Matches in SemOpenAlex for { <https://semopenalex.org/work/W4386365089> ?p ?o ?g. }
- W4386365089 endingPage "396" @default.
- W4386365089 startingPage "383" @default.
- W4386365089 abstract "In recent years, improvements in wireless communication have led to the development of microstrip or patch antennas. The article discusses using simulation, measurement, an RLC equivalent circuit model, and machine learning to assess antenna performance. The antenna's dimensions are 1.01 λ0×0.612λ0 with respect to the lowest operating frequency, the maximum achieved gain is 6.76 dB, the maximum directivity is 8.21 dBi, and the maximum efficiency is 83.05%. The prototype's measured return loss is compared to CST and ADS simulations. The prediction of gain and directivity of the antenna is determined using a different supervised regression machine learning (ML) method. The performance of ML models is measured by the variance score, R square, mean square error (MSE), mean absolute error (MAE), root mean square error (RMSE), and mean squared logarithmic error (MSLE), etc. With errors of less than unity and an accuracy of roughly 98%, Ridge regression gain prediction outperforms the other seven ML models. Gaussian process regression is the best method for predicting directivity. Finally, modeling results from CST and ADS, as well as measured and anticipated results from machine learning, reveal that the suggested antenna is a good candidate for LTE." @default.
- W4386365089 created "2023-09-02" @default.
- W4386365089 creator A5048553169 @default.
- W4386365089 creator A5050676855 @default.
- W4386365089 creator A5057252637 @default.
- W4386365089 creator A5062929789 @default.
- W4386365089 creator A5074295530 @default.
- W4386365089 creator A5074408924 @default.
- W4386365089 creator A5076988077 @default.
- W4386365089 creator A5091682523 @default.
- W4386365089 creator A5001964901 @default.
- W4386365089 date "2023-10-01" @default.
- W4386365089 modified "2023-10-06" @default.
- W4386365089 title "Quasi-Yagi antenna design for LTE applications and prediction of gain and directivity using machine learning approaches" @default.
- W4386365089 cites W1551260643 @default.
- W4386365089 cites W1967342452 @default.
- W4386365089 cites W2017954520 @default.
- W4386365089 cites W2147078268 @default.
- W4386365089 cites W2157562337 @default.
- W4386365089 cites W2177423987 @default.
- W4386365089 cites W2296609147 @default.
- W4386365089 cites W2564133618 @default.
- W4386365089 cites W2804446681 @default.
- W4386365089 cites W2809741222 @default.
- W4386365089 cites W2891741430 @default.
- W4386365089 cites W2904925972 @default.
- W4386365089 cites W2916900328 @default.
- W4386365089 cites W2948781496 @default.
- W4386365089 cites W2952297896 @default.
- W4386365089 cites W3033154016 @default.
- W4386365089 cites W3048100472 @default.
- W4386365089 cites W3091063916 @default.
- W4386365089 cites W3096308665 @default.
- W4386365089 cites W3117971949 @default.
- W4386365089 cites W3118250236 @default.
- W4386365089 cites W3124590480 @default.
- W4386365089 cites W3130074370 @default.
- W4386365089 cites W3133912952 @default.
- W4386365089 cites W3161577134 @default.
- W4386365089 cites W3173807287 @default.
- W4386365089 cites W3176318516 @default.
- W4386365089 cites W3201631728 @default.
- W4386365089 cites W3202590039 @default.
- W4386365089 cites W4211219865 @default.
- W4386365089 cites W4220728645 @default.
- W4386365089 cites W4285213604 @default.
- W4386365089 cites W4285301489 @default.
- W4386365089 cites W4288075652 @default.
- W4386365089 cites W4290839784 @default.
- W4386365089 cites W4301017997 @default.
- W4386365089 cites W4306737005 @default.
- W4386365089 cites W4317039772 @default.
- W4386365089 cites W4320492130 @default.
- W4386365089 cites W4366768680 @default.
- W4386365089 cites W4367175935 @default.
- W4386365089 cites W4380079037 @default.
- W4386365089 cites W4380684271 @default.
- W4386365089 cites W4382792161 @default.
- W4386365089 doi "https://doi.org/10.1016/j.aej.2023.08.059" @default.
- W4386365089 hasPublicationYear "2023" @default.
- W4386365089 type Work @default.
- W4386365089 citedByCount "0" @default.
- W4386365089 crossrefType "journal-article" @default.
- W4386365089 hasAuthorship W4386365089A5001964901 @default.
- W4386365089 hasAuthorship W4386365089A5048553169 @default.
- W4386365089 hasAuthorship W4386365089A5050676855 @default.
- W4386365089 hasAuthorship W4386365089A5057252637 @default.
- W4386365089 hasAuthorship W4386365089A5062929789 @default.
- W4386365089 hasAuthorship W4386365089A5074295530 @default.
- W4386365089 hasAuthorship W4386365089A5074408924 @default.
- W4386365089 hasAuthorship W4386365089A5076988077 @default.
- W4386365089 hasAuthorship W4386365089A5091682523 @default.
- W4386365089 hasBestOaLocation W43863650891 @default.
- W4386365089 hasConcept C105795698 @default.
- W4386365089 hasConcept C11413529 @default.
- W4386365089 hasConcept C119857082 @default.
- W4386365089 hasConcept C121332964 @default.
- W4386365089 hasConcept C127413603 @default.
- W4386365089 hasConcept C134306372 @default.
- W4386365089 hasConcept C137705275 @default.
- W4386365089 hasConcept C139945424 @default.
- W4386365089 hasConcept C151764478 @default.
- W4386365089 hasConcept C163716315 @default.
- W4386365089 hasConcept C185429906 @default.
- W4386365089 hasConcept C189039984 @default.
- W4386365089 hasConcept C21822782 @default.
- W4386365089 hasConcept C24326235 @default.
- W4386365089 hasConcept C33923547 @default.
- W4386365089 hasConcept C39927690 @default.
- W4386365089 hasConcept C41008148 @default.
- W4386365089 hasConcept C61326573 @default.
- W4386365089 hasConcept C62520636 @default.
- W4386365089 hasConcept C76155785 @default.
- W4386365089 hasConcept C81183938 @default.
- W4386365089 hasConcept C81692654 @default.
- W4386365089 hasConcept C90652560 @default.
- W4386365089 hasConceptScore W4386365089C105795698 @default.
- W4386365089 hasConceptScore W4386365089C11413529 @default.