Fundamentals of Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) Network
Reads0
Chats0
TLDR
A Machine Learning practitioner seeking guidance for implementing the new augmented LSTM model in software for experimentation and research will find the insights and derivations in this treatise valuable as well.About:
This article is published in Physica D: Nonlinear Phenomena.The article was published on 2020-03-01 and is currently open access. It has received 1795 citations till now.read more
Citations
More filters
Journal ArticleDOI
Predicting COVID-19 Incidence Through Analysis of Google Trends Data in Iran: Data Mining and Deep Learning Pilot Study.
Seyed Mohammad Ayyoubzadeh,Seyed Mehdi Ayyoubzadeh,Hoda Zahedi,Mahnaz Ahmadi,Sharareh R. Niakan Kalhori +4 more
TL;DR: This prediction might support policymakers and health care managers to plan and allocate health care resources accordingly and support data mining algorithms can be employed to predict trends of outbreaks.
Journal ArticleDOI
A review of deep learning with special emphasis on architectures, applications and recent trends
Saptarshi Sengupta,Sanchita Basak,Pallabi Saikia,Sayak Paul,Vasilios Tsalavoutis,Frederick Ditliac Atiah,Vadlamani Ravi,Alan Peters +7 more
TL;DR: The thrust of this review is to outline emerging applications of DL and provide a reference to researchers seeking to use DL in their work for pattern recognition with unparalleled learning capacity and the ability to scale with data.
Journal ArticleDOI
COVID-19 cough classification using machine learning and global smartphone recordings.
TL;DR: Although all classifiers were able to identify COVID-19 coughs, the best performance was exhibited by the Resnet50 classifier, which was best able to discriminate between the CO VID-19 positive and the healthy coughs with an area under the ROC curve (AUC) of 0.98.
Journal ArticleDOI
Time Series Forecasting of Covid-19 using Deep Learning Models: India-USA Comparative Case Study
TL;DR: Convolution LSTM outperformed the other two models and predicts the Covid-19 cases with high accuracy and very less error for all four datasets of both countries.
Journal ArticleDOI
Role of intelligent computing in COVID-19 prognosis: A state-of-the-art review.
TL;DR: A state-of-the-art analysis of the ongoing machine learning (ML) and deep learning (DL) methods in the diagnosis and prediction of COVID-19 has been done and a comparative analysis on the impact of machine learning and other competitive approaches like mathematical and statistical models on CO VID-19 problem has been conducted.
References
More filters
Journal ArticleDOI
Long short-term memory
TL;DR: A novel, efficient, gradient based method called long short-term memory (LSTM) is introduced, which can learn to bridge minimal time lags in excess of 1000 discrete-time steps by enforcing constant error flow through constant error carousels within special units.
Journal ArticleDOI
Equation of state calculations by fast computing machines
TL;DR: In this article, a modified Monte Carlo integration over configuration space is used to investigate the properties of a two-dimensional rigid-sphere system with a set of interacting individual molecules, and the results are compared to free volume equations of state and a four-term virial coefficient expansion.
Book ChapterDOI
Learning internal representations by error propagation
TL;DR: This chapter contains sections titled: The Problem, The Generalized Delta Rule, Simulation Results, Some Further Generalizations, Conclusion.
Book
Learning internal representations by error propagation
TL;DR: In this paper, the problem of the generalized delta rule is discussed and the Generalized Delta Rule is applied to the simulation results of simulation results in terms of the generalized delta rule.
Proceedings Article
Sequence to Sequence Learning with Neural Networks
TL;DR: The authors used a multilayered Long Short-Term Memory (LSTM) to map the input sequence to a vector of a fixed dimensionality, and then another deep LSTM to decode the target sequence from the vector.