Informatica

In this study, we evaluated the effects of the normalization procedures on decision outcomes of a given MADM method. For this aim, using the weights of a number of attributes calculated from FAHP method, we applied TOPSIS method to evaluate the financial performances of 13 Turkish deposit banks. In doing this, we used the most popular four normalization procedures. Our study revealed that vector normalization procedure, which is mostly used in the TOPSIS method by default, generated the most consistent results. Among the linear normalization procedures, max-min and max methods appeared as the possible alternatives to the vector normalization procedure.

The paper presents a novel algorithm for restoration of the missing samples in additive Gaussian noise based on the forward–backward autoregressive (AR) parameter estimation approach and the extrapolation technique. The proposed algorithm is implemented in two consecutive steps. In the first step, the forward–backward approach is used to estimate the parameters of the given neighbouring segments, while in the second step the extrapolation technique for the segments is applied to restore the samples of the missing segment. The experimental results demonstrate that the restoration error of the samples of the missing segment using the proposed algorithm is reduced as compared with the Burg algorithm.

An application of fuzzy modeling to the problem of telecommunications time-series prediction is proposed in this paper. The model building process is a two-stage sequential algorithm, based on Subtractive Clustering (SC) and the Orthogonal Least Squares (OLS) techniques. Particularly, the SC is first employed to partition the input space and determine the number of fuzzy rules and the premise parameters. In the sequel, an orthogonal estimator determines the input terms which should be included in the consequent part of each fuzzy rule and calculate their parameters. A comparative analysis with well-established forecasting models is conducted on real world telecommunications data, where the characteristics of the proposed forecaster are highlighted.

The optimal financial investment (Portfolio) problem was investigated by leading financial organizations and scientists. Nobel prizes were awarded for the Modern Portfolio Theory (MPT) and further developments. The aim of these works was to define the optimal diversification of the assets depending on the acceptable risk level.

The aim of this study is to predict the energy generated by a solar thermal system. To achieve this, a hybrid intelligent system was developed based on local regression models with low complexity and high accuracy. Input data is divided into clusters by using a Self Organization Maps; a local model will then be created for each cluster. Different regression techniques were tested and the best one was chosen. The novel hybrid regression system based on local models is empirically verified with a real dataset obtained by the solar thermal system of a bioclimatic house.

New asymmetric cipher based on matrix power function is presented. Cipher belongs to the class of recently intensively evolving non-commuting cryptography due to expectation of its resistance to potential quantum cryptanalysis.

Up to now, there was very little work on studying the revocation problem in existing hierarchical ID-based encryption (HIBE) systems. Certainly, all existing HIBE systems may inherit the revocation method suggested by Boneh and Franklin to revoke illegal or expired users, in which non-revoked users must periodically update their private keys using secure channels by contacting their ancestors in hierarchical structures. In this paper, we propose the first HIBE scheme with public revocation mechanism, called revocable HIBE (RHIBE), which is extended from Lewko and Waters's unbounded HIBE scheme presented in Eurocrypt 2011. We demonstrate that the proposed RHIBE scheme is fully secure while removing the requirement of secure channels for private key updating in Boneh and Franklin's revocation method. The public revocation mechanism is an exciting alternative to the existing revocation methods. Finally, we discuss the transformation technique from a HIBE scheme to a RHIBE scheme and employ it to another well-known HIBE scheme.

We present a new aggregation operator called the generalized ordered weighted proportional averaging (GOWPA) operator based on an optimal model with penalty function, which extends the ordered weighted geometric averaging (OWGA) operator. We investigate some properties and different families of the GOWPA operator. We also generalize the GOWPA operator. The key advantage of the GOWPA operator is that it is an aggregation operator with theoretic basis on aggregation, which focuses on its structure and importance of arguments. Moreover, we propose an orness measure of the GOWPA operator and indicate some properties of this orness measure. Furthermore, we introduce the generalized least squares method (GLSM) to determine the GOWPA operator weights based on its orness measure. Finally, we present a numerical example to illustrate the new approach in an investment selection decision making problem.