Zhitong Lee
ABSTRACT
In recent years, waste segregation, treatment, and recycling have become critical global issues, drawing significant attention worldwide. However, the efficiency of recycling processes can be influenced by numerous factors. Among these, waste classification plays a crucial role, and researchers have explored the application of machine learning to automate this step. Nonetheless, traditional machine learning approaches often require skilled professionals to invest substantial time in debugging, and achieving satisfactory accuracy can be challenging. To address this concern, we propose leveraging Automated Machine Learning (AutoML) to enhance the speed and accuracy of waste classification, thereby expediting the implementation of waste segregation policies. Our findings indicate that AutoML outperforms traditional machine learning models, requiring less time and energy, while achieving an impressive accuracy and precision rate of 95.1%.
- C. Marchesi, M. Rani, S. Federici, M. Lancini, and L. E. Depero, "Evaluating chemometric strategies and machine learning approaches for a miniaturized near-infrared spectrometer in plastic waste classification," Acta IMEKO, vol. 12, no. 2, p. 1-7, 2023.Google Scholar
Cross Ref
- H. Wang, Q. Zhuge, E. H.-M. Sha, R. Xu, and Y. Song, "Optimizing Efficiency of Machine Learning Based Hard Disk Failure Prediction by Two-Layer Classification-Based Feature Selection," Applied Sciences, vol. 13, no. 13, p. 7544, 2023.Google ScholarCross Ref
- M. Malik , "Machine Learning-Based Automatic Litter Detection and Classification Using Neural Networks in Smart Cities," International Journal on Semantic Web and Information Systems (IJSWIS), vol. 19, no. 1, p. 1-20, 2023.Google ScholarDigital Library
- E. Linß, J. Walz, and C. Könke, "Image analysis for the sorting of brick and masonry waste using machine learning methods," Acta IMEKO, vol. 12, no. 2, p. 1-5, 2023.Google ScholarCross Ref
- S. K. Karmaker, M. M. Hassan, M. J. Smith, L. Xu, C. Zhai, and K. Veeramachaneni, "Automl to date and beyond: Challenges and opportunities," ACM Computing Surveys (CSUR), vol. 54, no. 8, pp. 1-36, 2021.Google ScholarDigital Library
- K. Filippou, G. Aifantis, G. A. Papakostas, and G. E. Tsekouras, "Structure Learning and Hyperparameter Optimization Using an Automated Machine Learning (AutoML) Pipeline," Information, vol. 14, no. 4, p. 232, 2023.Google ScholarCross Ref
- S. M. Hussaine and L. Mu, "Intelligent Prediction of Maximum Ground Settlement Induced by EPB Shield Tunneling Using Automated Machine Learning Techniques," Mathematics, vol. 10, no. 24, p. 4637, 2022.Google ScholarCross Ref
- J. Ma , "Machine learning models for slope stability classification of circular mode failure: an updated database and automated machine learning (AutoML) approach," Sensors, vol. 22, no. 23, p. 9166, 2022.Google ScholarCross Ref
- W. Wu , "Application of local fully Convolutional Neural Network combined with YOLO v5 algorithm in small target detection of remote sensing image," PloS one, vol. 16, no. 10, p. e0259283, 2021.Google ScholarCross Ref
- M. Yang and G. Thung, "Classification of trash for recyclability status," CS229 project report, vol. 2016, no. 1, p. 3, 2016.Google Scholar
Index Terms
- Automated Machine Learning in Waste Classification: A Revolutionary Approach to Efficiency and Accuracy
Recommendations
Conventional Machine Learning Approach for Waste Classification
AICCC '23: Proceedings of the 2023 6th Artificial Intelligence and Cloud Computing ConferenceWaste management is a complex and challenging process, especially waste classification to sort waste by categories. The paper aims to overcome these challenges by proposing a waste classification approach that uses various feature extraction algorithms ...
Small Visual Object Detection in Smart Waste Classification Using Transformers with Deep Learning
Image and Vision ComputingAbstractSmart object waste classification is relatively essential for protecting the environment and saving resources. This is considered a vital pathway towards sustainability. In waste classification, we see that it is challenging to detect waste of ...
A Vision Based Approach to Localize Waste Objects and Geometric Features Exaction for Robotic Manipulation
AbstractThe quantity of waste generated is increasing daily, demanding an intelligent waste management system for effective and timely solutions. Pollution induced by garbage has been one of the major problems globally for a long time. The unawareness ...
Comments