Fuzzy Cognitive Maps (FCMs) have successfully been applied in numerous domains to show the relations between essential components in complex systems. In this paper, a novel learning method is proposed to construct FCMs based on historical data and by using meta-heuristic: Genetic Algorithm (GA), Simulated Annealing (SA), and Tabu Search (TS). Implementation of the proposed method has demonstrated via real data of a purchase system in order to simulate the system's behavior.

Fuzzy clustering methods are conveniently employed in constructing a fuzzy model of a system, but they need to tune some parameters. In this research, FCM is chosen for fuzzy clustering. Parameters such as the number of clusters and the value of fuzzifier significantly influence the extent of generalization of the fuzzy model. These two parameters require tuning to reduce the overfitting in the fuzzy model. Two new cost functions are developed to set the parameters of FCM algorithm properly and the two evolutionary optimization algorithms, i.e. the multi-objective simulated annealing and the multi-objective imperialist competitive algorithm, are employed to optimize the parameters of FCM according to the proposed cost functions. The multi-objective imperialist competitive algorithm is the proposed algorithm.

Due to the spread of the Internet and its pervasiveness, ``big data" is created daily. Processing this amount of data requires a system with high processing power. In fact, the production and collection of data from a wide range of different equipment and tools lead to the creation of large-scale databases. In dealing with large and unstructured databases and their management, there are always challenges. This study aims to present a model to increase the clustering accuracy of big data using a fuzzy clustering system based on data mining in a MatLab programming environment. For this purpose, first, the importance of each variable in the decision tree models in SPSSModeler software is determined, then with the help of these results, fuzzy rules are explained and a fuzzy inference system is formed in MATLAB software. This study uses data mining techniques such as C\&R Tree, Chaid and C5.0 to study the development of the FCM method to increase clustering accuracy in high volume data and related factors such as data preparation indicators, data type Data quality, data dimensions, data volume and number of clusters were evaluated as inputs and clustering accuracy index was evaluated as output. Then, with the help of these results, the rules of forming a fuzzy inference system were determined and by explaining the membership functions of the decision model, it showed what effect each input index has on the output index.

The objective of this study is to design a hybrid model based on Fuzzy C-Means (FCM) and Deep Learning in order to improve clustering accuracy in big data, particularly in the context of medical imaging. In this study, with the goal of enhancing clustering accuracy for skin lesion diagnosis, dermoscopic images were first collected and analyzed using Convolutional Neural Networks (CNN). Then, the FCM algorithm was combined with deep learning and the LCAOA optimization algorithm to optimize cluster centers. Fuzzy logic was also integrated into the system to improve the fitness function of the clustering algorithm. A hybrid method combining FCM, LCAOA, deep learning, and fuzzy logic was proposed. This approach improves clustering accuracy by refining the fitness function and optimizing cluster centers. The method was evaluated on medical images of skin cancer (melanoma) and showed significantly better performance and accuracy in automatic melanoma detection compared to conventional algorithms.

Extracting two component nanofibers from blend polymers is one of the interesting methods of industrial production of nanofibers. Knowing the morphology of nanofibers structure is essential for improving their efficiency. Fiber diameter is one of the major structural properties, which is typically determined by manual measuring methods. However, manual fiber diameter calculation is a tedious and time consuming task as well as being sensitive to human errors. Therefore, an accurate and automated technique to measure the diameter of fibers is desired. In recent years, image processing methods have been commonly used to measure the diameter of nanofibers. In this study, image segmentation based on Fuzzy Clustering Method (FCM) and Distance Transform Method (DTM) was used to measure the diameter of nanofibers extracted from blend fiber. The diameter of nanofibers was calculated using proposed method and results were compared with other image processing algorithms and the manual method. The presented results showed that the FCM approach can be helpful for measuring the nanofiber diameter within a fibrous network.