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All Journal International Journal of Applied and Advanced Multidisciplinary Research (IJAAMR)
M.A.Thalor
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Humanities Civil Engineering, Building, Construction & Architecture Economics, Econometrics & Finance Education Social Sciences

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Analytical Review of Machine Learning Algorithms (Models) for Stock Market Prediction Bose, Tejas; M.A.Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 2 (2023): October 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i2.517

Abstract

The realm of stock market forecasting presents a formidable challenge, given the intricate, noisy, chaotic, and ever-evolving nature of its time series data. However, the advent of computational advancements offers a ray of hope, as intelligent models hold the potential to assist investors and analysts in mitigating the inherent risks associated with financial markets. In recent years, Deep Learning models have garnered significant attention, with numerous studies delving into their application for predicting stock prices using historical data and technical indicator Yet, the ultimate goal in this pursuit is not merely prediction but validation, a crucial step in the context of the financial market. This systematic review sets its sights on Deep Learning models employed in stock market forecasting through the lens of technical analysis. It dissects the landscape based on four pivotal dimensions: predictor techniques, trading strategies, profitability metrics, and risk management. Unveiling the findings, it becomes apparent that the LSTM (Long Short-Term Memory) technique reigns supreme, representing a substantial 73.5% of the studies in this domain. However, the review uncovers notable limitations in the existing literature, with a mere 35.3% of studies addressing profitability metrics and a mere two articles delving into the intricacies of risk management.
Enabling the Future: a Virtualized Approach to 5G and Edge Computing Ankita Jamdade; M.A.Thalor
International Journal of Applied and Advanced Multidisciplinary Research Vol. 1 No. 4 (2023): December 2023
Publisher : MultiTech Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.59890/ijaamr.v1i4.614

Abstract

This abstract examines the evolution of wireless communications technologies beyond 5G (B5G) and the emergence of 6G. It highlights their key role in powering the Internet of Things (IoT) and enabling edge computing. Built on shared resources, this system is exposed to various real-time application scenarios and uses simulated user equipment (UE) and operational Nextcloud instances. Performance metrics are analyzed and the system can automatically scale during high network traffic to ensure high availability. Key concepts include Radio Access Network (RAN), Edge Computing, User Equipment and Virtual Network Functions (VNF). This framework relies on shared resources and undergoes rigorous testing with real-time application scenarios, using simulated User Equipment (UE) and operational Nextcloud instances.
Co-Authors Ankita Jamdade Bose, Tejas