Cryptocurrency Price Prediction using Machine Learning Algorithm

Bangroo, Rashika; Gupta, Utsav; Sah, Roshan; Kumar, Anil

doi:10.1109/icrito56286.2022.9964870

Cited by 10 publications

(3 citation statements)

References 7 publications

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“…Through these techniques, behaviors or movements within unstable cryptocurrency markets can be identified. In order to have a better prediction with good accuracy, Bangroo et al [7] used different machine learning algorithms like random forest regressor and gradient boosting regressor to predict cryptocurrencies like Bitcoin, XRP, Ethereum, and Stellar. Xiaolei et al [4] proposed three models: SVM, RF model, and light gradient boosting machine to forecast the price of the cryptocurrency market.…”

Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

Ladhari,

Boubaker

2024

Forecasting

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Since cryptocurrencies are among the most extensively traded financial instruments globally, predicting their price has become a crucial topic for investors. Our dataset, which includes fluctuations in Bitcoin’s hourly prices from 15 May 2018 to 19 January 2024, was gathered from Crypto Data Download. It is made up of over 50,000 hourly data points that provide a detailed view of the price behavior of Bitcoin over a five-year period. In this study, we used potent algorithms, including gradient descent, attention mechanisms, long short-term memory (LSTM), and artificial neural networks (ANNs). Furthermore, to estimate the price of Bitcoin, we first merged two deep learning algorithms, LSTM and attention mechanisms, and then combined LSTM-Attention with gradient-specific optimization to increase our model’s performance. Then we integrated ANN-LSTM and included gradient-specific optimization for the same reason. Our results show that the hybrid model with gradient-specific optimization can be used to anticipate Bitcoin values with better accuracy. Indeed, the hybrid model combines the best features of both approaches, and gradient-specific optimization improves predictive performance through frequent analysis of pricing data changes.

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Section: Literature Reviewmentioning

confidence: 99%

Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

Ladhari,

Boubaker

2024

Forecasting

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“…The scope of the present study, the impact of experiential emotions, interest in the subject topic and webbased Learning is about evaluating collaborative and non-cooperative behavior in the WBLE. [24] By providing relevant ideas in web-based learning contexts, Students check their answers, Evaluate their progress, Contribute to learning by allowing the cause of errors to be identified. Feedback will be helpful.…”

Section: Web-based Learning Environmentmentioning

confidence: 99%

Evaluation of Web-based Learning and Its Implications

2022

jitl

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Web based Learning often Also known as online learning or e-learning. Because it includes online course content. Because it is online Includes course content. All of the live lectures Possible through the Internet. Web-based course such as printed course materials can also provide static pages. Not involving active learning among students Traditional learning to be improved. So, in the twenty-first century, In the learning environment Development of Internet Technology Has become a core need of every student. One of the learning contexts of the teaching and learning process Web-based learning environment to meet needs. The students in this study are web-based Address learning needs; Characteristics of the learning environment its purpose is to identify.

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“…Janhavi Chaidhanya G.et.al / REST Journal on Data Analytics and Artificial Intelligence, 1(2) 2022,[33][34][35][36][37][38][39][40] …”

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confidence: 99%

Understanding the Performance of Micro and Small Entrepreneurs by (COPRAS)

2022

jdaai

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Micro and Small Entrepreneurs Micro-entrepreneurs, those who own and operate businesses with fewer than five employees, are the most common type of entrepreneurs in the world and make up a large part of the informal economy in low-income countries. Micro and Small Entrepreneurs of the proposed method Comparative analysis to illustrate the application Conducting and credit evaluation Example is used. COPRAS (Complex Proportional Evaluation is proposed to evaluate the possible maintenance strategy. Linguistic terms are used to evaluate the ratings and weights. The rankings of the alternatives are COPRAS Entrepreneur 1, Entrepreneur 2, Entrepreneur 3, and Entrepreneur 3 Entrepreneur. Credit histories, behavioral preference, performance skills, identity features, and interpersonal relationships. Entrepreneurs 2 got the first rank whereas Entrepreneurs have the lowest rank. In this paper, Micro and Small Entrepreneurs 2 is got the first rank whereas the Entrepreneurs3is has the lowest rank.

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Cryptocurrency Price Prediction using Machine Learning Algorithm

Cited by 10 publications

References 7 publications

Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

Deep Learning Models for Bitcoin Prediction Using Hybrid Approaches with Gradient-Specific Optimization

Evaluation of Web-based Learning and Its Implications

Understanding the Performance of Micro and Small Entrepreneurs by (COPRAS)

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