1. Artificial intelligence-based solution for sorting COVID related medical waste streams and supporting data-driven decisions for smart circular economy practice
- Author
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Nallapaneni Manoj Kumar, Robertas Damasevicius, Mashael S. Maashi, Shauhrat S. Chopra, Salama A. Mostafa, Karrar Hameed Abdulkareem, and Mazin Abed Mohammed
- Subjects
Waste sorting ,021110 strategic, defence & security studies ,Environmental Engineering ,business.industry ,Process (engineering) ,Computer science ,General Chemical Engineering ,Circular economy ,0211 other engineering and technologies ,Sorting ,Context (language use) ,02 engineering and technology ,010501 environmental sciences ,01 natural sciences ,Data-driven ,Support vector machine ,Environmental Chemistry ,Artificial intelligence ,Safety, Risk, Reliability and Quality ,business ,Remanufacturing ,0105 earth and related environmental sciences - 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.
- Published
- 2021