Testing accuracy and performance metrics for Alexnet in the original RGB, True (T) neutrosophic, and Falsity (F) neutrosophic domains after feature selection using GWO for DSWM1 and DSWM2.

Testing accuracy and performance metrics for Alexnet in the original RGB, True (T) neutrosophic, and Falsity (F) neutrosophic domains after feature selection using GWO for DSWM1 and DSWM2.

<p>Testing accuracy and performance metrics for Alexnet in the original RGB, True (T) neutrosophic, and Falsity (F) neutrosophic domains after feature selection using GWO for DSWM1 and DSWM2.</p>

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Bibliographic Details
Main Author:	Nour Eldeen Mahmoud Khalifa (19259450) (author)
Other Authors:	Mohamed Hamed N. Taha (19259453) (author), Heba M. Khalil (11237892) (author), Mazhar Hussain Malik (17081487) (author)
Published:	2024
Subjects:	Ecology Science Policy Biological Sciences not elsewhere classified Information Systems not elsewhere classified grey wolf optimization achieves competitive results xlink "> sustainability image features selection datasets exist publicly waste management 2 waste management 1 three neutrosophic domains deep transfer learning sustainability </ p p ), recall classify waste objects ta ), precision ondl model proved ondl model architecture different dtl models deep learning management practices waste classification classifying waste two datasets three classes sustainability ecosystem sustainability challenge selection process ondl model dswm1 ), two classes testing accuracy tested models related works r ), performance metrics neutrosophic domain key factor f1 score crucial role constructed based computer algorithms building components applied correctly
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