A PNN based Malign Attack Detection and Classification Model

Naaz, Sheenam; Parveen, Suraiya

doi:10.1109/icosec49089.2020.9215424

2020 International Conference on Smart Electronics and Communication (ICOSEC) 2020

DOI: 10.1109/icosec49089.2020.9215424

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A PNN based Malign Attack Detection and Classification Model

Sheenam Naaz

Suraiya Parveen

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2023

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Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer

Kong

Zhou

Bian

et al. 2023

Sci Rep

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Breast cancer is the second dangerous cancer in the world. Breast cancer data often contains more redundant information. Redundant information makes the breast cancer auxiliary diagnosis less accurate and time consuming. Dimension reduction algorithm combined with machine learning can solve these problems well. This paper proposes the single parameter decision theoretic rough set (SPDTRS) combined with the probability neural network (PNN) model for breast cancer diagnosis. We find that when the parameter value of SPDTRS is 2.5 and the SPREAD value is 0.75, the number of 30 attributes of the original breast cancer data dropped to 12, the accuracy of the SPDTRS-PNN model training set is 99.25%, the accuracy of the test set is 97.04%, and the test time is 0.093 s. The experimental results show that the SPDTRS-PNN model can improve the ac-curacy of breast cancer recognition, reduce the time required for diagnosis.

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Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer

Kong

Zhou

Bian

et al. 2023

Sci Rep

View full text Add to dashboard Cite

show abstract

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A PNN based Malign Attack Detection and Classification Model

Cited by 1 publication

References 4 publications

Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer

Research on SPDTRS-PNN based intelligent assistant diagnosis for breast cancer

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