Comparative analysis of machine learning techniques for metamaterial absorber performance in terahertz applications

Metamaterial absorbers (MMAs) have tremendous potential for controlling and modulating Terahertz electromagnetic (EM) waves. It is challenging to design MMAs for optimal performance using conventional methods due to their time-consuming and computationally demanding nature. Machine learning (ML) reg...

Full description

Bibliographic Details
Published in:	Alexandria Engineering Journal
Main Authors:	Prince Jain, Mohammad Tariqul Islam, Ahmed S. Alshammari
Format:	Article
Language:	English
Published:	Elsevier 2024-09-01
Subjects:	Bagging Extra trees K-nearest neighbors Metamaterial absorber Machine learning Random forest
Online Access:	http://www.sciencedirect.com/science/article/pii/S1110016824005799

Similar Items

Machine Learning Techniques for Predicting Metamaterial Microwave Absorption Performance: A Comparison
by: Prince Jain, et al.
Published: (2023-01-01)

The Use of Machine Learning in Real Estate Research
by: Lennon H. T. Choy, et al.
Published: (2023-03-01)

Analysis of the Effectiveness of Traditional and Ensemble Machine Learning Models for Mushroom Classification
by: Neny Sulistianingsih, et al.
Published: (2025-06-01)

Machine Learning Classifiers Based Classification For IRIS Recognition
by: Bahzad Taha Chicho, et al.
Published: (2021-05-01)

Switchable Metamaterial With Terahertz Buffering and Absorbing Performance
by: Chenxi Liu, et al.
Published: (2021-01-01)

Diabetes Classification Using Machine Learning Techniques
by: Methaporn Phongying, et al.
Published: (2023-05-01)

Ultra-broadband terahertz metamaterial absorber based on flexible wave-absorbing material
by: Dandan Chen, et al.
Published: (2023-09-01)

Enhanced the sensing sensitivity of the metamaterial absorbers with patterned convex graphene in the terahertz
by: Yun Liu, et al.
Published: (2024-01-01)

Utilizing machine learning for predictive maintenance of production machinery in small and medium enterprises
by: Yopa Eka Prawatya, et al.
Published: (2025-06-01)

Development of a machine learning framework for radiation biomarker discovery and absorbed dose prediction
by: Björn Andersson, et al.
Published: (2023-05-01)

Vanadium dioxide-based tunable broadband metamaterial absorber in the terahertz
by: Yuqi Zhu
Published: (2025-01-01)

Development of a Model for the Prediction of Lumpy Skin Diseases using Machine Learning Techniques
by: Olatayo Moses Olaniyan, et al.
Published: (2023-10-01)

A compact complementary split ring resonator (CSRR) based perfect metamaterial absorber for energy harvesting applications
by: Najeeb Ullah, et al.
Published: (2023-09-01)

Flexible Terahertz Metamaterials Absorber based on VO<sub>2</sub>
by: Zhaoxia Jiang, et al.
Published: (2023-05-01)

Tunable Dual Broadband Terahertz Metamaterial Absorber Based on Vanadium Dioxide
by: Xiao-Fei Jiao, et al.
Published: (2020-10-01)

Determining the Quality of Dairy Products Using Machine Learning Techniques
by: Nour Fadel, et al.
Published: (2024-03-01)

Graphene-based metamaterial ultrawideband absorber with enhanced terahertz performance
by: Abbas Movahednia, et al.
Published: (2024-10-01)

Tunable Dual-Broadband Terahertz Absorber with Vanadium Dioxide Metamaterial
by: Hengli Feng, et al.
Published: (2022-05-01)

Optimizing Methanol Injection Quantity for Gas Hydrate Inhibition Using Machine Learning Models
by: Mohammed Hilal Mukhsaf, et al.
Published: (2025-03-01)

Leveraging Artificial Intelligence in Public Health: A Comparative Evaluation of Machine-Learning Algorithms in Predicting COVID-19 Mortality
by: Eric B. Weiser
Published: (2025-03-01)

Sentiment classification of Roman-Urdu opinions using Naïve Bayesian, Decision Tree and KNN classification techniques
by: Muhammad Bilal, et al.
Published: (2016-07-01)

High-Sensitivity Temperature Sensor Based on the Perfect Metamaterial Absorber in the Terahertz Band
by: Yan Wang, et al.
Published: (2023-01-01)

Tailoring polarization and magnetization of absorbing terahertz metamaterials using a cut-wire sandwich structure
by: Hadi Teguh Yudistira, et al.
Published: (2018-05-01)

Tunable Broadband Terahertz Metamaterial Absorber Based on Vanadium Dioxide and Graphene
by: Laifang Zheng, et al.
Published: (2023-08-01)

An ultrathin and dual band metamaterial perfect absorber based on ZnSe for the polarization-independent in terahertz range
by: Yadgar I. Abdulkarim, et al.
Published: (2021-07-01)

Comparison of Sentiment Analysis Methods Based on Accuracy Value Case Study: Twitter Mentions of Academic Article
by: Muhamad Fahmi Fakhrezi, et al.
Published: (2023-02-01)

RADIO FREQUENCY BASED INPAINTING FOR INDOOR LOCALIZATION USING MEMORYLESS TECHNIQUES AND WIRELESS TECHNOLOGY
by: Tammineni Shanmukha Prasanthi, et al.
Published: (2024-12-01)

A Review on Metamaterial Absorbers: Microwave to Optical
by: Yadgar I. Abdulkarim, et al.
Published: (2022-04-01)

Vanadium dioxide enabled polarization insensitive tunable broadband terahertz metamaterial absorber
by: Yunji Wang, et al.
Published: (2025-03-01)

Quad-Band Terahertz Absorber Based on a Simple Design of Metamaterial Resonator
by: Ben-Xin Wang, et al.
Published: (2016-01-01)

Multi-resonant tunable absorber of terahertz metamaterial based on GaAs and VO2
by: Tingting Yuan, et al.
Published: (2023-06-01)

Design of Narrow Discrete Distances of Dual-/Triple-Band Terahertz Metamaterial Absorbers
by: Ben-Xin Wang, et al.
Published: (2019-02-01)

Predicting and Comparing Students’ Online and Offline Academic Performance Using Machine Learning Algorithms
by: Barnabás Holicza, et al.
Published: (2023-03-01)

Fatigue State Detection Through Multiple Machine Learning Classifiers Using EEG Signal
by: Md Mahmudul Hasan, et al.
Published: (2023-12-01)

Ultra-Broadband Tunable Terahertz Metamaterial Absorber Based on Double-Layer Vanadium Dioxide Square Ring Arrays
by: Pengyu Zhang, et al.
Published: (2022-04-01)

All-Metal Terahertz Metamaterial Absorber and Refractive Index Sensing Performance
by: Jing Yu, et al.
Published: (2021-05-01)

A Reconfigurable Terahertz Metamaterial Absorber for Gas Sensing Applications
by: Shruti, et al.
Published: (2023-01-01)

Investigating the performance of machine learning algorithms in predicting the survival of COVID‐19 patients: A cross section study of Iran
by: Azita Yazdani, et al.
Published: (2023-04-01)

Design of dual peak star shaped metamaterial absorber for S and C band sensing applications
by: Muhammad Amir Khalil, et al.
Published: (2024-11-01)

Urban monitoring application in new administrative capital in Egypt using machine learning techniques and Sentinel 2 data
by: Tamer ElGharbawi, et al.
Published: (2022-12-01)