| Issue |
E3S Web Conf.
Volume 391, 2023
4th International Conference on Design and Manufacturing Aspects for Sustainable Energy (ICMED-ICMPC 2023)
|
|
|---|---|---|
| Article Number | 01056 | |
| Number of page(s) | 9 | |
| DOI | https://doi.org/10.1051/e3sconf/202339101056 | |
| Published online | 05 June 2023 | |
Effective Machine Learning Garbage Data Filtering Algorithm for SNS Big Data Processing
Department of Information Technology, Gokaraju Rangaraju Institute of Engineering and Technology, Hyderabad, India
* Corresponding author: ledalla.sukanaya@gmail.com
Social network services (SNS) are used more often today, which results in more SNS data being generated. Furthermore, greater emphasis is being placed on extracting various sorts of information through the collection, processing, and analysis of massive volumes of SNS data. Although big data processing can extract a lot of information from SNS data, it takes a long time and a lot of resources. As a result, gaining insights from SNS data necessitates a significant investment of time and money. In this section, we propose a data filtering approach for removing unnecessary SNS data from the data stream. To improve filtering accuracy, the suggested method employs Random Forest, Decision Tree, and XGBoost. Research shows that the suggested algorithm filters the experimental keywords by more than 70%.
© The Authors, published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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