(Department of Informatic, Universitas Ahmad Dahlan Yogyakarta, Indonesia)(2) Sri Winiarti
(Department of Informatic, Universitas Ahmad Dahlan Yogyakarta, Indonesia)*corresponding author
AbstractThis paper proposes a convolutional neural network architectural design approach using the modified particle swarm optimization (MPSO) algorithm. Adjusting hyper-parameters and searching for optimal network architecture from convolutional neural networks (CNN) is an interesting challenge. Network performance and increasing the efficiency of learning models on certain problems depend on setting hyperparameter values, resulting in large and complex search spaces in their exploration. The use of heuristic-based searches allows for this type of problem, where the main contribution in this research is to apply the MPSO algorithm to find the optimal parameters of CNN, including the number of convolution layers, the filters used in the convolution process, the number of convolution filters and the batch size. The parameters obtained using MPSO are kept in the same condition in each convolution layer, and the objective function is evaluated by MPSO, which is given by classification rate. The optimized architecture is implemented in the Batik motif database. The research found that the proposed model produced the best results, with a classification rate higher than 94%, showing good results compared to other state-of-the-art approaches. This research demonstrates the performance of the MPSO algorithm in optimizing CNN architectures, highlighting its potential for improving image recognition tasks.
KeywordsBatik motif; CNN; Classification; Hyperparameter optimization; MPSO
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DOIhttps://doi.org/10.26555/ijain.v11i1.1303 |
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References
[1] A. Kamilaris and F. X. Prenafeta-Boldú, “Deep learning in agriculture: A survey,” Comput. Electron. Agric., vol. 147, no. February, pp. 70–90, 2018, doi: 10.1016/j.compag.2018.02.016.
[2] S. Kumar, B. Sharma, V. K. Sharma, H. Sharma, and J. C. Bansal, “Plant leaf disease identification using exponential spider monkey optimization,” Sustain. Comput. Informatics Syst., vol. 28, 2020, doi: 10.1016/j.suscom.2018.10.004.
[3] A. Waheed, M. Goyal, D. Gupta, A. Khanna, A. E. Hassanien, and H. M. Pandey, “An optimized dense convolutional neural network model for disease recognition and classification in corn leaf,” Comput. Electron. Agric., vol. 175, no. January, p. 105456, 2020, doi: 10.1016/j.compag.2020.105456.
[4] D. Rahadiyan, S. Hartati, Wahyono, and A. P. Nugroho, “Feature aggregation for nutrient deficiency identification in chili based on machine learning,” Artif. Intell. Agric., vol. 8, pp. 77–90, Jun. 2023, doi: 10.1016/j.aiia.2023.04.001.
[5] E. Elbasi et al., “Artificial Intelligence Technology in the Agricultural Sector: A Systematic Literature Review,” IEEE Access, vol. 11, pp. 171–202, 2023, doi: 10.1109/ACCESS.2022.3232485.
[6] T. A. Pham, V. Q. Tran, and H. L. T. Vu, “Evolution of Deep Neural Network Architecture Using Particle Swarm Optimization to Improve the Performance in Determining the Friction Angle of Soil,” Math. Probl. Eng., vol. 2021, pp. 19–22, 2021, doi: 10.1155/2021/5570945.
[7] N. Selvam, Y. Nagesa, and F. Negesa, “Deep Learning Approach with Optimization Algorithm for Reducing the Training and Testing Time in SAR Image Detection and Recognition,” Indian J. Sci. Technol., vol. 15, no. 9, pp. 371–385, 2022, doi: 10.17485/ijst/v15i9.1266.
[8] X. Liu, C. Zhang, Z. Cai, J. Yang, Z. Zhou, and X. Gong, “Continuous particle swarm optimization-based deep learning architecture search for hyperspectral image classification,” Remote Sens., vol. 13, no. 6, pp. 1–22, 2021, doi: 10.3390/rs13061082.
