This paper proposes a novel fuzzy multi-criteria decision-making method based on an improved score function of connection numbers and Choquet integral under interval-valued Pythagorean fuzzy environment. To do so, we first introduce a method to convert interval-valued Pythagorean fuzzy numbers into connection numbers based on the set pair analysis theory. Then an improved score function of connection numbers is proposed to make the ranking order of connection numbers more in line with reality in multi-criteria decision-making process. In addition, some properties of the proposed score function of connection numbers and some examples have been given to illustrate the advantages of conversion method proposed in the paper. Then, considering interactions among different criteria, we propose a fuzzy multi-criteria decision-making approach based on set pair analysis and Choquet integral under interval-valued Pythagorean fuzzy environment. Finally, a case of online learning satisfaction survey and a brief comparative analysis with other existing approaches are studied to show that the proposed method is simple,convenient and easy to implement. Comparing with previous studies, the method in this paper, from a new perspective, effectively deals with multi-criteria decision-making problems that the alternatives cannot be reasonably ranked in the decision-making process under interval-valued Pythagorean fuzzy environment.

Network security requires effective detection and proper analysis of abnormal network behavior. To address the uncertainty associated with the process of network intrusion detection, this article proposes a network intrusion-detection algorithm based on dynamic intuitionistic fuzzy sets (IFSs). We use the classic network intrusion datasets KDD 99, NSL-KDD, and the massive, high-dimensional dataset UNSW-NB15 to evaluate the performance of our proposed algorithm. First, we perform data preprocessing on these three datasets and select features based on the results of a chi-square test. Second, using time-series processing, we construct dynamic intuitionistic fuzzy patterns from the feature-selected datasets. At last, we use a proposed distance measure for the dynamic IFSs to generate a classifier that facilitates the detection of network intrusion. Experimental results show that the classification performance of the proposed algorithm is superior to that of other state-of-the-art algorithms on the three aforementioned datasets. The achieved improvement in classification performance is particularly significant for large datasets.

Adaptive image steganography embeds secret messages into areas of cover images with complex features, including rich edges and complex textures. In this paper, an adaptive image steganography technique based on the edge and complex texture areas of images is proposed, by comprehensively considering three rules in the design of image steganography. First, the embedding area is composed of the edge and complex texture areas of images, according to the complexity-first rule. Edge detection is realized by an improved fuzzy enhancement function, optimized by the Grey Wolf Optimizer (GWO) to detect both weak and strong edges. Second, the minimum average classification error rate is used to assess the choice of the complex texture areas. Third, under the spreading rule, two different average filters and one KB (KerBohme) filter are used to design the cost function in the embedding areas. Finally, confidential information is adaptively embedded through STCs (Syndrome-Trellis Codes). Experimental results show that the proposed algorithm outperforms seven classical adaptive image steganography algorithms on two steganalytic feature sets. The performance improvement is particularly significant when the payload is large.