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Interdisciplinary Implementation of Supervised Real-Time Waste Management

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Abstract

Over time, we are observing a colossal increase in the jillion of waste in the world, waste management is a standout among the most pivotal constituents for the comeliness surroundings and general wellbeing. Significantly influenced by referred components, data were collected from the liable government provincial and state authorities over surveys and consultations, and we saw failure in maintaining a timely collection of waste. To overcome this, a multi-disciplinary model was made, it used a multi-hybrid neural net to segregate the waste into either dry or wet which was previously uncategorized alongside the microcontroller which oversaw the level of garbage in the bins, also sustained restraint by ensuring regular collection of waste with an attendance system. This idea was then supported by an android application to track the full bins in the area. Besides this, it also assisted the managerial team to monitor the movement of liable workers for emptying the bin. This study hereby presents the graphical representations of recently gathered information and an inventive way of dealing with the problems utilizing the wide scope of technology.

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Authors and Affiliations

  1. Department of Computer Science and Engineering (DCSE), Amity School of Engineering and Technology (ASET), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Buddha Nagar, Noida, Uttar Pradesh, 201313, India

    Praveen Kumar & Rishabh Jain

  2. Amity Institute of Geoinformatics and Remote Sensing (AIGIRS), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Buddha Nagar, Noida, Uttar Pradesh, 201313, India

    Deepak Kumar

  3. Department of Information Technology (DIT), Amity School of Engineering & Technology (ASET), Amity University Uttar Pradesh (AUUP), Sector-125, Gautam Buddha Nagar, Noida, Uttar Pradesh, 201313, India

    Seema Rawat

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  1. Praveen Kumar
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Contributions

PK and DK conceived and designed the study, and RJ performed the research, analyzed the data, and SR contributed to editorial input.

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Correspondence to Deepak Kumar.

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This article is part of the topical collection “Deep learning approaches for data analysis: A practical perspective” guest edited by D. Jude Hemanth, Lipo Wang and Anastasia Angelopoulou.

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Kumar, P., Jain, R., Kumar, D. et al. Interdisciplinary Implementation of Supervised Real-Time Waste Management. SN COMPUT. SCI. 2, 270 (2021). https://doi.org/10.1007/s42979-021-00651-3

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