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Deep Neural Network Concepts for Classification using Convolutional Neural Network: A Systematic Review and Evaluation

Author

Listed:
  • Mohammad Gouse Galety

    (Department of Information Technology, Catholic University in Erbil, Erbil, Kurdistan Region, Iraq)

  • Firas Husham Al Mukthar

    (Department of Information Technology, Catholic University in Erbil, Erbil, Kurdistan Region, Iraq)

  • Rebaz Jamal Maaroof

    (Department of Information Technology, Catholic University in Erbil, Erbil, Kurdistan Region, Iraq)

  • Fanar Rofoo

    (Department of Information Technology, Catholic University in Erbil, Erbil, Kurdistan Region, Iraq)

Abstract

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.

Suggested Citation

  • Mohammad Gouse Galety & Firas Husham Al Mukthar & Rebaz Jamal Maaroof & Fanar Rofoo, 2021. "Deep Neural Network Concepts for Classification using Convolutional Neural Network: A Systematic Review and Evaluation," Technium, Technium Science, vol. 3(8), pages 58-70, September.
    • Handle: RePEc:tec:techni:ojs:4554
    DOI: 10.47577/technium.v3i8.4554
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