The effects of 5G network on people and the environment: A machine learning approach to the comprehensive analysis
1 Department of Electrical and Electronics Engineering, International Burch University, Faculty of Engineering, Natural and Medical Sciences, Francuske revolucije bb, 71000 Sarajevo, Bosnia and Herzegovina.
2 Department of Genetics and Bioengineering, International Burch University, Faculty of Engineering, Natural and Medical Sciences, Francuske revolucije bb, 71000 Sarajevo, Bosnia and Herzegovina.
Research Article
World Journal of Advanced Engineering Technology and Sciences, 2024, 11(01), 301–309.
Article DOI: 10.30574/wjaets.2024.11.1.0055
Publication history:
Received on 04 January 2024; revised on 13 February 2024; accepted on 16 February 2024
Abstract:
The progression of telecommunications, starting from the inception of 1G networks in 1979 to the advent of 5G technology in 2019, represents a significant journey of advancement for humanity. As we approach the era of 5G, characterized by heightened machine-to-machine connectivity and transformative applications in AI, IoT, and cloud computing, it becomes imperative to acknowledge and address concerns regarding its potential impacts on health and the environment. Utilizing machine learning algorithms, particularly implemented in Python for this research, provides a potent approach to analyzing intricate datasets concerning 5G signals and their potential correlations with healthcare outcomes. After carefully cleaning and preparing the data and conducting linear regression analysis, uncovered evidence backing the notion that 5G antennas emit greater levels of radiation compared to 4G antennas emerged - a fact often concealed by corporations. Despite relying on a restricted dataset, the results emphasize the necessity for more accurate data to improve model precision. Ongoing research endeavors are vital to alleviate public anxieties regarding 5G technology, thereby fostering trust and bolstering awareness on a wider front.
Keywords:
Fifth-generation network; Machine learning; Data analysis; Antennas; Radiation; Healthcare
Full text article in PDF:
Copyright information:
Copyright © 2024 Author(s) retain the copyright of this article. This article is published under the terms of the Creative Commons Attribution Liscense 4.0