Comparison of the EODA algorithm with existing algorithms in terms of precision.

<p>Comparison of the EODA algorithm with existing algorithms in terms of precision.</p>

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Bibliographic Details
Main Author:	Sunil Kumar (102321) (author)
Other Authors:	Sudeep Varshney (21453384) (author), Usha Jain (21453387) (author), Prashant Johri (21453390) (author), Abdulaziz S. Almazyad (21453393) (author), Ali Wagdy Mohamed (21453396) (author), Mehdi Hosseinzadeh (8383239) (author), Mohammad Shokouhifar (20547564) (author)
Published:	2025
Subjects:	Biotechnology Space Science Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Information Systems not elsewhere classified nearest neighbors algorithm nearest neighbor search nearest neighbor identification identifying unusual patterns gained significant attention disrupt system modeling 53 %, outperforming 07 %, recall rf feature selection learning repository datasets named eoda ), 49 %, parameter selection deep learning significantly deviate shadow features second stage results demonstrate relevant attributes rapid growth random forest parameter estimation often limited normal behavior inaccurate results highest z first stage existing techniques eoda approach enhanced knn data size data science clustering phase boruta method
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