Towards a Smart Interface-Based Automated Learning Environment Through Social Media for Disaster Management and Smart Disaster Education

Bouzidi, Zair; Boudries, Abdelmalek; Amad, Mourad

doi:10.1007/978-3-030-52249-0_31

Cited by 11 publications

(44 citation statements)

References 27 publications

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“…Twitter & Facebook (Bouzidi, 2020 This section presents the most relevant related works, namely information retrieval models, in general, and those from several sources, in particular.…”

Section: Identification Methodsmentioning

confidence: 99%

“…References Our New Approach (ONA) Elman ANN (EANN) (Adhikari, 2014), (Bouzidi, 2018), (Bouzidi, 2019) ONA Multi-Layer Feed-forward (MFF) (Adhikari, 2014), (Tk, 2016), (Pandey, 2018), (Bouzidi, 2020) ConvNets/Autoencoder (Pinheiro, 2017), (Lai, 2006), (Lasfer, 2013), (Gudelek, 2017), (Bao, 2017) RNN (Bouzidi, 2020b), (Ghazali, 2009), Mohammad2018) LSTM (Adhikari, 2014), (Cavalcante, 2016), (Sermpinis, 2019), (Calvez, 2018), (Fischer, 2018), (Pradeepkumar, 2017), (Tk, 2016), (Gudelek, 2017), (Pandey, 2018) Memory Networks (Adhikari, 2014), (Cavalcante, 2016), (Sermpinis, 2019), (Calvez, 2018), (Fischer, 2018), (Pradeepkumar, 2017), (Tk, 2016), (Gudelek, 2017), (Pandey, 2018) Social Media (Adhikari, 2014), (Cavalcante, 2016), (Sermpinis, 2019), (Calvez, 2018), Fischer, 2018), (Ghazali, 2009), (Pradeepkumar, 2017), (Tk, 2016), (Gudelek, 2017), (Pandey, 2018) Analysis (Sermpinis, 2019),…”

Section: Ai Conceptsmentioning

confidence: 99%

“…Automated learning is used for a wide range of applications, such as diagnostic aid (Abouelhoda, 2009), outlier detection, missing data detection, relevant information retrieval from multiple sources (social media) (Bouzidi, 2018), (Bouzidi, 2019), (Bouzidi, 2020), & (Bouzidi, 2020b), fraud detection, financial market analysis (Bouzidi, 2020b) & (Masseglia, 2005) and so on.…”

Section: Ai Conceptsmentioning

confidence: 99%

See 2 more Smart Citations

LSTM-Based Automated Learning With Smart Data to Improve Marketing Fraud Detection and Financial Forecasting

Bouzidi¹,

Boudries²,

Amad³

2021

International Scientific Conference EMAN – Economics and Management: How to Cope With Disrupted Times

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This proposed model is based on a deep recurrent neural network trained with Long Short- Term Memory Network (LSTM), used because of its ability to learn long term dependencies, taking the concatenated function and Financial data as input, while integrating encapsulations, using Smart Data and retrieving information by combining multiple search results (all the Web). It combines representation training with financial data while integrating encapsulations from multiple sources and retrieving information by combining multiple search results. It provides some good ideas that we have extended to improve Corporate Marketing and Business Strategies. We show that the proposed model learns to localize and recognize different aspects of Corporate Marketing and Business Strategies. We evaluate it on the challenging task of detecting Fraud in Financial Services and Financial Time Series Forecasting and show that it is more accurate than the state-of-the-art of other neural networks and that it uses fewer parameters and less computation.

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“…Twitter & Facebook (Bouzidi, 2020 This section presents the most relevant related works, namely information retrieval models, in general, and those from several sources, in particular.…”

Section: Identification Methodsmentioning

confidence: 99%

Section: Ai Conceptsmentioning

confidence: 99%

Section: Ai Conceptsmentioning

confidence: 99%

See 1 more Smart Citation

LSTM-Based Automated Learning With Smart Data to Improve Marketing Fraud Detection and Financial Forecasting

Bouzidi¹,

Boudries²,

Amad³

2021

International Scientific Conference EMAN – Economics and Management: How to Cope With Disrupted Times

Self Cite

View full text Add to dashboard Cite

show abstract

“…Elman ANN (EANN) [2], [23], [24] ONA Multi-Layer Feed-forward [2], [27], [25] ConvNets / Autoencoder [9] RNN [26], [11] LSTM [2], [5], [6], [8], [27] Memory Networks [2], [5], [6], [8], [27] Social Media [2], [5], [6], [8], [27] Analysis [5], [11], [27] Table 4: Comparative table of all AI Concepts used including our approach.…”

Section: Ai Concepts References Our New Approach (Ona)mentioning

confidence: 99%

“…Automated learning is used for a wide range of applications, such as diagnostic aid [16], outlier detection, missing data detection, relevant information retrieval from multiple sources (social media) [23][24][25][26], fraud detection, financial market analysis [26] & [14] and so on.…”

Section: Ai Concepts References Our New Approach (Ona)mentioning

confidence: 99%

Untitled

2021

Medicon Medical Sciences

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This proposed commercial model is based on deep Long Short-Term Memory Network (LSTM), used because of its ability to learn long term dependencies, in taking the concatenated function and market data as input, while integrating Smart Data encapsulations, retrieving information by combining various search results (all the Web). It provides some good ideas for that we have extended to improve Corporate Marketing and Business Strategies. We show that the proposed model learns to both localize and recognize their different aspects. We evaluate it on the challenging task of detecting Fraud in Financial Services and Financial Time Series Forecasting and show that it is both more accurate than the state-of-the-art of other neural networks and uses fewer parameters and less computation.

show abstract