Int J Performability Eng ›› 2023, Vol. 19 ›› Issue (5): 342-349.doi: 10.23940/ijpe.23.05.p6.342349

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State of the Art Convolutional Neural Networks

Shreshtha Singh* and Arun Sharma   

  1. Indira Gandhi Delhi Technical University for Women, Delhi, India
  • Contact: * E-mail address:

Abstract: Convolutional Neural Networks (CNNs) have become a powerful tool for a wide range of computer vision tasks, such as image classification, object detection, and semantic segmentation. This paper provides an overview of the fundamental concepts and architectures of CNNs, highlighting recent advancements and applications. We discuss the key components of CNNs including convolutional layers, pooling layers, and activation functions. The journey of CNN from its genesis to its evolution to the one we are familiar with today is covered. Although CNN is highly capable on its own many researchers have benefitted by hybridizing CNN with quality models. Therefore, we explore various tailor-made hybrid applications of CNN that are designed to solve very specific problems. We also discuss various innovative fields of applications of CNN and discuss the wide range of fields wherein CNN is performing surprisingly well. The paper concludes with future challenges and endeavors with respect to CNN.

Key words: convolution neural network (CNN), Deep learning applications, Deep neural network, GPU