Comparison of Madaline and Perceptron Algorithms on Classification with Quantum Computing Approach
Abstract
The fundamental problem in this research is to explore a more profound understanding of both performance and efficiency in quantity computing. Successful implementation of algorithms in computational computing environments can unlock the potential for significant improvements in information processing and neural network modeling. This research focuses on developing Madaline and Perceptron algorithms using a quantum approach. This study compares the two algorithms regarding the accuracy and epoch of the test results. The data set used in this study is a lens data set. There are four attributes: 1) patient age: young, prepresbyopia, presbyopia 2) eyeglass prescription: myopia, hypermetropia, 3) astigmatic: no, yes. 4) tear production rate: reduced, normal. There are three classes: 1) patients must have hard contact lenses installed, 2) patients must have soft contact lenses installed, and 3) patients cannot have contact lenses installed. The number of data is 24 data. The result of this research is the development of the Madaline and Perceptron algorithms with a quantum computing approach. Comparing these algorithms shows that the best accuracy is the Perceptron algorithm, namely 100%. In comparison, Madaline is 62.5%, and the smallest epoch is the Madaline algorithm, namely 4 epochs, while the smallest Perceptron epoch is 317. This research significantly contributes to the development of computing and neural networks, with potential applications extending from data processing to more accurate modeling in artificial intelligence, data analysis, and understanding complex patterns.
Downloads
References
C. Güzeliş, “An experience on problem-based learning in an Engineering Faculty,” Turkish J. Electr. Eng. Comput. Sci., vol. 14, no. 1, pp. 67–76, 2006.
N. Wiebe, A. Kapoor, and K. M. Svore, “Quantum perceptron models,” Adv. Neural Inf. Process. Syst., pp. 4006–4014, 2016.
I. Technology and U. S. Utara, “Using Quantum Circuits For Data Classification,” vol. 16, no. 11, pp. 1139–1146, 2022.
M. Joorabian, S. S. Mortazavi, and A. A. Khayyami, “Author’s copy Harmonic estimation in a power system using a novel hybrid Least Squares-Adaline algorithm.”
M. Zhu, G. Zhang, L. Zhang, W. Han, Z. Shi, and X. Lv, “Object Segmentation by Spraying Robot Based on Multi-Layer Perceptron,” Energies, vol. 16, no. 1, 2023, doi: 10.3390/en16010232.
X. Tan and Z. Xue, “Spectral-spatial multi-layer perceptron network for hyperspectral image land cover classification,” Eur. J. Remote Sens., vol. 55, no. 1, pp. 409–419, 2022, doi: 10.1080/22797254.2022.2087540.
F. Yang, H. Moayedi, and A. Mosavi, “Predicting the degree of dissolved oxygen using three types of multi-layer perceptron-based artificial neural networks,” Sustain., vol. 13, no. 17, pp. 1–20, 2021, doi: 10.3390/su13179898.
K. Sharma, M. Cerezo, L. Cincio, and P. J. Coles, “Trainability of Dissipative Perceptron-Based Quantum Neural Networks,” Phys. Rev. Lett., vol. 128, no. 18, pp. 1–28, 2022, doi: 10.1103/PhysRevLett.128.180505.
S. Janpong, K. Areerak, and K. Areerak, “Harmonic detection for shunt active power filter using adaline neural network,” Energies, vol. 14, no. 14, 2021.
Y. Han, L. Xu, M. M. Khan, C. Chen, G. Yao, and L. D. Zhou, “Robust deadbeat control scheme for a hybrid APF with resetting filter and Adaline-based harmonic estimation algorithm,” IEEE Trans. Ind. Electron., vol. 58, no. 9, pp. 3893–3904, 2011.
D. Erdogmus and J. C. Principe, “Convergence properties and data efficiency of the minimum error entropy criterion in adaline training,” IEEE Trans. Signal Process., vol. 51, no. 7, pp. 1966–1978, 2003.
A. L. Lestari, D. M. Midyanti, and R. Hidayati, “Prediksi ketersediaan pangan di kalimantan barat dengan menggunakan metode,” Komput. dan Apl., vol. 11, no. 01, pp. 119–127, 2023.
S. Samadianfard et al., “Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm,” Energy Reports, vol. 6, pp. 1147–1159, 2020, doi: 10.1016/j.egyr.2020.05.001.
W. T. Sewunetie and L. Kovács, “Comparison of template-based and multilayer perceptron-based approach for automatic question generation system,” Indones. J. Electr. Eng. Comput. Sci., vol. 28, no. 3, pp. 1738–1748, 2022, doi: 10.11591/ijeecs.v28.i3.pp1738-1748.
A. Sagheer, M. Zidan, and M. M. Abdelsamea, “A novel autonomous perceptron model for pattern classification applications,” Entropy, vol. 21, no. 8, pp. 1–24, 2019, doi: 10.3390/e21080763.
S. Nosratabadi, S. F. Ardabili, Z. Lakner, C. Makó, and A. Mosavi, “Prediction of Food Production Using Machine Learning Algorithms of Multilayer Perceptron and ANFIS,” SSRN Electron. J., pp. 1–13, 2021, doi: 10.2139/ssrn.3836565.
M. Desai and M. Shah, “An anatomization on breast cancer detection and diagnosis employing multi-layer perceptron neural network (MLP) and Convolutional neural network (CNN),” Clin. eHealth, vol. 4, no. 2021, pp. 1–11, 2021, doi: 10.1016/j.ceh.2020.11.002.
A. A. Heidari, H. Faris, S. Mirjalili, I. Aljarah, and M. Mafarja, “Ant lion optimizer: Theory, literature review, and application in multi-layer perceptron neural networks,” Stud. Comput. Intell., vol. 811, no. January, pp. 23–46, 2020, doi: 10.1007/978-3-030-12127-3_3.
D. Li, F. Huang, L. Yan, Z. Cao, J. Chen, and Z. Ye, “Landslide susceptibility prediction using particle-swarm-optimized multilayer perceptron: Comparisons with multilayer-perceptron-only, BP neural network, and information value models,” Appl. Sci., vol. 9, no. 18, 2019, doi: 10.3390/app9183664.
H. P. L. A. T. D. A. for C. L. Pan, X. Zhu, S. Atici, and A. E. Cetin, “DCT Perceptron Layer: A Transform Domain Approach for Convolution Layer,” pp. 1–10, 2022, [Online]. Available: https://arxiv.org/abs/2211.08577v1
R. Ren, J. Su, B. Yang, R. Y. K. Lau, and Q. Liu, “Novel Low-Power Construction of Chaotic S-Box in Multilayer Perceptron,” Entropy, vol. 24, no. 11, p. 1552, 2022, doi: 10.3390/e24111552.
Copyright (c) 2024 Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi)
This work is licensed under a Creative Commons Attribution 4.0 International License.
Copyright in each article belongs to the author
- The author acknowledges that the RESTI Journal (System Engineering and Information Technology) is the first publisher to publish with a license Creative Commons Attribution 4.0 International License.
- Authors can enter writing separately, arrange the non-exclusive distribution of manuscripts that have been published in this journal into other versions (eg sent to the author's institutional repository, publication in a book, etc.), by acknowledging that the manuscript has been published for the first time in the RESTI (Rekayasa Sistem dan Teknologi Informasi) journal ;
