Aybars Ugur

An interactive simulation and analysis software for solving TSP using Ant Colony Optimization algorithms

Traveling salesman problem (TSP) is one of the extensively studied combinatorial optimization problems and tries to find the shortest route for salesperson which visits each given city precisely once. Ant colony optimization (ACO) algorithms have been used to solve many optimization problems in various fields of engineering. In this paper, a web-based simulation and analysis software (TSPAntSim) is developed for solving TSP using ACO algorithms with local search heuristics. Algorithms are tested on benchmark problems from TSPLIB and test results are presented. Importance of TSPAntSim providing also interactive visualization with real-time analysis support for researchers studying on optimization and people who have problems in form of TSP is discussed.

GENETIC ALGORITHM–BASED SENSOR DEPLOYMENT WITH AREA PRIORITY

We are introducing a new design goal called area priority to determine optimal sensor node distribution. The environment in which the wireless sensor network (WSN) will be placed is divided into parts and priorities are attached to these parts. Priorities make the deployment problem adaptable to nonhomogeneous environments with regions that have different importance levels such as forests. Various tree/animal types and densities, residential in the forest can be classified by the area priority concept that we propose. We also develop a genetic algorithm–based method to optimize the total importance in a fully connected WSN. Experimental results obtained for different priorities are presented and discussed.

Extraction of flower regions in color images using ant colony optimization

Abstract Extraction of flower regions from complex background is a difficult task and it is an important part of a flower image retrieval and recognition. In this article, we propose an Ant Colony Optimization (ACO) algorithm as a general color clustering method, and test it on flower images as a case study of object boundary extraction. The segmentation methodology on flower images consists of six steps: color space conversion, generation of candidate color cluster centers, ant colony optimization method to select optimum color cluster centers, merging of cluster centers which are close to each other, image segmentation by clustering, and extraction of flower region from the image. To evince that ACO algorithm can be a general segmentation method, some results of natural images in Berkeley segmentation benchmark have been presented. The method as a case study on flower region extraction has also been tested on the images of Oxford-17 Flowers dataset, and the results have confronted with other well established flower region extraction approaches.

Recognition of leaves based on morphological features derived from two half-regions

Leaf recognition systems can be used for automatic plant taxonomy and provide understanding and managing of plants in botany, medicine, industry and food sector. Trees and flowery plants can be classified by using leaf recognition. This paper proposes a simple method based on bisection of leaves for recognition. After preprocessing techniques are applied for leaves, 7 low-cost morphological features are extracted which are used in the literature. We produced 3 additional features using half leaf images. Most of leaf species have morphological structure that resembles each other a lot. For these leaves, while structural features of one half resemble, features of other half differ. Taking advantage of this knowledge, leaf is oriented according to its major axis and two parts are acquired by slicing leaf on its centroid vertically. Area, extent and eccentricity features are extracted for each part and their proportions to each other are taken as new features in this study. These all 10 features are used as an input to probabilistic neural network (PNN). PNN is trained with 1120 leaf images from 32 different plant species which are taken from FLAVIA dataset. 160 leaf images from the plant species are used for testing. Our experiments and comparisons show that method based on half leaf features has reached one of the best results in the literature for PNN with 92.5% recognition accuracy.

Automatic number plate information extraction and recognition for intelligent transportation system

Abstract In this paper, a new license plate information retrieval system is designed and developed. The system has two main modules: segmentation and recognition. In segmentation, interested information on the image is extracted through the processes of Kaiser resizing, morphological filtering, artificial shifting and bi-directional vertical thresholding. In recognition module, a novel approach for principal component analysis (PCA) and fast backpropagation neural net composition is used as a recognizer. The novel approach is about the construction of Eigen space through the PCA that is used for feature extraction. Our approach is more tolerable to the problems of classical application PCA such as rotation, scaling and character width dependence. The outputs of the new feature extractor used as inputs to the fastbackpropagation neural net recognizer module. This neural network trained with scaled conjugate gradient function. For each module, alternative available methods are mentioned and proper sequence of operations is developed. Finally, overall performance of the system is exported.

