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...

Part III is concluded with this chapter that proposes a Fuzzy Cognitive Map (FCM)-based modeling of the Quality of Interaction (QoI) of the Learning Management System (LMS) users within a blended (b)learning context, namely FCM-QoI model. Two training/testing scenarios were conducted and explored here, i.e., time-dependent and time-independent, using as pre-validated QoI data the ones presented...

The S-FCM uses flow cytometry technology to measure sperm concentrations. Semen samples from 104 men attending a male infertility clinic were used to evaluate the reproducibility of results and the carryover rate with the S-FCM by performing between- and within-run imprecision analyses. In addition, sperm concentrations measured with the S-FCM were compared with those obtained by manual analyse...

Fuzzy c-means (FCM) has been considered as an effective algorithm for image segmentation. However, it still suffers from two problems: one is insufficient robustness to image noise, and the other is the Euclidean distance in FCM, which is sensitive to outliers. In this paper, we propose two new algorithms, generalized FCM (GFCM) and hierarchical FCM (HFCM), to solve these two problems. Traditio...

In fuzzy clustering, the fuzzy c-means (FCM) clustering algorithm is the best known and used method. An interesting extension of FCM is the fuzzy ISODATA (FISODATA) algorithm; it updates cluster number during the algorithm. That's why we can have more or less clusters than the initialization step. It's the power of the fuzzy ISODATA algorithm comparing to FCM. The aim of this paper is...