[9] G. Zhang, S. Zhao, W. Li, Q. Du, Q. Ran, and R. Tao, “HTD-Net: A deep convolutional neural network for target detection in hyperspectral imagery,” Remote Sens., vol. 12, no. 9, pp. 1–21, 2020, doi: 10.3390/RS12091489.
[10] L. Mukku and J. Thomas, “Deep learning-based cervical lesion segmentation in colposcopic images,” Appl. Eng. Technol., vol. 3, no. 1, pp. 16–25, Apr. 2024, doi: 10.31763/aet.v3i1.1345.
[11] H. K. Jeon, S. Kim, J. Edwin, and C. S. Yang, “Sea fog identification from GOCI images using CNN transfer learning models,” Electron., vol. 9, no. 2, pp. 1–9, 2020, doi: 10.3390/electronics9020311.
[12] T. Majeed, R. Rashid, D. Ali, and A. Asaad, “Issues associated with deploying CNN transfer learning to detect COVID-19 from chest X-rays,” Phys. Eng. Sci. Med., vol. 43, no. 4, pp. 1289–1303, 2020, doi: 10.1007/s13246-020-00934-8.
[13] J. Geldmacher, C. Yerkes, and Y. Zhao, “Convolutional neural networks for feature extraction and automated target recognition in synthetic aperture radar images,” CEUR Workshop Proc., vol. 2819, pp. 86–91, 2020. [Online]. Available at: https://apps.dtic.mil/sti/trecms/pdf/AD1114517.pdf.
[14] D. P. Ismi and N. Khoirunnisa, “Enhancing the performance of heart arrhythmia prediction model using Convolutional Neural Network based architectures,” Sci. Inf. Technol. Lett., vol. 5, no. 2, pp. 1–12, 2024, [Online]. Available at: https://pubs2.ascee.org/index.php/sitech/article/view/1794.
[15] H. Jayadianti, B. A. Arianti, N. H. Cahyana, S. Saifullah, and R. Dre?ewski, “Improving sentiment analysis on PeduliLindungi comments: a comparative study with CNN-Word2Vec and integrated negation handling,” Sci. Inf. Technol. Lett., vol. 4, no. 2, pp. 75–89, Nov. 2023, doi: 10.31763/sitech.v4i2.1184.
[16] A. H. Pratomo, N. H. Cahyana, and S. N. Indrawati, “Optimizing CNN hyperparameters with genetic algorithms for face mask usage classification,” Sci. Inf. Technol. Lett., vol. 4, no. 1, pp. 54–64, May 2023, doi: 10.31763/sitech.v4i1.1182.
[17] M. Masum, H. Shahriar, and H. M. Haddad, “A Transfer Learning with Deep Neural Network Approach for Network Intrusion Detection,” vol. 12, no. 1, pp. 1087–1095, 2021, doi: 10.20533/ijicr.2042.4655.2021.0132.
[18] T. B. Shahi, C. Sitaula, A. Neupane, and W. Guo, “Fruit classification using attention-based MobileNetV2 for industrial applications,” PLoS One, vol. 17, no. 2, p. e0264586, Feb. 2022, doi: 10.1371/journal.pone.0264586.
[19] H. Ali Khan, W. Jue, M. Mushtaq, and M. Umer Mushtaq, “Brain tumor classification in MRI image using convolutional neural network,” Math. Biosci. Eng., vol. 17, no. 5, pp. 6203–6216, 2020, doi: 10.3934/mbe.2020328.
[20] Z. P. Jiang, Y. Y. Liu, Z. E. Shao, and K. W. Huang, “An improved VGG16 model for pneumonia image classification,” Appl. Sci., vol. 11, no. 23, 2021, doi: 10.3390/app112311185.
[21] S. W. P. Listio, “Performance of Deep Learning Inception Model and MobileNet Model on Gender Prediction Through Eye Image,” Sinkron, vol. 7, no. 4, pp. 2593–2601, 2022, doi: 10.33395/sinkron.v7i4.11887.
