Artificial intelligence-based solution for sorting COVID related medical waste streams and supporting data-driven decisions for smart circular economy practice

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

1 Scopus Citations
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Author(s)

  • Mazin Abed Mohammed
  • Karrar Hameed Abdulkareem
  • Robertas Damasevicius
  • Salama A. Mostafa
  • Mashael S. Maashi

Related Research Unit(s)

Detail(s)

Original languageEnglish
Pages (from-to)482-494
Journal / PublicationProcess Safety and Environmental Protection
Volume152
Online published19 Jun 2021
Publication statusPublished - Aug 2021

Abstract

Waste generation is a continuous process that needs to be managed effectively to ensure environmental safety and public health. The recent circular economy (CE) practices have brought a new shape for the waste management industry, creating value from the generated waste. The shift to a CE represents one of the most significant challenges, particularly in sorting and classifying generated waste. Addressing these challenges would facilitate the recycling industry and helps in promoting remanufacturing. But in the COVID times, most of the generated waste is getting mixed with conventional waste types, especially in the global south. The pandemic has resulted in colossal infectious waste generation. Its handling became the most significant challenge raising fears and concerns over sorting and classifying. Hence, this study proposes an Artificial Intelligence (AI) based automated solution for sorting COVID related medical waste streams from other waste types and, at the same time, ensures data-driven decisions for recycling in the context of CE. Metal, paper, glass waste categories, including the polyethylene terephthalate (PET) waste from the pandemic, are considered. The waste type classification is done based on the image-texture-dependent features, which provided an accurate sorting and classification before the recycling process starts. The features are fused using the proposed decision-level feature fusion scheme. The classification model based on the support vector machine (SVM) classifier performs best (with 96.5 % accuracy, 95.3 % sensitivity, and 95.9 % specificity) in classifying waste types in the context of circular manufacturing and exhibiting the abilities to manage the COVID related medical waste mixed.

Research Area(s)

  • Medical waste streams, Smart circular economy, COVID waste management, Waste sorting, Feature fusion, Machine learning

Citation Format(s)

Artificial intelligence-based solution for sorting COVID related medical waste streams and supporting data-driven decisions for smart circular economy practice. / Kumar, Nallapaneni Manoj; Mohammed, Mazin Abed; Abdulkareem, Karrar Hameed; Damasevicius, Robertas; Mostafa, Salama A.; Maashi, Mashael S.; Chopra, Shauhrat S.

In: Process Safety and Environmental Protection, Vol. 152, 08.2021, p. 482-494.

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review