Machine learning-based estimation and optimization of phoenix Dactylifera Seed Powder reinforced vinyl ester bio-composites.

V Vignesh, S Sathees Kumar, A M Arun Mohan, I Vijay Arasu, N Nagaprasad, Ramaswamy Krishnaraj

Abstract read

In one paragraph

Article in Scientific reports, 2026. 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.

Tribological performance of Al5052/B₄C/Granite hybrid composites: machine learning prediction and multi-response optimization.Scientific reports · 2026
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

6 authors.

V VigneshCenter for Advanced Energy Materials, SRM TRP Engineering College, Tiruchirappalli, Tamil Nadu, 621105, India.

S Sathees KumarDepartment of Mechanical Engineering, Saveetha Engineering College, Thandalam, Chennai, Tamil Nadu, 602 105, India. shrutishyami@gmail.com.

A M Arun MohanDepartment of Civil Engineering, Sethu Institute of Technology, Kariapatti, Tamil Nadu, 626115, India.

I Vijay ArasuDepartment of Mechanical Engineering, K. Ramakrishnan College of Engineering, Samayapuram, Tiruchirappalli, Tamil Nadu, 621112, India.

N NagaprasadDepartment of Mechanical Engineering, ULTRA College of Engineering and Technology, Madurai, Tamil Nadu, 625104, India.

Ramaswamy KrishnarajDepartment of Mechanical Engineering, Dambi Dollo University, Dambi Dollo, Ethiopia. prof.dr.krishnaraj@dadu.edu.et.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study aims to develop a robust machine learning (ML) framework for accurately predicting the mechanical and thermal properties of sustainable vinyl ester composites reinforced with Phoenix Dactylifera Seed Powder (PDSP). To achieve this, multiple ML algorithms such as Linear Regression, Support Vector Machine (SVM), Random Forest and Decision Tree were evaluated to predict the mechanical and thermal using both experimental data generated in-house and data collected from past literature review, Among which the SVM model demonstrated superior predictive accuracy, achieving high R

Indexed as

EstersMachine LearningPhoeniceaeSeedsVinyl CompoundsPowdersPrediction AlgorithmsPredictive Learning ModelsRandom ForestSupport Vector MachineTensile StrengthEstersPowdersVinyl CompoundsDactylifera seed powderMachine learning modelMechanical propertiesThermal propertiesVinyl ester resin

Identifiers

PMID41617751

DOI10.1038/s41598-026-37202-w

PMCPMC12913890

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.