Ugur Ayan

Ensemble classification over stock market time series and economy news

Aim of this study is applying the ensemble classification methods over the stock market closing values, which can be assumed as time series and finding out the relation between the economy news. In order to keep the study back ground clear, the majority voting method has been applied over the three classification algorithms, which are the k-nearest neighborhood, support vector machine and the C4.5 tree. The results gathered from two different feature extraction methods are correlated with majority voting meta classifier (ensemble method) which is running over three classifiers. The results show the success rates are increased after the ensemble at least 2 to 3 percent success rate.

A Novel String Distance Function Based on Most Frequent K Characters

This study aims to publish a novel similarity metric to increase the speed of comparison operations. Also the new metric is suitable for distance-based operations among strings. Most of the simple calculation methods, such as string length are fast to calculate but does not represent the string correctly. On the other hand the methods like keeping the histogram over all characters in the string are slower but good to represent the string characteristics in some areas, like natural language. We propose a new metric, easy to calculate and satisfactory for string comparison. Method is built on a hash function, which gets a string at any size and outputs the most frequent K characters with their frequencies. The outputs are open for comparison and our studies showed that the success rate is quite satisfactory for the text mining operations.

Autonomous unmanned aerial vehicle route planning

In this study, a methodology detecting the radar make caught in real time during the flight or pre-flight route planning has been developed for an autonomous unmanned aerial vehicles (UAVs). The proposed method and genetic algorithms are implemented in parallel and duration is reduced to a large extent. The developed methodology can provide fast and safe routes for autonomous single or multiple UAV or operator-assisted flight.

Cooperative multi-task assignment for heterogonous UAVs

This research is focused on the cooperative multi-task assignment problem for heterogeneous UAVs, where a set of multiple tasks, each requiring a predetermined number of UAVs, have to be completed at specific locations. We modeled this as an optimization problem to minimize the number of uncompleted tasks while also minimizing total airtime and total distance traveled by all the UAVs. By taking into account the UAV flight capacities. For the solution of the problem, we adopted a multi-Traveling Salesman Problem (mTSP) method [1] and designed a new genetic structure for it so that it can be applied to cooperative multi-task assignment problems. Furthermore, we developed two domain specific mutation operators to improve the quality of the solutions in terms of number of uncompleted tasks, total airtime and total distance traveled by all the UAVs. The simulation experiments showed that these operators significantly improve the solution quality. Our main contributions are the application of the Multi Structure Genetic Algorithm (MSGA) to cooperative multi-task assignment problem and the development of two novel mutation operators to improve the solution of MSGA.

Correlation between the Economy News and Stock Market in Turkey

Depending on the market strength and structure, it is a known fact that there is a correlation between the stock market values and the content in newspapers. The correlation increases in weak and speculative markets, while they never get reduced to zero in the strongest markets. This research focuses on the correlation between the economic news published in a highly circulating newspaper in Turkey and the stock market closing values in Turkey. In the research several feature extraction methodologies are implemented on both of the data sources, which are the stock market values and economic news. Since the economic news is in natural language format, the text mining technique, term frequency-inverse document frequency is implemented. On the other hand, the time series analysis methods like random walk, Bollinger band, moving average or difference are applied over the stock market values. After the feature extraction step, the classification methods are built on the well-known classifiers support vector machine, k-nearest neighborhood and decision tree. Moreover, an ensemble classifier based on majority voting is implemented on top of these classifiers. The success rates show that the results are satisfactory to claim the methods implemented in this study can be spread to future research with similar data sets from other countries.

Novel comment filtering approach based on outlier on streaming data

This is the preliminary work for a project which will be filtering comments made on news and papers automatically. Our database has over 1 million news and comments. Due to the intensity of our data, 30.677 comments made on 15.064 articles on 44 different categories are used as experimental data. Proposed anomaly based method have been obtained fast and high accuracy results without the high storage requirement and high computational complexity with respect to other classification based methods on literature.

Autonomous multi unmanned aerial vehicles path planning on 3 dimensional terrain

Leave Secure route planning in environments with obstacles and threats, which is of first priority for single and multi-Unmanned Aerial Vehicles (UAVs), is the main focus of this study. Planning the optimum route by using genetic algorithms by considering kinematic constraints and terrain conditions is investigated. The terrain used in this study consists of real 3D satellite data of NASA. Thus, access to altitude data is achieved and mountains over the terrain are detected. Real geographic coordinate system is used and the geometric shape of the earth is considered for high precision calculations. While global route planning is made by the GA, local route planning is considered for transitions between waypoints. According to experimental result, usage of multi-UAV brings a great benefit in the fulfillment of the missions in terms of time and performance.

New learning approach for drug design

Although protien classification for Drug design is one of the most widely studied area in the past few years, it is difficult to obtain high accuracy. We used a feature weighting algorithm in order to represent the whole needed feature set. Because of scarce labeled data and high computational complexity of supervised learning methods, a new semi-supervised learning algorithm extended from Gaussian Random Field methodology combined with active query learning is developed. The proposed approach is applied to newly extracted data from DrugBank database contains nearly 4800 drug entries including FDA approved drugs and synthetic drug and 2640 non-drug proteins. We found that our new approach has better accuracy then the other traditional semi-supervised methods and lower computational complexity than the supervised methods.

Improved Naïve Bayesian algorithm for author detection

Author detection is a challenging problem for articles on the Internet (forums, blogs, etc.) due to lack of substantial content. Therefore, author detection for these articles is much more difficult than books, reports and other documents. In this study, the authors of newspaper articles are categorized. We tried to detect authors of articles of test data by using frequently used words in these categories. In doing so, we applied the improved Naïve Bayesian machine learning algorithm.