Evidence map›Paper›PMID 40044756›Full record

ArticleScientific reports2025

Multiband THz MIMO antenna with regression machine learning techniques for isolation prediction in IoT applications.

Md Ashraful Haque, Kamal Hossain Nahin, Jamal Hossain Nirob, Md Kawsar Ahmed, Narinderjit Singh Sawaran Singh, Liton Chandra Paul, Abeer D Algarni, Mohammed ElAffendi, Ahmed A Abd El-Latif, Abdelhamied A Ateya

Abstract read
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 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing 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

15 citing papers in PubMed.

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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

10 authors.

Md Ashraful HaqueDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.
Kamal Hossain NahinDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.
Jamal Hossain NirobDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.
Md Kawsar AhmedDepartment of Electrical and Electronic Engineering, Daffodil International University, Dhaka, 1207, Bangladesh.
Narinderjit Singh Sawaran SinghFaculty of Data Science and Information Technology, INTI International University, Persiaran Perdana BBN, Putra Nilai, 71800, Nilai, Negeri Sembilan, Malaysia.
Liton Chandra PaulDepartment of Electrical, Electronic and Communication Engineering, Pabna University of Science and Technology, Pabna, Bangladesh.
Abeer D AlgarniDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Mohammed ElAffendiEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
Ahmed A Abd El-LatifEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia.
Abdelhamied A AteyaEIAS Data Science Lab, College of Computer and Information Sciences, Prince Sultan University, 11586, Riyadh, Saudi Arabia. aateya@psu.edu.sa.

Funding

Princess Nourah Bint Abdulrahman University PNURSP2025R51
6 · The paper itself

Abstract

The rapid evolution of Internet of Things (IoT) applications demands advancements in wireless communication technologies to handle increasing data rates and connectivity requirements. This article presents our novel research on utilizing machine learning techniques to enhance the efficiency of MIMO antennas for Wireless Communication and IoT applications in the Terahertz (THz) frequency band. Our research assesses antenna performance using various methodologies, including simulation and RLC equivalent circuit models. The proposed design operates at 6.51 THz, 7.48 THz, and 8.46 THz, with bandwidths of 0.7 THz, 0.69 THz, and 0.89 THz, respectively. It features a maximum gain of 13.53 dBi and compact dimensions of 160 × 75 μm

Indexed as

6G communicationGrapheneHigh efficiencyMIMO antennaMLRLCTHz antenna

Identifiers

PMID40044756
PMCPMC11882809

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.