Comparative analysis of machine learning techniques for metamaterial absorber performance in terahertz applications Article Swipe
Prince Jain
,
Mohammad Tariqul Islam
,
Ahmed S. Alshammari
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.05.111
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1016/j.aej.2024.05.111
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) regressor approaches have emerged as potential tools for optimizing the predictive modeling of MMAs in recent years. This article examined different regression techniques, such as Decision Tree, k-Nearest Neighbors, Random Forest, Extra Trees (ET), Extreme Gradient Boosting, Bagging, and Categorical Boosting. The primary goal is to assess the effectiveness of each regressor technique in forecasting the performance of MMAs using various performance metrics. The study focuses on a representative spectrum of MMAs, chosen for its key features that are common across a wide range of MMAs. The extra tree Regressor outperforms other models in the comparative analysis due to its capacity to integrate multiple decision trees and include randomness into the feature selection process, resulting in improved predictive skills. The ET regressor forecasts MMA performance remarkably well for a 70 % test size, with a Root Mean Squared Error of 0.0251, a Mean Absolute Error of 0.0074, a Mean Squared Error of 0.006, and an Adjusted R-squared of 0.9873. These findings can assist researchers in optimizing metamaterial absorber designs for Terahertz applications using ML regressor techniques and provide insights into how these methods can be generalized to other spectra with similar characteristics.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.aej.2024.05.111
- OA Status
- gold
- Cited By
- 51
- References
- 52
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All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4399565108Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1016/j.aej.2024.05.111Digital Object Identifier
- Title
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Comparative analysis of machine learning techniques for metamaterial absorber performance in terahertz applicationsWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-06-12Full publication date if available
- Authors
-
Prince Jain, Mohammad Tariqul Islam, Ahmed S. AlshammariList of authors in order
- Landing page
-
https://doi.org/10.1016/j.aej.2024.05.111Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1016/j.aej.2024.05.111Direct OA link when available
- Concepts
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Terahertz radiation, Metamaterial, Materials science, Computer science, Electronic engineering, Optoelectronics, EngineeringTop concepts (fields/topics) attached by OpenAlex
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51Total citation count in OpenAlex
- Citations by year (recent)
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2025: 39, 2024: 12Per-year citation counts (last 5 years)
- References (count)
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52Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.process, | 150 |
| abstract_inverted_index.spectrum | 106 |
| abstract_inverted_index.Boosting, | 73 |
| abstract_inverted_index.Boosting. | 77 |
| abstract_inverted_index.R-squared | 193 |
| abstract_inverted_index.Regressor | 126 |
| abstract_inverted_index.Terahertz | 10, 207 |
| abstract_inverted_index.absorbers | 1 |
| abstract_inverted_index.demanding | 32 |
| abstract_inverted_index.different | 57 |
| abstract_inverted_index.forecasts | 159 |
| abstract_inverted_index.integrate | 139 |
| abstract_inverted_index.k-Nearest | 64 |
| abstract_inverted_index.potential | 5, 42 |
| abstract_inverted_index.regressor | 37, 88, 158, 211 |
| abstract_inverted_index.resulting | 151 |
| abstract_inverted_index.selection | 149 |
| abstract_inverted_index.technique | 89 |
| abstract_inverted_index.Neighbors, | 65 |
| abstract_inverted_index.approaches | 38 |
| abstract_inverted_index.modulating | 9 |
| abstract_inverted_index.optimizing | 45, 202 |
| abstract_inverted_index.predictive | 47, 154 |
| abstract_inverted_index.randomness | 145 |
| abstract_inverted_index.regression | 58 |
| abstract_inverted_index.remarkably | 162 |
| abstract_inverted_index.techniques | 212 |
| abstract_inverted_index.tremendous | 4 |
| abstract_inverted_index.Categorical | 76 |
| abstract_inverted_index.challenging | 16 |
| abstract_inverted_index.comparative | 132 |
| abstract_inverted_index.controlling | 7 |
| abstract_inverted_index.forecasting | 91 |
| abstract_inverted_index.generalized | 222 |
| abstract_inverted_index.outperforms | 127 |
| abstract_inverted_index.performance | 22, 93, 98, 161 |
| abstract_inverted_index.researchers | 200 |
| abstract_inverted_index.techniques, | 59 |
| abstract_inverted_index.Metamaterial | 0 |
| abstract_inverted_index.applications | 208 |
| abstract_inverted_index.conventional | 24 |
| abstract_inverted_index.metamaterial | 203 |
| abstract_inverted_index.effectiveness | 85 |
| abstract_inverted_index.representative | 105 |
| abstract_inverted_index.time-consuming | 29 |
| abstract_inverted_index.computationally | 31 |
| abstract_inverted_index.electromagnetic | 11 |
| abstract_inverted_index.characteristics. | 228 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 99 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.98572723 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |