Table of Content

Open Access iconOpen Access

ARTICLE

Personalized Nutrition Recommendation for Diabetic Patients Using Optimization Techniques

Bhavithra Janakiraman1,*, Saradha Arumugam2

1 Department of Computer Science and Engineering, Dr. Mahalingam College of Engineering and Technology, Pollachi, Tamilnadu 642003, India
2 Department of Computer Science and Engineering, Institute of Road and Transport Technology, Erode, Tamilnadu 638316, India

* Corresponding Author: Bhavithra Janakiraman, email

Intelligent Automation & Soft Computing 2020, 26(2), 269-280. https://doi.org/10.31209/2019.100000150

Abstract

Personalization in recommendation system has been emerging as the most predominant area in service computing. Collaborative filtering and content based approaches are two major techniques applied for recommendation. However, to improve the accuracy and enhance user satisfaction, optimization techniques such as Ant Colony and Particle Swarm Optimization were analyzed in this paper. For theoretical analysis, this paper investigates web page recommender system. For experimentation, Diabetic patient’s health records were investigated and recommendation algorithms are applied to suggest appropriate nutrition for improving their health. Experiment result shows that Particle Swarm Optimization outperforms other traditional methods with improved performance and accuracy.

Keywords


Cite This Article

APA Style
Janakiraman, B., Arumugam, S. (2020). Personalized nutrition recommendation for diabetic patients using optimization techniques. Intelligent Automation & Soft Computing, 26(2), 269-280. https://doi.org/10.31209/2019.100000150
Vancouver Style
Janakiraman B, Arumugam S. Personalized nutrition recommendation for diabetic patients using optimization techniques. Intell Automat Soft Comput . 2020;26(2):269-280 https://doi.org/10.31209/2019.100000150
IEEE Style
B. Janakiraman and S. Arumugam, "Personalized Nutrition Recommendation for Diabetic Patients Using Optimization Techniques," Intell. Automat. Soft Comput. , vol. 26, no. 2, pp. 269-280. 2020. https://doi.org/10.31209/2019.100000150



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
  • 1665

    View

  • 1452

    Download

  • 0

    Like

Share Link