ONDL: An optimized Neutrosophic Deep Learning model for classifying waste for sustainability

by Nour Eldeen Mahmoud Khalifa, Mohamed Hamed N. Taha, Heba M. Khalil, Mazhar Hussain Malik

Sustainability has become a key factor on our planet. If this concept is applied correctly, our planet will be greener and more eco-friendly. Nowadays, waste classification and management practices have become more evident than ever. It plays a crucial role in the sustainability ecosystem. Computer algorithms and deep learning can help in this sustainability challenge. In this paper, An Optimized Neutrosophic Deep Learning (ONDL) model was proposed to classify waste objects. Two datasets were tested in this research {Dataset for Waste Management 1 (DSWM1), and Dataset for Waste Management 2 (DSWM2)}. DSWM1 consists of two classes (Organic or Recycled) objects. The DSWM2 consists of three classes (Organic, Recycled, or Non-Recyclable) objects. Both datasets exist publicly on the internet. The ONDL model architecture is constructed based on Alexnet as a Deep Transfer Learning (DTL) model and the conversion of images to True (T) neutrosophic domain and Grey Wolf Optimization (GWO) for the image features selection. The selection process of the building components of the ONDL model is comprehensive as different DTL models (Alexnet, Googlenet, and Resnet18) are tested, and three neutrosophic domains (T, I, and F) domain are included. The ONDL model proved its efficiency against all the tested models, moreover, it achieves competitive results with related works in terms of testing accuracy and performance metrics. In DSWM1, the ONDL model achieved 0.9189, 0.9177, 0.9176, and 0.9177 in Testing Accuracy (TA), Precision (P), Recall (R), and F1 score. In DSWM2, it achieved 0.8532, 0.7728, 0.7944, and 0.7835 in TA, P, R, and F1 Score consequently.