Integrating Multiple Methodologies, Segment Anything Model and AlexNet, for Enhanced Accuracy in Garbage Classification

This study aimed to classify garbage images using the deep convolutional neural network model AlexNet. Over 150 epochs of validation were performed. Our method demonstrates how deep learning models can be used to improve and automate waste management systems. This research is novel because it examines multiple approaches for a potential fusion technique and evaluates how well-suited and improved various machine learning and image detection models are. This study is the first in a planned series of research using SAM and AlexNet models, providing a foundation for future research in this area. Through incorporating novel methodologies and capitalizing on the advantages of diverse frameworks, this study advances the creation of more effective and efficient waste management strategies. This fundamental work highlights the usefulness and suitability of cutting-edge machine learning methods for environmental sustainability initiatives by laying the groundwork for significant advancements in waste classification precision and system resilience.