A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

Santosa, Paulus Insap; Pramunendar, Ricardus Anggi

doi:10.2478/cait-2022-0045

Cited by 2 publications

(5 citation statements)

References 33 publications

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“…The experiments included a comparative analysis of the optimum and maximal average results. The statistical analysis demonstrates that the P-value is amount 1.10E-08, suggesting a major disparity across the experimental groups of GA [19], GWO, MBO [20], and PSO [3] concerning the observed mean values. This discovery provides more evidence supporting the conclusions shown in Fig.…”

Section: Feature Selection Comparisonmentioning

confidence: 93%

“…This research aimed to assess and contrast the efficacy of two optimization strategies. However, the assessment process involves not only comparing the two approaches but also considering various additional optimization strategies to get the ideal feature outcomes, such as genetic algorithm (GA) [19] and swarm magnetic optimiser (SMO) [20].…”

Section: Methodsmentioning

confidence: 99%

“…Birdsongs are prioritised, while other noises are disregarded. In this stage, The YAMNet AudioSet ontology is utilised to extract the portion of the audio stream classified as birdsongs [6,19,20].…”

Section: Data Collection and Pre-processingmentioning

confidence: 99%

“…Grey wolf optimizer (GWO) was employed to identify the optimal features [19]. In GWO, the wolf mimics the leadership role and intelligent hunting behavior seen in nature, such as exploring, encircling, and assaulting prey.…”

Section: Feature Selection Based On Grey Wolf Optimizationmentioning

confidence: 99%

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Integrating Grey Wolf Optimizer for Feature Selection in Birdsong Classification Using K-Nearest Neighbours Algorithm

2023

IJIES

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This study aims to improve the classification accuracy of birdsongs by selecting the most pertinent features. This is important because birds are exceptional environmental regulators, but many species are endangered. The community can be assisted in distinguishing bird species and conserving the local environment if the classification is more precise. Nevertheless, because of disruptive noise and unfavorable qualities in the whispering of these bird species, feature selection focuses on enhancing performance accuracy. The use of the gray wolf optimizer (GWO) technique has been employed to identify the most optimum features from the data after outlier removal by the application of k-means clustering, reducing noise through YAMNet, and feature synthesis using gammatone cepstral coefficients (GFCC). This work utilizes the GWO algorithm to address the constraint management challenges associated with high-dimensional data in birdsong classification. The fitness functions used in this research are derived from the K-nearest neighbors (KNN) algorithm. The objective is to devise innovative ways for effectively managing constraints in the context of high-dimensional data. The number of features was reduced by more than 30.7% compared to the initial number of features and obtained an accuracy of 81.06%, as determined by the evaluation outcomes. This discovery improves precision by 4% and surpasses prior research. In summary, this work showcases the effectiveness of the optimization method, especially of GWO. It makes a valuable contribution to advancing a new workflow for analyzing high-dimensional data, specifically in enhancing the classification of birdsongs.

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Section: Feature Selection Comparisonmentioning

confidence: 93%

Section: Methodsmentioning

confidence: 99%

Section: Data Collection and Pre-processingmentioning

confidence: 99%

Section: Feature Selection Based On Grey Wolf Optimizationmentioning

confidence: 99%

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Integrating Grey Wolf Optimizer for Feature Selection in Birdsong Classification Using K-Nearest Neighbours Algorithm

2023

IJIES

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“…The gray wolf algorithm (Insap & Anggi, 2022; Karami et al, 2018) simulates the social class system and the hunting behavior of the gray wolf, and then uses the searching, surrounding and hunting behavior of the gray wolf in the hunting process to achieve the goal of optimization. Suppose the gray wolf population is

N

and the search area is

d

dimension, where the position of the i th gray wolf can be expressed as:

X_{i} = (X_{i 1}, X_{i 2}, X_{i 3}, \dots, X_{id})

.…”

Section: Methodsmentioning

confidence: 99%

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

Zhang,

Li,

Tian

et al. 2023

J Food Process Engineering

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Cracks in walnuts during processing and storage can adversely affect their quality and cause economic losses. To achieve efficient identification of cracked walnuts, this study proposed a method of walnut crack identification based on acoustic vibration and feature fusion. First, the sound signals of intact and cracked walnuts were collected using an acoustic signal acquisition system, and 44 time‐domain features, 13 frequency‐domain features, and 768 Mel spectrogram features (the number of pixel frequencies corresponding to the gray‐scale values of R, G, and B channels) of the sound signals were extracted. Then, the classification models of support vector machines (SVM), least squares support vector machines (LSSVM), and extreme learning machines (ELM) were established based on single class features data and fusion of different feature groups data respectively. The results indicated that the LSSVM model with the fusion of the three feature sets was optimal, with an accuracy of 85% in the testing set. Next, three feature selection methods were employed to reduce the dimensionality of the best fused feature data. Subsequently, the LSSVM classification model was established based on the feature selection data. Finally, arithmetic optimization algorithm (AOA), particle swarm optimization (PSO), and gray wolf optimization (GWO) were introduced to optimize the parameters c and of the classification model. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, with 95% accuracy in the testing set. This study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.Practical applicationsCracks in walnut during harvesting, transportation and peeling may lead to economic losses and food safety problems. Aiming at the difficulty and low accuracy of crack identification in Yunnan walnut, this paper proposed a method for crack detection based on acoustic vibration identification combine with feature fusion. The features of time domain, frequency domain and Mel spectrogram were extracted from the effective sound signal, and the features were fused. The influence of three feature selection methods, three models and three optimization algorithms on walnut sound signal recognition was analyzed and compared. The results indicated that the best classification model was VISSA‐IRIV‐GWO‐LSSVM, and the method proposed in this study provides theoretical support for the research and development of online detection equipment for walnut crack in Yunnan Yangbi.

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A Robust Feature Construction for Fish Classification Using Grey Wolf Optimizer

Cited by 2 publications

References 33 publications

Integrating Grey Wolf Optimizer for Feature Selection in Birdsong Classification Using K-Nearest Neighbours Algorithm

Integrating Grey Wolf Optimizer for Feature Selection in Birdsong Classification Using K-Nearest Neighbours Algorithm

Crack identification of Yunnan walnuts based on acoustic vibration and feature fusion

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