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dc.contributor.authorSyeed, M M Mahbubul
dc.contributor.authorKarim, Md. Rajaul
dc.contributor.authorHossain, Md Shakhawat
dc.contributor.authorFatema, Kaniz
dc.contributor.authorUddin, Mohammad Faisal
dc.contributor.authorKhan, Razib Hayat
dc.date.accessioned2023-10-25T08:43:22Z
dc.date.available2023-10-25T08:43:22Z
dc.date.issued2023-05
dc.identifier.urihttps://ar.iub.edu.bd/handle/123456789/586
dc.description.abstractSurface water is heavily exposed to contamination as this is the ubiquitous source for the majority of water needs. This situation is exaggerated by excessive population, heavy industrialization, rapid urbanization, and ad-hoc monitoring. Comprehensive measurement and knowledge extraction of surface water pollution is therefore pivotal for ensuring safe and hygienic water use. However, current process of surface water quality profiling involves laboratory-based manual sample collection and testing, which is tardy, expensive, error-prone, and untraceable. This paper, therefore presents the design and development of an IoT integrated water quality profiling system that possesses a novel plug-and-play physical layer for the sensor actuation, and an AI powered fog computing based cloud application layer for remote water quality parameter measurement and data acquisition, remote data logging, monitoring and control, with data analytic for critical reasoning and decision makingen_US
dc.publisherIndependent University, Bangladeshen_US
dc.subjectIoTen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectSurface Water Qualityen_US
dc.titleAn IoT Intensive AI-integrated System for Optimized Surface Water Quality Profilingen_US
dc.typeArticleen_US


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  • 2023 [67]
    Research articles produced by the CSE department in the year 2023

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