Multi-step Methods for Machine Learning Models with Web Metrics

Authors

  • Ivan Popchev Institute of Information and Communication Technologies, Bulgarian Academy of Sciences
  • Daniela Orozova Trakia University, Faculty of Economics, Bulgaria

DOI:

https://doi.org/10.7546/CRABS.2023.11.08

Keywords:

web metrics, machine learning, multistep methods

Abstract

The purpose of the article is to outline methods as a multi-step process for developing an information flow with machine learning models that can be used for research, analysis, and training. Web metrics analysis of available web content can provide useful information about user behaviour. The results show that the formulated solutions can be successfully used for different tasks and can be adapted to new technologies and applications.

Author Biographies

Ivan Popchev, Institute of Information and Communication Technologies, Bulgarian Academy of Sciences

Mailing Address:
Institute of Information and Communication Technologies,
Bulgarian Academy of Sciences
Akad. G. Bonchev St, Bl. 2
1113 Sofia, Bulgaria

E-mail: ivan.popchev@iict.bas.bg

Daniela Orozova, Trakia University, Faculty of Economics, Bulgaria

Mailing Address:
Trakia University,
Faculty of Economics
6015 Stara Zagora, Bulgaria

E-mail: daniela.orozova@trakia-uni.bg

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Published

27-11-2023

How to Cite

[1]
I. Popchev and D. Orozova, “Multi-step Methods for Machine Learning Models with Web Metrics”, C. R. Acad. Bulg. Sci., vol. 76, no. 11, pp. 1707–1716, Nov. 2023.

Issue

Section

Engineering Sciences