State of the art of machine learning: an overview of the past, current, and the future research trends in the era of quantum computing

Irawan, Mohammad Isa and Jamhuri, Mohammad (2022) State of the art of machine learning: an overview of the past, current, and the future research trends in the era of quantum computing. Presented at 7th International Conference on Mathematics: Pure, Applied and Computation: Mathematics of Quantum Computing, 2 Oct 2021, Surabaya, Indonesia.

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Abstract

This paper describes data science history and behavioral trends on the largest platform for learning and competition in analyzing and modeling data; Kaggle. We analyze the history of methods commonly used in linear predictor to predict, classify, cluster, and explore data sets. In addition, we also examine the use of the most widely used tools and frameworks to help make data modeling easier. The analysis was carried out on the forum discussion data for the last ten years based on the data available on meta-Kaggle. To see the future trend of data science and linear predictor models, we analyzed the abstracts on the articles available on the Elsevier search page. We extracted information from them using a machine learning method.

Item Type:	Conference (Keynote)
Subjects:	01 MATHEMATICAL SCIENCES > 0103 Numerical and Computational mathematics > 010399 Numerical and Computational Mathematics not elsewhere classified
Depositing User:	Mr Jamhuri Mohammad
Date Deposited:	15 Jun 2023 09:10

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