Design and Development of Flood Disaster Game-based Learning based on Learning Domain

Zaini, Nur Atiqah; Noor, Siti Fadzilah Mat; Zailani, Syafiah Zahra Mohd

doi:10.35940/ijeat.c6216.049420

Cited by 12 publications

(3 citation statements)

References 0 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Used OSN (Zaini, 2020) Flood Disaster Game-based Learning Twitter (Vivakaran, 2018) Educational Purposes among the Faculty of Higher Education with Special Reference (He, 2016) Summarization with social-temporal context (Dussart, 2020) Capitalizing on a TREC Track to Build a Tweet Summarization Dataset (Lamsal, 2020) Semi-automated artificial intelligence-based classifier for Disaster Response (Rudra, 2019) Summarizing situational tweets in crisis scenarios: An extractive-abstractive approach (Bouzidi, 2018), (Bouzidi, 2019) Based on Artificial Neural Network (ANN)…”

Section: Identification Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Section: Identification Methodsmentioning

confidence: 99%

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

View full text Add to dashboard Cite

show abstract

“…Previous experiences have shown the positive effects of education in disaster risk management, especially children education. It has turned out that those who have been made aware of the phenomenon of disasters and how to respond to such situations are still able to react quickly and appropriately, while warning others and protecting themselves in an emergency [22]. This model is designed to support an introductory traineeship in disaster management for citizens, trainees and future disaster managers [3].…”

Section: Smart Educationmentioning

confidence: 99%

Untitled

2024

Adv Mach Lear Art Inte

View full text Add to dashboard Cite

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.

show abstract

“…It is also available for effective use of satellite positioning, remote sensing and GIS, for disaster monitoring and management. Dealing only with natural disasters, Internet GIS for Crisis Management as Approaches Methods [23] Summarizy situational tweets in crisis events: An extractiveabstractive approach [35] Educational Purpose of the Faculty of Higher Education with Special Reference [1] Game-based Learning of Flood Disasters [26] The importance of education on disasters and emergencies [25] Challenges and opportunities of education programs [27] A tabletop simulation system for disaster education [28] Flood protection computer game for disaster education [29] Using immersive game-based virtual reality to teach fire-safety skills to children [30] Penumbral Tourism: Place-based Disaster Education via Realworld Disaster Simulation [31] A mixed reality game to investigate coordination in disaster response [3] FeedForward The neural network-based disaster alert models [2] is one of the first to use multiple sources, namely Twitter and Facebook, for capturing messages during a crisis. Followed by the smart interface-based automated learning environment to improve [3] disaster warning, while introducing smart education.…”

Section: Related Workmentioning

confidence: 99%

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

View full text Add to dashboard Cite

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.

show abstract

Design and Development of Flood Disaster Game-based Learning based on Learning Domain

Cited by 12 publications

References 0 publications

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

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

Untitled

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

Contact Info

Product

Resources

About