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[Home](https://www.jsr.org/hs/index.php/path/index) / 2. [Archives](https://www.jsr.org/hs/index.php/path/issue/archive) / 3. [Vol. 12 No. 4 (2023)](https://www.jsr.org/hs/index.php/path/issue/view/61) / 4. HS Review Articles # The CNN: The Architecture Behind Artificial Intelligence Development ## Authors - Yechan Lee Dublin High School ## DOI: <https://doi.org/10.47611/jsrhs.v12i4.5579> ## Keywords: Convolutional Neural Network, Convolutional Layer, Transfer Function, Artificial Intelligence, Machine Learning - [PDF](https://www.jsr.org/hs/index.php/path/article/view/5579/2731) ## Abstract The convolutional neural network (CNN) is a multilayer network architecture that is capable of training itself using an advanced algorithm to produce increasingly accurate results. The CNN is especially effective in spatial image recognition and is used in a multitude of fields, such as image recognition in the medical industry, and image segmentation in the security field. The ability of the CNN to show impressive results is seen in its multilayer composition. This multilayer network consists of the convolutional layer, the pooling layer, the activation layer, and the fully connected layer. Here, the convolutional layer, the pooling layer, and the activation layer have their own parameters which will influence the flow of input data throughout the CNN until it produces an ultimate output in the connected layer. As such, this paper will delve into the individual characteristics of each layer, and introduce its relationship with not only its immediate surrounding layers but with the entirety of the CNN. Additionally, this paper will stress important modules and parameters that can help improve the layers. ### Downloads Download data is not yet available. ## References or Bibliography Ali, S. H., Al-Sultan, H. A., & Al Rubaie, M. T. (2022). Fifth industrial revolution. International Journal of Business, Management and Economics, 3(3), 196–212. <https://doi.org/10.47747/ijbme.v3i3.694> Assael, Y., Sommerschield, T., Shillingford, B., Bordbar, M., Pavlopoulos, J., Chatzipanagiotou, M., Androutsopoulos, I., Prag, J., & de Freitas, N. (2022). Restoring and attributing ancient texts using deep neural networks. Nature, 603(7900), 280–283. <https://doi.org/10.1038/s41586-022-04448-z> Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of Deep Learning: Concepts, CNN Architectures, challenges, applications, Future Directions. Journal of Big Data, 8(1). <https://doi.org/10.1186/s40537-021-00444-8> Callaghan, C. (2022). The Fifth Industrial Revolution: An Unfolding Knowledge Productivity Revolution. SSRN Electronic Journal. <https://doi.org/10.2139/ssrn.4307753> Campbell, M., Hoane, A. J., & Hsu, F (2002) Deep Blue, Artificial Intelligence, 134(2), 57-83,, <https://doi.org/10.1016/S0004-3702(01)00129-1>. Carbone, M. R. (2022). When not to use machine learning: A perspective on potential and limitations. MRS Bulletin, 47(9), 968–974. <https://doi.org/10.1557/s43577-022-00417-z> Crafts, N. F. R (1996). The First Industrial Revolution: A Guided Tour for Growth Economists. The American Economic Review, 86(2), 197–201. <http://www.jstor.org/stable/2118122> Deiana, A. M., Tran, N., Agar, J., Blott, M., Di Guglielmo, G., Duarte, J., Harris, P., Hauck, S., Liu, M., Neubauer, M. S., Ngadiuba, J., Ogrenci-Memik, S., Pierini, M., Aarrestad, T., Bahr, S., Becker, J., Berthold, A.