Smart garbage classification for recycling uses machine learning algorithms to classify waste materials like plastic, paper, and metal into their respective categories. This involves training a model with labeled images and using it to predict new images.

Using Intel® oneAPI Deep Neural Network Library (oneDNN) is critical to improving recycling efficiency and accuracy. oneDNN optimizes deep learning operations, leading to faster run times and better performance on modern CPUs. This optimization is crucial in recovering more recyclable materials, reducing human error, and improving accuracy. Overall, oneDNN has significant implications for promoting a sustainable future through improved recycling processes.

Speaker

Melbin Martin is doing his final year as a Master of Computer Applications (MCA) student. He is passionate about technology and its boundless potential. Melbin's interests lie in the fields of data analytics and cricket analytics, where he enjoys analyzing data to derive meaningful insights. With a good foundation in programming languages and software development, he has gained his skills in data collection, processing, and interpretation. He is excited to apply his knowledge in these areas to make data-driven decisions and deliver valuable insights to organizations. Outside of his academic pursuits, he is an avid movie enthusiast.