cihan mert

Trend Analysis of Long-term Temperatures in Tbilisi, Georgia

The study examines the trend analysis of temperatures in Tbilisi City, Georgia by using non-param... more The study examines the trend analysis of temperatures in Tbilisi City, Georgia by using non-parametric Mann Kendall test. Data covered monthly air temperature of Tbilisi for the period of 1901-2012. Although no significant trend was seen for the whole temperature data according to the 95% confidence level, some months showed significantly increasing trends. January, April, July and September months showed significantly increasing trend while no trend was identified for the other months with respect to 95% confidence level. Annual temperatures were also analyzed and a highly increasing trend was identified for the Tbilisi City.

Heuristic Analysis of Time Series Internal Structure

A method of analysis of Time Series Internal Structures based on Singular Spectrum Analysis is di... more A method of analysis of Time Series Internal Structures based on Singular Spectrum Analysis is discussed. It has been shown that in the case when the Time Series contains deterministic additive components rank of the trajectory matrices equal to number of parameters of the components. Also it was proved that both eigen and factor vectors repeat shapes of the additive components and both eigen values and eigen vectors can be divided into additive groups. Some useful patterns of deterministic components were identified, which permit to provide graphical analysis of times series Internal Structures.

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Comparison of multi-gene genetic programming and dynamic evolving neural-fuzzy inference system in modeling pan evaporation

Hydrology Research

Accurately modeling pan evaporation is important in water resources planning and management and a... more Accurately modeling pan evaporation is important in water resources planning and management and also in environmental engineering. This study compares the accuracy of two new data-driven methods, multi-gene genetic programming (MGGP) approach and dynamic evolving neural-fuzzy inference system (DENFIS), in modeling monthly pan evaporation. The climatic data, namely, minimum temperature, maximum temperature, solar radiation, relative humidity, wind speed, and pan evaporation, obtained from Antakya and Antalya stations, Mediterranean Region of Turkey were utilized in the study. The MGGP and DENFIS methods were also compared with genetic programming (GP) and calibrated version of Hargreaves Samani (CHS) empirical method. For Antakya station, GP had slightly better accuracy than the MGGP and DENFIS models and all the data-driven models performed were superior to the CHS while the DENFIS provided better performance than the other models in modeling pan evaporation at Antalya station. The effect of periodicity input to the models' accuracy was also investigated and it was found that adding periodicity significantly increased the accuracy of MGGP and DENFIS models.

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Survey of different data-intelligent modeling strategies for forecasting air temperature using geographic information as model predictors

Computers and Electronics in Agriculture

Comparison of four heuristic regression techniques in solar radiation modeling: Kriging method vs RSM, MARS and M5 model tree

Renewable and Sustainable Energy Reviews

Singular Spectrum Analysis Method as a universal filter

2011 5th International Conference on Application of Information and Communication Technologies (AICT), 2011

ABSTRACT An application of Singular Spectrum Analysis(SSA) Method, based on a new elaborated tens... more ABSTRACT An application of Singular Spectrum Analysis(SSA) Method, based on a new elaborated tensorial approach of computation of singular values and left and right singular vectors of arbitrary non-square matrices, for time series is presented. All necessary calculations of singular values and both types (left and right) singular vectors are performed on the base of elaborated tensorial approach. It is showed that non parametric SSA can be efficiently used as a universal filter to separate Low and High frequencies components in long signals and time series.

Technological Impact of Employment Web Sites in Caucasus Region

European Journal of Business Research, 2014

ABSTRACT This research conducts the web statistics of the employment web sites with the technolog... more ABSTRACT This research conducts the web statistics of the employment web sites with the technological impact on the macroeconomics. The statistical information gathered from web-o-metrics of the Caucasus region job seeking web sites like the number of visitors, Facebook likes or shares, twitter messages about web site, number of back links counted by google, bing or Alexa. On the other hand, the macroeconomic and demographic facts like the population, unemployment rate, median age or migration rate.

Rsa Şi̇frelme Si̇stemi̇ne Karşi Yeni̇ Bi̇r Çarpanlara Ayirma Saldirisi

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, 2014

Sabah Imkb Format YEDEK47

Is the concept of stock market speculations, related with the news in the news papers? This study... more Is the concept of stock market speculations, related with the news in the news papers? This study mainly focus on the correlation between economy news from one of the highest circulation rate news paper in Turkey and Istanbul stock market closing values. Data set is collected from the web page of news paper in natural language and text mining technique, term frequency -inverse document frequency is applied over these news. On the other hand the stock market values are evaluated as a signal processing job and random walk method has been applied on it. The two feature vectors are correlated with several classification algorithms such as support vector machines, k-nearest neighborhood and artificial neural networks. The results show that there is a weakly relation over 43% between the news and stock market closing values. We believe this research would be beneficiary for the literature to create some stock market estimation tools from the economy news or market strength analysis.