[22] R. U. Khan, X. Zhang, R. Kumar, and E. O. Aboagye, “Evaluating the performance of ResNet model based on image recognition,” ACM Int. Conf. Proceeding Ser., pp. 86–90, 2018, doi: 10.1145/3194452.3194461.
[23] N. Hasan, Y. Bao, A. Shawon, and Y. Huang, “DenseNet Convolutional Neural Networks Application for Predicting COVID-19 Using CT Image,” SN Comput. Sci., vol. 2, no. 5, pp. 1–11, 2021, doi: 10.1007/s42979-021-00782-7.
[24] J. Bergstra and Y. Bengio, “Random search for hyper-parameter optimization,” J. Mach. Learn. Res., vol. 13, pp. 281–305, 2012, [Online]. Available at: https://www.jmlr.org/papers/volume13/bergstra12a/bergstra12a.pdf.
[25] S. Rana, S. Jasola, and R. Kumar, “A review on particle swarm optimization algorithms and their applications to data clustering,” Artif. Intell. Rev., vol. 35, no. 3, pp. 211–222, 2011, doi: 10.1007/s10462-010-9191-9.
[26] J. R. Challapalli and N. Devarakonda, “A novel approach for optimization of convolution neural network with hybrid particle swarm and grey wolf algorithm for classification of Indian classical dances,” Knowl. Inf. Syst., vol. 64, no. 9, pp. 2411–2434, 2022, doi: 10.1007/s10115-022-01707-3.
[27] S. Oh, J. Yoon, Y. Choi, Y. A. Jung, and J. Kim, “Genetic Algorithm for the Optimization of a Building Power Consumption Prediction Model,” Electron., vol. 11, no. 21, 2022, doi: 10.3390/electronics11213591.
[28] R. D. Logan et al., “Hyperspectral imaging and machine learning for monitoring produce ripeness,” p. 25, 2020, doi: 10.1117/12.2560968.
[29] X. Zhang, X. Wang, Q. Kang, and J. Cheng, “Differential mutation and novel social learning particle swarm optimization algorithm,” Inf. Sci. (Ny)., vol. 480, pp. 109–129, 2019, doi: 10.1016/j.ins.2018.12.030.
[30] E. Çomak, “A particle swarm optimizer with modified velocity update and adaptive diversity regulation,” Expert Syst., vol. 36, no. 1, pp. 1–18, 2019, doi: 10.1111/exsy.12330.
[31] Murinto, A. Prahara, and E. I. H. Ujianto, “Multilevel Thresholding Image Segmentation Based-Logarithm Decreasing Inertia Weight Particle Swarm Optimization,” Int. J. Adv. Soft Comput. its Appl., vol. 14, no. 3, pp. 64–77, 2022, doi: 10.15849/IJASCA.221128.05.
[32] W. H. Bangyal, H. T. Rauf, H. Batool, S. A. Bangyal, J. Ahmed, and S. Pervaiz, “An improved Particle Swarm Optimization algorithm with Chi-Square mutation strategy,” Int. J. Adv. Comput. Sci. Appl., vol. 10, no. 3, pp. 481–491, 2019, doi: 10.14569/IJACSA.2019.0100362.
[33] Murinto, A. Harjoko, S. Hartati, and P. Danoedoro, “Modified Particle Swarm Optimization With Chaos-Based Particle Initialization and Logarithmic Decreasing Inertia Weight,” ICIC Express Lett., vol. 16, no. 2, pp. 195–203, 2022. [Online]. Available at: http://www.icicel.org/ell/contents/2022/2/el-16-02-11.pdf.
[34] I. M. Wismadi, D. C. Khrisne, and I. M. A. Suyadnya, “Detecting the Ripeness of Harvest-Ready Dragon Fruit using Smaller VGGNet-Like Network,” J. Electr. Electron. Informatics, vol. 3, no. 2, p. 35, 2020, doi: 10.24843/jeei.2019.v03.i02.p01.
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