Shape and Texture Based Novel Features for Automated Juxtapleural Nodule Detection in Lung CTs

Lung cancer is one of the types of cancer with highest mortality rate in the world. In case of early detection and diagnosis, the survival rate of patients significantly increases. In this study, a novel method and system that provides automatic detection of juxtapleural nodule pattern have been developed from cross-sectional images of lung CT (Computerized Tomography). Shape-based and both shape and texture based 7 features are contributed to the literature for lung nodules. System that we developed consists of six main stages called preprocessing, lung segmentation, detection of nodule candidate regions, feature extraction, feature selection (with five feature ranking criteria) and classification. LIDC dataset containing cross-sectional images of lung CT has been utilized, 1410 nodule candidate regions and 40 features have been extracted from 138 cross-sectional images for 24 patients. Experimental results for 10 classifiers are obtained and presented. Adding our derived features to known 33 features has increased nodule recognition performance from 0.9639 to 0.9679 AUC value on generalized linear model regression (GLMR) for 22 selected features and being reached one of the most successful results in the literature.

A web‐based tool for teaching neural network concepts

Although neural networks (NN) are important especially for engineers, scientists, mathematicians and statisticians, they may also be hard to understand. In this article, application areas of NN are discussed, basic NN components are described and it is explained how an NN work. A web‐based simulation and visualization tool (EasyLearnNN) is developed using Java and Java 2D for teaching NN concepts. Perceptron, ADALINE, Multilayer Perceptron, LVQ and SOM models and related training algorithms are implemented. As a result, comparison with other teaching methods of NN concepts is presented and discussed.

Design and Implementation of a Software Presenting Information in DVB Subtitles in Various Forms

Subtitle data carried by MPEG-2 transport stream includes texts of dialogs in bitmap graphics format. However, this data is not reachable for the blind audience. In addition, carrying extra compatible data with subtitle packets can be helpful for testing subtitle software automatically and for presenting live content (i.e. latest news or special advertisements). In this paper, we propose a method for converting subtitle data into text format by recognizing characters using neural networks. The converted data can be used for blind television users and testing subtitle software automatically. We also define a new subtitle segment for carrying test comparison data and a new subtitle page for representing live content. We present our implementation on Linux platform.

A new regression based software cost estimation model using power values

The paper aims to provide for the improvement of software estimation research through a new regression model. The study design of the paper is organized as follows. Evaluation of estimation methods based on historical data sets requires that these data sets be representative for current or future projects. For that reason the data set for software cost estimation model the International Software Benchmarking Standards Group (ISBSG) data set Release 9 is used. The data set records true project values in the real world, and can be used to extract information to predict new projects cost in terms of effort. As estimation method regression models are used. The main contribution of this study is the new cost production function that is used to obtain software cost estimation. The new proposed cost estimation function performance is compared with related work in the literature. In the study same calibration on the production function is made in order to obtain maximum performance. There is some important discussion on how the results can be improved and how they can be applied to other estimation models and datasets.

Area-priority-based sensor deployment optimisation with priority estimation using K-means

Deployment in wireless sensor networks (WSN) addresses maximising the coverage of sensors and reducing the total cost of deployment. The area-priority concept for WSN deployment that the authors contributed to the literature recently allows environments with regions that have different importance or priority levels. In this study, the authors propose the first priority-estimation method for area-priority-based WSN deployments. First, a satellite image of the environment that will be used in the deployment of the sensors is clustered by a K -means algorithm using the colour features of the regions. In the sensor deployment phase, this cluster information is used to determine the priorities of the sensor coverage areas on positions of the image. Sensors are initially deployed quickly using a priority queue-based technique. Then, a simulated annealing algorithm is used to maximise the total covered area priority and to minimise the gaps between the sensors. Various experiments are performed for different scenarios (land, sea, and forest) on images captured from Google Maps using different parameter values. The experiments confirm that the proposed approach performs well and outperforms the random deployment of sensors.