Car Investigation by utilizing Machine Learning
Abstract
In a world with technology of an upcoming future of machine working as humans. In this procedure Artificial Intelligence, Machine Learning, Knowledge Engineering plays a crucial function. AI plays a crucial role in today’s life and is that the future if upcoming world. Machine learning is that the core part of the AI. In this, using problem classification the matter is recognized and that we have solved this world issue of recognition prediction for Auto Company by utilizing machine learning approach.
How to cite this article:
Shaikh TH, Kapote RA, Memon AS et al. Car Investigation by utilizing Machine Learning. J Adv Res Embed Sys 2020; 7(1): 23-24.
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depth study on SP 500 stocks.”Big Data (Big Data), 2017
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machine learning: A review of classification techniques.
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Khan A, Baharudin B, Lee LH et al. A review of machine
learning algorithms for text-documents Classification.
Journal of advances in information technology 2010;
1(1): 4-20.
Jiang J. A literature survey on domain adaptation of
statistical classifiers. URL: http://sifaka. Cs. Uiuc.Edu/
jiang4/domainadaptation/survey. 2008 Mar 6;3.
Kaelbling LP, Littman ML, Moore AW. Reinforcement
learning: A survey. Journal of AI Research 1996; 4:
237-285.
Ban, Tao, Zhang R etal. Referential knn regression
for Financial statistic forecasting. In International
Conference on Neural information science. Springer,
Berlin, Heidelberg 2013;601-608.
Yue R, Xiling X, Heng L et al. Quantum Algorithm for
K-Nearest Neighbors Classification Based on the Metric
of Hamming Distance. NASA Astrophysics Data System
(ADS), 2017-11-01.
Alsharif MH, Kelechi AH, Yahya K et al. Machine Learning
Algorithms for Smart Data Analysis in Internet of
Things Environment: Taxonomies and Research Trends.
Symmetry 2020; 12(1): 88; Published: 2 January 2020.
Published
2020-07-27
How to Cite
SHAIKH, Tayyaba H.
Car Investigation by utilizing Machine Learning.
Journal of Advanced Research in Embedded System, [S.l.], v. 7, n. 1, p. 23-24, july 2020.
ISSN 2395-3802.
Available at: <http://thejournalshouse.com/index.php/ADR-Journal-Embedded-Systems/article/view/207>. Date accessed: 18 may 2024.
Section
Articles
