A Review of Artificial Fish Swarm Optimization Methods and Applications

Neshat, Mehdi; Adeli, Ali; Sepidnam, Ghodrat; Sargolzaei, Mehdi; Toosi, Adel Najaran

doi:10.21307/ijssis-2017-474

Cited by 43 publications

(21 citation statements)

References 52 publications

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“…The comparison is made between the proposed Modified Convolutional neural network based Liver disease prediction system (MCNN-LDPS) and the existing methodologies Multi layer perceptron neural network (MLPNN) (17) .…”

Section: Resultsmentioning

confidence: 99%

Accurate liver disease prediction system using convolutional neural network

Jeyalakshmi¹,

Rangaraj²

2021

IJST

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Objectives: To introduce the technique which can ensure the accurate and reliable prediction of liver disease by adapting the deep learning technique. Methods: In this work Modified Convolutional Neural Network based Liver Disease Prediction System (MCNN-LDPS) is introduced for the accurate liver disease prediction outcome. In the proposed research work, Dimensionality reduction is carried out using Modified Principal Component Analysis. Optimal feature selection is carried out using Score based Artificial Fish Swarm Algorithm (SAFSA). In SAFSA algorithm, information gain and entropy values are taken as input values which proved accurate outcome. This research method has been analysed over Indian Liver patient dataset. Findings: The analysis of the research work proves that the proposed method MCNN-LDPS obtains better outcome in terms of increased accuracy, precision. Here comparison analysis proved that MCNN-LDPS obtains 4.05% increased accuracy, 21.23% F-measure, 4.22% precision and 34.26% recall. This research method has been compared with the existing Multi layer Perceptron Neural Network (MLPNN) for the performance analysis. Novelty: The major limitation of CNN is its inability to encode Orientational and relative spatial relationships, view angle. CNN do not encode the position and orientation of data. Lack of ability to be spatially invariant to the input data sample. This is resolved in this research work by combining the genetic algorithm with the CNN method.

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Section: Resultsmentioning

confidence: 99%

Accurate liver disease prediction system using convolutional neural network

Jeyalakshmi¹,

Rangaraj²

2021

IJST

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“…First of all, input variables with their membership functions are introduced. Second step is to describe the output variable with its membership function, [22].…”

Section: Fuzzy Modelingmentioning

confidence: 99%

Adhesion Stress Prediction in Polymer Concrete Using Fuzzy Logic Model

Rusu¹,

Bejan²,

Bărbuţă³

et al. 2019

Mat.Plast.

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In recent years, in many areas of civil engineering applications, fuzzy logic models have been used. The aim of this paper is to develop a fuzzy logic model to predict the adhesion stress in polymer concrete. Availability of experimental data was required to develop the relationship between the mixture variables of polymer concrete and its measured properties. The basic parameters considered in this study were epoxy resin, silica fume, aggregate sort I and aggregate sort II dosage. Using Mamdani fuzzy model, with the said parameters, we effectively predicted the adhesion stress in polymer concrete accurately by taking into account the parameters of the problem. In these circumstances, data can be acquired in a short period of time without wasting material, with decreased design cost, all this without attempting experiments and thus saving time. This study showed that the model used has a good prediction and generalization capacity with acceptable errors.

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“…For that purpose, modified-PSO (Particle Swarm Optimization) proposed by Jatmiko et al [19] or artificial swarm optimization as explained in [20] could be used to search the best kernel parameters before being improved using SA-SVM.…”

Section: A Window Length Experimentsmentioning

confidence: 99%