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MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification

Author

Listed:
  • Koon Meng Ang

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Wei Hong Lim

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Sew Sun Tiang

    (Faculty of Engineering, Technology and Built Environment, UCSI University, Kuala Lumpur 56000, Malaysia)

  • Abhishek Sharma

    (Department of Computer Science and Engineering, Graphic Era Deemed to be University, Dehradun 248002, India)

  • S. K. Towfek

    (Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura 35111, Egypt
    Computer Science and Intelligent Systems Research Center, Blacksburg, VA 24060, USA)

  • Abdelaziz A. Abdelhamid

    (Department of Computer Science, Faculty of Computer and Information Sciences, Ain Shams University, Cairo 11566, Egypt
    Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra 11961, Saudi Arabia)

  • Amal H. Alharbi

    (Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

  • Doaa Sami Khafaga

    (Department of Computer Science, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi Arabia)

Abstract

Convolutional neural networks (CNNs) have excelled in artificial intelligence, particularly in image-related tasks such as classification and object recognition. However, manually designing CNN architectures demands significant domain expertise and involves time-consuming trial-and-error processes, along with substantial computational resources. To overcome this challenge, an automated network design method known as Modified Teaching-Learning-Based Optimization with Refined Knowledge Sharing (MTLBORKS-CNN) is introduced. It autonomously searches for optimal CNN architectures, achieving high classification performance on specific datasets without human intervention. MTLBORKS-CNN incorporates four key features. It employs an effective encoding scheme for various network hyperparameters, facilitating the search for innovative and valid network architectures. During the modified teacher phase, it leverages a social learning concept to calculate unique exemplars that effectively guide learners while preserving diversity. In the modified learner phase, self-learning and adaptive peer learning are incorporated to enhance knowledge acquisition of learners during CNN architecture optimization. Finally, MTLBORKS-CNN employs a dual-criterion selection scheme, considering both fitness and diversity, to determine the survival of learners in subsequent generations. MTLBORKS-CNN is rigorously evaluated across nine image datasets and compared with state-of-the-art methods. The results consistently demonstrate MTLBORKS-CNN’s superiority in terms of classification accuracy and network complexity, suggesting its potential for infrastructural development of smart devices.

Suggested Citation

  • Koon Meng Ang & Wei Hong Lim & Sew Sun Tiang & Abhishek Sharma & S. K. Towfek & Abdelaziz A. Abdelhamid & Amal H. Alharbi & Doaa Sami Khafaga, 2023. "MTLBORKS-CNN: An Innovative Approach for Automated Convolutional Neural Network Design for Image Classification," Mathematics, MDPI, vol. 11(19), pages 1-44, September.
  • Handle: RePEc:gam:jmathe:v:11:y:2023:i:19:p:4115-:d:1250331
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    Cited by:

    1. Wenbo Zhu & Yongcong Hu & Zhengjun Zhu & Wei-Chang Yeh & Haibing Li & Zhongbo Zhang & Weijie Fu, 2024. "Searching by Topological Complexity: Lightweight Neural Architecture Search for Coal and Gangue Classification," Mathematics, MDPI, vol. 12(5), pages 1-24, March.

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