Internet Service Classification Using Swarm-Intelligent K-Nearest Neighbour Algorithm

Authors

  • Harshita Kaushik Assistant Professor, Department of Computer Science & Engineering, Vivekananda Global University, Jaipur, India
  • Shambhu Bhardwaj Associate Professor, College of Computing Science and Information Technology, Teerthanker Mahaveer University, Moradabad, Uttar Pradesh, India
  • Sovers Singh Bisht Assistant Professor & Dy. HoD, Department of Data Science (DS), Noida Institute of Engineering and Technology, Greater Noida, Uttar Pradesh, India
  • Kalaiarasan C. Associate Dean, Department of Computer Science and Engineering, Presidency University, Bangalore, India

Keywords:

Internet data, classification, Dragonfly Optimised K-Nearest Neighbour (DF KNN), swarm-intelligent

Abstract

Effective ways for identifying and organizing this data are now essential due to the Internet's explosive growth and the growing amount of data produced every day. We provide a novel Dragonfly Optimised K-Nearest Neighbour (DF-KNN) algorithm for classifying internet data in this work. The DF-KNN method combines the KNN classifier and DF, a swarm-intelligent optimization algorithm drawn from dragonfly swarms as its primary source of inspiration. By using the DF to find the KNN algorithm's ideal parameter values, categorization accuracy, and efficiency are improved. We ran tests on an actual internet database to measure how well the suggested strategy performed. We contrasted the DF-KNN algorithm's classification outcomes with those of more established methods. The results of the experiments show that the DF-KNN technique performs superior than the conventional KNN algorithm and classifies internet data with more accuracy and efficiency.

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Published

11.07.2023

How to Cite

Kaushik, H. ., Bhardwaj, S. ., Bisht, S. S. ., & C., K. (2023). Internet Service Classification Using Swarm-Intelligent K-Nearest Neighbour Algorithm. International Journal of Intelligent Systems and Applications in Engineering, 11(8s), 192–197. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/3040

Issue

Vol. 11 No. 8s (2023): Advances on Machine Learning and Artificial Intelligence in Computer Technology

Section

Research Article

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