Hybrid of swarm intelligent algorithms in medical applications
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...
| Main Authors: | , , , , , |
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| Other Authors: | |
| Format: | Proceeding Paper |
| Language: | English English |
| Published: |
Springer Nature Singapore
2019
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| Subjects: | |
| Online Access: | http://irep.iium.edu.my/74294/ http://irep.iium.edu.my/74294/1/Proceedings%2Bof%2Bthe%2BInternational%2BConfere.pdf http://irep.iium.edu.my/74294/7/74294_Hybrid%20of%20Swarm%20Intelligent%20Algorithms%20in%20Medical%20Applications_scopus.pdf |
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| author | Abubakar, Adamu Haruna, Chiroma Abdullah Muaz, Sanah Ya'u Gital, Abdulsalam Baba Dauda, Ali Joda Usman, Muhammed |
| author2 | Abawajy, Jemal H. |
| author_facet | Abawajy, Jemal H. Abubakar, Adamu Haruna, Chiroma Abdullah Muaz, Sanah Ya'u Gital, Abdulsalam Baba Dauda, Ali Joda Usman, Muhammed |
| author_sort | Abubakar, Adamu |
| building | IIUM Repository |
| collection | Online Access |
| description | 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 |
| first_indexed | 2025-11-14T17:32:56Z |
| format | Proceeding Paper |
| id | iium-74294 |
| institution | International Islamic University Malaysia |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T17:32:56Z |
| publishDate | 2019 |
| publisher | Springer Nature Singapore |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | iium-742942019-10-29T14:24:38Z http://irep.iium.edu.my/74294/ Hybrid of swarm intelligent algorithms in medical applications Abubakar, Adamu Haruna, Chiroma Abdullah Muaz, Sanah Ya'u Gital, Abdulsalam Baba Dauda, Ali Joda Usman, Muhammed Q350 Information theory 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 Springer Nature Singapore Abawajy, Jemal H. Othman, Mohamed Ghazali, Rozaida Mat Deris, Mustafa Mahdin, Hairulnizam Herawan, Tutut 2019 Proceeding Paper PeerReviewed application/pdf en http://irep.iium.edu.my/74294/1/Proceedings%2Bof%2Bthe%2BInternational%2BConfere.pdf application/pdf en http://irep.iium.edu.my/74294/7/74294_Hybrid%20of%20Swarm%20Intelligent%20Algorithms%20in%20Medical%20Applications_scopus.pdf 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. https://link.springer.com/chapter/10.1007/978-981-13-1799-6_63 https://doi.org/10.1007/978-981-13-1799-6_63 |
| spellingShingle | Q350 Information theory Abubakar, Adamu Haruna, Chiroma Abdullah Muaz, Sanah Ya'u Gital, Abdulsalam Baba Dauda, Ali Joda Usman, Muhammed Hybrid of swarm intelligent algorithms in medical applications |
| title | Hybrid of swarm intelligent algorithms in medical applications |
| title_full | Hybrid of swarm intelligent algorithms in medical applications |
| title_fullStr | Hybrid of swarm intelligent algorithms in medical applications |
| title_full_unstemmed | Hybrid of swarm intelligent algorithms in medical applications |
| title_short | Hybrid of swarm intelligent algorithms in medical applications |
| title_sort | hybrid of swarm intelligent algorithms in medical applications |
| topic | Q350 Information theory |
| url | http://irep.iium.edu.my/74294/ http://irep.iium.edu.my/74294/ http://irep.iium.edu.my/74294/ http://irep.iium.edu.my/74294/1/Proceedings%2Bof%2Bthe%2BInternational%2BConfere.pdf http://irep.iium.edu.my/74294/7/74294_Hybrid%20of%20Swarm%20Intelligent%20Algorithms%20in%20Medical%20Applications_scopus.pdf |