Abstract
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.
Highlights
Object recognition is an essential and fundamental task in computer vision, which finds or identifies objects in digital images
A combination of Mamdani Type-2 fuzzy system with adaptive rules with a Convolutional Neural Network convolutional neural networks (CNNs) is applied to identify and distinguish various animals using different datasets consisting of about 27,307 images
The results showed that the K-nearest neighbors (KNN) classifier achieved a high accuracy rate for 70% of samples compared to probabilistic neural network (PNN) in training (82.70, 78.09, respectively) using single block segmentation
Summary
Object recognition is an essential and fundamental task in computer vision, which finds or identifies objects in digital images. The problem with most recognition models is that they require image objects without background and correct categorical labels. This enables the model to predict the right title of the item [2]. Cat is the term in a level semantic hierarchy, which comes to mind and does not in any way happen by chance or accident [4,5]. Researchers put a lot of effort into other aspects of computer vision, with only a few developments in image recognition [6]. Detecting Fuzzy edge is conducted automatically for each image depending on the. Fuzzy Type-2 rules that chThoeoisneittiahlevamluaexsiomf tuhme Ugprpaedr imenemt vbearlsuheips.fuTnhcetiotnwsoarsetcehpossebnewloiwth ailnluexsp-erimentrate the proposed fuztsaezlpymaraeapttheployrdof.oaIrcnehaccothontifrmianastgd,etthuheseilnoegwdaegsretamstieosmtficbaaenlrasiphmpipraogfauecnh.c.tMionor’seoinvietri,atlhveamluuelstia-trheredsehteorldmOintesdu Performs Gaussiaalngobrlituhrmrinisgemwpitlhoyaed3f×or3fiknedrinngelthforershaonldins panudt uimtilaizgeed. in the settings of the Lower
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
