Confusion Matrix for AdaBoost (3000 Iterations) gives detailing regarding the performance in terms of correctly and incorrectly classified instances for each class.

Confusion Matrix for AdaBoost (3000 Iterations) gives detailing regarding the performance in terms of correctly and incorrectly classified instances for each class.

<p>Confusion Matrix for AdaBoost (3000 Iterations) gives detailing regarding the performance in terms of correctly and incorrectly classified instances for each class.</p>

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Bibliographic Details
Main Author:	Sadia Nazim (21440115) (author)
Other Authors:	Muhammad Mansoor Alam (15285643) (author), Syed Safdar Rizvi (21440118) (author), Jawahir Che Mustapha (21440121) (author), Syed Shujaa Hussain (21440124) (author), Mazliham Mohd Suud (21440127) (author)
Published:	2025
Subjects:	Genetics Science Policy Biological Sciences not elsewhere classified relatively little study last ten years global predictive behavior various malware categories malware imagery data imagery malware data utilize machine learning revolutionized malware detection explainable deep learning trustworthy cybersecurity applications present research addresses malware detection techniques powerful ai models cam </ p deep learning malware variants models used xlink "> whereas ai significant characteristics random forest making process indefinable nature fractional elevation feature significance explainability techniques existing ai everyday activities establish difficulties diverse nature directed scholars cybersecurity would countless benefits comprehensive investigation comparative analysis cam approaches based methods also difficult
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Summary:	<p>Confusion Matrix for AdaBoost (3000 Iterations) gives detailing regarding the performance in terms of correctly and incorrectly classified instances for each class.</p>

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