An Integration of Archerfish Hunter Spotted Hyena Optimization and Improved ELM Classifier for Multicollinear Big Data Classification Tasks

Chidambaram, S.; Alam, M. M. Gowthul

doi:10.1007/s11063-021-10718-0

Cited by 8 publications

(4 citation statements)

References 36 publications

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“…For the best fused feature data, the testing set accuracy of the LSSVM model (85%) was higher than that of ELM (71.67%) and SVM (81.67%) reflecting the superiority of the LSSVM classification algorithm for processing walnut acoustic signals. Compared with LSSVM, ELM is slow and easy to fall into local values (Chidambaram & Gowthul Alam, 2022). SVM is more suitable for features of the same nature, such as continuous features, but has no better support for feature diversity (Fan & Sharma, 2021).…”

Section: Results and Analysismentioning

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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Section: Results and Analysismentioning

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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“…The authors use the MapReduce model to deal with the large-scale data set, where the presented model outperforms the compared methods with a maximum accuracy of 98%. Chidambaram and Gowthul Alam [104] proposed a novel MapReduce framework based on the improved Archerfish hunter spotted hyena optimization-based ELM (AHSHO-IELM) classifier for big data classification. Finally, a novel MapReduce based on parallel feature selection and ELM method was proposed in Hira and Bai [105] for classifying microarray cancer data.…”

Section: Mapreducementioning

confidence: 99%

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

Gelvez-Almeida,

Mora,

Barrientos

et al. 2024

MCA

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The randomization-based feedforward neural network has raised great interest in the scientific community due to its simplicity, training speed, and accuracy comparable to traditional learning algorithms. The basic algorithm consists of randomly determining the weights and biases of the hidden layer and analytically calculating the weights of the output layer by solving a linear overdetermined system using the Moore–Penrose generalized inverse. When processing large volumes of data, randomization-based feedforward neural network models consume large amounts of memory and drastically increase training time. To efficiently solve the above problems, parallel and distributed models have recently been proposed. Previous reviews of randomization-based feedforward neural network models have mainly focused on categorizing and describing the evolution of the algorithms presented in the literature. The main contribution of this paper is to approach the topic from the perspective of the handling of large volumes of data. In this sense, we present a current and extensive review of the parallel and distributed models of randomized feedforward neural networks, focusing on extreme learning machine. In particular, we review the mathematical foundations (Moore–Penrose generalized inverse and solution of linear systems using parallel and distributed methods) and hardware and software technologies considered in current implementations.

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“…A metaheuristic is a high‐level problem‐oriented structure 8,9 used to evolve the heuristic optimization algorithm by considering the set of procedures provided by it 10 . Because financial and periodic resources are always limited, optimization is an unavoidable process in all fields.…”

Section: Introductionmentioning

confidence: 99%

“…Due to the simplicity of the query expansion technique, it has been used in many applications such as e-commerce, 4 information filtering, 5 sports and games, 6 and medical. 7 A metaheuristic is a high-level problem-oriented structure 8,9 used to evolve the heuristic optimization algorithm by considering the set of procedures provided by it. 10 Because financial and periodic resources are always limited, optimization is an unavoidable process in all fields.…”

Section: Introductionmentioning

confidence: 99%

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

Srivel¹,

Kalaiselvi

Shanthi

et al. 2022

Concurrency and Computation

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Due to the technical words employed, which are primarily recognized by medical specialists, information retrieval in the medical area is sometimes described as sophisticated. Because of this, users frequently have trouble coming up with queries utilizing these medical phrases. However, this problem may be readily fixed by an information retrieval system that finds the pertinent terms that fit the user's query and automatically creates a ranking document using these keywords. To enhance the IR performance, the Automatic Query expansion method is applied by appending additional query terms for the medical domain. We propose a novel fuzzy-based Grasshopper Optimization Algorithm (GOA) based on automatic query expansion. This work is mainly focused on filtering the most relevant augmented query by utilizing the synchronization score of IR evidence like normalized term frequency, inverse document frequency, and normalization of document length. The main aim of this work is to identify the medical terms that appropriately match the user's queries. The GOA algorithm ranks the terms based on relevance and then identifies the terms with the maximum synchronization value. The documents formed using the optimal expanded query are classified into three types, namely totally relevant, moderately relevant, and marginally relevant. Besides, the comparison of the proposed work is carried out for different performance metrics like Mean-Average Precision, F-measure, Precision-recall, and Precision rank are evaluated and analyzed by using TREC-COVID, TREC Genomics 2007, and MEDLARs medical datasets for the proposed and some of the state-of-art works. For a total of 60 queries, the proposed model offers an F1-Score of 0.964, 0.959, and 0.968 for the MEDLARS, TREC Genomics, and TREC COVID19 datasets, respectively. The E1-score and Mean Reciprocal Rate (MRR) of the proposed model is 0.8 and 0.9 when evaluated using the TREC COVID19 dataset. Performance analyses show that the proposed approach outperforms the other automatic keyword expansion approaches in the medical domain.

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An Integration of Archerfish Hunter Spotted Hyena Optimization and Improved ELM Classifier for Multicollinear Big Data Classification Tasks

Cited by 8 publications

References 36 publications

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

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

A Review on Large-Scale Data Processing with Parallel and Distributed Randomized Extreme Learning Machine Neural Networks

An automation query expansion strategy for information retrieval by using fuzzy based grasshopper optimization algorithm on medical datasets

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