Evidence map›Paper›PMID 38555354›Full record

ArticleScientific reports2024

Integrated smart dust monitoring and prediction system for surface mine sites using IoT and machine learning techniques.

Abhishek Kumar Tripathi, Mangalpady Aruna, Satyajeet Parida, Durgesh Nandan, P V Elumalai, E Prakash, Joshua Stephen Chellakumar Isaac JoshuaRamesh Lalvani, Koppula Srinivas Rao

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Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Review
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  3. 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

8 authors.

Abhishek Kumar TripathiDepartment of Mining Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, 53347, India. abhishekkumar@aec.edu.in.
Mangalpady ArunaDepartment of Mining Engineering, National Institute of Technology Karnataka, Surathkal, 575025, India.
Satyajeet ParidaDepartment of Mining Engineering, Aditya Engineering College, Surampalem, Andhra Pradesh, 53347, India.
Durgesh NandanSchool of Computer Science & Artificial Intelligence, SR University, Warangal, Telangana, 506004, India.
P V ElumalaiDepartment of Mechanical Engineering, Aditya Engineering College, Surampalem, India.
E PrakashDepartment of Mechtronics Engineering, Rajalaskhmi Engineering College, Mevalurkuppam, India.
Joshua Stephen Chellakumar Isaac JoshuaRamesh LalvaniFaculty of Mechanical Engineering, Arba Minch Institute of Technology, Arba Minch University, Arba Minch, Ethiopia. isaac.jrl@amu.edu.et.
Koppula Srinivas RaoDepartment of Computer Science and Engineering, MLR Institute of Technology, Hyderabad, Telangana, India.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The mining industry confronts significant challenges in mitigating airborne particulate matter (PM) pollution, necessitating innovative approaches for effective monitoring and prediction. This research focuses on the design and development of an Internet of Things (IoT)-based real-time monitoring system tailored for PM pollutants in surface mines, specifically PM 1.0, PM 2.5, PM 4.0, and PM 10.0. The novelty of this work lies in the integration of IoT technology for real-time measurement and the application of machine learning (ML) techniques for accurate prediction based on recorded dust pollutants data. The study's findings indicate that PM 1.0 pollutants exhibited the highest concentration in the atmosphere of the ball clay surface mine sites, with the stockyard site registering the maximum levels of PM pollutants (28.45 µg/m

Indexed as

Dust pollutantsIoT technologyMachine learning modelsMiner's safetyMining industry

Identifiers

PMID38555354
PMCPMC10981741

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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.