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In this paper we propose a rough classification modeling algorithm based on Ant Colony Optimization (ACO) reduction. We used ACO to compute the rough set reduct and later a modified rules generation method is employed to generate the classification rules. The rules generation algorithm used is the simplification of the Default Rules Generation Framework (DRGF) in order to fit with the ACO reduct....
Having an accurate Signature Detection Classification (SDC) Model has become highly demanding for Intrusion Detection Systems (IDS) to secure networks, especially when dealing with large and complex security audit data set. Selecting appropriate network features is one of the factors that influence the accuracy of SDC model. Past research has shown that the Hidden Marcov Chain, Genetic Algorithm,...
Outlier is strange data values that stand out from datasets. In some applications, finding outliers are more interesting than finding inliers in datasets, such as fraud detection, network system, financial and others. In this research, an algorithm is proposed to find minimum non-Reduct based on Rough set using Particle Swarm Optimization (PSO) for outlier detection. Like Genetic Algorithm (GA), PSO...
The advantages of soft c-means over its hard and fuzzy versions render it more attractive to use in a wide variety of applications. Its main merit lies in its relatively higher convergence speed, which is more obvious in the presence of huge high dimensional data. This work presents a new approach to accelerate the convergence of the original soft c-means. It is mainly based on an iterative optimization...
This paper proposes a new feature-selection strategy by integrating the Rough Set Theory (RST) and Particle Swarm Optimisation (PSO) algorithms to generate a set of discriminatory features for the classification problem. The proposed method is seen as a marriage between filter and wrapper approaches in which the RST is used to pre-reduce the feature set before optimisation by PSO, a meta-heuristic...
Stress and its related comorbid diseases are responsible for a large proportion of disability worldwide. In particular, chronic stress is the main responsible for the dramatic increase of premature mortality in the Western countries. However, advanced simulation and sensing technologies, such as virtual reality and mobile biosensors offer interesting opportunities for innovative personal health-care...
Individual protection, physically or mentally, is very important for someone living in a risk environment. Insurance is one of the individual protections due to accident, blaze, critical diseases or death. Insurance company plays a critical role in providing competitive product insurance that covers flexible features depend on customer requirements. In order to compete with other competitors and fulfill...
Security problems arise in software systems are very challenging. Using program analysis techniques and some language based security rules can help in enforcing application-level security through control access to program resources and verification of control flow of the information inside the program based on some security properties. This paper presents a new job analyzer component for an intrusion...
In business analysis, models are sometimes oversimplified. We pragmatically approach many problems with a single financial objective and include monetary values for non-monetary variables. We enforce constraints which may not be as strict in reality. Based on a case in distributed energy production, we illustrate how we can avoid simplification by modeling multiple objectives, solving it with an NSGA-II...
This paper introduces a relational fuzzy c-means clustering algorithm that is able to partition objects taking into account simultaneously several dissimilarity matrices. The aim is to obtain a collaborative role of the different dissimilarity matrices in order to obtain a final consensus partition. These matrices could have been obtained using different sets of variables and dissimilarity functions...
Due to the development of internet rapidly, the secure transmission of information has become more and more importance. Until now, there are many scholars study in the topic of data hiding. Specially, the reversible data hiding scheme catch the researcher's attention. No matter how the researchers use the different technology to embed the secret information, they always try to increase the hide space...
Mining techniques are needed to extract important information from huge high dimensional gene expression sets. Targeting unique expression behavior as over/under-expression is specific to gene expression data and is needed to explore another direction in the relation of genes to tumor conditions. This research proposes criteria for filtering over-expression genes, identifying over-expression related...
Paper deals with the problem of designing efficient classifiers for a special case of incremental concept drift. We focus on its classification based on the multiple classifier system. For the problem under consideration we propose four simple methods of combining classification and evaluate them via computer experiments.
In the past few years, online social data visualization has emerged as a new platform for users to construct, share, and comment on data visualizations online. The most well known online data visualization tools include Many Eyes, Swivel, and Tableau Public. In this paper, we report our analysis of Many Eyes - an IBM research project. By analyzing all the data visualizations constructed by users from...
Increasing use of computerized systems in our daily lives creates new adversarial opportunities for which complex mechanisms are exploited to mend the rapid development of new attacks. Behavioral Biometrics appear as one of the promising response to these attacks. But it is a relatively new research area, specific frameworks for evaluation and development of behavioral biometrics solutions could not...
Combining pattern recognition is the promising direction in designing an effective classifier systems. There are several approaches of collective decision-making, among them voting methods, where the decision is a combination of individual classifiers' outputs are quite popular. This article focuses on the problem of fuser design which uses continuous outputs of individual classifiers to make a decision...
This paper deals with a support tool for an automation of simulation of Coloured Petri nets and selected simulation experiments conducted with the aid of the tool. The tool is called CPN Assistant, it has been developed at the home institution of the authors and cooperates with the CPN Tools software. It allows to run and manage multiple customized simulations in a network environment. The experiments...
Control of interior permanent magnet (IPMSM) is difficult because its nonlinearity and parameter uncertainty. In this paper, a fuzzy c-regression models clustering algorithm which is based on T-S fuzzy is used to model IPMSM with a series linear model and weight them by memberships. Lagrangian of constrained function is built for calculating clustering centers where training output data are considered...
This paper deals with the problem of multi-agent learning of a population of players, engaged in a repeated normal-form game. Assuming boundedly-rational agents, we propose a model of social learning based on trial and error, called “social reinforcement learning”. This extension of well-known Q-learning algorithm, allows players within a population to communicate and share their experiences with...
Linearization of T-S fuzzy model is difficult to be achieved by using existing linearization methods because fuzzy rules and membership functions are included in T-S fuzzy models. In this paper, a new linearization method is proposed for discrete time T-S fuzzy system based on the properties of T-S fuzzy theorem. The local linear models of a T-S fuzzy model are transformed to a controllable canonical...
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