Revolutionizing Recycling: Smart Garbage Classification using oneDNN for Improved Efficiency and Sustainability
Smart garbage classification for recycling utilizes 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. The use of oneAPI Deep Neural Network Library (oneDNN) is critical to improve recycling efficiency and accuracy. oneDNN optimizes deep learning operations, leading to faster execution 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(s)
Melbin Martin - Christ University/Masters Student
Melbin Martin is doing his final year as a Master of Computer Applications (MCA) student. He is passionate about technology and its boundless potential. His particular 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 and enjoy immersing myself in the world of cinema.
