Big Data Analytics and Implementation Challenges of Machine Learning in Big data

Rajeswari P.; Sathishkumar V E.; Chunduru Anilkumar; P Thilakaveni; Usha Moorthy

doi:10.54254/2755-2721/2/20220584

Rajeswari P.¹ , Sathishkumar V E.^2,* , Chunduru Anilkumar³ , P Thilakaveni⁴ , Usha Moorthy⁵

¹ Department of Computer Engineering, PSG Polytechnic College, Peelamedu, Coimbatore, Tamilnadu- 641004

² Department of Industrial Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul, Republic of Korea

³ Department of Information Technology, GMR Institute of Technology, Srikakulam, AP-532127

⁴ Department of Computer Engineering, PSG Polytechnic College, Peelamedu,, Coimbatore, Tamilnadu-641004.

⁵ School of Computing and Information Technology, REVA University, Bengaluru, Karnataka, India

^* Corresponding author: srisathishkumarve@gmail.com

Published: 22 March 2023

DOI: https://doi.org/10.54254/2755-2721/2/20220584

Abstract

The term "big data" refers to an information processing system that combines different conventional data techniques. Big data also includes a large amount of personally identifiable and authenticated data, making privacy a major concern. Various techniques have been developed to provide security and efficient data processing. Machine learning is a form of data technology that deals with one of the most important and least understood aspects of the data collected. Deep learning algorithms, similar to machine learning algorithms, learn programmers automatically from data and are thought to improve the efficiency and security of large data sets. The efficiency of machine learning and deep learning in a sensitive environment was evaluated in this paper, which reviewed security problems in big data. This paper begins by providing an overview of machine learning and deep learning algorithms. The research then moves on to machine learning problems and challenges, as well as potential solutions. The investigation into deep learning principles of big data continues after that. Finally, the report examines approaches used in recent research developments and concludes with recommendations for the future.

Keywords: Machine learning , big data , Deep learning , artificial intelligence , data mining , security issues.

ACE Vol.2

ISSN (Print): 2755-2721

ISSN (Online): 2755-273X

ISBN (Print): 978-1-915371-19-5

ISBN (Online): 978-1-915371-20-1

Download Cover

Article Information

Received: 20 February 2023

Published: 22 March 2023

DOI: https://doi.org/10.54254/2755-2721/2/20220584

Article Type: Research Article

Cite this article

P.,R.;E.,S.V.;Anilkumar,C.;Thilakaveni,P.;Moorthy,U. (2023). Big Data Analytics and Implementation Challenges of Machine Learning in Big data. Applied and Computational Engineering,2,484-489.

Data availability

The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.

Disclaimer/Publisher's Note

The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

About this Volume

Volume Title: ACE Vol.2

Part of Series: Applied and Computational Engineering

ISSN: 2755-2721 (Print) / 2755-273X (Online)

Article Information

Cite this article

Data availability

Disclaimer/Publisher's Note

About this Volume

Copyright & License