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An Experimental Review on Deep Learning Architectures for Time Series Forecasting
In recent years, deep learning techniques have outperformed traditional ...
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Decentralized Flood Forecasting Using Deep Neural Networks
Predicting flood for any location at times of extreme storms is a longst...
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Rethinking Representations in P C Actuarial Science with Deep Neural Networks
Insurance companies gather a growing variety of data for use in the insu...
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Composite Monte Carlo Decision Making under High Uncertainty of Novel Coronavirus Epidemic Using Hybridized Deep Learning and Fuzzy Rule Induction
In the advent of the novel coronavirus epidemic since December 2019, gov...
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Inaccuracy Minimization by Partioning Fuzzy Data Sets - Validation of Analystical Methodology
In the last two decades, a number of methods have been proposed for fore...
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A Survey on Knowledge integration techniques with Artificial Neural Networks for seq-2-seq/time series models
In recent years, with the advent of massive computational power and the ...
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Deep learning of stochastic contagion dynamics on complex networks
Forecasting the evolution of contagion dynamics is still an open problem...
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Deep Neural Networks and Neuro-Fuzzy Networks for Intellectual Analysis of Economic Systems
In tis paper we consider approaches for time series forecasting based on deep neural networks and neuro-fuzzy nets. Also, we make short review of researches in forecasting based on various models of ANFIS models. Deep Learning has proven to be an effective method for making highly accurate predictions from complex data sources. Also, we propose our models of DL and Neuro-Fuzzy Networks for this task. Finally, we show possibility of using these models for data science tasks. This paper presents also an overview of approaches for incorporating rule-based methodology into deep learning neural networks.
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