Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm

In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages...

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Main Authors: Ahmad Ihsan, Khairul Muttaqin, Rahmatul Fajri, Mursyidah Mursyidah, Islam Md Rizwanul Fattah
Format: Article
Language:English
Published: MDPI AG 2023-11-01
Series:Journal of Imaging
Subjects:
Online Access:https://www.mdpi.com/2313-433X/9/12/263
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author Ahmad Ihsan
Khairul Muttaqin
Rahmatul Fajri
Mursyidah Mursyidah
Islam Md Rizwanul Fattah
author_facet Ahmad Ihsan
Khairul Muttaqin
Rahmatul Fajri
Mursyidah Mursyidah
Islam Md Rizwanul Fattah
author_sort Ahmad Ihsan
collection DOAJ
description In this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA’s performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.
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spelling doaj.art-488d492b19634f82a6a7e4b40004a9542023-12-22T14:18:10ZengMDPI AGJournal of Imaging2313-433X2023-11-0191226310.3390/jimaging9120263Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm AlgorithmAhmad Ihsan0Khairul Muttaqin1Rahmatul Fajri2Mursyidah Mursyidah3Islam Md Rizwanul Fattah4Department of Informatics, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, IndonesiaDepartment of Informatics, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, IndonesiaDepartment of Chemistry, Faculty of Engineering, Universitas Samudra, Langsa 24416, Aceh, IndonesiaDepartment of Multimedia Engineering Technology, Politeknik Negeri Lhokseumawe, Kota Lhokseumawe 24301, Aceh, IndonesiaCentre for Technology in Water and Wastewater, School of Civil and Environmental Engineering, Faculty of Engineering and IT, University of Technology Sydney, Ultimo, NSW 2007, AustraliaIn this paper, we introduce a new and advanced multi-feature selection method for bacterial classification that uses the salp swarm algorithm (SSA). We improve the SSA’s performance by using opposition-based learning (OBL) and a local search algorithm (LSA). The proposed method has three main stages, which automate the categorization of bacteria based on their unique characteristics. The method uses a multi-feature selection approach augmented by an enhanced version of the SSA. The enhancements include using OBL to increase population diversity during the search process and LSA to address local optimization problems. The improved salp swarm algorithm (ISSA) is designed to optimize multi-feature selection by increasing the number of selected features and improving classification accuracy. We compare the ISSA’s performance to that of several other algorithms on ten different test datasets. The results show that the ISSA outperforms the other algorithms in terms of classification accuracy on three datasets with 19 features, achieving an accuracy of 73.75%. Additionally, the ISSA excels at determining the optimal number of features and producing a better fit value, with a classification error rate of 0.249. Therefore, the ISSA method is expected to make a significant contribution to solving feature selection problems in bacterial analysis.https://www.mdpi.com/2313-433X/9/12/263bacterial colonymulti-feature selectionclassification accuracyimproved salp swarm algorithm
spellingShingle Ahmad Ihsan
Khairul Muttaqin
Rahmatul Fajri
Mursyidah Mursyidah
Islam Md Rizwanul Fattah
Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
Journal of Imaging
bacterial colony
multi-feature selection
classification accuracy
improved salp swarm algorithm
title Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
title_full Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
title_fullStr Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
title_full_unstemmed Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
title_short Innovative Bacterial Colony Detection: Leveraging Multi-Feature Selection with the Improved Salp Swarm Algorithm
title_sort innovative bacterial colony detection leveraging multi feature selection with the improved salp swarm algorithm
topic bacterial colony
multi-feature selection
classification accuracy
improved salp swarm algorithm
url https://www.mdpi.com/2313-433X/9/12/263
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