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

Mohammad Isa Irawan, Mohammad Jamhuri

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publication7th International Conference on Mathematics - Pure, Applied and Computation
Subtitle of host publicationMathematics of Quantum Computing
EditorsMuhammad Syifa�ul Mufid, Dieky Adzkiya
PublisherAmerican Institute of Physics Inc.
ISBN (Electronic)9780735442917
DOIs
Publication statusPublished - 19 Dec 2022
Event7th International Conference on Mathematics: Pure, Applied and Computation: , ICoMPAC 2021 - Surabaya, Indonesia
Duration: 2 Oct 2021 → …

Publication series

NameAIP Conference Proceedings
Volume2641
ISSN (Print)0094-243X
ISSN (Electronic)1551-7616

Conference

Conference7th International Conference on Mathematics: Pure, Applied and Computation: , ICoMPAC 2021
Country/TerritoryIndonesia
CitySurabaya
Period2/10/21 → …

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