Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach
- Title
- Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach
- Creator
- Karahyla J.K.; Sharma N.; Chamoli S.; Shirgire A.; Kant R.; Chauhan A.
- Description
- Bitcoin is a rapidly growing but extremely risky cryptocurrency. It marks a watershed moment in the history of cash. These days, digital currency is preferred to actual money. Bitcoin has decentralized authority and placed it in the hands of its users. Many people are joining the largest and most well-known Bitcoin mining pools as the risk of working alone is too great. In order to enhance their chances of creating the next block in the Bitcoins blockchain and decrease the mining reward volatility, users can band together to form Bitcoin pools. This tendency toward consolidation may also be seen in the rise of large-scale mining farms equipped with powerful mining resources and speedy processing capability. Because of the risk of a 51% assault, this pattern shows that Bitcoin's pure, decentralized protocol is moving toward greater centralization in its distribution network. Not to be overlooked is the resulting centralization of the bitcoin network as a result of cloud wallets making it simple for new users to join. Because of the easily hackable nature of Bitcoin technologies, this could lead to a wide range of security vulnerabilities. The proposed approach uses normalization and filling missing values in preprocessing, PCA for feature Extraction and finally training the model using LSTM-DNN Models. The proposed approach outperforms other two models such as CNN and DNN. 2023 IEEE.
- Source
- 2023 4th International Conference on Electronics and Sustainable Communication Systems, ICESC 2023 - Proceedings, pp. 953-958.
- Date
- 2023-01-01
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Bitcoin; Dense Neural Network (DNN); Normalization
- Coverage
- Karahyla J.K., Pgcc Hospital Management Professor and Head Respiratory Medicine Mmimsr Mm Deemed University, Human Rights, Haryana,Mullana, Ambala, India; Sharma N., Lovely Professional University, Physical Education, Punjab, Phagwara, India; Chamoli S., Graphic Era Hill University, Department of Computer Science and Engineering, Dehradun, India; Shirgire A., Dr D y Patil Institute of Technology, Department of Civil Engineering, Maharashtra,Pimpri, Pune, India; Kant R., Shoolini University, Cse Department, Himachal Pradesh, Solan, India; Chauhan A., Christ (Deemed to Be University), Department of Life Sciences, Karnataka, Bengaluru, India
- Rights
- Restricted Access
- Relation
- ISBN: 979-835030009-3
- Format
- Online
- Language
- English
- Type
- Conference paper
Collection
Citation
Karahyla J.K.; Sharma N.; Chamoli S.; Shirgire A.; Kant R.; Chauhan A., “Predicting Price Direction of Bitcoin based on Hybrid Model of LSTM and Dense Neural Network Approach,” CHRIST (Deemed To Be University) Institutional Repository, accessed February 23, 2025, https://archives.christuniversity.in/items/show/19899.