Cover Image

Forecasting and Trading of the Stable Cryptocurrencies With Machine Learning and Deep Learning Algorithms for Market Conditions

The digital market trend is rapidly expanding due to key characteristics like decentralization, accessibility, and market diversity enabled by blockchain technology. This study proposes a Predictive Analytics System to provide simplified reporting for the three most popular cryptocurrencies with var...

Full description

Saved in:

Bibliographic Details
Main Authors:	Shamshad, H., Ullah, F., Ullah, A., Kebande, V.R., Ullah, S., Al-Dhaqm, A.
Format:	Article
Published:	Institute of Electrical and Electronics Engineers Inc. 2023
Online Access:	http://scholars.utp.edu.my/id/eprint/38011/ https://www.scopus.com/inward/record.uri?eid=2-s2.0-85176343169&doi=10.1109%2fACCESS.2023.3327440&partnerID=40&md5=50ce468cc316983814a0cc5d6ded2ac5
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Comparative performance of machine learning algorithms for cryptocurrency forecasting
by: Hitam, Nor Azizah, et al.
Published: (2018)

Machine learning for plant disease detection: an investigative comparison between support vector machine and deep learning
by: Muhammad Abdu, Aliyu, et al.
Published: (2020)

A review of deep learning and machine learning techniques for hydrological inflow forecasting
by: Latif S.D., et al.
Published: (2024)

Elucidating cryptocurrency with trading dashboard.
by: Ghazali, Masitah, et al.
Published: (2023)

A Dynamic Compact Deep Learning Architecture For Pattern Recognition
by: Khan, Farhat Ullah
Published: (2023)

Modelling and Forecasting the Trend in Cryptocurrency Prices
by: Abdul Rashid, Nurazlina, et al.
Published: (2023)

Subdued trading forecast for the market this week
Published: (2024)

Automatic detection, segmentation and classification of Abdominal Aortic Aneurysm using deep learning
by: Ho, Aik Hong, et al.
Published: (2016)

An Optimized Support Vector Machine (SVM) based on Particle Swarm Optimization (PSO) for cryptocurrency forecasting
by: Hitam, Nor Azizah, et al.
Published: (2019)

A review of hybrid deep learning applications for streamflow forecasting
by: Ng K.W., et al.
Published: (2024)

A comparative study of deep learning algorithms in univariate and multivariate forecasting of the Malaysian stock market
by: Mohd. Ridzuan Ab. Khalil,, et al.
Published: (2023)

Robotics architectures based machine learning and deep learning approaches
by: Alatabani, Lina E. Alatabani, et al.
Published: (2022)

Deep learning and explainable machine learning on hair disease detection
by: Heng, Wei Wei, et al.
Published: (2023)

Stress detection using machine learning and deep learning
by: Zainudin, Z., et al.
Published: (2021)

Air quality forecasting using deep learning and transfer learning: A survey
by: Yang, Junzi, et al.
Published: (2022)

A collective machine learning and deep learning prototypical to expect diabetic retinopathy
by: Srisuma, V., et al.
Published: (2023)

Machine Learning and Dyslexia-Diagnostic and Classification System (DCS) for Kids with Learning Disabilities
by: Rehman Ullah, Khan, et al.
Published: (2018)

Deep learning and machine learning for early detection of stroke and haemorrhage
by: Al-Mekhlafi, Zeyad Ghaleb, et al.
Published: (2022)

Financial market predictions with deep learning
by: Yap, Zhong Jing, et al.
Published: (2023)

Deep learning neural network for time series water level forecasting
by: Zaini N., et al.
Published: (2023)

Structural change analysis of active cryptocurrency market
by: Yen, Tan Chia, et al.
Published: (2022)

Three steps towards better forecasting for streamflow deep learning
by: Tan, Woon Yang, et al.
Published: (2022)

Deep Learning Techniques for Wind Speed Forecasting at Palembang Airport
by: Akbar Rizki, Ramadhan, et al.
Published: (2024)

Application of Machine Learning for Daily Forecasting Dam Water Levels
by: Almubaidin, et al.
Published: (2024)

Waste management using machine learning and deep learning algorithms
by: Sami, Khan Nasik, et al.
Published: (2020)

Machine learning and deep learning approaches for cybersecurity: a review
by: Halbouni, Asmaa Hani, et al.
Published: (2022)

Comparative performance of deep learning and machine learning algorithms on imbalanced handwritten data
by: Amri, A’inur A’fifah, et al.
Published: (2018)

Hybrid deep learning model for ozone concentration prediction: comprehensive evaluation and comparison with various machine and deep learning algorithms
by: Yafouz A., et al.
Published: (2023)

Dynamic user preference parameters selection and energy consumption optimization for smart homes using deep extreme learning machine and bat algorithm
by: Shah, Abdul Salam, et al.
Published: (2020)

Forecasting of Carbon Monoxide Concentration Based on Sequence-to-Sequence Deep Learning Approach
by: Zaini N., et al.
Published: (2023)

A Comparative Analysis of Machine Learning and Deep Learning Algorithms for Image Classification
by: Madanan M., et al.
Published: (2024)

MRI Imaging, Comparison of MRI with other Modalities, Noise in MRI Images and Machine Learning Techniques for Noise Removal : A Review
by: Sajid Ullah, Khan, et al.
Published: (2019)

Non-trading days model for Tesco stock market forecasting
by: Mohd. Zainudin, Che Normelissa, et al.
Published: (2023)

A review of machine learning and deep learning techniques for anomaly detection in iot data
by: Al-Amri R., et al.
Published: (2023)

Factors affecting cryptocurrency adoption in digital market of Malaysia
by: Miraz, Mahadi Hasan
Published: (2021)

Blockchain, cryptocurrency and FinTech market growth in Malaysia
by: Ku-Mahamud, Ku Ruhana, et al.
Published: (2018)

Cryptocurrencies: a survey on acceptance, governance and market dynamics
by: Hairudin, Aiman, et al.
Published: (2020)

Change vulnerability forecasting using deep learning algorithm for Southeast Asia
by: Amelia Ritahani, Ismail, et al.
Published: (2018)

Deep learning-based forecast and warning of floods in Klang River, Malaysia
by: Faruq, Amrul, et al.
Published: (2020)

Regional manufacturing industry demand forecasting: A deep learning approach
by: Dou, Zixin, et al.
Published: (2021)