-S., Bonventre, R. J., Bravo, T. E. M., … Weng, O. (2021, October 25). Applications and techniques for fast machine learning in science. arXiv.org. <https://arxiv.org/abs/2110.13041> Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022). Activation functions in Deep learning: A comprehensive survey and benchmark. Neurocomputing, 503, 92–108. <https://doi.org/10.1016/j.neucom.2022.06.111> Ford, P. (2020, April 2). Our fear of Artificial Intelligence. MIT Technology Review. <https://www.technologyreview.com/2015/02/11/169210/our-fear-of-artificial-intelligence/> Geetha, T. V., & Sendhilkumar, S. (2023). Machine learning applications. Machine Learning, 295–315. <https://doi.org/10.1201/9781003290100-12> Gassenmaier, S., Küstner, T., Nickel, D., Herrmann, J., Hoffmann, R., Almansour, H., Afat, S., Nikolaou, K., & Othman, A. E. (2021). Deep learning applications in Magnetic Resonance Imaging: Has the future become present? Diagnostics, 11(12), 2181. <https://doi.org/10.3390/diagnostics11122181> Gholamalinezhad, H., & Khosravi, H. (2020, September 16). Pooling methods in Deep Neural Networks, a review. arXiv.org. <https://arxiv.org/abs/2009.07485> Groumpos, P. P. (2021). A critical historical and scientific overview of all industrial revolutions. IFAC-PapersOnLine, 54(13), 464–471. <https://doi.org/10.1016/j.ifacol.2021.10.492> IBM100 - Deep Blue. (2023). <https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/> Hao, W., Yizhou, W., Yaqin, L., & Zhili, S. (2020). The role of activation function in CNN. 2020 2nd International Conference on Information Technology and Computer Application (ITCA). <https://doi.org/10.1109/itca52113.2020.00096> Hashemi, M. (2019). Enlarging smaller images before inputting into convolutional neural network: Zero-padding vs. interpolation. Journal of Big Data, 6(1). <https://doi.org/10.1186/s40537-019-0263-7> Islam, M. A., Wimmer, H., & Rebman, C. M. (2021). Examining sigmoid vs relu activation functions in deep learning. Interdisciplinary Research in Technology and Management, 432–437. <https://doi.org/10.1201/9781003202240-68> Lewis, B. (2018, February 8). Predictive maintenance for more resilient self-service vending. <https://www.insight.tech/retail/predictive-maintenance-for-more-resilient-self-service-vending> Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., Hubert, T., Choy, P., de Masson d’Autume, C., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Cherepanov, A., … Vinyals, O. (2022). Competition-level code generation with AlphaCode. Science, 378(6624), 1092–1097. <https://doi.org/10.1126/science.abq1158> Mahima, R., Maheswari, M., Roshana, S., Priyanka, E., Mohanan, N., & Nandhini, N. (2023). A comparative analysis of the most commonly used activation functions in deep neural network. 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC). <https://doi.org/10.1109/icesc57686.2023.10193390> Malallah, H., Zeebaree, S. R., Zebari, R. R., Sadeeq, M. A., Ageed, Z. S., Ibrahim, I. M., Yasin, H. M., & Merceedi, K. J. (2021). A comprehensive study of kernel (issues and concepts) in different operating systems. Asian Journal of Research in Computer Science, 16–31. <https://doi.org/10.9734/ajrcos/2021/v8i330201> Malleswaran, M., Vaidehi, V., & Deborah, S. A. (2011). CNN based GPS/INS data integration using new dynamic learning algorithm. 2011 International Conference on Recent Trends in Information Technology (ICRTIT). <https://doi.org/10.1109/icrtit.2011.5972270> Milosevic, N. (2020). Convolutions and Convolutional Neural Networks. Introduction to Convolutional Neural Networks. <https://doi.org/10.1007/978-1-4842-5648-0_12> Mohajan, H. (2019, October 21). The Second Industrial Revolution has brought modern social and economic developments. Munich Personal RePEc Archive. <https://mpra.ub.uni-muenchen.de/98209/> Mohajan, H. (2021). Third Industrial Revolution Brings Global Development. 7. 