Creator of Knowledge
Information Resources Management Association
Advancing the Concepts & Practices of Information Resources Management in Modern Organizations

The Automatic Detection of Diabetes Based on Swarm of Fish

The Automatic Detection of Diabetes Based on Swarm of Fish
View Sample PDF
Author(s): Reda Mohamed Hamou (Dr. Moulay Tahar University of Saida, Algeria)
Copyright: 2018
Pages: 21
Source title: Handbook of Research on Biomimicry in Information Retrieval and Knowledge Management
Source Author(s)/Editor(s): Reda Mohamed Hamou (Dr. Tahar Moulay University of Saida, Algeria)
DOI: 10.4018/978-1-5225-3004-6.ch010


View The Automatic Detection of Diabetes Based on Swarm of Fish on the publisher's website for pricing and purchasing information.


Diabetes is a major health problem and a disease that can be very dangerous in developing and developed countries, and its incidence is increasing dramatically. In this chapter, the authors propose a system of automatic detection of diabetes based on a bioinspired model called a swarm of fish (fish swarm or AFSA). AFSA (artificial fish swarm algorithm) represents one of the best methods of optimization among swarm intelligence algorithms. This algorithm is inspired by the collective, the movement of fish and their different social behaviors in order to achieve their objectives. There are several parameters to be adjusted in the AFSA model. The visual step is very significant, given that the fish artificial essentially moves according to this parameter. Large parameter values increase the capacity of the global search algorithm, while small values tend to improve local search capability. This algorithm has many advantages, including high speed convergence, flexibility, and high accuracy. In this chapter, the authors evaluate their model of AFSA for the purpose of automatic detection of diabetes.

Related Content

Tlou Maggie Masenya, Collence Takaingenhamo Chisita. © 2022. 21 pages.
Mmaphuti Carron Teffo, Ignitia Motjolopane, Tlou Maggie Masenya. © 2022. 18 pages.
Neha Lata, Valentine Joseph Owan. © 2022. 17 pages.
Rexwhite Tega Enakrire, Joseph Kehinde Fasae. © 2022. 13 pages.
Madireng Monyela. © 2022. 18 pages.
Valentine Joseph Owan, Daniel Clement Agurokpon. © 2022. 16 pages.
Nkholedzeni Sidney Netshakhuma. © 2022. 18 pages.
Body Bottom