Evidence map›Paper›PMID 39521430›Full record

ArticleJournal of advanced research2025

Advanced machine learning schemes for prediction CO

Yongjun Wang, Mingze Guo, Hung Vo Thanh, Hemeng Zhang, Xiaoying Liu, Qian Zheng, Xiaoming Zhang, Mohammad Sh Daoud, Laith Abualigah

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Journal of advanced research, 2025. 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

9 authors.

Yongjun WangCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China; Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Huludao 125105, China. Electronic address: wangyongjun@lntu.edu.cn.
Mingze GuoCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China.
Hung Vo ThanhLaboratory for Computational Mechanics, Institute for Computational Science and Artificial Intelligence, Van Lang University, Ho Chi Minh City, Viet Nam; Applied Science Research Center. Applied Science Private University, Amman, Jordan; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India.
Hemeng ZhangCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China; Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Huludao 125105, China.
Xiaoying LiuCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China; Key Laboratory of Mine Thermodynamic Disasters and Control of Ministry of Education, Huludao 125105, China.
Qian ZhengCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China.
Xiaoming ZhangCollege of Safety Science and Engineering, Liaoning Technical University, Huludao 125105, China; Institute of Engineering and Environment, Liaoning Technical University, Huludao 125105, China.
Mohammad Sh DaoudCollege of Engineering, Al Ain University, Abu Dhabi, United Arab Emirates.
Laith AbualigahComputer Science Department, Al al-Bayt University, Mafraq 25113, Jordan; Artificial Intelligence and Sensing Technologies (AIST) Research Center, University of Tabuk, Tabuk 71491, Saudi Arabia; Applied science research center, Applied science private university, Amman 11931, Jordan; Centre for Research Impact & Outcome, Chitkara University Institute of Engineering and Technology, Chitkara University, Rajpura, 140401, Punjab, India; School of Engineering and Technology, Sunway University Malaysia, Petaling Jaya 27500, Malaysia.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionUnderground coal fires pose significant environmental and health risks due to releasing CO

objectivesBy leveraging the power of advanced machine learning schemes and experimental approaches, this research aims to provide valuable insights into CO

methodsThe study involves the collection of an experimental dataset specific to underground coal fire areas, encompassing various parameters related to CO

resultsThe NGRB model yields the best predictive performances with R

Indexed as

Climate changeCO(2) fluxEnvironmental monitoringNatural gradient boosting regressionUnderground coal fire

Identifiers

PMID39521430
PMCPMC11976562

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

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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.