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, Zair; Amad, Mourad; Boudries, Abdelmalek

doi:10.1007/978-3-031-06971-0_26

Cited by 4 publications

(17 citation statements)

References 38 publications

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“…Followed by the smart interface-based automated learning environment to improve disaster warning, while introducing smart education [3]. While consists of an RNN-based Automated Learning Environment to improve awareness [5]. Based on LSTM, it is to improve the lack of RNN of the previous model [5,8,9].…”

Section: Related Workmentioning

confidence: 99%

“…While consists of an RNN-based Automated Learning Environment to improve awareness [5]. Based on LSTM, it is to improve the lack of RNN of the previous model [5,8,9]. It is introduced to improve also education.…”

Section: Related Workmentioning

confidence: 99%

“…• Demonstration of all the examples learned and analysis of any problem already explained or resolved. This mode provides access to the material learned from the course as a reference [3,5,8,16,9,30]. Thereby supporting example-based online help.…”

Section: Smart Educationmentioning

confidence: 99%

“…Disaster education consists of constantly rehashing these tips on all information channels, websites and all social and networking media to have maximum awareness [3,5,8,9,16,30]. Health education and promotion can be integrated into curriculum-based or training-based programs as drills, modules, and visual media.…”

Section: Smart Educationmentioning

confidence: 99%

“…This new approach allows the integration of artificial intelligence technologies, deep learning, and social media, in the managing crisis model [4]. This is based on an extension of our previous approach: this experience forms the background for this model [3,5]. It combines representation training with alertness, awareness, and education while integrating encapsulations from multiple sources and retrieving information by combining various search results, providing some good ideas for its extending to improve Managing Emergency.…”

Section: Introductionmentioning

confidence: 99%

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2024

Adv Mach Lear Art Inte

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Networking continues to increase day by day to invest increasingly our daily, creating a 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 the primary tool of a hybrid of Deep Convolutional Neural Networks (CNN)-Long Short-Term Memory (LSTM)-based model to retrieve 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 experiences that have shown overwhelmingly positive effects of education for vulnerability reduction and disaster risk management, in the pursuit of Environmental Sustainability.

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