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JournalISSN: 1300-0632

Turkish Journal of Electrical Engineering and Computer Sciences 

Scientific and Technological Research Council of Turkey (TUBITAK)
About: Turkish Journal of Electrical Engineering and Computer Sciences is an academic journal published by Scientific and Technological Research Council of Turkey (TUBITAK). The journal publishes majorly in the area(s): Computer science & Electric power system. It has an ISSN identifier of 1300-0632. Over the lifetime, 2705 publications have been published receiving 20968 citations.


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Journal Article
TL;DR: From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms, and seems to be a promising approach for engineering optimization problems.
Abstract: Differential Evolution (DE) algorithm is a new heuristic approach mainly having three advantages; finding the true global minimum regardless of the initial parameter values, fast convergence, and using few control parameters. DE algorithm is a population based algorithm like genetic algorithms using similar operators; crossover, mutation and selection. In this work, we have compared the performance of DE algorithm to that of some other well known versions of genetic algorithms: PGA, Grefensstette, Eshelman. In simulation studies, De Jong's test functions have been used. From the simulation results, it was observed that the convergence speed of DE is significantly better than genetic algorithms. Therefore, DE algorithm seems to be a promising approach for engineering optimization problems.

344 citations

Journal Article
TL;DR: This paper has presented a preliminary taxonomy for swarm robotics and classified existing studies into this taxonomy after investigating the existing surveys related to swarm robotics literature.
Abstract: Swarm robotics is a new approach to the coordination of large numbers of relatively simple robots The approach takes its inspiration from the system-level functioning of social insects which demonstrate three desired characteristics for multi-robot systems: robustness, flexibility and scalability In this paper we have presented a preliminary taxonomy for swarm robotics and classified existing studies into this taxonomy after investigating the existing surveys related to swarm robotics literature Our parent taxonomic units are modeling, behavior design, communication, analytical studies and problems We are classifying existing studies into these main axes Since existing reviews do not have enough number of studies reviewed or do have less numbers of or less appropriate categories, we believe that this review will be helpful for swarm robotics researchers

217 citations

Journal Article
TL;DR: In this article, the authors used the magnetic field generated by the injected currents, for the purpose of reconstructing the conductivity distribution, and calculated the sensitivity matrix relating the magnetic fields to the element conductivities using the Finite Element Method and Biot-Savart law.
Abstract: In two dimensional conventional Electrical Impedance Tomography (EIT), volume conductor is probed by means of injected currents, and peripheral voltage measurements are used as input to the reconstruction algorithm. The current that flows in the 2D object creates magnetic fields that are perpendicular to the plane of imaging. Such magnetic fields can be measured using magnetic resonance tomography. In this study, use of this magnetic field generated by the injected currents, for the purpose of reconstructing the conductivity distribution, is studied. Sensitivity matrix relating the magnetic field to the element conductivities is calculated using the Finite Element Method and Biot-Savart law. Linearization is made during sensitivity matrix formation. This matrix is inverted using singular value decompostion. Simulations for objects placed in different parts of the imaging region are made to understand the spatial dependency of the proposed method and it is seen that the method has uniform sensitivity throughout the imaging region. Finally, images reconstructed using data taken from an experimental phantom are presented.

162 citations

Journal Article
TL;DR: The proposed GLCM based face recognition system not only outperforms well-known techniques such as principal component analysis and linear discriminant analysis, but also has comparable performance with local binary patterns and Gabor wavelets.
Abstract: In this paper, a new face recognition technique is introduced based on the gray-level co-occurrence matrix (GLCM). GLCM represents the distributions of the intensities and the information about relative positions of neighboring pixels of an image. We proposed two methods to extract feature vectors using GLCM for face classification. The first method extracts the well-known Haralick features from the GLCM, and the second method directly uses GLCM by converting the matrix into a vector that can be used in the classification process. The results demonstrate that the second method, which uses GLCM directly, is superior to the first method that uses the feature vector containing the statistical Haralick features in both nearest neighbor and neural networks classifiers. The proposed GLCM based face recognition system not only outperforms well-known techniques such as principal component analysis and linear discriminant analysis, but also has comparable performance with local binary patterns and Gabor wavelets.

154 citations

Journal ArticleDOI
TL;DR: The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning, and the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.
Abstract: The timely and accurate diagnosis of plant diseases plays an important role in preventing the loss of productivity and loss or reduced quantity of agricultural products. In order to solve such problems, methods based on machine learning can be used. In recent years, deep learning, which is especially widely used in image processing, offers many new applications related to precision agriculture. In this study, we evaluated the performance results using different approaches of nine powerful architectures of deep neural networks for plant disease detection. Transfer learning and deep feature extraction methods are used, which adapt these deep learning models to the problem at hand. The utilized pretrained deep models are considered in the presented work for feature extraction and for further fine-tuning. The obtained features using deep feature extraction are then classified by support vector machine (SVM), extreme learning machine (ELM), and K-nearest neighbor (KNN) methods. The experiments are carried out using data consisting of real disease and pest images from Turkey. The accuracy, sensitivity, specificity, and F1-score are all calculated for performance evaluation. The evaluation results show that deep feature extraction and SVM/ELM classification produced better results than transfer learning. In addition, the fc6 layers of the AlexNet, VGG16, and VGG19 models produced better accuracy scores when compared to the other layers.

145 citations

Performance
Metrics
No. of papers from the Journal in previous years
YearPapers
202340
2022141
202191
2020200
2019316
2018263