Scopus İndeksli Yayınlar Koleksiyonu
Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/395
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Article Citation - WoS: 10Citation - Scopus: 11Knowledge Based Response Correction Method for Design of Reconfigurable N-Shaped Microstrip Patch Antenna Using Inverse Anns(Wiley, 2015-12-18) Aoad, Ashrf; Simsek, Murat; Aydin, ZaferArtificial neural networks (ANNs) have been often used for engineering design problems. In this work, an inverse model of a reconfigurable N-shaped microstrip patch antenna which is formed by ANN is considered to find design parameters. For this task, knowledge-based response correction consists of two steps, which include generating response using multilayer perceptron as a first step and correcting this response using knowledge based methods such as source difference, prior knowledge input, and prior knowledge input with difference as a second step. The proposed antenna has four states of operation controlled by two Positive-Intrinsic-Negative (PIN) diodes with ON/OFF states. The two-step ANN models are inversely trained using the optimum of the resonant frequency parameter as the input and the physical dimensions of the proposed antenna as outputs of the multilayer perceptron. The outputs and, in some methods, the input parameters of the multilayer perceptron are sent as input to the knowledge-based models while the obtained outputs from the two steps are the results of the new physical dimensions of the redesigned reconfigurable antenna that will be compared and analyzed. This input/output complexity of the proposed reconfigurable antenna allows an accurate and fast inverse model to be developed with less training data. Users may use this antenna and its ANN models to develop new products in the market where any frequency in the operating region can be given to the input to result an appropriate form of the new reconfigurable antenna. Copyright (c) 2015 John Wiley & Sons, Ltd.Article Citation - WoS: 13Citation - Scopus: 20IGPRED: Combination of Convolutional Neural and Graph Convolutional Networks for Protein Secondary Structure Prediction(Wiley, 2021-05-25) Gormez, Yasin; Sabzekar, Mostafa; Aydin, ZaferThere is a close relationship between the tertiary structure and the function of a protein. One of the important steps to determine the tertiary structure is protein secondary structure prediction (PSSP). For this reason, predicting secondary structure with higher accuracy will give valuable information about the tertiary structure. Recently, deep learning techniques have obtained promising improvements in several machine learning applications including PSSP. In this article, a novel deep learning model, based on convolutional neural network and graph convolutional network is proposed. PSIBLAST PSSM, HHMAKE PSSM, physico-chemical properties of amino acids are combined with structural profiles to generate a rich feature set. Furthermore, the hyper-parameters of the proposed network are optimized using Bayesian optimization. The proposed model IGPRED obtained 89.19%, 86.34%, 87.87%, 85.76%, and 86.54% Q3 accuracies for CullPDB, EVAset, CASP10, CASP11, and CASP12 datasets, respectively.Article Citation - WoS: 7Citation - Scopus: 5Effect of Interpolation on Specular Reflections in Texture-Based Automatic Colonic Polyp Detection(Wiley, 2020-06-26) Kacmaz, Rukiye Nur; Yilmaz, Bulent; Aydin, ZaferReflections of LED light cause unwanted noise effects called specular reflection (SR) on colonoscopic images. The aim of this study was to seek answers to the following two questions. (a) How are the texture features used in automatic detection of polyps affected by the interpolation on specular reflections? (b) If they are affected does it really affect the classification performance? In order to answer these questions, we used 610 colonoscopy images, and divided each image into tiles whose sizes were 32-by-32 pixels. From these tiles, we selected the ones without any specular reflection. We added different shape and size specular reflections cropped from real images onto the reflection-free tiles. We then used the nearest neighbors, bilinear and bicubic interpolation techniques on the tiles on which SRs were added. On these tiles we extracted 116 texture features using 3 second-order approaches, and 4 first-order statistics. First, we used paired samplettest. Second, we performed automatic classification of polyps and background using random forest and k nearest neighbors (k-NN) approaches using the texture features for different combinations of specular reflections added on the tiles from the polyp or background. The results showed that depending on the size of specular reflection, interpolation can cause a significant difference between the texture features that were coming from reflection-free tiles and the same tiles on which interpolation was performed. In addition, we note that bicubic interpolation may be preferred to eliminate specular reflection when texture features are used for background and polyp discrimination.Article Citation - WoS: 14Citation - Scopus: 20A Deep Learning Approach With Bayesian Optimization and Ensemble Classifiers for Detecting Denial of Service Attacks(Wiley, 2020-05-06) Gormez, Yasin; Aydin, Zafer; Karademir, Ramazan; Gungor, Vehbi C.Detecting malicious behavior is important for preventing security threats in a computer network. Denial of Service (DoS) is among the popular cyber attacks targeted at web sites of high-profile organizations and can potentially have high economic and time costs. In this paper, several machine learning methods including ensemble models and autoencoder-based deep learning classifiers are compared and tuned using Bayesian optimization. The autoencoder framework enables to extract new features by mapping the original input to a new space. The methods are trained and tested both for binary and multi-class classification on Digiturk and Labris datasets, which were introduced recently for detecting various types of DDoS attacks. The best performing methods are found to be ensembles though deep learning classifiers achieved comparable level of accuracy.