Aplikasi dan Kerentanan Algoritma Probabilistic Neural Network (PNN): Systematic Literature Review
DOI:
https://doi.org/10.37859/coscitech.v4i3.5676
Keywords:
Probabilistic Neural Network, PNN, Medical Detection, Biometric Identification Probabilistic Neural Network, PNN, Medical Detection, Biometric Identification
Abstract
PNN (Probabilistic Neural Network) is an artificial neural network that can be used for various applications, such as prediction, classification, word embedding, medical detection, biometric identification, and other applications. Although PNN performs well in most cases, this algorithm also has specific weaknesses to attacks and flaws. Therefore, research on PNN applications and vulnerabilities is fundamental in developing more secure and reliable machine learning systems. This study aims to conduct a systematic literature review on PNN applications and vulnerabilities. The systematic literature review method identifies and analyzes PNN-related publications from various sources, such as scientific journals. The results of this literature review indicate that PNN has been successfully used in various applications and shows good performance. However, several studies have also revealed the vulnerabilities and weaknesses of PNN. This research provides insight into PNN applications and vulnerabilities, which can be used to develop more secure and reliable techniques in machine learning. The results of this literature review can also be used as a reference source for researchers interested in developing better and more reliable machine learning systems using the PNN algorithm.
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References
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[12] A. Savchenko, “Probabilistic neural network with complex exponential activation functions in image recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 651–660, 2020.
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[16] Vital, T. P. Ranga, dkk., “Probabilistic Neural Network‑based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data,” Arabian Journal for Science and Engineering, vol. 46, p. 3383–3407, 2021.
[17] Yang, C., Yang, J., Liu, Y., & Geng, X. “Cancer Risk Analysis Based on Improved Probabilistic Neural Network,” Frontiers in Computational Neuroscience, 14. 2020.
[18] Capizzi, Giacomo, dkk., “Small Lung Nodules Detection based on Fuzzy-Logic and Probabilistic Neural Network with Bio-inspired Reinforcement Learning,” IEEE Transactions on Fuzzy Systems, 2020.
[19] Isfahani, Z. N., Jannat-Dastjerdi, I., Eskandari, F., Ghoushchi, S. J., & Pourasad, Y, “Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network,” Computational Intelligence and Neuroscience, 2021.
[20] Sathishkumar, S & R. Devi Priya, “Efficient Classification of ECG Signals Using Probabilistic Neural Network in the Detection of Cardiovascular Diseases,” Intelligent Systems And Applications In Engineering, vol. 10, no. 3, p. 247–255, 2022.
[21] Goshvarpour, A., & Goshvarpour, A. (2019). Human identification using a new matching pursuit-based feature set of ECG. Computer methods and programs in biomedicine, 172, 87-94.
[22] Lotfi, A., & Benyettou, A. (2018). Cross-validation probabilistic neural network based face identification. Journal of Information Processing Systems, 14(5), 1075-1086.
[23] Zhang, H., & Qiao, F. (2020). Face recognition method based on probabilistic neural network optimizing two-dimensional subspace analysis. In IOP Conference Series: Materials Science and Engineering (Vol. 719, No. 1, p. 012074). IOP Publishing.
[24] Indasyah, E., Septian Enggar, S., Horng, S. J., Ketut Edi, P., & Purnomo, M. H. Fingerprint Identification Based on Minutiae Point Using Probabilistic Neural Network.
[25] Dar, S. A., Palanivel, S., Geetha, M. K., & Balasubramanian, M. (2022). Mouth Image Based Person Authentication Using DWLSTM and GRU. Inf. Sci. Lett, 11(3), 853-862.
[26] Davydenko, A., Vysotska, O., & Shmelova, T. (2019). Methods of Primary Processing Handwriting Samples at User Authentication Using a Probabilistic Neural Network. In CybHyg (pp. 723-735).
[27] Wenando, F. A., Adji, T. B., & Ardiyanto, I. (2017). Text classification to detect student level of understanding in prior knowledge activation process. Advanced Science Letters, 23(3), 2285-2287.
[28] Wenando, F. A., Hayami, R., & Novermahakim, A. Y. (2020, October). Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various Classification Algorithms. In 2020 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE) (pp. 279-282). IEEE.
[2] Mohebali, B., Tahmassebi, A., Meyer-Baese, A., & Gandomi, A. H. (2020). Probabilistic neural networks: a brief overview of theory, implementation, and application. Handbook of probabilistic models, 347-367.
[3] Azizah, P. D. I, "Penerapan Probabilistic Neural Network pada Klasifikasi Berat Bayi Baru Lahir" Journal Riset Statistika. 2021.
[4] Jatmiko, I. Maulana., Munir, M., Putra, N. P. Uman., Rohiem, N. Hananur., Masfufiah, I., “Analisa Sisa Umur Transformator Berdasarkan Pengaruh Pembebanan Menggunakan Metode Probabilistik Neural Network (PNN),” Seminar Nasional Sains dan Teknologi Terapan X 2022, 2022.
[5] Zhang, Shuai, Lina Yao, and Xingquan Zhu. "Deep learning based recommender system: A survey and new perspectives." ACM Computing Surveys (CSUR) 52.1 (2019): 1-38.
[6] N. Aljeri and A. Boukerche, “A probabilistic neural network-based road side unit prediction scheme for autonomous driving,” ICC 2019 - 2019 IEEE International Conference on Communications (ICC), 2019.
[7] V. Chandrasekara, C. Tilakaratne, and M. Mammadov, “An improved probabilistic neural network model for directional prediction of a stock market index,” Applied Sciences, vol. 9, no. 24, p. 5334, 2019.
