APPLICATION OF ARTIFICIAL INTELLIGENCE TO BITCOIN COURSE MODELLING

Artificial neural networks are modern methods suitable for solving the problem of nonlinear dependency approximation, which is successfully applied in many fields. This paper compares the predictive capabilities of Back Propagation, Radial Basis Function, Extreme Learning Machine, and Long-Short Term Memory neural networks to determine which artificial intelligence algorithm is best for modeling the price of Bitcoin opening. The criterion for comparing network performance was the standard deviation, the mean absolute deviation, and the accuracy of predicting the direction of change of course. At the same time, in the study of time series, it is recommended to perform a comprehensive data analysis using appropriate networks, depending on the length of the series and the specificity of the database.

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Comparison of Back Propagation, Long Short-Term Memory (LSTM), Attention-Based LSTM Neural Networks Application in Futures Market of China using R Programming

International Journal of Advanced Computer Science and Information Technology ◽

10.23953/cloud.ijacsit.461 ◽

2020 ◽

Vol 6 (1) ◽

pp. 101-114

Author(s):

Wang Shuangao ◽

◽

Liu Yi ◽

Rajchandar Padmanaban ◽

Mohamed Shamsudeen ◽

...

Keyword(s):

Neural Networks ◽

Short Term Memory ◽

Back Propagation ◽

Futures Market ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

R Programming

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Stock price trend prediction with long short-term memory neural networks

International Journal of Computational Intelligence Studies ◽

10.1504/ijcistudies.2019.103619 ◽

2019 ◽

Vol 8 (4) ◽

pp. 289

Author(s):

Varun Gupta ◽

Mujahid Ahmad

Keyword(s):

Neural Networks ◽

Stock Price ◽

Short Term Memory ◽

Short Term ◽

Trend Prediction ◽

Price Trend ◽

Term Memory ◽

Long Short Term Memory

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ECT-LSTM-RNN: An Electrical Capacitance Tomography Model-based Long Short-Term Memory Recurrent Neural Networks for Conductive Materials

IEEE Access ◽

10.1109/access.2021.3079447 ◽

2021 ◽

pp. 1-1

Author(s):

Wael Deabes ◽

Alaa Sheta ◽

Malik Braik

Keyword(s):

Neural Networks ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Electrical Capacitance Tomography ◽

Electrical Capacitance ◽

Short Term ◽

Term Memory ◽

Conductive Materials ◽

Long Short Term Memory ◽

Capacitance Tomography

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Empirical modeling of ethanol production dynamics using long short-term memory recurrent neural networks

Bioresource Technology Reports ◽

10.1016/j.biteb.2021.100724 ◽

2021 ◽

pp. 100724

Author(s):

Felipe Matheus Mota Sousa ◽

Rodolpho Rodrigues Fonseca ◽

Flávio Vasconcelos da Silva

Keyword(s):

Neural Networks ◽

Ethanol Production ◽

Recurrent Neural Networks ◽

Short Term Memory ◽

Empirical Modeling ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory ◽

Production Dynamics

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Research of semantic role labeling based on long short-term memory neural networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/1966/1/012005 ◽

2021 ◽

Vol 1966 (1) ◽

pp. 012005

Author(s):

Xuheng Liang

Keyword(s):

Neural Networks ◽

Short Term Memory ◽

Semantic Role ◽

Semantic Role Labeling ◽

Short Term ◽

Term Memory ◽

Long Short Term Memory

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Using long short term memory and convolutional neural networks for driver drowsiness detection

Accident Analysis & Prevention ◽

10.1016/j.aap.2021.106107 ◽

2021 ◽

Vol 156 ◽

pp. 106107

Author(s):

Azhar Quddus ◽

Ali Shahidi Zandi ◽

Laura Prest ◽

Felix J.E. Comeau

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Drowsiness Detection ◽

Driver Drowsiness ◽

Long Short Term Memory

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Adaptive Multi-Sensor Data Fusion Positioning Algorithm Based On Long Short Term Memory Neural Networks

2020 IEEE 20th International Conference on Communication Technology (ICCT) ◽

10.1109/icct50939.2020.9295914 ◽

2020 ◽

Author(s):

Jie Zhou ◽

Guanghui Xu ◽

Donglin Zhu ◽

Enbiao Di

Keyword(s):

Neural Networks ◽

Data Fusion ◽

Short Term Memory ◽

Sensor Data ◽

Short Term ◽

Term Memory ◽

Sensor Data Fusion ◽

Multi Sensor Data Fusion ◽

Long Short Term Memory ◽

Positioning Algorithm

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Comprehensive comparison of artificial neural networks and long short-term memory networks for rainfall-runoff simulation

Physics and Chemistry of the Earth Parts A/B/C ◽

10.1016/j.pce.2021.103026 ◽

2021 ◽

pp. 103026

Author(s):

Ganquan Mao ◽

Meng Wang ◽

Junguo Liu ◽

Zifeng Wang ◽

Kai Wang ◽

...

Keyword(s):

Neural Networks ◽

Artificial Neural Networks ◽

Short Term Memory ◽

Rainfall Runoff ◽

Short Term ◽

Runoff Simulation ◽

Term Memory ◽

Comprehensive Comparison ◽

Long Short Term Memory ◽

Artificial Neural

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An Intelligent Approach to Detect Fake News Using Artificial Intelligence Technique

International Journal of Distributed Artificial Intelligence ◽

10.4018/ijdai.2021070101 ◽

2021 ◽

Vol 13 (2) ◽

pp. 1-12

Author(s):

Sumit Das ◽

Manas Kumar Sanyal ◽

Sarbajyoti Mallik

Keyword(s):

Artificial Intelligence ◽

Web Application ◽

Short Term Memory ◽

Real Data ◽

Fake News ◽

Artificial Intelligence Technique ◽

News Reports ◽

Proposed Model ◽

Long Short Term Memory ◽

Intelligent Approach

There is a lot of fake news roaming around various mediums, which misleads people. It is a big issue in this advanced intelligent era, and there is a need to find some solution to this kind of situation. This article proposes an approach that analyzes fake and real news. This analysis is focused on sentiment, significance, and novelty, which are a few characteristics of this news. The ability to manipulate daily information mathematically and statistically is allowed by expressing news reports as numbers and metadata. The objective of this article is to analyze and filter out the fake news that makes trouble. The proposed model is amalgamated with the web application; users can get real data and fake data by using this application. The authors have used the AI (artificial intelligence) algorithms, specifically logistic regression and LSTM (long short-term memory), so that the application works well. The results of the proposed model are compared with existing models.

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