Enhancing waste management through municipal solid waste classification: a convolutional neural network approach

dc.contributor.authorMd. Tarequzzaman
dc.contributor.authorMojahidul Alom Akash
dc.contributor.authorZakir Hossain
dc.contributor.authorMd. Sabbir Reza
dc.contributor.authorShajjadul Haque
dc.date.accessioned2026-03-06T08:07:48Z
dc.date.issued2025-12-06
dc.description.abstractThe escalation of population, economic expansion, and industrialization has resulted in an increase in waste production.This has made waste management more challenging and has resulted in environmental deterioration, negatively impacting the quality of life. Recycling, reducing, and reusing are viable methods to eradicate the escalating waste issue, requiring the appropriate classification of municipal solid waste. This study focuses on comparing six advanced waste classification systems that employ a pre-trained convolutional neural network (CNN) designed to recognize twelve distinct categories of municipal waste.
dc.identifier.citationTarequzzaman, M., Akash, M. A., Hossain, Z., Reza, M. S., & Haque, S. (2025). Enhancing waste management through municipal solid waste classification: a convolutional neural network approach. Int J Artif Intell, 14(6), 4775-4786.
dc.identifier.issn20894872
dc.identifier.urihttp://dspace.uttarauniversity.edu.bd:4000/handle/123456789/1342
dc.language.isoen_US
dc.publisherIAES International Journal of Artificial Intelligence
dc.subjectWaste Management
dc.subjectDeep Learning
dc.subjectArtificial Intelligence (AI)
dc.subjectWaste Classification
dc.titleEnhancing waste management through municipal solid waste classification: a convolutional neural network approach
dc.typeArticle

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