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As the use of network services increase, security is considered as a crucial and major issue in the network. Several computers connected with the network play an essential role in business and other applications running over the network to provide services. Therefore, we need to search out the best ways to protect the system. One of the methods is to provide security to the system and analyze network traffic through intrusion detection or intrusion prevention. In this paper, a hybrid intrusion detection framework is suggested. Proposed hybrid IDS is a combination of two machine learning algorithms J48 DT and SVM. To select relevant features from the KDD CUP dataset Particle Swarm Optimization is used. WEKA is used to implement classification on the KDD CUP dataset. The dataset is divided into ratios of 60:40, 70:30, and 80:20 for training and testing purpose. The experiment result showed 99.1% accuracy, 99.6% detection rate and 1.0% FAR for 60:40 datasets whereas accuracy, detection rate and FAR for 70:30 datasets are 99.2%, 99.6% and 0.9% respectively for 80:20 datasets 99.1%, 99.6% and 0.9% respectively.

The Single Transferable Vote (STV) is an attractive way of achieving representation that is proportional in terms of whatever characteristics of candidates voters value. Increasingly sophisticated methods of implementing STV have been advanced to overcome identified limitations of earlier methods. But every refinement comes at a cost of increased difficulty of understanding the vote-counting algorithm and increased cost of undertaking the count. This paper uses votes from actual elections to provide evidence about the frequency with which the choice of a particular STV method affects the outcome, and about the type of difference that different methods make. The most sophisticated STV method is CPO-STV, the comparison of pairs of outcomes by STV. This method avoids sequential exclusions and therefore overcomes the limitations of previous methods, that a paucity of votes in the early stages of a count can lead to the exclusion of a candidate who is the consensus choice of voters whose preferred candidates will be excluded at later stages of the count.

Pilot projects in the area of remote Internet voting have been carried out in several countries. However, legally binding Internet-cast voting is valid in few countries, Estonia being the first country to introduce it at national level. A good communications infrastructure, voters’ high e-readiness, the widespread use of the national e-ID card, which enables secure authentication of on-line voters, and its relatively small population of 1.3 million are the key factors for the success of I-voting in Estonia. This case-study provides a detailed description of the Estonian remote I-voting system, the motivation behind implementing such a system, a short analysis of the experience and problems emerged as well as about future prospects.

This paper presents results of experiments on some data sets using bagging on the MLEM2 rule induction algorithm. Three different methods of ensemble voting, based on support (a non-democratic voting in which ensembles vote with their strengths), strength only (an ensemble with the largest strength decides to which concept a case belongs) and democratic voting (each ensemble has at most one vote) were used. Our conclusions are that though in most cases democratic voting was the best, it is not significantly better than voting based on support. The strength voting was the worst voting method.

There are many situations wherein a group of individuals (e.g., voters, experts, sports writers) must produce an ordered list of ‘best’ alternatives selected from a given group of alternatives (e.g., candidates, proposals, sports teams). Two long established mechanisms that have been used for this task are ‘Zermelo’s Ranking Method’ (1929) and ‘Borda’s Voting Scheme’ (1781). The main purpose of this paper is to point out that they are, under certain common circumstances, identical. We then show that Zermelo’s Method can be used in situations that Borda’s Method is not designed to handle.

It has been demonstrated that combining the decisions of several classifiers can lead to better recognition results. The combination can be implemented using a variety of schemes, among which voting method is the simplest, but it has been found to be just as effective as more complicated strategies in improving the recognition results. In this paper, we propose a voting method for object location, which can be viewed as generalization of majority vote rule. Using this method, we locate eye centers in face region. The experimental results demonstrate that the locating performance is comparable with other newly proposed eye locating methods. The voting method can be considered as a general fusion scheme for precise location of object.

The discussion on electronic voting has so far mostly focused on technical issues, mainly concerning security and privacy. This paper reports an empirical study on how the symbolic values of democracy, as manifested in the act of voting, are considered by e-voters. The study found that the voters in a student election in actions as well as in stated views gave priority to convenience over security and privacy. They voted electronically from home despite uncertainty about the security of the technical system. We argue that this is an indication that the view of the principles of democratic practices will change, and that what might be called an "e-practices mode of thinking" will to some extent prevail over a "rigid democracy mode".

We demonstrate that the process of identifying differentially expressed genes in microarray studies with small sample sizes can be substantially improved by extracting information from a large number of datasets accumulated in public databases. The improvement comes from more reliable estimates of gene-specific variances based on other datasets. For a two-group comparison with two arrays in each group, for example, the result of our method was comparable to that of a t-test analysis with five samples in each group or to that of a regularized t-test analysis with three samples in each group. Our results are further improved by weighting the results of our approach with the regularized t-test results in a hybrid method.

The Multiple Classifier Systems are nowadays one of the most promising directions in pattern recognition. There are many methods of decision making by the ensemble of classifiers. The most popular are methods that have their origin in vote method, where the decision of the common classifier is a combination of individual classifiers decisions. This work presents method of weighted classifiers combination and experimental results of proposed algorithm.

Background Genes work in concert as a system, not as independent entities, to mediate disease states. There has been considerable interest in understanding variations in molecular signatures between normal and disease states. The selective-voting convex-hull ensemble procedure accommodates molecular heterogeneity within and between groups and allows retrieval of sample-specific sets and investigation of variations in individual networks relevant to personalized medicine[1]. The work here describes using the convex-hull voting method on a large data set. Using parallelization techniques, we predict that we can execute the convex-hull voting algorithm on the University of Kentucky cluster (DLX) using a dataset much too large to run in a feasible time on a single machine.