Evolving Artificial Neural Networks Using Butterfly Optimization Algorithm for Data Classification

Jalali, Seyed Mohammad Jafar; Ahmadian, Sajad; Kebria, Parham M.; Khosravi, Abbas; Lim, Chee Peng; Nahavandi, Saeid

doi:10.1007/978-3-030-36708-4_49

Cited by 42 publications

(18 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries. For future works, the authors suggest the researchers categorize the statistical models including regression and time series models and artificial intelligence models that can be fitted on Covid-19 datasets [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , [41] , [42] , [43] , [44] , [45] , [46] , [47] , [48] , [49] , [50] , [51] , [52] , [53] , [54] , [55] , [56] , [57] , [58] , [59] , [60] , [61] .…”

Section: Discussionmentioning

confidence: 99%

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

Mahmoudi

Băleanu

Mansor

et al. 2020

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

The numbers of confirmed cases of new coronavirus (Covid-19) are increased daily in different countries. To determine the policies and plans, the study of the relations between the distributions of the spread of this virus in other countries is critical. In this work, the distributions of the spread of Covid-19 in Unites States America, Spain, Italy, Germany, United Kingdom, France, and Iran were compared and clustered using fuzzy clustering technique. At first, the time series of Covid-19 datasets in selected countries were considered. Then, the relation between spread of Covid-19 and population's size was studied using Pearson correlation. The effect of the population's size was eliminated by rescaling the Covid-19 datasets based on the population's size of USA. Finally, the rescaled Covid-19 datasets of the countries were clustered using fuzzy clustering. The results of Pearson correlation indicated that there were positive and significant between total confirmed cases, total dead cases and population's size of the countries. The clustering results indicated that the distribution of spreading in Spain and Italy was approximately similar and differed from other countries.

show abstract

Section: Discussionmentioning

confidence: 99%

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

Mahmoudi

Băleanu

Mansor

et al. 2020

Chaos, Solitons & Fractals

View full text Add to dashboard Cite

show abstract

“…Since January until now (18 April 2020), this epidemic changed to pandemic in the whole of the world and the number of patients with this disease and the number of deaths due to this disease is increasing day by day in most of countries, particularly in France [2] , [3] , [4] , [5] , Germany [6] , Iran [7] , Italy [8] , [9] , [10] , Spain [11] , United Kingdom [12] , [13] , [14] , and Unites States America [15] . The modeling of the different natural phenomena were studied using different data analysis techniques, such as statistical and mathematical modeling containing time series analysis, regression modeling, optimization and numerical analysis [16] , [17] , [18] , [19] , [20] , [21] , [22] , [23] , [24] , [25] , [26] , [27] , [28] , [29] , [30] , [31] , [32] , [33] , [34] , [35] , [36] , [37] , [38] , [39] , [40] , and artificial intelligence modeling containing deep learning, machine learning and clustering [41] , [42] , [43] , [44] , [45] , [46] . Because of different social, economic and environmental effects of Covid-19, it is critical to study and compare the spread rate of this disease in different countries.…”

Section: Introductionmentioning

confidence: 99%

Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries

Mahmoudi

Heydari

Qasem

et al. 2021

Alexandria Engineering Journal

141

View full text Add to dashboard Cite

In this research, the number of patients with Covid-19 and the number of deaths due to this disease in France, Germany, Iran, Italy, Spain, United Kingdom, and Unites States America are considered. First, the relations between the considered countries are studied using Pearson’s correlation. Then, based on the spread rate of Covid-19, these countries are categorized using principal component analysis.

show abstract

“…[35] used BOA for vehicle designs. [36] used BOA for finding the optimal biases and weights of artificial neural networks (ANN) and used it for Data Classification. [37] used BOA for stock forecasting.…”

Section: Related Workmentioning

confidence: 99%

Dynamic Butterfly Optimization Algorithm for Feature Selection

et al. 2020

View full text Add to dashboard Cite

Feature selection represents an essential pre-processing step for a wide range of Machine Learning approaches. Datasets typically contain irrelevant features that may negatively affect the classifier performance. A feature selector can reduce the number of these features and maximise the classifier accuracy. This paper proposes a Dynamic Butterfly Optimization Algorithm (DBOA) as an improved variant to Butterfly Optimization Algorithm (BOA) for feature selection problems. BOA represents one of the most recently proposed optimisation algorithms. BOA has demonstrated its ability to solve different types of problems with competitive results compared to other optimisation algorithms. However, the original BOA algorithm has problems when optimising high-dimensional problems. Such issues include stagnation into local optima and lacking solutions diversity during the optimisation process. To alleviate these weaknesses of the original BOA, two significant improvements are introduced in the original BOA: the development of a Local Search Algorithm Based on Mutation (LSAM) operator to avoid local optima problem and the use of LSAM to improve BOA solutions diversity. To demonstrate the efficiency and superiority of the proposed DBOA algorithm, 20 benchmark datasets from the UCI repository are employed. The classification accuracy, the fitness values, the number of selected features, the statistical results, and convergence curves are reported for DBOA and its competing algorithms. These results demonstrate that DBOA significantly outperforms the comparative algorithms on the majority of the used performance metrics. INDEX TERMS Butterfly optimisation algorithm, feature selection, local search algorithm based on mutation.

show abstract

Evolving Artificial Neural Networks Using Butterfly Optimization Algorithm for Data Classification

Cited by 42 publications

References 22 publications

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

Fuzzy clustering method to compare the spread rate of Covid-19 in the high risks countries

Principal component analysis to study the relations between the spread rates of COVID-19 in high risks countries

Dynamic Butterfly Optimization Algorithm for Feature Selection

Contact Info

Product

Resources

About