Social-Spider Optimization-Based Artificial Neural Networks Training and Its Applications for Parkinson's Disease Identification

2014 ◽  
Author(s):  
L.A.M. Pereira ◽  
D. Rodrigues ◽  
P.B. Ribeiro ◽  
J.P. Papa ◽  
Silke A.T. Weber
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Artificial Neural Networks ◽  
Social Spider ◽  
Disease Identification ◽  
Social Spider Optimization ◽  
Artificial Neural

scholarly journals Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks

Sensors ◽  
2018 ◽  
Vol 19 (1) ◽  
pp. 16 ◽  
Author(s):  
Lucijano Berus ◽  
Simon Klancnik ◽  
Miran Brezocnik ◽  
Mirko Ficko
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Feature Selection ◽  
Artificial Neural Networks ◽  
Correlation Coefficient ◽  
Principal Component ◽  
Test Accuracy ◽  
Artificial Neural ◽  
Prediction Problems

In recent years, neural networks have become very popular in all kinds of prediction problems. In this paper, multiple feed-forward artificial neural networks (ANNs) with various configurations are used in the prediction of Parkinson’s disease (PD) of tested individuals, based on extracted features from 26 different voice samples per individual. Results are validated via the leave-one-subject-out (LOSO) scheme. Few feature selection procedures based on Pearson’s correlation coefficient, Kendall’s correlation coefficient, principal component analysis, and self-organizing maps, have been used for boosting the performance of algorithms and for data reduction. The best test accuracy result has been achieved with Kendall’s correlation coefficient-based feature selection, and the most relevant voice samples are recognized. Multiple ANNs have proven to be the best classification technique for diagnosis of PD without usage of the feature selection procedure (on raw data). Finally, a neural network is fine-tuned, and a test accuracy of 86.47% was achieved.

scholarly journals Handwritten dynamics assessment through convolutional neural networks: An application to Parkinson's disease identification

2018 ◽  
Vol 87 ◽  
pp. 67-77 ◽  
Author(s):  
Clayton R. Pereira ◽  
Danilo R. Pereira ◽  
Gustavo H. Rosa ◽  
Victor H.C. Albuquerque ◽  
Silke A.T. Weber ◽  
...  
Keyword(s):  
Neural Networks ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Convolutional Neural Networks ◽  
Disease Identification

Social Spider Algorithm for Training Artificial Neural Networks

2021 ◽  
pp. 1100-1119
Author(s):  
Burak Gülmez ◽  
Sinem Kulluk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Heuristic Algorithms ◽  
Experimental Studies ◽  
Benchmark Problems ◽  
Real World Data ◽  
Social Spider ◽  
Local Optima ◽  
Artificial Neural

Artificial neural networks (ANNs) are one of the most widely used techniques for generalization, classification, and optimization. ANNs are inspired from the human brain and perform some abilities automatically like learning new information and making new inferences. Back-propagation (BP) is the most common algorithm for training ANNs. But the processing of the BP algorithm is too slow, and it can be trapped into local optima. The meta-heuristic algorithms overcome these drawbacks and are frequently used in training ANNs. In this study, a new generation meta-heuristic, the Social Spider (SS) algorithm, is adapted for training ANNs. The performance of the algorithm is compared with conventional and meta-heuristic algorithms on classification benchmark problems in the literature. The algorithm is also applied to real-world data in order to predict the production of a factory in Kayseri and compared with some regression-based algorithms and ANNs models. The obtained results and comparisons on classification benchmark datasets have shown that the SS algorithm is a competitive algorithm for training ANNs. On the real-world production dataset, the SS algorithm has outperformed all compared algorithms. As a result of experimental studies, the SS algorithm is highly capable for training ANNs and can be used for both classification and regression.

Social Spider Algorithm for Training Artificial Neural Networks

2019 ◽  
Vol 6 (4) ◽  
pp. 32-49
Author(s):  
Burak Gülmez ◽  
Sinem Kulluk
Keyword(s):  
Neural Networks ◽  
Artificial Neural Networks ◽  
Real World ◽  
Heuristic Algorithms ◽  
Experimental Studies ◽  
Benchmark Problems ◽  
Real World Data ◽  
Social Spider ◽  
Local Optima ◽  
Artificial Neural

Artificial neural networks (ANNs) are one of the most widely used techniques for generalization, classification, and optimization. ANNs are inspired from the human brain and perform some abilities automatically like learning new information and making new inferences. Back-propagation (BP) is the most common algorithm for training ANNs. But the processing of the BP algorithm is too slow, and it can be trapped into local optima. The meta-heuristic algorithms overcome these drawbacks and are frequently used in training ANNs. In this study, a new generation meta-heuristic, the Social Spider (SS) algorithm, is adapted for training ANNs. The performance of the algorithm is compared with conventional and meta-heuristic algorithms on classification benchmark problems in the literature. The algorithm is also applied to real-world data in order to predict the production of a factory in Kayseri and compared with some regression-based algorithms and ANNs models. The obtained results and comparisons on classification benchmark datasets have shown that the SS algorithm is a competitive algorithm for training ANNs. On the real-world production dataset, the SS algorithm has outperformed all compared algorithms. As a result of experimental studies, the SS algorithm is highly capable for training ANNs and can be used for both classification and regression.

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