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A New Approach to Detecting Deterministic Periodic Components in Noise

ABSTRACT A new approach to detecting Deterministic Periodic Components of non-stationary time ser... more ABSTRACT A new approach to detecting Deterministic Periodic Components of non-stationary time series corrupted by random component is considered. The approach is based on usage of the new time series, which is the union of the principal singular vectors of the data matrix of the original time series. The approach has universal character as it allows significant improvement of resolving capacity of any existed method of pseudospectra estimation. The comparative examples, illustrated practical efficiency of the approach relatively to existed methods of pseudospetra estimation, are considered.

Application of a Displacement Structure for Acceleration of Novel Matrix Spectral Factorization Algorithm

S-Box Hashing for Text Mining

Rsa Şi̇freleme Si̇stemi̇ne Karşi Yeni̇ Bi̇r Çarpanlara Ayirma Saldirisi a Novel Factorization Attack for Rsa Crypto System

quadratic sieve), Eratosthene Sieve (Atkin Kalburu) ve Polllar-Rho yöntemleridir.

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Time Series Analysis on Stock Market for Text Mining Correlation of Economy News

─Abstract─ This paper proposes an information retrieval method for the economy news. The effect o... more

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A Novel Feature Hashing for Text Mining

Because of the increasing studies on the big data, holding text as data source, the importance of... more Because of the increasing studies on the big data, holding text as data source, the importance of feature hashing has a major role in the literature now. A usual way of text mining on big data, mostly requires a layer of feature hashing, which reduces the size of feature vector. For example getting the word count yields hundreds of thousands of features in most of the cases and taking the postagging would reduce this number into features about 50. By the feature hashing the size of feature vector reduces reasonably and the data mining processes like classification, clustering or association can run faster. And in some cases, executing some algorithms are impossible with current hardware, where parallel or distributed programming takes into account.

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Reverse Factorization and Comparison of Factorization Algorithms in attack to RSA

Factorization algorithms have a major role in the computer security and cryptography. Most of the... more Factorization algorithms have a major role in the computer security and cryptography. Most of the widely used cryptographic algorithms, like RSA, are built on the mathematical difficulty of factorization for big prime numbers. This research, proposes a new approach to the factorization by using two new enhancements. The new approach is also compared with six different factorization algorithms and evaluated the performance on a big data environment. The algorithms covered are elliptic curve method, quadratic sieve, Fermat's method, trial division and Pollard rho methods. Success rates are compared over a million of integer numbers with different difficulties. We have implemented our own algorithm for random number generation, which is also explained in the paper. We also empirically show that the new approach has an advantage on the factorization attack to RSA.

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Correlation Between Turkish Stock Market and Economy

Is the concept of stock market speculations, related with the news in the news papers? This study... more Is the concept of stock market speculations, related with the news in the news papers? This study mainly focus on the correlation between economy news from one of the highest circulation rate news paper in Turkey and Istanbul stock market closing values. Data set is collected from the web page of news paper in natural language and text mining technique, term frequency -inverse document frequency is applied over these news. On the other hand the stock market values are evaluated as a signal processing job and random walk method has been applied on it. The two feature vectors are correlated with several classification algorithms such as support vector machines, k-nearest neighborhood and artificial neural networks. The results show that there is a weakly relation over 43% between the news and stock market closing values. We believe this research would be beneficiary for the literature to create some stock market estimation tools from the economy news or market strength analysis.

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A Novel String Distance Function Based on Most Frequent K Characters

International Journal of Machine Learning and Computing, 2014

This study aims to publish a novel similarity metric to increase the speed of comparison operatio... more

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Correlation between the Economy News and Stock Market in Turkey

International Journal of Business Intelligence Research, 2013

Depending on the market strength and structure, it is a known fact that there is a correlation be... more 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 class...

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Ensemble classification over stock market time series and economy news

2013 IEEE International Conference on Intelligence and Security Informatics, 2013

Aim of this study is applying the ensemble classification methods over the stock market closing v... more 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.

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