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This article represents a comprehensive review of the research carried out on analytical and numerical methods modeling of electromagnetic band‐gap (EBG) structures used in around last two decades. Because of the unique characteristics of the surface wave reduction as well as perfect magnetic conductor (PMC) like behavior, the EBG structures have created their separate existence in antenna engineering society. These structures are being widely used in designing of several microwave planar circuits including printed antennas, printed microwave filters, etc. The purpose of this article is to present an inclusive review of analytical methods as well as numerical methods in the context of modeling of EBG‐structures. Such a review process is rarely carried out in the open literature to the best of authors' knowledge. The review exercise might be helpful to the researchers working on modeling of EBG‐structures as well as of EBG‐structured printed antennas, microwave planar filters, etc.

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In this article, the slotted microstrip‐ fed elliptical patch antenna is designed and optimized to operate at the resonance frequency of 5.38 GHz. Then, the designed patch antenna is further analyzed in a resonant cavity formed by a frequency selective surface (FSS) superstrate and antenna ground plane. The highly reflective behavior of FSS from the offset of the resonance is utilized for improving the performance of the antenna. The optimized slotted geometry produces a gain improvement of 2.5 dBi, impedance bandwidth improvement of 0.21 GHz, simultaneously at the operating resonance frequency of 5.38 GHz. After placing an FSS superstrate at a height of 28.5 mm above the antenna substrate, further improvement of 5.1 dBi gain and 0.13 GHz bandwidth is attained, For validation purpose, the prototypes of the slotted patch antenna with and without FSS superstrate are fabricated and characterized. A fairly good agreement is achieved in the measured and simulated results.

In the medical domain, early identification of cardiovascular issues poses a significant challenge. This study enhances heart disease prediction accuracy using machine learning techniques. Six algorithms (random forest, K-nearest neighbor, logistic regression, Naïve Bayes, gradient boosting, and AdaBoost classifier) are utilized, with datasets from the Cleveland and IEEE Dataport. Optimizing model accuracy, GridsearchCV, and five-fold cross-validation are employed. In the Cleveland dataset, logistic regression surpassed others with 90.16% accuracy, while AdaBoost excelled in the IEEE Dataport dataset, achieving 90% accuracy. A soft voting ensemble classifier combining all six algorithms further enhanced accuracy, resulting in a 93.44% accuracy for the Cleveland dataset and 95% for the IEEE Dataport dataset. This surpassed the performance of the logistic regression and AdaBoost classifiers on both datasets. This study’s novelty lies in the use of GridSearchCV with five-fold cross-validation for hyperparameter optimization, determining the best parameters for the model, and assessing performance using accuracy and negative log loss metrics. This study also examined accuracy loss for each fold to evaluate the model’s performance on both benchmark datasets. The soft voting ensemble classifier approach improved accuracies on both datasets and, when compared to existing heart disease prediction studies, this method notably exceeded their results.