Open AccessDeep Neural Network Concepts for Classification using Convolutional Neural Network: A Systematic Review and Evaluation
Author(s) -
Mohammad Gouse Galety,
Firas Hussam Al Mukthar,
Rebaz Maaroof,
Fanar Fareed Hanna Rofoo
Publication year - 2021
Publication title -
technium
Language(s) - English
Resource type - Journals
ISSN - 2668-778X
DOI - 10.47577/technium.v3i8.4554
Subject(s) - computer science , artificial intelligence , convolutional neural network , machine learning , deep learning , artificial neural network , construct (python library) , categorization , process (computing) , nervous system network models , margin (machine learning) , set (abstract data type) , time delay neural network , types of artificial neural networks , programming language , operating system
In recent years, artificial intelligence (AI) has piqued the curiosity of researchers. Convolutional Neural Networks (CNN) is a deep learning (DL) approach commonly utilized to solve problems. In standard machine learning tasks, biologically inspired computational models surpass prior types of artificial intelligence by a considerable margin. The Convolutional Neural Network (CNN) is one of the most stunning types of ANN architecture. The goal of this research is to provide information and expertise on many areas of CNN. Understanding the concepts, benefits, and limitations of CNN is critical for maximizing its potential to improve image categorization performance. This article has integrated the usage of a mathematical object called covering arrays to construct the set of ideal parameters for neural network design due to the complexity of the tuning process for the correct selection of the parameters used for this form of neural network.