Sort Data Faster: Comparing Algorithms
Clicks: 1
ID: 312433
2025
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This
article has not been analysed, so there is no overall score —
reader engagement is measured and shown alongside.
Reader Engagement
0.0
/100
1 views
0 readers
AI Quality Assessment
Not analyzed
Readership in this journal
Ranked #3 of 15 articles by views in Southern Journal of Computer Science
Most read
Least read
Bar heights use a square-root scale.
Mint this article as an NFT
Not yet mintedCreate a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.
5
SUSD
one-off · no wallet required
Abstract
Sorting algorithms form the backbone of efficient data processing across computing systems, from database management to machine learning pipelines. This study presents a comprehensive empirical evaluation of five fundamental sorting algorithms—Bubble Sort, Insertion Sort, Selection Sort, Merge Sort, and Quick Sort—analyzing their performance characteristics under varied input conditions. We designed a systematic testing framework using Python 3.9, implementing each algorithm with standardized optimization techniques and measuring their behavior across three distinct dataset profiles: randomly distributed integers (100 to 1,000,000 elements), partially ordered sequences (90% sorted), and reverse-sorted arrays.
| Reference Key |
imported_1776990083_69eab7831fd9c
Use this key to autocite in the manuscript while using
SciMatic Manuscript Manager or Thesis Manager
|
|---|---|
| Authors | Muhammad Asim Rajwana |
| Journal | Southern Journal of Computer Science |
| Year | 2025 |
| DOI |
DOI not found
|
| URL | |
| Keywords | Keywords not found |
Citations
No citations found. To add a citation, contact the admin at info@scimatic.org
Comments
No comments yet. Be the first to comment on this article.