Showing papers in "Information Sciences in 2013"

TL;DR: The components and concepts that are used in various metaheuristics are outlined in order to analyze their similarities and differences and the classification adopted in this paper differentiates between single solution based metaheURistics and population based meta heuristics.

TL;DR: This work carries out a thorough discussion on the main issues related to using data intrinsic characteristics in this classification problem, and introduces several approaches and recommendations to address these problems in conjunction with imbalanced data.

TL;DR: The experimental results show that the proposed black hole algorithm outperforms other traditional heuristic algorithms for several benchmark datasets.

TL;DR: This paper is a brief survey on the existing problems and challenges inherent in model-based control (MBC) theory, and some important issues in the analysis and design of data-driven control (DDC) methods are here reviewed and addressed.

TL;DR: A new linguistic group decision model is proposed that facilitates the elicitation of flexible and rich linguistic expressions, in particular through the use of comparative linguistic expressions close to human beings' cognitive models for expressing linguistic preferences based on hesitant fuzzy linguistic term sets and context-free grammars.

TL;DR: The proposed local Shannon entropy measure overcomes several weaknesses of the conventional global Shannon entropyMeasure, including unfair randomness comparisons between images of different sizes, failure to discern image randomness before and after image shuffling, and possible inaccurate scores for synthesized images.

TL;DR: Experimental studies show that TSF using simple features such as mean, standard deviation and slope is computationally efficient and outperforms strong competitors such as one-nearest-neighbor classifiers with dynamic time warping.

TL;DR: This paper proposes a new method to detect unknown malware families based on the frequency of the appearance of opcode sequences, and describes a technique to mine the relevance of each opcode and assess the Frequency of Each opcode sequence.

TL;DR: A hybrid PSO algorithm is proposed, called DNSPSO, which employs a diversity enhancing mechanism and neighborhood search strategies to achieve a trade-off between exploration and exploitation abilities.

TL;DR: A new visualization approach based on a Sensitivity Analysis (SA) to extract human understandable knowledge from supervised learning black box data mining models, such as Neural Networks, Support Vector Machines and ensembles, including Random Forests.

TL;DR: The primary goal of the study is to suggest the systematic transformation of the entropy into the similarity measure for HFSs and vice versa, and two clustering algorithms are developed under a hesitant fuzzy environment.

TL;DR: A comparative study of the effect of the application of some different distance functions for measuring consensus in GDM, using the nonparametric Wilcoxon matched-pairs signed-ranks test, and concludes that differentdistance functions can produce significantly different results.

TL;DR: This paper develops a wide range of hesitant fuzzy power aggregation operators for hesitant fuzzy information and demonstrates several useful properties of the operators and discusses the relationships between them.

TL;DR: An MCDM model combining VIKOR, DEMATEL, and ANP is proposed to solve the problem of conflicting criteria that show dependence and feedback and the results show that the proposed method can be effective in helping IT managers validate the effectiveness of their risk controls.

TL;DR: A fuzzy c-means clustering hybrid approach that combines support vector regression and a genetic algorithm yields sufficient and sensible imputation performance results.

TL;DR: An overview of techniques from the 1980s to the present is provided, revealing the wide range of factors that can influence problem difficulty and emphasising the need for a shift in focus away from predicting problem hardness towards measuring characteristics.

TL;DR: This is the first paper to introduce local suppression to achieve a tailored privacy model for trajectory data anonymization and compared with the previous works in the literature, this proposed local suppression method can significantly improve the data utility in anonymous trajectory data.

TL;DR: The induced 2-tuple linguistic generalized ordered weighted averaging (2-TILGOWA) operator is presented and the applicability of this new approach in a multi-person linguistic decision-making problem concerning product management is analysed.

TL;DR: A new definition of attribute reduct for decision-theoretic rough set models is provided and a heuristic approach, a genetic approach and a simulated annealing approach to the new problem are proposed.

TL;DR: This paper defines the strongest model of PEKS which is secure channel free and secure against chosen keyword attack, chosen ciphertext attack, and keyword guessing attack and presents two important security notions namely IND-SCF-CKCA and IND-KGA.

TL;DR: This work extends SN P systems by introducing some new ingredients (such as three types of neurons, fuzzy logic and new firing mechanism) and proposes the fuzzy reasoning spiking neural P systems (FRSN P systems), which are particularly suitable to model fuzzy production rules in a fuzzy diagnosis knowledge base and their reasoning process.

TL;DR: Three types of covering based multigranulation rough sets are presented, in which set approximations are defined by different covering approximation operators, which will enrich the MGRS theory and enlarge its application scope.

TL;DR: This paper will provide a general taxonomy of attack tactics against IDSs, an extensive description of how such attacks can be implemented by exploiting IDS weaknesses at different abstraction levels, and highlight the most promising research directions for the design of adversary-aware, harder-to-defeat IDS solutions.

TL;DR: In this paper, the authors introduce the notion of local synchronization into asynchronous SN P systems, where the use of spiking rules (even if they are enabled by the contents of neurons) is not obligatory.

TL;DR: An optimization approach based on artificial bee colony algorithm for optimal selection of cutting parameters in multi-pass turning operations is presented and a comparison of evolutionary-based optimization techniques to solve multi- pass turning optimization problems is presented.

TL;DR: The proposed hybrid approach combines the characteristics of neural networks and evolutionary techniques to obtain more precise solutions to solve the inverse kinematics problem of a six-joint Stanford robotic manipulator to minimize the error at the end effector.

TL;DR: The present research adopts particle swarm optimization techniques to obtain appropriate parameter settings for subtractive clustering and integrates the adaptive-network-based fuzzy inference system (ANFIS) model to construct a model for predicting business failures and shows that the proposed model is superior to other models.

TL;DR: This study develops a hybrid recommendation approach which combines user-based and item-based collaborative filtering techniques with fuzzy set techniques and applies it to mobile product and service recommendation and implements the proposed approach in an intelligent recommender system software called Fuzzy-based Telecom Product Recommender System (FTCP-RS).

TL;DR: The concept of intuitionistic fuzzy set theory is applied to generalize results concerning hypergraphs to determine what properties of the sequence of crisp structures characterize a given property of the intuitionists' fuzzy structure.

TL;DR: The paper presents theoretical results on the global exponential periodicity and stability of a class of memristor-based recurrent neural networks with multiple delays and uses the theory of differential equations with discontinuous right-hand side for the dynamic analysis.