Abstract-Three different techniques are applied for accurate constitutive parameters determination of isotropic split-ring resonator (SRR) and SRR with a cut wire (Composite) metamaterial (MM) slabs. The first two techniques use explicit analytical calibrationdependent and calibration-invariant expressions while the third technique is based on Lorentz and Drude dispersion models. We have tested these techniques from simulated scattering (S-) parameters of two classic SRR and Composite MM slabs with various level of losses and different calibration plane factors. From the comparison, we conclude that whereas the extracted complex permittivity of both slabs by the analytical techniques produces unphysical results at resonance regions, that by the dispersion model eliminates this shortcoming and retrieves physically accurate constitutive parameters over the whole analyzed frequency region. We argue that incorrect retrieval of complex permittivity by analytical methods comes from spatial dispersion effects due to the discreteness of conducting elements within MM slabs which largely vary simulated S-parameters in the resonance regions where the slabs are highly spatially dispersive.
Abstract. Inducing classification rules on domains from which information is gathered at regular periods lead the number of such classification rules to be generally so huge that selection of interesting ones among all discovered rules becomes an important task. At each period, using the newly gathered information from the domain, the new classification rules are induced. Therefore, these rules stream through time and are so called streaming classification rules. In this paper, an interactive rule interestingness-learning algorithm (IRIL) is developed to automatically label the classification rules either as "interesting" or "uninteresting" with limited user interaction. In our study, VFP (Voting Feature Projections), a feature projection based incremental classification learning algorithm, is also developed in the framework of IRIL. The concept description learned by the VFP algorithm constitutes a novel approach for interestingness analysis of streaming classification rules.
The most important mode of long-distance transportation for people to use time efficiently is air transportation. One of the most significant factors determining air traffic performance is the scheduling created by companies. When creating schedules, various parameters are used, mainly the demand for the determined route, and the availability of airplanes and flight crews. Known climatic conditions on the route are generally neglected. Within the scope of this study, the creation of flight schedules, which are normally the preparation of flight schedules by considering the monthly, daily, and hourly constraints that could cause flight delays, were determined by also considering weather conditions. In this determination process, 32-year flight data between 1987 and 2018 were used as a basis. Apache Spark, one of the big data technologies, was used to determine these constraints. The scheduling was optimized by using the constraints used to prevent flight delays. It was observed in the results obtained that simulated annealing (SA) achieved the most optimal results compared to the genetic algorithm (GA) and the artificial bee colony (ABC) algorithm. As the dimensions of the searched space increased, solving the problem with metaheuristic approaches was more advantageous in terms of time compared to the classical method.
Pneumonia is a seasonal infectious lung tissue inflammatory disease. According to the World Health Organization (WHO), early diagnosis of the disease reduces the risk of its transmission and death. Various deep learning and machine learning algorithms were used for pneumonia detection. This study aims to analyze the lung images and diagnose pneumonia disease by employing deep learning approaches. We have suggested a novel deep learning framework for the detection of pneumonia in lung. A comparison was made between the proposed new deep learning model and pre-trained deep learning models. 88.62% accuracy rate has been obtained from the proposed deep learning structure. It was observed that by utilizing the new deep neural network developed, the accuracy results of VGG16 (88.78%) and VGG19 (88.30%), which are among the popular deep learning architectures, can be approximated. The test results show that our proposed model has a better recall value (97.43%) (VGG16 (93.33%) and VGG19 (96.92%)), and a better F1-Score (91.45%) (VGG16 (91.22%) and VGG19 (91.19%)).
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