[8] R. Sharma and L. Srivastava, “Power quality disturbance prediction using PNN,” 2018 2nd IEEE International Conference on Power Electronics, Intelligent Control and Energy Systems (ICPEICES), 2018.
[9] S. A. El-Dabaa, F. I. Metwalli, A. T. Amin, and A. A. Basheer, “Prediction of porosity and water saturation using a probabilistic neural network for the Bahariya Formation, Nader Field, North Western Desert, Egypt,” Journal of African Earth Sciences, vol. 196, p. 104638, 2022.
[10] M. Alweshah, L. Rababa, M. H. Ryalat, A. Al Momani, and M. F. Ababneh, “African Buffalo Algorithm: Training the probabilistic neural network to solve classification problems,” Journal of King Saud University - Computer and Information Sciences, vol. 34, no. 5, pp. 1808–1818, 2022.
[11] C. Sun, Y. Hu, and P. Shi, “Probabilistic neural network based seabed sediment recognition method for side-scan sonar imagery,” Sedimentary Geology, vol. 410, p. 105792, 2020.
[12] A. Savchenko, “Probabilistic neural network with complex exponential activation functions in image recognition,” IEEE Transactions on Neural Networks and Learning Systems, vol. 31, no. 2, pp. 651–660, 2020.
[13] Z. Tang, “Leaf image recognition and classification based on GBDT-probabilistic neural network,” Journal of Physics: Conference Series, vol. 1592, no. 1, p. 012061, 2020.
[14] S. Alam and N. Yao, “Probabilistic neural network and word embedding for sentiment analysis,” International Journal of Advanced Computer Science and Applications, vol. 9, no. 7, 2018.
[15] Kalaiselvi, K., & Sujarani, P. “Correlation Feature Selection (CFS) and Probabilistic Neural Network (PNN) for Diabetes Disease Prediction,” International Journal of Engineering & Technology. 2018.
[16] Vital, T. P. Ranga, dkk., “Probabilistic Neural Network‑based Model for Identification of Parkinson’s Disease by using Voice Profile and Personal Data,” Arabian Journal for Science and Engineering, vol. 46, p. 3383–3407, 2021.
[17] Yang, C., Yang, J., Liu, Y., & Geng, X. “Cancer Risk Analysis Based on Improved Probabilistic Neural Network,” Frontiers in Computational Neuroscience, 14. 2020.
[18] Capizzi, Giacomo, dkk., “Small Lung Nodules Detection based on Fuzzy-Logic and Probabilistic Neural Network with Bio-inspired Reinforcement Learning,” IEEE Transactions on Fuzzy Systems, 2020.
[19] Isfahani, Z. N., Jannat-Dastjerdi, I., Eskandari, F., Ghoushchi, S. J., & Pourasad, Y, “Presentation of Novel Hybrid Algorithm for Detection and Classification of Breast Cancer Using Growth Region Method and Probabilistic Neural Network,” Computational Intelligence and Neuroscience, 2021.
[20] Sathishkumar, S & R. Devi Priya, “Efficient Classification of ECG Signals Using Probabilistic Neural Network in the Detection of Cardiovascular Diseases,” Intelligent Systems And Applications In Engineering, vol. 10, no. 3, p. 247–255, 2022.
[21] Goshvarpour, A., & Goshvarpour, A. (2019). Human identification using a new matching pursuit-based feature set of ECG. Computer methods and programs in biomedicine, 172, 87-94.
[22] Lotfi, A., & Benyettou, A. (2018). Cross-validation probabilistic neural network based face identification. Journal of Information Processing Systems, 14(5), 1075-1086.
[23] Zhang, H., & Qiao, F. (2020). Face recognition method based on probabilistic neural network optimizing two-dimensional subspace analysis. In IOP Conference Series: Materials Science and Engineering (Vol. 719, No. 1, p. 012074). IOP Publishing.
[24] Indasyah, E., Septian Enggar, S., Horng, S. J., Ketut Edi, P., & Purnomo, M. H. Fingerprint Identification Based on Minutiae Point Using Probabilistic Neural Network.
[25] Dar, S. A., Palanivel, S., Geetha, M. K., & Balasubramanian, M. (2022). Mouth Image Based Person Authentication Using DWLSTM and GRU. Inf. Sci. Lett, 11(3), 853-862.
[26] Davydenko, A., Vysotska, O., & Shmelova, T. (2019). Methods of Primary Processing Handwriting Samples at User Authentication Using a Probabilistic Neural Network. In CybHyg (pp. 723-735).
[27] Wenando, F. A., Adji, T. B., & Ardiyanto, I. (2017). Text classification to detect student level of understanding in prior knowledge activation process. Advanced Science Letters, 23(3), 2285-2287.
[28] Wenando, F. A., Hayami, R., & Novermahakim, A. Y. (2020, October). Tweet Sentiment Analysis for 2019 Indonesia Presidential Election Results using Various Classification Algorithms. In 2020 1st International Conference on Information Technology, Advanced Mechanical and Electrical Engineering (ICITAMEE) (pp. 279-282). IEEE.
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Published
2023-09-18
How to Cite
Apri Wenando, F., Fatma, Y., Ulfa, A., Salma, & Taurin, J. (2023). Aplikasi dan Kerentanan Algoritma Probabilistic Neural Network (PNN): Systematic Literature Review. Jurnal CoSciTech (Computer Science and Information Technology), 4(2), 491–499. https://doi.org/10.37859/coscitech.v4i3.5676
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