A Spleen Infection Recognition Approach Using Shallow Neural Network in Comparison with Support Vector Machine

K. Fakruddin, N. Deepa

2023

Abstract

The research primarily investigates the accuracy differences between spleen infection segmentation and classification using the Novel Shallow Neural Network (NSNN) versus the SVM. For the study, spleen infections in patients were detected using the NSNN (15 samples) and compared against results from SVM (another 15 samples), operating with an 80% G-power. Findings indicated the NSNN had an accuracy of 75.27%, marginally superior to the SVM's 66.33%. Despite this disparity in accuracy, there was no statistically significant difference between the two methods, evidenced by an independent sample T-Test result of p=0.25. In conclusion, NSNN offers a slightly enhanced accuracy rate in contrast to SVM within the realm of machine learning.

Paper Citation

in Harvard Style

Fakruddin K. and Deepa N. (2023). A Spleen Infection Recognition Approach Using Shallow Neural Network in Comparison with Support Vector Machine. In Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT; ISBN 978-989-758-661-3, SciTePress, pages 480-486. DOI: 10.5220/0012518900003739

in Bibtex Style

@conference{ai4iot23,
author={K. Fakruddin and N. Deepa},
title={A Spleen Infection Recognition Approach Using Shallow Neural Network in Comparison with Support Vector Machine},
booktitle={Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT},
year={2023},
pages={480-486},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0012518900003739},
isbn={978-989-758-661-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 1st International Conference on Artificial Intelligence for Internet of Things: Accelerating Innovation in Industry and Consumer Electronics - Volume 1: AI4IoT
TI - A Spleen Infection Recognition Approach Using Shallow Neural Network in Comparison with Support Vector Machine
SN - 978-989-758-661-3
AU - Fakruddin K.
AU - Deepa N.
PY - 2023
SP - 480
EP - 486
DO - 10.5220/0012518900003739
PB - SciTePress