239-251. Monkam, P., Qi, S., Ma, H., Gao, W., Yao, Y., & Qian, W. (2019). Detection and classification of pulmonary nodules using Convolutional Neural Networks: A survey. IEEE Access, 7, 78075–78091. <https://doi.org/10.1109/access.2019.2920980> Nedeljkovic, D., & Jakovljevic, Z. (2022). CNN based method for the development of cyber-attacks detection algorithms in industrial control systems. Computers & Security, 114, 102585. <https://doi.org/10.1016/j.cose.2021.102585> Newborn, M. (1997). Kasparov versus Deep Blue. <https://doi.org/10.1007/978-1-4612-2260-6> Noble, S. M., Mende, M., Grewal, D., & Parasuraman, A. (2022). The Fifth Industrial Revolution: How Harmonious Human–machine collaboration is triggering a retail and service \[r\]evolution. Journal of Retailing, 98(2), 199–208. <https://doi.org/10.1016/j.jretai.2022.04.003> Onyema, E. M., Almuzaini, K. K., Onu, F. U., Verma, D., Gregory, U. S., Puttaramaiah, M., & Afriyie, R. K. (2022). Prospects and challenges of using machine learning for academic forecasting. Computational Intelligence and Neuroscience, 2022, 1–7. <https://doi.org/10.1155/2022/5624475> Özdemir, C. (2023). Avg-topk: A new pooling method for Convolutional Neural Networks. Expert Systems with Applications, 223, 119892. <https://doi.org/10.1016/j.eswa.2023.119892> Prasad, P. S., & R. Upadhyay, A. (2012). Design of hybrid kernel and the performance improvement of the operating system. International Journal of Engineering and Technology, 4(2), 162–165. <https://doi.org/10.7763/ijet.2012.v4.340> Sarker, I.H. AI-Based Modeling: Techniques, Applications and Research Issues Towards Automation, Intelligent and Smart Systems. Sn Computer Science. 3, 158 (2022). <https://doi.org/10.1007/s42979-022-01043-x> Shaheen, F., Verma, B., & Asafuddoula M. (2016). 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In PUBLIC POLICY IMPLICATIONS OF THE FOURTH INDUSTRIAL REVOLUTION FOR SINGAPORE (pp. 5–7). S. Rajaratnam School of International Studies. <http://www.jstor.org/stable/resrep17650.5> Taye, M. M. (2023). Theoretical Understanding of Convolutional Neural Network: Concepts, Architectures, Applications, Future Directions. Computation, 11(3), 52. <https://doi.org/10.3390/computation11030052> Thakur, D. (2019, September 4). ML Basics \#4: Replace negatives with Zeros!. dhruvs space RSS. <https://dhruvs.space/posts/ml-basics-issue-4/> Theodoridis, S. (2015). Parameter learning. Machine Learning, 327–402. <https://doi.org/10.1016/b978-0-12-801522-3.00008-2> The Strategic Foresight Initiative. (2013). The Third Industrial Revolution. In Envisioning 2030: US Strategy for the Coming Technology Revolution (pp. 15–22). Atlantic Council. <http://www.jstor.org/stable/resrep03584.8> Varoquaux, G., & Cheplygina, V. (2022). Machine Learning for Medical Imaging: Methodological Failures and recommendations for the future. Npj Digital Medicine, 5(1). <https://doi.org/10.1038/s41746-022-00592-y> Wandelt, B. D., & Bailer-Jones, C. A. L. (2008). Precision parameter estimation and machine learning. AIP Conference Proceedings. <https://doi.org/10.1063/1.3059073> Xu, C., & Wang, H. (2022). Research on a convolution kernel initialization method for speeding up the convergence of CNN. Applied Sciences, 12(2), 633. <https://doi.org/10.3390/app12020633> Zafar, A., Aamir, M., Mohd Nawi, N., Arshad, A., Riaz, S., Alruban, A., Dutta, A. K., & Almotairi, S. (2022). A comparison of pooling methods for Convolutional Neural Networks. Applied Sciences, 12(17), 8643. <https://doi.org/10.3390/app12178643> - [PDF](https://www.jsr.org/hs/index.php/path/article/view/5579/2731) ## Published 11-30-2023 ## How to Cite Lee, Y. (2023). The CNN: The Architecture Behind Artificial Intelligence Development. *Journal of Student Research*, *12*(4). https://doi.org/10.47611/jsrhs.v12i4.5579 More Citation Formats - [ACM](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/acm-sig-proceedings?submissionId=5579&publicationId=4574) - [ACS](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/acs-nano?submissionId=5579&publicationId=4574) - [APA](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/apa?submissionId=5579&publicationId=4574) - [ABNT](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/associacao-brasileira-de-normas-tecnicas?submissionId=5579&publicationId=4574) - [Chicago](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/chicago-author-date?submissionId=5579&publicationId=4574) - [Harvard](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/harvard-cite-them-right?submissionId=5579&publicationId=4574) - [IEEE](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/ieee?submissionId=5579&publicationId=4574) - [MLA](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/modern-language-association?submissionId=5579&publicationId=4574) - [Turabian](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/turabian-fullnote-bibliography?submissionId=5579&publicationId=4574) - [Vancouver](https://www.jsr.org/hs/index.php/path/citationstylelanguage/get/vancouver?submissionId=5579&publicationId=4574) Download Citation - [Endnote/Zotero/Mendeley (RIS)](https://www.jsr.org/hs/index.php/path/citationstylelanguage/download/ris?submissionId=5579&publicationId=4574) - [BibTeX](https://www.jsr.org/hs/index.php/path/citationstylelanguage/download/bibtex?submissionId=5579&publicationId=4574) ## Issue [Vol. 12 No. 4 (2023)](https://www.jsr.org/hs/index.php/path/issue/view/61) ## Section HS Review Articles Copyright (c) 2023 Yechan Lee [](https://creativecommons.org/licenses/by-nc-sa/4.0/) This work is licensed under a [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](https://creativecommons.org/licenses/by-nc-sa/4.0/). 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## Authors ## DOI: <https://doi.org/10.47611/jsrhs.v12i4.5579> ## Keywords: Convolutional Neural Network, Convolutional Layer, Transfer Function, Artificial Intelligence, Machine Learning - [PDF](https://www.jsr.org/hs/index.php/path/article/view/5579/2731) ## Abstract The convolutional neural network (CNN) is a multilayer network architecture that is capable of training itself using an advanced algorithm to produce increasingly accurate results. The CNN is especially effective in spatial image recognition and is used in a multitude of fields, such as image recognition in the medical industry, and image segmentation in the security field. The ability of the CNN to show impressive results is seen in its multilayer composition. This multilayer network consists of the convolutional layer, the pooling layer, the activation layer, and the fully connected layer. Here, the convolutional layer, the pooling layer, and the activation layer have their own parameters which will influence the flow of input data throughout the CNN until it produces an ultimate output in the connected layer. As such, this paper will delve into the individual characteristics of each layer, and introduce its relationship with not only its immediate surrounding layers but with the entirety of the CNN. Additionally, this paper will stress important modules and parameters that can help improve the layers. ### Downloads Download data is not yet available. ## References or Bibliography Ali, S. H., Al-Sultan, H. A., & Al Rubaie, M. T. (2022). Fifth industrial revolution. International Journal of Business, Management and Economics, 3(3), 196–212. <https://doi.org/10.47747/ijbme.v3i3.694> Assael, Y., Sommerschield, T., Shillingford, B., Bordbar, M., Pavlopoulos, J., Chatzipanagiotou, M., Androutsopoulos, I., Prag, J., & de Freitas, N. (2022). Restoring and attributing ancient texts using deep neural networks. Nature, 603(7900), 280–283. <https://doi.org/10.1038/s41586-022-04448-z> Alzubaidi, L., Zhang, J., Humaidi, A. J., Al-Dujaili, A., Duan, Y., Al-Shamma, O., Santamaría, J., Fadhel, M. A., Al-Amidie, M., & Farhan, L. (2021). Review of Deep Learning: Concepts, CNN Architectures, challenges, applications, Future Directions. Journal of Big Data, 8(1). <https://doi.org/10.1186/s40537-021-00444-8> Callaghan, C. (2022). The Fifth Industrial Revolution: An Unfolding Knowledge Productivity Revolution. SSRN Electronic