## 1 Introduction  
Solid waste management stands among the most pressing global challenges today. The generation of waste has steadily increased, driven by factors such as rapid population growth, accelerated industrialization, and economic development [1]. This trend has given rise to a series of environmental and social problems, particularly regarding public health and the conservation of the natural environment [2]. Proper waste management is essential to mitigate these negative effects, requiring the implementation of efficient processes that encompass waste collection at the source, transportation, treatment, segregation, and final disposal [3]. Developing countries, in particular, face the challenge of optimizing these processes, whose effectiveness largely depends on the knowledge and resources available to the institutions responsible for their implementation [4]. Within the framework of environmental policies, solid waste management holds a fundamental position. Its proper implementation aims not only to reduce environmental impact but also to improve living conditions, prevent public health issues, and promote sustainability [5]. Solid waste classification has gained prominence as a key measure to achieve sustainable economic development goals, reduce poverty, and preserve ecosystems. The adoption of efficient solid waste classification systems is crucial to creating jobs, stimulating the recycling sector, and fostering social well-being [6]. Therefore, efforts must focus on improving waste separation practices at the source, benefiting both the environment and the communities that rely on these economic activities. Given this scenario, there is a need to develop innovative technologies that optimize waste classification at its origin, enabling a more effective resolution of this issue. With the advancement of emerging technologies such as the Internet of Things (IoT) and Artificial Intelligence, governments worldwide are working toward the creation of \"smart cities,\" where efficient waste management plays a fundamental role [7]. These technologies have great potential to automate and optimize waste classification processes. However, most solutions developed for waste classification focus on image recognition using techniques such as convolutional neural networks, optical methods, and spectrophotometry, among others. Below are some of the works related to this field:  
Machine learning-based systems for specific materials:  
* In [8], researchers designed recycling machines equipped with ML to classify glass and plastic bottles. Although effective, these solutions are limited to transparent materials due to the optical properties required for their detection. Hybrid solutions with spectrophotometry and ML:
* In [9], a study integrated spectrophotometry with ML, achieving high accuracy in waste classification. However, the high cost of implementation makes it difficult to adopt in resource-limited environments. Classification using convolutional neural networks (CNN):
* In [10], researchers proposed YOLO-Green, a model optimized for real-time detection that classifies waste from images. Although efficient, its performance decreases considerably when processing contaminated waste, a common problem in recycling centers. In [11], researchers developed a system to classify recyclable materials using CNN, achieving an accuracy of 94.5%. However, its implementation depends on expensive machine vision equipment and controlled conditions, which limits its applicability in contaminated environments. Deep Learning Techniques:
* In [12], researchers propose ConvoWaste, which uses deep neural networks to classify waste into categories such as plastics and metals, achieving an accuracy of 98%. * In [13], the research proposes an intelligent model to categorize waste using deep learning techniques. AlexNet, DenseNet121, and SqueezeNet have been implemented to perform the classification tasks. The obtained results showed great success in the classification process.