Hybrid of swarm intelligent algorithms in medical applications

Abubakar, Adamu and Haruna, Chiroma and Abdullah Muaz, Sanah and Ya'u Gital, Abdulsalam and Baba Dauda, Ali and Joda Usman, Muhammed (2019) Hybrid of swarm intelligent algorithms in medical applications. In: The Second International Conference on Advanced Data and Information Engineering (DaEng-2015), 25-26 Apr 2015, Bali, Indonesia.

	PDF (conf paper) - Published Version Restricted to Registered users only Download (18MB) | Request a copy
Preview	PDF (scopus) Download (113kB) | Preview

Abstract

In this paper, we designed a hybrid of swarm intelligence algorithms to diagnose hepatitis, breast tissue, and dermatology conditions in patients with such infection. The effectiveness of hybrid swarm intelligent algorithms was studied since no single algorithm is effective in solving all types of problems. In this study, feed forward and Elman recurrent neural network (ERN) with swarm intelligent algorithms is used for the classification of the mentioned diseases. The capabilities of six (6) global optimization learning algorithms were studied and their performances in training as well as testing were compared. These algorithms include: hybrid of Cuckoo Search algorithm and Levenberg-Marquardt (LM) (CSLM), Cuckoo Search algorithm (CS) and backpropagation (BP) (CSBP), CS and ERN (CSERN), Artificial Bee Colony (ABC) and LM (ABCLM), ABC and BP (ABCBP), Genetic Algorithm (GA) and BP (GANN). Simulation comparative results indicated that the classification accuracy and run time of the CSLM outperform the CSERN, GANN, ABCBP, ABCLM, and CSBP in the breast tissue dataset. On the other hand, the CSERN performs better than the CSLM, GANN, ABCBP, ABCLM, and CSBP in both the

Item Type:	Conference or Workshop Item (Plenary Papers)
Additional Information:	7132/74294
Uncontrolled Keywords:	Hepatitis · Breast tissue · Dermatology · Genetic algorithm · Cuckoo search algorithm · Neural network · Artificial bee colony
Subjects:	Q Science > Q Science (General) > Q300 Cybernetics > Q350 Information theory
Kulliyyahs/Centres/Divisions/Institutes (Can select more than one option. Press CONTROL button):	Kulliyyah of Information and Communication Technology > Department of Computer Science Kulliyyah of Information and Communication Technology > Department of Computer Science
Depositing User:	Dr Adamu Abubakar
Date Deposited:	29 Oct 2019 22:22
Last Modified:	29 Oct 2019 22:24
URI:	http://irep.iium.edu.my/id/eprint/74294

Actions (login required)

View Item