Emerging Trends in Data Structures for Modern Machine Learning Applications

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

    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


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


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