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What is AE Learning Neural Network

Revolutionizing Financial Services and Markets Through FinTech and Blockchain
AE (Autoencoder) learning neural network refers to a type of machine learning model that aims to recreate the input data in an unsupervised manner through a series of hidden layers. The objective of an AE learning neural network is to learn the underlying patterns in the data by reconstructing the input data with high accuracy.
Published in Chapter:
Predicting Cryptocurrency Prices Model Using a Stacked Sparse Autoencoder and Bayesian Optimization
S. Baranidharan (CHRIST University (Deemed), India), Raja Narayanan (Dayananda Sagar University, India), and V. Geetha (Seshadripuram Evening College, India)
DOI: 10.4018/978-1-6684-8624-5.ch005
Abstract
In recent years, digital currencies, also known as cybercash, digital money, and electronic money, have gained significant attention from researchers and investors alike. Cryptocurrency has emerged as a result of advancements in financial technology and has presented a unique opening for research in the field. However, predicting the prices of cryptocurrencies is a challenging task due to their dynamic and volatile nature. This study aims to address this challenge by introducing a new prediction model called Bayesian optimization with stacked sparse autoencoder-based cryptocurrency price prediction (BOSSAE-CPP). The main objective of this model is to effectively predict the prices of cryptocurrencies. To achieve this goal, the BOSSAE-CPP model employs a stacked sparse autoencoder (SSAE) for the prediction process and resulting in improved predictive outcomes. The results were compared to other models, and it was found that the BOSSAE-CPP model performed significantly better.
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