Deep Learning and Social Media for Managing Disaster: Survey

Bouzidi, Zair; Boudries, Abdelmalek; Amad, Mourad

doi:10.1007/978-3-030-82193-7_2

Cited by 6 publications

(30 citation statements)

References 64 publications

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“…Long Short-Term Memory, efficient RNN architecture for sequence learning, introduces the memory cell, a computation unit that replaces artificial neurons in the hidden layer [11]. A memory cell is a component of LSTM units that can hold information in memory for a long time.…”

Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

“…Research on extracting content from social media can be considered as sequence learning problem [11]. Thus, we propose a new approach of managing emergency model, based on a hybrid of deep convolutional neural network with Long Short-Term Memory Network used thanks to its ability of learning long term dependencies.…”

mentioning

confidence: 99%

“…Thus, we propose a new approach of managing emergency model, based on a hybrid of deep convolutional neural network with Long Short-Term Memory Network used thanks to its ability of learning long term dependencies. This new approach allows integrating artificial intelligence technologies, deep learning and social media, in the managing crisis model [11]. This is based on an extension of our previous approach [3,12]: this experience forms the background for this model.…”

mentioning

confidence: 99%

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Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Bouzidi

Boudries

Amad

2022

Preprint

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Networking continues to increase day by day to invest increasingly our daily, creating huge volume of various and precise data: it is not easy to collect content, especially in crisis times. We focus on Smart Education proposed as primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieving content efficiently: CNN is used to extract meaningful features from multiple sources, enabling to have qualitative and sure information, notably with an efficient fake news security, and LSTM is used to maintain long-term dependencies in the extracted features with recurrent connections. This model has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach makes it possible to integrate artificial intelligence technologies, deep learning, social media and detecting or avoiding fake news into the crisis management model. It is based on an extension of our previous approach, namely disaster management based on short-term memory and education: this experience constitutes a background for this model. It combines representation training with awareness and education, while retrieving pattern information by combining various search results from multiple sources. We have extended it to improve our disaster management model and evaluate it in the case of Covid-19 while obtaining promising results, through past programs and experience that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pusuit of Environmental Sustainability.

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Section: Long Short-term Memory (Lstm)mentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Bouzidi

Boudries

Amad

2022

Preprint

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“…Research on extracting content from social media can be considered as sequence learning problem [ 6 , 7 ]. Thus, we propose a new approach of managing emergency model, based on a hybrid of deep convolutional neural network with long short-term memory network used thanks to its ability of learning long-term dependencies.…”

Section: Introductionmentioning

confidence: 99%

“…Thus, we propose a new approach of managing emergency model, based on a hybrid of deep convolutional neural network with long short-term memory network used thanks to its ability of learning long-term dependencies. This new approach allows integrating artificial intelligence technologies, deep learning and social media, in the managing crisis model [ 7 ]. This is based on an extension of our previous approach [ 4 , 8 ]: this experience forms the background for this model.…”

Section: Introductionmentioning

confidence: 99%

Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19

2022

Self Cite

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The volume of network and Internet traffic is increasing extraordinarily fast daily, creating huge data. With this volume, variety, speed, and precision of data, it is hard to collect crisis information in such a massive data environment. This paper proposes a hybrid of deep convolutional neural network (CNN)-long short-term memory (LSTM)-based model to efficiently retrieve crisis information. Deep CNN is used to extract significant characteristics from multiple sources. LSTM is used to maintain long-term dependencies in extracted characteristics while preventing overfitting on recurring connections. This method has been compared to previous approaches to the performance of a publicly available dataset to demonstrate its highly satisfactory performance. This new approach allows integrating artificial intelligence technologies, deep learning and social media in managing crisis model. It is based on an extension of our previous approach namely long short-term memory-based disaster management and education: this experience forms a background for this model. It combines representation training with situational awareness and education, while retrieving template information by combining various search results from multiple sources. We have extended it to improve our managing disaster model and evaluate it in the case of the coronavirus disease 2019 (COVID-19) while achieving promising results.

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Deep Learning-Based Automated Learning Environment Using Smart Data to Improve Corporate Marketing, Business Strategies, Fraud Detection in Financial Services, and Financial Time Series Forecasting

Bouzidi

Amad²,

Boudries³

2022

International Conference on Managing Business Through Web Analytics

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Deep Learning and Social Media for Managing Disaster: Survey

Cited by 6 publications

References 64 publications

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Smart Education with Deep Learning and Social Media for Disaster Management in the pursuit of Environmental Sustainability While avoiding Fake News

Enhancing Warning, Situational Awareness, Assessment and Education in Managing Emergency: Case Study of COVID-19

Deep Learning-Based Automated Learning Environment Using Smart Data to Improve Corporate Marketing, Business Strategies, Fraud Detection in Financial Services, and Financial Time Series Forecasting

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