Essential Building Blocks of Convolutional Neural Network for Deep Learning

References

[1] B. Avijeet, Top 10 Deep Learning Algorithms you Should Know in 2022, https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-algorithm, 2021

[2] R. Yamashita, M. Nishio, R.K.G Do, et al. Convolutional neural networks: an overview and application in radiology. Insights Imaging 9, 611–629 (2018). https://doi.org/10.1007/s13244-018-0639-9

[3] Y. Tang, “Deep learning using linear support vector machines,” arXiv prprint arXiv:1306.0239, 2013.

[4] T. Guo, J. Dong, H. Li and Y. Gao, "Simple convolutional neural network on image classification," 2017 IEEE 2nd International Conference on Big Data Analysis (ICBDA), 2017, pp. 721-724, doi: 10.1109/ICBDA.2017.8078730.

[5] A. A. M. Al-Saffar, H. Tao and M. A. Talab, "Review of deep convolution neural network in image classification," 2017 International Conference on Radar, Antenna, Microwave, Electronics, and Telecommunications (ICRAMET), 2017, pp. 26-31, doi: 10.1109/ICRAMET.2017.8253139.

[6] M. N. Islam, T. T. Inan, S. Rafi, S. S. Akter, I. H. Sarker and A. K. M. N. Islam, "A Systematic Review on the Use of AI and ML for Fighting the COVID-19 Pandemic," in IEEE Transactions on Artificial Intelligence, vol. 1, no. 3, pp. 258-270, Dec. 2020, doi: 10.1109/TAI.2021.3062771.

[7] M. Mishra and M. Srivastava, "A view of Artificial Neural Network," 2014 International Conference on Advances in Engineering & Technology Research (ICAETR - 2014), 2014, pp. 1-3, doi: 10.1109/ICAETR.2014.7012785.

[8] C. Zhang and W. Xu, "Neural networks: Efficient implementations and applications," 2017 IEEE 12th International Conference on ASIC (ASICON), 2017, pp. 1029-1032, doi: 10.1109/ASICON.2017.8252654.

[9] R. E. Uhrig, "Introduction to artificial neural networks," Proceedings of IECON '95 - 21st Annual Conference on IEEE Industrial Electronics, 1995, pp. 33-37 vol.1, doi: 10.1109/IECON.1995.483329.

[10] T. Maimaitiaili, L. Dai. Deep neural network based uyghur large vocabulary continuous speech recognition Journal of Data Acquisition and Processing, 2015, 30 (2): 365-371.

[11] R. Chauhan, K. K. Ghanshala and R. C. Joshi, "Convolutional Neural Network (CNN) for Image Detection and Recognition," 2018 First International Conference on Secure Cyber Computing and Communication (ICSCCC), 2018, pp. 278-282, doi: 10.1109/ICSCCC.2018.8703316.

[12] M. D. Zeiler and Fergus, “Visualizing and understanding convolutional networks”. European Conference on Computer Vision, vol 8689. Springer, Cham, pp. 818-833, 2014.

[13] Hyeonuk Kim, Jaehyeong Sim, Yeongjae Choi and Lee-Sup Kim, "A kernel decomposition architecture for binary-weight Convolutional Neural Networks," 2017 54th ACM/EDAC/IEEE Design Automation Conference (DAC), 2017, pp. 1-6, doi: 10.1145/3061639.3062189.

[14] A. Ghosh, A. Sufian, F. Sultana, A. Chakrabarti, “Fundamental Concept of Convolutional Neural Network”,2020, DOI: 10.1007/978-3-030-32644-9_36

[15] V. Badrinarayanan, A. Kendall, and R. Cipolla. Segnet: A deep convolutional encoder-decoder architecture for image segmentation. CoRR, abs/1511.00561, 2015.

[16] Binod Suman Academy, “Convolutional Neural Networks | CNN | Kernel | Stride | Padding | Pooling | Flatten | Formula”, https://www.youtube.com/watch?v=Y1qxI-Df4Lk&t=13s

@article{1703854,
      author = {Olasunkanmi Felix Oyadokun, Danjuma Shadrach Sunday, Haruna Bege, Kolawole Samuel F},
      title = {Essential Building Blocks of Convolutional Neural Network for Deep Learning},
      journal = {Iconic Research And Engineering Journals},
      year = {2022},
      volume = {6},
      number = {4},
      pages = {89-94},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1703854.pdf},
      abstract = {Computations for Deep Learning (DL) are designed to mimic the functionality of the neurons found in the brains of mammals. To achieve this, DL utilizes different algorithms to learn patterns in number fed as input to the system. One among the algorithms it uses to achieve this is the Convolutional Neural Network (CNN). CNN is best used to manipulate images in order to enable machines learn the patterns in them. In this article, kernel or filter, stride, padding, pooling and flattening shall be considered as fundamental building blocks of CNN for DL.},
      keywords = {CNN, DL, Neural network, kernel, stride, padding, pooling, flattening},
      month = {October},
  }