Uso De Redes Neurais Recorrentes Para Previsão De Tendências Em Séries Temporais De Criptomoedas

Antunes, Willian Felipe; Milhomem, Danilo; Dantas, Maria José Pereira

doi:10.37885/230111845

Cited by 3 publications

(4 citation statements)

References 2 publications

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“…The internal architecture of the neural cell presents mechanisms (gates) that control the information coming from the temporal states of the cells, that is, functions of the sigmoid type and hyperbolic tangent that control the propagation of signals inside the cell. Consequently, the model is able to select information that will be forgotten or propagated [40][41][42].…”

Section: Recurrent Neural Network-rnnsmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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Wind energy has achieved a leading position among renewable energies. The global installed capacity in 2022 was 906 GW of power, with a growth of 8.4% compared to the same period in the previous year. The forecast is that the barrier of 1,000,000 MW of installed wind capacity in the world will be exceeded in July 2023, according to data from the World Association of Wind Energy. In order to support the expected growth in the wind sector, maintenance strategies for wind turbines must provide the reliability and availability necessary to achieve these goals. The usual maintenance procedures may present difficulties in keeping up with the expansion of this energy source. The objective of this work was to carry out a systematic review of the literature focused on research on the predictive and prescriptive maintenance of wind turbines based on the implementation of data-oriented models with the use of artificial intelligence tools. Deep machine learning models involving the detection, diagnosis, and prognosis of failures in this equipment were addressed.

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Section: Recurrent Neural Network-rnnsmentioning

confidence: 99%

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Santiago,

Barbosa,

Mergulhão

et al. 2024

Energies

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show abstract

“…Downsampling techniques are then applied to obtain more discriminant features and reduce the frame rate dimension without compromising results. The proposed model, based on Recurrent Neural Network (RNN) (Nelson, 2017) with Long Short-Term Memory (LSTM) (Nelson, 2017), Dropout (Nelson, 2017), Dense, and Softmax layers (Nelson, 2017), outperforms other methods and shows promising competitiveness for heartbeat sound classification.…”

Section: Related Workmentioning

confidence: 99%

Predicting Cardiovascular Disorders Through Stethoscope Audio Using Convolutional Neural Network

2023

RHSS

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Cardiovascular disorders pose a significant global health challenge, resulting in a substantial number of annual deaths. Early and accurate prediction of heart disorders is crucial to mitigate their impact on individuals and healthcare systems. In this study, we explore the potential of Convolutional Neural Network in automating heart disease prediction using spectrogram data. The dataset comprises audio recordings collected from the general public via an iPhone app and a clinical trial using a digital stethoscope. We preprocess the data to obtain spectrograms and design a Convolutional Neural Network architecture to classify heart sounds into distinct categories. The Convolutional Neural Network exhibits promising performance, achieving an accuracy of approximately 77%. Our research highlights the opportunity to leverage Convolutional Neural Network in this context, paving the way for advanced automated cardiac diagnostics.

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“…Consiste também de válvulas de três unidades multiplicativas: válvula de entrada, válvula de saída e válvula de esquecimento. Estas válvulas têm a função de manter o fluxo do erro constante através de unidades especiais chamadas "gates" (portões) permitindo os ajustes de pesos da mesma forma que o truncamento da sequência quando a informação não é necessária, simbolizando um esquecimento [Nelson 2017].…”

Section: Redes Long Short-term Memory (Lstm)unclassified

“…Segundo [Nelson 2017] desde sua criação, este método foi ramificado em diversas variações. Entretanto, quando avaliadas em relação à original por [Greff et al 2015] demonstrado na Figura 1, nenhuma foi capaz de apresentar nenhuma melhoria considerável em termos de resultados.…”

Section: Redes Long Short-term Memory (Lstm)unclassified

Influência da quantidade de caulim e de agente compatibilizante no índice de fluidez de compósitos PEAD/Caulim

Lima¹,

Bandeira²,

Mota³

et al. 2022

Engenharia, Gestão E Inovação – Volume 1

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Uso De Redes Neurais Recorrentes Para Previsão De Tendências Em Séries Temporais De Criptomoedas

Cited by 3 publications

References 2 publications

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Data-Driven Models Applied to Predictive and Prescriptive Maintenance of Wind Turbine: A Systematic Review of Approaches Based on Failure Detection, Diagnosis, and Prognosis

Predicting Cardiovascular Disorders Through Stethoscope Audio Using Convolutional Neural Network

Influência da quantidade de caulim e de agente compatibilizante no índice de fluidez de compósitos PEAD/Caulim

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