WBA-DNN: A hybrid weight bat algorithm with deep neural network for classification of poisonous and harmful wild plants

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dc.contributor.authorIbrahim, H. Mohammed
dc.date.accessioned2024-02-23T14:02:36Z
dc.date.available2024-02-23T14:02:36Z
dc.date.issued2021
dc.departmentNEÜen_US
dc.description.abstractIn this study, a deep neural network structure is proposed for the classification of poisonous and harmful wild plants in fields. Furthermore, a novel metaheuristic weight bat-inspired algorithm is developed for training the devised deep neural network. The harmful wild plant dataset is obtained from the agricultural field contains the purslane plants and harmful plants. The feature of the plants extract with mean absolute deviation, the dataset is consists of four features, two classes information, and contains 3452 samples, one-third of these samples are classified as purslane plants. Firstly, the performance of the proposed weight bat-inspired algorithm based deep neural network is evaluated by using ten UCI data repository datasets and the obtained results are compared with state-of-the-art classification algorithms. Then, classification of the harmful wild plant dataset is performed, results of the proposed weight bat-inspired algorithm based deep neural network are compared to two categories of classification algorithms, including (i) the most well-known classification algorithms, including decision tree, k-nearest neighbors, backpropagation based deep neural network, naive bayes, random forest, AdaBoost, and support vector machine; (ii) optimization-based deep neural network, including bat algorithm, genetic algorithm, particle swarm optimization, equilibrium optimizer, A bio-inspired based optimization algorithm, and salp swarm algorithm. The proposed weight bat-inspired algorithm based deep neural network has outperformed the most well-known classification algorithms and optimization-based deep neural network in terms of CA, FPR, REC, PRE, TNR, AUC, F1-M, and F-M by 0.980, 0.020, 0.980, 0.980, 0.980, 0.980, 0.980, and 0.980, respectively. The highest performance has indicated that the training of deep neural networks by the weight bat-inspired algorithm is proven to be a very effective and useful tool for the classification of poisonous and harmful wild plants.en_US
dc.identifier.doi10.1016/j.compag.2021.106478
dc.identifier.issn0168-1699
dc.identifier.issn1872-7107
dc.identifier.scopus2-s2.0-85118737920en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.compag.2021.106478
dc.identifier.urihttps://hdl.handle.net/20.500.12452/11749
dc.identifier.volume190en_US
dc.identifier.wosWOS:000703125000002en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherElsevier Sci Ltden_US
dc.relation.ispartofComputers And Electronics In Agricultureen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectClassificationen_US
dc.subjectDeep Learningen_US
dc.subjectPoisonous Wild Plantsen_US
dc.subjectWeight Bat Algorithmen_US
dc.titleWBA-DNN: A hybrid weight bat algorithm with deep neural network for classification of poisonous and harmful wild plantsen_US
dc.typeArticleen_US

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