An ensemble multi-stream classifier for infant needs detection.

Hesham Ahmed Fahmy, Sherif Fadel Fahmy, Alberto A Del Barrio García, Guillermo Botella Juan

Open access · goldAbstract read

In one paragraph

Article in Heliyon, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

1.3field-weighted citation impact, top 18% of its field

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

0 citing papers in PubMed, 3 citations in OpenAlex.

No citing paper in PubMed yet.

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 at 2 institutions in 2 countries.

Hesham Ahmed FahmyDept. of Electronics and Communications, Arab Academy for Science, Technology and Maritime Transport, 2401 Smart Village Campus, Giza, 12577, Egypt.

Sherif Fadel FahmyDept. of Computer Engineering, Arab Academy for Science, Technology and Maritime Transport, El Moshir Ismail st., Cairo, 2033, Egypt.

Alberto A Del Barrio GarcíaDept. of Computer Architecture and Automation, Complutense University of Madrid, C/ Jose Santesmases 9, Madrid, 28040, Spain.

Guillermo Botella JuanDept. of Computer Architecture and Automation, Complutense University of Madrid, C/ Jose Santesmases 9, Madrid, 28040, Spain.

Arab Academy for Science, Technology, and Maritime Transport · EGUniversidad Complutense de Madrid · ES

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

In this paper, we propose a novel multi-stream video classifier for infant needs detection. The proposed system is an ensemble-based system that combines several machine learning to improve the overall result of the state-of-the-art algorithms. It is a multi-stream in the sense that it combines the output predictions of both audio and images of infants from every single classifier employed in the system for a unified result. This produces better performance and results compared to the previous other research techniques, which relied on only one of these modalities. For training and testing the proposed system, from the Dunstan Baby Language video collection, we built three separate datasets for videos, images, and sounds encompassing the five primary infant needs that require predicting. These are:

Indexed as

68T0568T0768T1068U10Deep learningDunstan baby languageEnsemble classifierInfant needsMachine learningVideo classifier

Identifiers

PMID37123937

DOI10.1016/j.heliyon.2023.e15098

PMCPMC10130778

OpenAlexW4362603295

What OpenQuestion holds

Textmetadata

LicenceCC BY-NC-ND

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.