Evidence map›Paper›PMID 41047407›Full record

ArticleScientific reports2025

Predictive modelling and high-performance enhancement smart thz antennas for 6 g applications using regression machine learning approaches.

Md Ashraful Haque, Md Mostafa Arafat, Isha Das, Geamel Alyami, Maruf Billah, Narinderjit Singh Sawaran Singh, Hanaa A Abdallah, Abdelhamied A Ateya, Hussein Shaman

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In one paragraph

Article in Scientific reports, 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

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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

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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. 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

9 authors.

Md Ashraful HaqueDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh. limon.ashraf@gmail.com.
Md Mostafa ArafatDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
Isha DasNetwork Communication and IoT Lab, Chittagong University of Engineering and Technology, Chittagong, Bangladesh.
Geamel AlyamiKing Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia. Galyami@kacst.gov.sa.
Maruf BillahDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1341, Bangladesh.
Narinderjit Singh Sawaran SinghFaculty of Data Science and Information Technology, INTI International University, Nilai, Malaysia.
Hanaa A AbdallahDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Abdelhamied A AteyaEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
Hussein ShamanKing Abdulaziz City for Science and Technology (KACST), Riyadh, Saudi Arabia. hshaman@kacst.gov.sa.

Funding

Princess Nourah bint Abdulrahman University, Riyadh,Saudi Arabia. (PNURSP2025R749)
6 · The paper itself

Abstract

This research introduces a novel design for a graphene-based multiple-input multiple-output (MIMO) antenna, specifically developed for sixth generation (6G) terahertz (THz) applications. The proposed antenna demonstrates multi-resonant behavior across three broad bandwidths: 2.479 THz (1.00-3.49 THz), 0.516 THz (3.64-4.156 THz), and 1.694 THz (4.50-6.20 THz). It achieves a peak gain of 13.41 dB, exceptional isolation of -34.2 dB, and a high efficiency of up to 90%. The design was developed using CST Studio Suite and validated through an RLC equivalent circuit model in ADS. This ensured accurate representation of its electromagnetic behavior. To improve the predictive capabilities of the antenna design process, five supervised regression-based machine learning (ML) models were employed. The models used were Extra Trees, Random Forest, Decision Tree, Ridge Regression, and Gaussian Process Regression. Among these, the Extra Trees Regression model delivered the highest prediction accuracy, achieving a mean absolute error (MAE) of 2.51%, a mean squared error (MSE) of 0.44%, and an R

Indexed as

6GMachine learningMIMO antennaTHz

Identifiers

PMID41047407
PMCPMC12497878

What OpenQuestion holds

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LicenceCC BY-NC-ND
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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.