Informatica

Fuzzy C-Means (FCM) algorithm is one of the commonly preferred fuzzy algorithms for image segmentation applications. Even though FCM algorithm is sufficiently accurate, it suffers from the computational complexity problem which prevents the usage of FCM in real-time applications. In this work, this convergence problem is tackled through the proposed Modified FCM (MFCM) algorithm. In this algorithm, several clusters among the input data are formed based on similarity measures and one representative data from each cluster is used for FCM algorithm. Hence, this methodology minimizes the convergence time period requirement of the conventional FCM algorithm to higher extent. This proposed approach is experimented on Magnetic Resonance (MR) brain tumor images. Experimental results suggest promising results for the MFCM algorithm in terms of the performance measures.

The choice of natural image prior decides the quality of restored image. Recently successful algorithms exploit heavy-tailed gradient distribution as image prior to restore latent image with piecewise smooth regions. However, these prior assumed also remove the mid-frequency component such as textural details regions while they preserve sharp edges. That because gradient profile in fractal-like texture do not have sparse characteristic.

Visual appearance can be phenomenologically modeled through an integral equation, known as reflectance equation. It describes the surface radiance which depends on the interaction between incident light field and surface Bidirectional Reflectance Distribution Function (BRDF). Being defined on the Cartesian product of the incident and outgoing hemispheres, hemispherical basis is the natural way to represent surface BRDFs. Nonetheless, due to their compactness in the frequency space, spherical harmonics have been extensively used for this purpose. Addressing the geometrical compliance of hemispherical basis, this paper proposes a Cartesian product of the hemispherical harmonics to provide a compact representation of plausible BRDFs, while satisfying the Helmholtz reciprocity property. We provide an analytical analysis and experimental justification that our basis provides better approximation accuracy when compared to similar bases in literature.

The nonlinear stochastic programming problem involving CVaR in the objective and constraints is considered. Solving the latter problem in a framework of bi-level stochastic programming, the extended Lagrangian is introduced and the related KKT conditions are derived. Next, the sequential simulation-based approach has been developed to solve stochastic problems with CVaR by finite sequences of Monte Carlo samples. The approach considered is grounded by the rule for iterative regulation of the Monte Carlo sample size and the stochastic termination procedure, taking into account the stochastic model risk. The rule is introduced to regulate the size of the Monte Carlo sample inversely proportionally to the square of the stochastic gradient norm allows us to solve stochastic nonlinear problems in a rational way and ensures the convergence. The proposed termination procedure enables us to test the KKT conditions in a statistical way and to evaluate the confidence intervals of the objective and constraint functions in a statistical way as well. The results of the Monte Carlo simulation with test functions and solution of the practice sample of trade-offs of gas purchases, storage and service reliability, illustrate the convergence of the approach considered as well as the ability to solve in a rational way the nonlinear stochastic programming problems handling CVaR in the objective and constraints, with an admissible accuracy, treated in a statistical manner.

In current paper a problem of classification of T-distributed random field observation into one of two populations specified by common scaling function is considered. The ML and LS estimators of the mean parameters are plugged into the linear discriminant function. The closed form expressions for the Bayes error rate and the actual error rate associated with the aforementioned discriminant functions are derived. This is the extension of one for the Gaussian case. The actual error rates are used to evaluate and compare the performance of the plug-in discriminant function by means of Monte Carlo study.

In a multi-decrypter encryption (MDE) scheme, a message encrypted under the public keys of multiple receivers can be recovered only when all the receivers designated by the sender are available (e.g. in a national security setting where a “Top Secret” document can only be decrypted and recovered when all the designated “keyholders” present the respective keys). Despite its effectiveness (i.e. without heavy computational overheads) in ensuring a message can only be read when all the designated parties are available, this is an under-researched topic (there are only two published MDE schemes in the literature, to the best of our knowledge). In this paper, we propose an efficient MDE scheme and prove its CCA2 security in the standard model under the decisional bilinear Diffie–Hellman assumption.

This paper investigates group decision making problems in which the criterion values take the form of interval-valued intuitionistic uncertain linguistic numbers (IIULNs). First, some additive operational laws of IIULNs are defined. Subsequently, some new arithmetic aggregation operators, such as the interval-valued intuitionistic uncertain linguistic weighted averaging (IIULWA) operator, interval-valued intuitionistic uncertain linguistic ordered weighted averaging (IIULOWA) operator and interval-valued intuitionistic uncertain linguistic hybrid aggregation (IIULHA) operator, are proposed which are based on the operational laws. Furthermore, an approach to group decision making with interval-valued intuitionistic uncertain linguistic information is developed, which is based on the IIULWA and IIULHA operators. Finally, an illustrative example is provided to demonstrate the feasibility and effectiveness of the proposed method.

The Generalized Traveling Salesman Problem is one of a well known complex combinatorial optimization problems. Equality-Generalized Traveling Salesman Problem is a particular case of it. The main objective of the problem it is to find a minimum cost tour passing through exactly one node from each cluster of a large-scale undirected graph. Multi-agent approaches are successfully used nowadays for solving real life complex problems. The aim of the current paper is to illustrate some agent-based algorithms, including particular ant-based models and virtual robots-agents with specific properties for solving Equality-Generalized Traveling Salesman Problem.

This paper shows a few novel calculations for wind speed estimation, which is focused around soft computing. The inputs of to the estimators are picked as the wind turbine power coefficient, rotational rate and blade pitch angle. Polynomial and radial basis function (RBF) are applied as the kernel function of Support Vector Regression (SVR) technique to estimate the wind speed in this study. Instead of minimizing the observed training error, SVR_poly and SVR_rbf attempt to minimize the generalization error bound so as to achieve generalized performance. The results are compared with the adaptive neuro-fuzzy (ANFIS) results.

In this paper, we propose a new aggregation operator under uncertain pure linguistic environment called the induced uncertain pure linguistic hybrid averaging aggregation (IUPLHAA) operator. Some of the main advantages and properties of the new operator are studied. Moreover, in the situations where the given arguments about all the attribute weights, the attribute values and the expert weights are expressed in the form of linguistic labels variables, we develop an approach based on the IUPLHAA operator for multiple attribute group decision making with uncertain pure linguistic environment. Finally, an illustrative example is given to verify the developed approach and to demonstrate its feasibility and practicality.