In this work we concentrate on the long-term and short term cycles of Ukrainian stock market, being based on the nonlinear approach of the analysis of open systems. First, the paper gives an algorithmic model for the investigation of the nonlinear nature of stock market, which comprises five individual stages. Then, by analyzing the Hurst coefficient for the PFTS index for the Ukrainian stock market it is shown that it is persistent, i.e. contains the fractals. As the results, a parabolic function is used for the approximation of a nonlinear trend in the PFTS series. Moreover, the major tendency of the PFTS index gives the correlation trends of “blue chips”. The elimination of trends and the usage of Fourier analysis allow one to determine the long-term and short-term cycles in the index and shares. Finally, by investigating the weight of the long-term harmonics in the cyclic component of the PFTS index, the stability of Ukrainian stock market is studied in a short-time period. The application of the results involves the forecasting of the crisis points of stock market and proves the effectiveness of shareholders.

For more relevant informational retrieval and matching of user request with metadata about biomedical informational recourses it is necessary to formulize the user knowledge about this subject domain. We propose to use the ontologies and associated with them thesauri of the appropriate subject domains for representation of biomedicine knowledge. The algorithms of formation and normalization of the multilinguistic thesauruses, and also methods of their comparison are given in this work.

The famous K-means clustering algorithm is sensitive to the selection of the initial centroids and may converge to a local minimum of the criterion function value. A new algorithm for initialization of the K-means clustering algorithm is presented. The proposed initial starting centroids procedure allows the K-means algorithm to converge to a “better” local minimum. Our algorithm shows that refined initial starting centroids indeed lead to improved solutions. A framework for implementing and testing various clustering algorithms is presented and used for developing and evaluating the algorithm.

A large number of the world business is going on using “INTERNET” and the data over the internet which is vulnerable for attacks from the hackers. Thus, uses of highly efficient methods are required for sensitive data transmission over the internet to ensure data security. One of the solutions to data security is to use an efficient method of steganography. The goal of steganography is to hide messages inside other ‘harmless’ messages in a way that does not allow any enemy to even detect that there is a second message present. Steganography can be used with a large number of file formats most commonly used in the digital world of today. The different file formats popularly used are .bmp, .gif, .txt etc. Thus the techniques of steganography are going to play a very important part in the future of data security and privacy on open systems such as the Internet.       

This paper presents an efficient method for hiding data into an image and send to the destination in a safe manner. This technique does not need any key for embedding and extracting data. Also, it allows hiding four bits in a block of size 5×5 with minimal distortion. The proposed algorithm ensures security and safety of the hidden information. The experimental results presented in this paper show the efficacy of the proposed method.

The paper presents a new method for electronic toll collection system and to match car number plate detection system. The electronic toll collection system (ETS) has been fabricated based on microcontroller. Here a system is developed to collect tolls according to the weight of the vehicle. The car number plate detection method utilizes template matching technique to approximate the location of car number plate. Then, using this output from the template matching method, color information is used to eliminate the unwanted color areas from the approximate number plate region without affecting the correct color regions. Hence, the number plate region can be determined more accurately. This work can easily be done by image processing system using MATLAB. The method has low complexity and reduced the processing time magnificently. This automated system also shows a better performance in highway traffic management. This paper shows the gateway to fabricate a highly automated toll-plaza.

The output power produced by high-concentration solar thermal and photovoltaic systems is directly related to the amount of solar energy acquired by the System, and it is therefore necessary to track the sun’s position with a high degree of accuracy. This paper presents sun tracking generating power system designed and implemented in real time. A tracking mechanism composed of photovoltaic module, stepper motor ,sensors, input/output interface and expert FLC implemented on FPGA, that to track the sun and keep the solar cells always face the sun in most of the day time. The proposed sun tracking fuzzy controller has been tested using Matlab/Simulink program; the simulation results verify the effectiveness of the proposed controller and shows an excellent result.

Due to many applications need the management of spatial data; clustering large spatial databases is an important problem which tries to find the densely populated regions in the feature space to be used in data mining, knowledge discovery, or efficient information retrieval. A good clustering approach should be efficient and detect clusters of arbitrary shapes. It must be insensitive to the outliers (noise) and the order of input data. In this paper Cosine Cluster is proposed based on cosine transformation, which satisfies all the above requirements. Using multi-resolution property of cosine transforms, arbitrary shape clusters can be effectively identified at different degrees of accuracy. Cosine Cluster is also approved to be highly efficient in terms of time complexity. Experimental results on very large data sets are presented, which show the efficiency and effectiveness of the proposed approach compared to other recent clustering methods.

We describe an agents-based contractual workflow paradigm for Self-organization and autonomy in computational networks. The agent-based paradigm can be interpreted as the outcome arising out of deterministic, nondeterministic or stochastic interaction among a set of agents that includes the environment. These interactions are like chemical reactions and result in self-organization. Since the reaction rules are inherently parallel, any number of actions can be performed cooperatively or competitively among the subsets of elements, so that the agents carry out the required actions. Also we describe the application of this paradigm in finding short duration paths, chemical- patent mining, and in cloud computing services.