Journal. <https://doi.org/10.2139/ssrn.4307753> Campbell, M., Hoane, A. J., & Hsu, F (2002) Deep Blue, Artificial Intelligence, 134(2), 57-83,, <https://doi.org/10.1016/S0004-3702(01)00129-1>. Carbone, M. R. (2022). When not to use machine learning: A perspective on potential and limitations. MRS Bulletin, 47(9), 968–974. <https://doi.org/10.1557/s43577-022-00417-z> Crafts, N. F. R (1996). The First Industrial Revolution: A Guided Tour for Growth Economists. The American Economic Review, 86(2), 197–201. <http://www.jstor.org/stable/2118122> Deiana, A. M., Tran, N., Agar, J., Blott, M., Di Guglielmo, G., Duarte, J., Harris, P., Hauck, S., Liu, M., Neubauer, M. S., Ngadiuba, J., Ogrenci-Memik, S., Pierini, M., Aarrestad, T., Bahr, S., Becker, J., Berthold, A.-S., Bonventre, R. J., Bravo, T. E. M., … Weng, O. (2021, October 25). Applications and techniques for fast machine learning in science. arXiv.org. <https://arxiv.org/abs/2110.13041> Dubey, S. R., Singh, S. K., & Chaudhuri, B. B. (2022). Activation functions in Deep learning: A comprehensive survey and benchmark. Neurocomputing, 503, 92–108. <https://doi.org/10.1016/j.neucom.2022.06.111> Ford, P. (2020, April 2). Our fear of Artificial Intelligence. MIT Technology Review. <https://www.technologyreview.com/2015/02/11/169210/our-fear-of-artificial-intelligence/> Geetha, T. V., & Sendhilkumar, S. (2023). Machine learning applications. Machine Learning, 295–315. <https://doi.org/10.1201/9781003290100-12> Gassenmaier, S., Küstner, T., Nickel, D., Herrmann, J., Hoffmann, R., Almansour, H., Afat, S., Nikolaou, K., & Othman, A. E. (2021). Deep learning applications in Magnetic Resonance Imaging: Has the future become present? Diagnostics, 11(12), 2181. <https://doi.org/10.3390/diagnostics11122181> Gholamalinezhad, H., & Khosravi, H. (2020, September 16). Pooling methods in Deep Neural Networks, a review. arXiv.org. <https://arxiv.org/abs/2009.07485> Groumpos, P. P. (2021). A critical historical and scientific overview of all industrial revolutions. IFAC-PapersOnLine, 54(13), 464–471. <https://doi.org/10.1016/j.ifacol.2021.10.492> IBM100 - Deep Blue. (2023). <https://www.ibm.com/ibm/history/ibm100/us/en/icons/deepblue/> Hao, W., Yizhou, W., Yaqin, L., & Zhili, S. (2020). The role of activation function in CNN. 2020 2nd International Conference on Information Technology and Computer Application (ITCA). <https://doi.org/10.1109/itca52113.2020.00096> Hashemi, M. (2019). Enlarging smaller images before inputting into convolutional neural network: Zero-padding vs. interpolation. Journal of Big Data, 6(1). <https://doi.org/10.1186/s40537-019-0263-7> Islam, M. A., Wimmer, H., & Rebman, C. M. (2021). Examining sigmoid vs relu activation functions in deep learning. Interdisciplinary Research in Technology and Management, 432–437. <https://doi.org/10.1201/9781003202240-68> Lewis, B. (2018, February 8). Predictive maintenance for more resilient self-service vending. <https://www.insight.tech/retail/predictive-maintenance-for-more-resilient-self-service-vending> Li, Y., Choi, D., Chung, J., Kushman, N., Schrittwieser, J., Leblond, R., Eccles, T., Keeling, J., Gimeno, F., Dal Lago, A., Hubert, T., Choy, P., de Masson d’Autume, C., Babuschkin, I., Chen, X., Huang, P.-S., Welbl, J., Gowal, S., Cherepanov, A., … Vinyals, O. (2022). Competition-level code generation with AlphaCode. Science, 378(6624), 1092–1097. <https://doi.org/10.1126/science.abq1158> Mahima, R., Maheswari, M., Roshana, S., Priyanka, E., Mohanan, N., & Nandhini, N. (2023). A comparative analysis of the most commonly used activation functions in deep neural network. 2023 4th International Conference on Electronics and Sustainable Communication Systems (ICESC). <https://doi.org/10.1109/icesc57686.2023.10193390> Malallah, H., Zeebaree, S. R., Zebari, R. R., Sadeeq, M. A., Ageed, Z. S., Ibrahim, I. M., Yasin, H. 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