No abstract

No abstract

In this paper, based on power aggregation (PA) operators, neutrality average and neutrality geometric aggregation operators are proposed. Furthermore, a general score function for q-rung orthopair fuzzy sets (q-ROFSs) is proposed. PA can reduce the impact of excessively high or excessively low arguments. It also emphasizes the interrelationship between attributes on the decision matrix. PA and neutrality aggregations (NAs) were hybrid with more fair and neutral decisions of the DMs evaluations. Also, neutrality aggregation operator provides reliable results by taking into account neutrality among decision-makers. On the one hand, through its dynamic structure, q-ROFSs provide a wider evaluation for decision-makers (DMs). q-ROFSs include many fuzzy sets with varying ≥ q 1 parameters. One of the most important parts of multiattribute group decision-making problems is that DMs do not take into account their bias. On the other hand, the NA operator evaluates the decisions of the DMs from a neutral attitude perspective. The power neutrality aggregation operator proposed in this study produces more objective results. The proposed operator is more consistent with the advantages of both operators and it deals with the attitude of DMs more objectively. Validity tests are applied to the proposed methods. Also, the superior aspects of the

The increase in the frequency of use of the Internet causes the attacks on computer networks to increase. Such phenomena also increase the importance of intrusion detection systems. In this paper, KDD Cup 99 dataset is used for the classification of the network attacks. Four different classification algorithms were used, and the results were compared. These algorithms were multilayer perceptron network, decision trees, fuzzy unordered rule induction algorithm (FURIA) and support vector machines. The most successful algorithm in this dataset found as FURIA. As the second part of this study, the most important feature sets were found by correlation-based feature selection and best first search algorithm. Then, the results of classification algorithms were compared with these new feature sets according to the performance of the algorithms.

No abstract