapid development of Internet technology and media, the image data spread on the Internet is growing exponentially every day.  ... 
doi:10.1155/2021/9092589 fatcat:sep3lpggzfbglfbjuofu3cysry

Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach

Valerio Lorini, Carlos Castillo, Francesco Dottori, Milan Kalas, Domenico Nappo, Peter Salamon
2019 International Conference on Information Systems for Crisis Response and Management  
Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data.  ...  This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model.  ...  Thanks to Gabriele Mantovani Banella and Sasa Vranic for helping in the development of web layers and web services for the integration of SMFR into the existing EFAS/GloFAS systems.  ... 
dblp:conf/iscram/LoriniCDKNS19 fatcat:2oxyyrfllzfodgavnu7twqwoyy

The Application of Artificial Intelligence Decision-Making Algorithm in Crisis Analysis and Optimization of the International Court System

Yuan Zhang, Yuepeng Zhao, Yueqin Zhao, Chia-Huei Wu
2022 Mobile Information Systems  
Therefore, this study proposes a fusion CNN-GRU network result model that uses a text classification neural network structure convolutional neural network with a good effect to combine the GRU neural network  ...  Promote the development of the connotation of smart courts through the collaborative integration of artificial intelligence technology and system to promote the deep integration of judicial theory and  ...  Acknowledgments is work was supported by the e Project of Baoding City Science and Technology Bureau (Research on the construction strategy of Baoding grassroots smart court under the background of AI,  ... 
doi:10.1155/2022/8150122 fatcat:mcd56gfxznfprox4zxs5eckywq

Integrating Social Media into a Pan-European Flood Awareness System: A Multilingual Approach [article]

V. Lorini, Ispra, Italy, Universitat Pompeu Fabra, Barcelona, Spain), C. Castillo (Universitat Pompeu Fabra, Barcelona, Spain), F. Dottori (European Commission, Joint Research Centre, D. Nappo, P. Salamon
2019 arXiv   pre-print
Both approaches can be used to bootstrap a classifier of social media messages for a new language with little or no labeled data.  ...  This integration allows the collection of social media data to be automatically triggered by flood risk warnings determined by a hydro-meteorological model.  ...  Thanks to Gabriele Mantovani Banella and Sasa Vranic for helping in the development of web layers and web services for the integration of SMFR into the existing EFAS/GloFAS systems.  ... 
arXiv:1904.10876v1 fatcat:yvzbrxn6wrf23ixfvse7r5ocba

Microblog Sentiment Classification via Recurrent Random Walk Network Learning

Zhou Zhao, Hanqing Lu, Deng Cai, Xiaofei He, Yueting Zhuang
2017 Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence  
We propose a novel recurrent random walk network learning framework for the problem by exploiting both users' posted tweets and their social relations in microblogs.  ...  In this paper, we consider the problem of microblog sentiment classification from the viewpoint of heterogeneous MSC network embedding.  ...  Table 1 : 1 Experimental results on microblog sentiment classification using STS dataset with different proportions of training data.  ... 
doi:10.24963/ijcai.2017/494 dblp:conf/ijcai/ZhaoLCHZ17 fatcat:qmdeox7u5vgpnmbfmdylfcpcvq

Research on Fine-Grained Classification of Rumors in Public Crisis ——Take the COVID-19 incident as an example

Shuaipu Chen, A. Ghadouani, F. Wu
2020 E3S Web of Conferences  
[Method / Process] Based on the rumor data of several mainstream rumor refuting platforms, the pre-training model of BERT was used to fine-tuning in the context of COVID-19 events to obtain the feature  ...  of rumors about COVID-19 based on the BERT model.  ...  in convolutional neural networks.  ... 
doi:10.1051/e3sconf/202017902027 fatcat:3n6nv7tiyncvffhbvj6s3to5ne

Public health face mask detection of Covid-19 utilizing convolutional neural network (CNN)

Tri Septiana Nadia Puspita Putri, Mohamad Al Fikih, Nur Kasan, Novendra Setyawan
2022 AIP Conference Proceedings  
More specifically, Neural Networks' Convolutional learning algorithm uses the extraction of features from imagery that would be further studied by several hidden layers.  ...  Face mask detection is commonly acknowledged as the detection of whether a person wears a mask. The study applied the Convolutional Neural Networks (CNN) method.  ...  ACKNOWLEDGMENTS Author would like to thank Direktorat Penelitian dan Pengabdian kepada Masyarakat (DPPM), the internal Engineering Faculty, and the Department of Electrical Engineering, University of Muhammadiyah  ... 
doi:10.1063/5.0094485 fatcat:qd6umgrh2re2xoeidxbhxyhejy

Language Processing Model Construction and Simulation Based on Hybrid CNN and LSTM

Shujing Zhang, Syed Hassan Ahmed
2021 Computational Intelligence and Neuroscience  
In order to further improve the text language processing effect, a convolutional neural network model, Hybrid convolutional neural network (CNN), and Long Short-Term Memory (LSTM) based on the fusion of  ...  Convolutional neural networks have been widely used in image classification, target detection, semantic segmentation, and natural language processing because they can automatically learn the feature representation  ...  Acknowledgments is study was supported by the Faculty of International Studies in Henan Normal University.  ... 
doi:10.1155/2021/2578422 fatcat:crkf7pazx5gt3gjjwc5wf62ukq
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