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All Journal International Journal of Intelligent Systems and Applications in Engineering
Kahramanli, Humar
Advanced Technology and Science (ATScience)
Computer Science & IT

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Training Product-Unit Neural Networks with Cuckoo Optimization Algorithm for Classification Kahramanli, Humar
International Journal of Intelligent Systems and Applications in Engineering Vol 5, No 4 (2017)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2017533900

Abstract

In this study Product-Unit Neural Networks (PUNN) which is the special class of feed-forward neural network, has been trained using Cuckoo Optimization algorithm. The trained model has been applied to two classification problem. BUPA liver disorders and Habermans Survival Data have been used for application. The both data have been obtained from UCI machine Learning Repository. For comparison Backpropagation (BP) and Levenberg–Marquardt (LM) algorithms have been used. The application results show that the PUNN trained with Cuckoo Optimization algorithm is achieved better classification accuracy.
Determining the Carrot Volume via Radius and Length Using ANN Örnek, Mustafa Nevzat; Kahramanli, Humar
International Journal of Intelligent Systems and Applications in Engineering Vol 6, No 2 (2018)
Publisher : Advanced Technology and Science (ATScience)

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.18201/ijisae.2018642081

Abstract

In this study a total of 464 carrots were taken from Kaşınhanı, where the most carrots are produces in Turkey. The length and radiuses with an interval of 5 cm and volume were measured and recorded. Three different Artificial Neural Network models: BP, LM and PUNN were designed for predicting the carrot volume. To assess the success of the system, statistical measures such as Root Mean Squared Error, Mean Absolute Error and R2 were used. The results were showed that all three methods are successful in this problem, while LM and PUNN seems bit.
Co-Authors Örnek, Mustafa Nevzat