Evidence map›Paper›PMID 33611749›Full record

ArticleEnvironmental science and pollution research international2021

Prediction of lead (Pb) adsorption on attapulgite clay using the feasibility of data intelligence models.

Suraj Kumar Bhagat, Mariapparaj Paramasivan, Mustafa Al-Mukhtar, Tiyasha Tiyasha, Konstantina Pyrgaki, Tran Minh Tung, Zaher Mundher Yaseen

Abstract read
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In one paragraph

Article in Environmental science and pollution research international, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Solidification of chromium-containing sludge with attapulgite combined alkali slag.Environmental science and pollution research international · 2022
    Article
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

7 authors.

Suraj Kumar BhagatFaculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Mariapparaj ParamasivanDepartment of Automotive Engineering, Arba Minch University, Arba Minch, Ethiopia.
Mustafa Al-MukhtarCivil Engineering Department, University of Technology, Baghdad, Iraq.
Tiyasha TiyashaFaculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Konstantina PyrgakiDepartment of Geology & Geoenvironment, National and Kapodistrian University of Athens, Panepistimiopolis Zographou, 15784, Athens, Greece.
Tran Minh TungFaculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam.
Zaher Mundher YaseenInstitute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam. zahermundheryaseen@duytan.edu.vn.ORCID http://orcid.org/0000-0003-3647-7137

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study investigates the performance of support vector machine (SVM), multivariate adaptive regression spline (MARS), and random forest (RF) models for predicting the lead (Pb) adsorption by attapulgite clay. Models are constructed using batch stochastic data of heavy metal (HM) concentrations under different physicochemical conditions. Implementation of auto-hyper-parameter tuning using grid-search approach and comparative analysis is performed against the benchmark artificial intelligence (AI) models. Models are constructed based on Pb concentration (IC), the dosage of attapulgite clay (dose), contact time (CT), pH, and NaNO

Indexed as

Artificial IntelligenceLeadAdsorptionClayFeasibility StudiesIntelligenceMagnesium CompoundsSilicon CompoundsattapulgiteClayLeadMagnesium CompoundsSilicon CompoundsArtificial intelligenceAttapulgite clayCopper adsorptionMARS modelPrediction

Identifiers

PMID33611749

What OpenQuestion holds

Textmetadata
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.