ArticleHeliyon2019
A multi-class classification system for continuous water quality monitoring.
Article in Heliyon, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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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.
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.
Who cites it
2 citing papers in PubMed.
- The water quality of the Upper Citarum: Applying the overall index of pollution, Said-WQI, and pollution index methods.Heliyon · 2025Article
- BenchMetrics Prob: benchmarking of probabilistic error/loss performance evaluation instruments for binary classification problems.International journal of machine learning and cybernetics · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The issue addressed in this exposition is the classification of multivariate data collected through different sensors for water quality monitoring. Multivariate data are sequences that have various attributes in every instance of the sequences. A few endeavours exist to address this issue; however, none of them has given full emphasis on continuous dataset. Another solution for this issue is to reduce the instances to a single attribute while losing significant information. Different arrangements address both the multivariate and the sequential part of the data yet give an un-versatile solution. The proposed algorithm is not only able to monitor continuous water quality, but it also produces a better classification model for other continuous datasets as well. Instead of decreasing the attributes of the dataset, we introduce three additional reference indicators which are dependent on the actual attributes. We compare the classification accuracy of our proposed algorithm with standard classification models. The proposed method gives better classification accuracy compared to existing methods.
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Registered trials
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.