Evidence map›Paper›PMID 36554676›Full record

ArticleInternational journal of environmental research and public health2022

Forecasting the Status of Municipal Waste in Smart Bins Using Deep Learning.

Sabbir Ahmed, Sameera Mubarak, Jia Tina Du, Santoso Wibowo

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Sabbir AhmedUniSA STEM, University of South Australia, Adelaide, SA 5001, Australia.ORCID 0000-0001-8496-8745
Sameera MubarakUniSA STEM, University of South Australia, Adelaide, SA 5001, Australia.
Jia Tina DuUniSA STEM, University of South Australia, Adelaide, SA 5001, Australia.
Santoso WibowoSchool of Engineering and Technology, Central Queensland University, Melbourne, VIC 3000, Australia.ORCID 0000-0002-5318-8428

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The immense growth of the population generates a polluted environment that must be managed to ensure environmental sustainability, versatility and efficiency in our everyday lives. Particularly, the municipality is unable to cope with the increase in garbage, and many urban areas are becoming increasingly difficult to manage. The advancement of technology allows researchers to transmit data from municipal bins using smart IoT (Internet of Things) devices. These bin data can contribute to a compelling analysis of waste management instead of depending on the historical dataset. Thus, this study proposes forecasting models comprising of 1D CNN (Convolutional Neural Networks) long short-term memory (LSTM), gated recurrent units (GRU) and bidirectional long short-term memory (Bi-LSTM) for time series prediction of public bins. The execution of the models is evaluated by Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Coefficient determination (R

Indexed as

Deep LearningCitiesForecastingNeural Networks, ComputerTime Factorsdeep learningmunicipaltime serieswaste managementwaste prediction

Identifiers

PMID36554676
PMCPMC9779277

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.