Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction
Md. Hasan Al Banna,Tapotosh Ghosh,Md. Jaber Al Nahian,Kazi Abu Taher,M. Shamim Kaiser,Mufti Mahmud,Mohammad Shahadat Hossain,Karl Andersson +7 more
TLDR
In this article, an earthquake occurrence and location prediction model is proposed, which is composed of combinations of various LSTM architectures and dense layers, and an attention mechanism was added to the LSTMs architecture to improve the model's earthquake occurrence prediction accuracy.Abstract:
An earthquake is a tremor felt on the surface of the earth created by the movement of the major pieces of its outer shell. Till now, many attempts have been made to forecast earthquakes, which saw some success, but these attempted models are specific to a region. In this paper, an earthquake occurrence and location prediction model is proposed. After reviewing the literature, long short-term memory (LSTM) is found to be a good option for building the model because of its memory-keeping ability. Using the Keras tuner, the best model was selected from candidate models, which are composed of combinations of various LSTM architectures and dense layers. This selected model used seismic indicators from the earthquake catalog of Bangladesh as features to predict earthquakes of the following month. Attention mechanism was added to the LSTM architecture to improve the model’s earthquake occurrence prediction accuracy, which was 74.67%. Additionally, a regression model was built using LSTM and dense layers to predict the earthquake epicenter as a distance from a predefined location, which provided a root mean square error of 1.25.read more
Citations
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Journal ArticleDOI
Artificial intelligence and internet of things in screening and management of autism spectrum disorder
Tapotosh Ghosh,Md. Hasan Al Banna,Md. Sazzadur Rahman,M. Shamim Kaiser,Mufti Mahmud,A. S. M. Sanwar Hosen,Gihwan Cho +6 more
TL;DR: In this paper, some of the research works in the field of application of AI, ML, and IoT in autism were reviewed and incorporation of the autism facilities in smart city environment is described.
Book ChapterDOI
An Attention-Based Mood Controlling Framework for Social Media Users
Tapotosh Ghosh,Md. Hasan Al Banna,Tazkia Mim Angona,Md. Jaber Al Nahian,Mohammed Nasir Uddin,M. Shamim Kaiser,Mufti Mahmud +6 more
TL;DR: In this article, an emotion detection-based mood control framework that reorganizes social media posts to match the user's mental state was proposed, which can detect six emotions from Bangla text with 66.98% accuracy.
Journal ArticleDOI
Attention-based LSTM-FCN for earthquake detection and location
Tao Bai,Pejman Tahmasebi +1 more
TL;DR: An attention-based Long Short-Term Memory Fully Convolutional Network (LSTM-FCN) model is trained to improve the detection and location accuracy on the same dataset and it is demonstrated that the incorporated attention mechanism can effectively improve the classification performance by automatically and selectively enhancing the significant feature maps and inputs.
Proceedings ArticleDOI
A deep learning approach for the development of an Early Earthquake Warning system
TL;DR: In this article , the authors investigated the development of an early earthquake warning system based on a novel deep learning system using both Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM).
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Autoencoder based Consensus Mechanism for Blockchain-enabled Industrial Internet of Things
Md. Murshedul Arifeen,Tapotosh Ghosh,Rakibul M. Islam,Akm Ashiquzzaman,Juncheol Yoon,Jinsul Kim +5 more
TL;DR: Li et al. as discussed by the authors proposed an autoencoder-integrated chaincode (CC)-based consensus mechanism in which the AE differentiates normal data from anomalous data, and the result returned from the AE to the CC is stored in the ledger.
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