Emerging Trends in Data Structures for Modern Machine Learning Applications

Year : 2024 | Volume :02 | Issue : 01 | Page : 1-7
By

    Ikvinderpal Singh

  1. Sapandeep Kaur Dhillon

  1. Assistant Professor, Department of Computer Science & Applications, TSGGS Khalsa College, Amritsar., Punjab, India
  2. Assistant Professor, Department of Computer Science, Guru Nanak Dev University, Amritsar., Punjab, India

Abstract

In the realm of machine learning, data structures play a pivotal role in facilitating efficient data manipulation, storage, and retrieval, thereby significantly impacting the performance and scalability of machine learning algorithms. In recent years, the field of machine learning has witnessed the emergence of novel data structures tailored to address scalability and efficiency challenges inherent in handling large-scale and high-dimensional data. This paper provides a look at the data preprocessing, data cleaning and feature extraction in machine learning using data structures. This paper explores three key emerging trends: tensor representations, sparse data structures, and graph-based neural networks, highlighting their potential to revolutionize modern machine learning applications. Furthermore, we discusseda case study of the Leveraging Emerging Data Structures in Healthcare Analytics.

Keywords: Machine Learning, Data Structures, Tensor Representations, Sparse Data Structures, Graph-Based Neural Networks

[This article belongs to International Journal of Data Structure Studies(ijdss)]

How to cite this article: Ikvinderpal Singh, Sapandeep Kaur Dhillon , Emerging Trends in Data Structures for Modern Machine Learning Applications ijdss 2024; 02:1-7
How to cite this URL: Ikvinderpal Singh, Sapandeep Kaur Dhillon , Emerging Trends in Data Structures for Modern Machine Learning Applications ijdss 2024 {cited 2024 Feb 21};02:1-7. Available from: https://journals.stmjournals.com/ijdss/article=2024/view=133432


References

[1] Datasets | Research | Canadian Institute for Cybersecurity | UNB . Www.unb.ca. 2023. Available from: https://www.unb.ca/cic/datasets/index.html ‌

[2] DDoS 2019 | Datasets | Research | Canadian Institute for Cybersecurity | UNB . Www.unb.ca. 2019. Available from: https://www.unb.ca/cic/datasets/ddos-2019.html ‌

[3] Ardabili SF, Mosavi A, Ghamisi P, Ferdinand F, Varkonyi-Koczy AR, Reuter U, Rabczuk T, Atkinson PM. Covid-19 outbreak prediction with machine learning. Algorithms. 2020 Oct 1;13(10):249.

[4] Boukerche A, Wang J. Machine learning-based traffic prediction models for intelligent transportation systems. Computer Networks. 2020 Nov 9;181:107530.

[5] Chollet F. Xception: Deep learning with depthwise separable convolutions. InProceedings of the IEEE conference on computer vision and pattern recognition 2017 (pp. 1251-1258).

[6] DATA STRUCTURES FOR MACHINE LEARNING. Engpaper.com. 2022 . Available from: https://www.engpaper.com/data-structures-for-machine-learning.htm ‌

[7]Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, Wieser E, Taylor J, Berg S, Smith NJ, Kern R. Array programming with NumPy. Nature. 2020 Sep 17;585(7825):357-62.

[8] Niemeyer J, Rottensteiner F, Soergel U. Contextual classification of lidar data and building object detection in urban areas. ISPRS journal of photogrammetry and remote sensing. 2014 Jan 1;87:152-65. [9] Kuang L, Hao F, Yang LT, Lin M, Luo C, Min G. A tensor-based approach for big data representation and dimensionality reduction. IEEE transactions on emerging topics in computing. 2014 Jun 12;2(3):280-91.

[10]Sadeghi M, Babaie-Zadeh M, Jutten C. Dictionary learning for sparse representation: A novel approach. IEEE Signal Processing Letters. 2013 Oct 9;20(12):1195-8.

[11] Allamanis M, Brockschmidt M, Khademi M. Learning to represent programs with graphs. arXiv preprint arXiv:1711.00740. 2017 Nov 1.

[12] Dettmers T, Minervini P, Stenetorp P, Riedel S. Convolutional 2d knowledge graph embeddings. InProceedings of the AAAI conference on artificial intelligence 2018 Apr 25 (Vol. 32, No. 1).

[13] Koniusz P, Wang L, Cherian A. Tensor representations for action recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence. 2021 Aug 24;44(2):648-65.

[14] Hua C, Rabusseau G, Tang J. High-order pooling for graph neural networks with tensor decomposition. Advances in Neural Information Processing Systems. 2022 Dec 6;35:6021-33.


Regular Issue Subscription Review Article
Volume 02
Issue 01
Received February 16, 2024
Accepted February 19, 2024
Published February 21, 2024