International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems

In this paper, we have formulated a simple SIS type epidemic model in the presence of treatment control, and we have discussed the dynamical behavior of the system. The system is modified by considering both the disease transmission rate and the treatment function as fuzzy numbers, and also the fuzzy expected value of the infected individuals is calculated. Furthermore, the fuzzy basic reproduction number is investigated and a threshold condition of pathogen is obtained at which the system undergoes a transcritical bifurcation.

Many clustering algorithms have been proposed in literature to identify the parameters involved in the Takagi–Sugeno fuzzy model, we can quote as an example the Fuzzy C-Means algorithm (FCM), the Possibilistic C-Means algorithm (PCM), the Allied Fuzzy C-Means algorithm (AFCM), the NEPCM algorithm and the KNEPCM algorithm. The main drawback of these algorithms is the sensitivity to initialization and the convergence to a local optimum of the objective function. In order to overcome these problems, the particle swarm optimization is proposed. Indeed, the particle swarm optimization is a global optimization technique. Thus, the incorporation of local research capacity of the KNEPCM algorithm and the global optimization ability of the PSO algorithm can solve these problems. In this paper, a new clustering algorithm called KNEPCM-PSO is proposed. This algorithm is a combination between Kernel New Extended Possibilistic C-Means algorithm (KNEPCM) and Particle Swarm Optimization (PSO). The effectiveness of this algorithm is tested on nonlinear systems and on an electro-hydraulic system.

Sharing data to a third party for the mutual benefit of the data owners plays a vital need in all computer applications. Ensuring the privacy of sensitive data is an important issue in data sharing and hence there is a need for privacy preserving data mining algorithms. Traditional privacy preserving algorithms are prone to attacks, have high information loss and sometimes fail to achieve the privacy constraints. In this paper, a (G,S) clustering algorithm is proposed to achieve clusters each with S diverse sensitive values for the sensitive attribute. The number of distinct sensitive values in each cluster would be exactly equal to the number of sensitive values in the given data set. The clusters’ formed are anonymized by replacing the actual values with the centroid(G) of the cluster. The algorithm achieves the maximum possible diverse sensitive values in each cluster making it resilient to similarity attack. The method guarantees high degree of privacy with very minimal loss of information. The privacy degree of the clusters formed is at least equal to the number of distinct values in the sensitive attribute. The worst case linking probability would be as low as 0.5. The significance of this algorithm is its resilience to similarity attack. The performance of the algorithm is proved to be better than the existing algorithms in terms of KL Divergence, Discernibility metric and few other metrics.

In this paper we propose clustering methods based on weighted quasiarithmetic means of T-transitive fuzzy relations. We first generate a T-transitive closure R^T from a proximity relation R based on a max-T composition and produce a T-transitive lower approximation or opening R_T from the proximity relation R through the residuation operator. We then aggregate a new T-indistinguishability fuzzy relation by using a weighted quasiarithmetic mean of R^T and R_T. A clustering algorithm based on the proposed T-indistinguishability is thus created. We compare clustering results from three critical t_i-indistinguishabilities: minimum (t₃), product (t₂), and Łukasiewicz (t₁). A weighted quasiarithmetic mean of a t₁-transitive closure $R^{t_1}$ and a t₁-transitive lower approximation or opening $R_{t_1}$ with the weight $p=0.5$, demonstrates the superiority and usefulness of clustering begun by using a proximity relation R based on the proposed clustering algorithm. The algorithm is then applied to the practical evaluation of the performance of higher education in Taiwan.

A new multi-output interval type-2 fuzzy logic system (MOIT2FLS) is introduced for protein secondary structure prediction in this paper. Three outputs of the MOIT2FLS correspond to three structure classes including helix, strand (sheet) and coil. Quantitative properties of amino acids are employed to characterize twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Three clustering tasks are performed using the adaptive vector quantization method to construct an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS. The genetic fitness function is designed based on the Q3 measure. Experimental results demonstrate the dominance of the proposed approach against the traditional methods that are Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

Many information fusion methods have been widely investigated to tackle group decision making (GDM) problem under dual hesitant fuzzy (DHF) environment. Nevertheless, traditional DHF information fusion methods are not quite perfect and they could be improved. In this study, we introduce the Archimedean t-conorm and t-norm (ATT) to DHF environment. We first introduce the ATT operations for DHF information, some existing operations are proved to be the exceptional cases of the proposed ones. Then, several new aggregation operators for DHF information are proposed based on ATT operations. Finally, an actual case about the selection of cooperative hospital as an emergency hospital for a company is studied based on the proposed methods.

Motivated by application in BitTorrent-like Peer-to-Peer resource sharing system, we introduce the maximization and minimization semi-latticized fuzzy relation geometric programming problems with max-min composition in this paper. The objective function in the proposed problem in nonlinear and the feasible domain is non-convex. The maximization problem is converted into a fuzzy relation monomial geometric programming and then solved. However, this approach is not effective for the minimization one. The linear programming method is applied to deal with the minimization problem. A step-to-step algorithm is develop to carried out the linear programming method and tow illustrative examples are provided at last.