Optimized Weighted Ensemble Using Dipper Throated Optimization Algorithm in Metamaterial Antenna

Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance. The bandwidth restriction associated with small antennas can be solved using metamaterial antennas. Machine learning is gaining popularity as a way to improve solutions in a range of fields. Machine learning a...

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Veröffentlicht in:Computers, materials & continua Jg. 73; H. 3; S. 5771 - 5788
Hauptverfasser: Sami Khafaga, Doaa, M. El-kenawy, El-Sayed, Khalid Karim, Faten, Alshetewi, Sameer, Ibrahim, Abdelhameed, A. Abdelhamid, Abdelaziz
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Henderson Tech Science Press 2022
Schlagworte:
Algorithms
Antennas
Bandwidths
Decision trees
Machine learning
Metamaterials
Multilayer perceptrons
Multilayers
Optimization
Support vector machines
ISSN:1546-2226, 1546-2218, 1546-2226
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Zusammenfassung:Metamaterial Antennas are a type of antenna that uses metamaterial to enhance performance. The bandwidth restriction associated with small antennas can be solved using metamaterial antennas. Machine learning is gaining popularity as a way to improve solutions in a range of fields. Machine learning approaches are currently a big part of current research, and they’re likely to be huge in the future. The model utilized determines the accuracy of the prediction in large part. The goal of this paper is to develop an optimized ensemble model for forecasting the metamaterial antenna’s bandwidth and gain. The basic models employed in the developed ensemble are Support Vector Regression (SVR), K-Nearest Regression (KNR), Multi-Layer Perceptron (MLP), Decision Trees (DT), and Random Forest (RF). The percentages of contribution of these models in the ensemble model are weighted and optimized using the dipper throated optimization (DTO) algorithm. To choose the best features from the dataset, the binary (bDTO) algorithm is exploited. The proposed ensemble model is compared to the base models and results are recorded and analyzed statistically. In addition, two other ensembles are incorporated in the conducted experiments for comparison. These ensembles are average ensemble and K-nearest neighbors (KNN)-based ensemble. The comparison is performed in terms of eleven evaluation criteria. The evaluation results confirmed the superiority of the proposed model when compared with the basic models and the other ensemble models.
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ISSN:1546-2226
1546-2218
1546-2226
DOI:10.32604/cmc.2022.032229