Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

Banna, Md. Hasan Al; Ghosh, Tapotosh; Nahian, Md. Jaber Al; Taher, Kazi Abu; Kaiser, M. Shamim; Mahmud, Mufti; Hossain, Mohammad Shahadat; Andersson, Karl

doi:10.1109/access.2021.3071400

Cited by 53 publications

(8 citation statements)

References 58 publications

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“…A fundamental aim of Apache Drill is to expedite the discovery of overlapping data segments, an operation essential for thorough data scrutiny. This capability sets it apart in the arena of expansive interactive analysis, where tailored queries demand intricate feedback, as demonstrated in mechanisms utilized by HDFS for data retention or rigorous batch scrutiny through the MapReduce algorithm [49].…”

Section: A Optimal Production Managementmentioning

confidence: 99%

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Kazbekova,

Ismagulova,

Zhussipbek

et al. 2024

IJACSA

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In the dynamic milieu of Industry 4.0, characterized by the deluge of big data, this research promulgates a groundbreaking framework that harnesses machine learning (ML) to optimize big data modeling processes, addressing the intricate requirements and challenges of contemporary industrial domains. Traditional data processing mechanisms falter in the face of the sheer volume, velocity, and variety of big data, necessitating more robust, intelligent solutions. This paper delineates the development and application of an innovative ML-augmented framework, engineered to interpret and model complex, multifaceted data structures more efficiently and accurately than has been feasible with conventional methodologies. Central to our approach is the integration of advanced ML strategies-including but not limited to deep learning and neural networks-with sophisticated analytics tools, collectively capable of automated decisionmaking, predictive analysis, and trend identification in real-time scenarios. Beyond theoretical formulation, our research rigorously evaluates the framework through empirical analysis and industrial case studies, demonstrating tangible enhancements in data utility, predictive accuracy, operational efficiency, and scalability within various Industry 4.0 contexts. The results signify a marked improvement over existing models, particularly in handling high-dimensional data and facilitating actionable insights, thereby empowering industrial entities to navigate the complexities of digital transformation. This exploration underscores the potential of machine learning as a pivotal ally in evolving data strategies, setting a new precedent for data-driven decision-making paradigms in the era of Industry 4.0.

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Section: A Optimal Production Managementmentioning

confidence: 99%

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Kazbekova,

Ismagulova,

Zhussipbek

et al. 2024

IJACSA

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“…Lastly, the EBAS method is exploited for optimum hyperparameter adjustment of the ABiLSTM model. In BSA, we consider the search for food resources (viz., locations with a maximal intensity of food smell) as an optimization issue for mathematical modelling of the behaviour of a beetle ( Al Banna et al, 2021 ). The maps of odor concentration in the atmosphere correspond to the value of the objective function.…”

Section: The Proposed Modelmentioning

confidence: 99%

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

Alotaibi,

Selvi Sundarapandi,

et al. 2023

PeerJ Computer Science

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Computational intelligence and nature-inspired computing have changed the way biologically and linguistically driven computing paradigms are made. In the last few decades, they have been used more and more to solve optimisation problems in the real world. Computational linguistics has its roots in linguistics, but most of the studies being done today are led by computer scientists. Data-driven and machine-learning methods have become more popular than handwritten language rules, which shows this shift. This study uses a new method called Computational Linguistics-based mood Analysis using Enhanced Beetle Antenna Search with deep learning (CLSA-EBASDL) to tackle the important problem of mood analysis during the COVID-19 pandemic. We sought to determine how people felt about the COVID-19 pandemic by studying social media texts. The method is made up of three main steps. First, data pre-processing changes raw data into a shape that can be used. After that, word embedding is done using the ‘bi-directional encoder representations of transformers (BERT) process. An attention-based bidirectional long short-term memory (ABiLSTM) network is at the heart of mood classification. The Enhanced Beetle Antenna Search (EBAS) method, in particular, fine-tunes hyperparameters so that the ABiLSTM model works at its best. Many tests show that the CLSA-EBASDL method works better than others. Comparative studies show that it works, making it the best method for analysing opinion during the COVID-19 pandemic.

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“…Based on, the AM selectively focuses on some of the more influential information, dismisses unnecessary information, and boosts desirable information. AM is commonly used in various fields such as image captioning [37], machine translation [38], earthquake prediction [39]. AM acts based on weight allocation, determining the most effective information by distributing higher weights.…”

Section: Attention Mechanismmentioning

confidence: 99%

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

Kavianpour¹,

Kavianpour²,

Jahani³

et al. 2021

Preprint

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Earthquakes, as natural phenomena, have continuously caused damage and loss of human life historically. Earthquake prediction is an essential aspect of any society's plans and can increase public preparedness and reduce damage to a great extent. Nevertheless, due to the stochastic character of earthquakes and the challenge of achieving an efficient and dependable model for earthquake prediction, efforts have been insufficient thus far, and new methods are required to solve this problem. Aware of these issues, this paper proposes a novel prediction method based on attention mechanism (AM), convolution neural network (CNN), and bi-directional long shortterm memory (BiLSTM) models, which can predict the number and maximum magnitude of earthquakes in each area of mainland China-based on the earthquake catalog of the region. This model takes advantage of LSTM and CNN with an attention mechanism to better focus on effective earthquake characteristics and produce more accurate predictions. Firstly, the zero-order hold technique is applied as pre-processing on earthquake data, making the model's input data more proper. Secondly, to effectively use spatial information and reduce dimensions of input data, the CNN is used to capture the spatial dependencies between earthquake data. Thirdly, the Bi-LSTM layer is employed to capture the temporal dependencies. Fourthly, the AM layer is introduced to highlight its important features to achieve better prediction performance. The results show that the proposed method has better performance and generalize ability than other prediction methods.

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Attention-Based Bi-Directional Long-Short Term Memory Network for Earthquake Prediction

Cited by 53 publications

References 58 publications

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Machine Learning Enhanced Framework for Big Data Modeling with Application in Industry 4.0

Computational linguistics based text emotion analysis using enhanced beetle antenna search with deep learning during COVID-19 pandemic

A CNN-BiLSTM Model with Attention Mechanism for Earthquake Prediction

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