In fuzzy clustering, the fuzzy c-means (FCM) algorithm is the most commonly used clustering method. However, the FCM algorithm is usually affected by initializations. Incorporating FCM into switching regressions, called the fuzzy c-regressions (FCR), has also the same drawback as FCM, where bad initializations may cause difficulties in obtaining appropriate clustering and regression results. In...

In medical applications it is very important for a physician to be informed of patient situation as soon as possible especially in emergency circumstances. Therefore all efficient agents in patient health must be fast even medical algorithms such as clustering ones. Among clustering methods Fuzzy C-Means (FCM) clustering has been frequently used for segmentation of medical images. In this paper...

Fuzzy Cognitive Maps (FCM) are a proper knowledge-based tool for modeling and simulation. They are denoted as directed weighted graphs with feedback allowing causal reasoning. According to the transformation function used for updating the activation value of concepts, FCM can be grouped in two large clusters: discrete and continuous. It is notable that FCM having discrete outputs never exhibit ...

BACKGROUND Unexplained fatigue is often left untreated or treated with antidepressants. This randomized, placebo-controlled, single-blinded study evaluated the efficacy and tolerability of single-dose intravenous ferric carboxymaltose (FCM) in iron-deficient, premenopausal women with symptomatic, unexplained fatigue. METHODS Fatigued women (Piper Fatigue Scale [PFS] score ≥5) with iron defici...

Standard FCM algorithm takes the pixel gray-scale information into account only, while ignoring the spatial location of pixels, so the standard FCM algorithm is sensitive to noise. This paper present a pavement image segmentation algorithm based on FCM algorithm using neighborhood information. The presented algorithm introduces neighborhood information into membership function to improve the st...

 Fuzzy Cognitive Maps (FCM) is a technique to represent models of causal inference networks. Data driven FCM learning approach is a good way to model FCM. We present a hybrid FCM learning method that combines Nonlinear Hebbian Learning (NHL) and Extended Great Deluge Algorithm (EGDA), which has the efficiency of NHL and global optimization ability of EGDA. We propose using NHL to train FCM at ...

Dendritic cells (DCs) induce pathogen-specific T cell responses. We comprehensively studied the effects of addition of maturation stimulus, fibroblasts (fibroblast conditioned medium), PHA activated T cells (T cell conditioned medium), and mixture of fibroblast & PHA activated T cells (FCM-TCCM) conditioned media on maturation of DCs. Monocytes were cultured with GM-CSF and IL-4 for five days. ...

Modeling natural systems is challenging due to their complexity in terms of variables, interactions, and dynamics. Much of this complexity is rooted in the existence of multiple ways through which acting variables affect each other. Besides the simple direct effects, numerous indirect effects emerge in ecological systems. Through an illustrative example, I exemplify here several advantages of f...