Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images
1
Faculty of Computer Science and Mathematics, University of Kufa, P.O. Box 21, Najaf 540011, Iraq
2
School of Engineering, Edith Cowan University, Joondalup, WA 6027, Australia
*
Author to whom correspondence should be addressed.
Academic Editor: Yudong Zhang
Computation 2021, 9(3), 35; https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030035
Received: 15 February 2021
/
Revised: 8 March 2021
/
Accepted: 12 March 2021
/
Published: 17 March 2021
(This article belongs to the Section Computational Engineering)
Accurate, fast, and automatic detection and classification of animal images is challenging, but it is much needed for many real-life applications. This paper presents a hybrid model of Mamdani Type-2 fuzzy rules and convolutional neural networks (CNNs) applied to identify and distinguish various animals using different datasets consisting of about 27,307 images. The proposed system utilizes fuzzy rules to detect the image and then apply the CNN model for the object’s predicate category. The CNN model was trained and tested based on more than 21,846 pictures of animals. The experiments’ results of the proposed method offered high speed and efficiency, which could be a prominent aspect in designing image-processing systems based on Type 2 fuzzy rules characterization for identifying fixed and moving images. The proposed fuzzy method obtained an accuracy rate for identifying and recognizing moving objects of 98% and a mean square error of 0.1183464 less than other studies. It also achieved a very high rate of correctly predicting malicious objects equal to recall = 0.98121 and a precision rate of 1. The test’s accuracy was evaluated using the F1 Score, which obtained a high percentage of 0.99052.
View Full-Text
Keywords:
image classification; animal identification; convolution neural network CNN; Mamdani fuzzy rules; gaussian membership
▼
Show Figures
Figure 1
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited
MDPI and ACS Style
Mohammed, H.R.; Hussain, Z.M. Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images. Computation 2021, 9, 35. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030035
AMA Style
Mohammed HR, Hussain ZM. Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images. Computation. 2021; 9(3):35. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030035
Chicago/Turabian StyleMohammed, Hind R., and Zahir M. Hussain 2021. "Hybrid Mamdani Fuzzy Rules and Convolutional Neural Networks for Analysis and Identification of Animal Images" Computation 9, no. 3: 35. https://0-doi-org.brum.beds.ac.uk/10.3390/computation9030035
Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.
