Application of flow cytometry (FCM) to microbial analysis of milk is hampered by the presence of milk proteins and lipid particles. Here we report on the development of a rapid (/= 0.98) between the FCM assay and the more conventi...

Image segmentation plays an important role in medical image processing. Fuzzy c-means (FCM) clustering is one of the popular clustering algorithms for medical image segmentation. However, FCM has the problems of depending on initial clustering centers, falling into local optimal solution easily, and sensitivity to noise disturbance. To solve these problems, this paper proposes a hybrid artifici...

Semi-supervised learning is somewhere between unsupervised and supervised learning. In fact, most semi-supervised learning strategies are based on extending either unsupervised or supervised learning to include additional information typical of the other learning paradigm. Constraint fuzzy c-means a novel semi-supervised fuzzy c-means algorithm proposed by Li et al [1]. Constraint FCM like FCM ...

In order to make up some deficiencies of the fuzzy c-means clustering algorithm, a new FCM algorithm based on pretreatment of similarity relation between samples is proposed in the paper, which is utilized to estimate the fuzzy clustering centers and the weight coefficient of samples effecting on the fuzzy clustering centers during iteration process. The new FCM algorithm makes the clustering q...

Fuzzy C-Mean (FCM) is an unsupervised clustering algorithm based on fuzzy set theory that allows an element to belong to more than one cluster. Where fuzzy means “unclear” or “not defined” and c denotes “clustering”. In FCM the number of cluster are randomly selected. [15] FCM is the advanced version of K-means clustering algorithm and doing more work than K-means. K-Means just needs to do a di...

In this study, we address the problem of medical diagnosis by applying Fuzzy Cognitive Map (FCM). A distinctive feature of the FCM is its ability to simulate the development of the disease in time. By this simulation, it is possible to predict the severity of the disease by having future knowledge on current medical investigations. For the first time in this paper, we construct an FCM-based cla...

BACKGROUND Quantifying plant gene expression by flow cytometry (FCM) would allow multidimensional cell-parameter analysis on a per-cell basis, thereby providing insight into the cellular mechanisms of plant gene regulation. Here we sought to establish quantitation by FCM of plant hormone (abscisic acid, ABA)-inducible green fluorescent protein (GFP) expression and to compare the method directly...

In medical applications all effectual agents in patient health must be fast, even medical algorithms such as clustering ones. In this paper an optimized technique is presented to decrease execution time and iterations of standard Fuzzy C-Means (FCM) alghorythm. New approach calculates cluster center in each iteration by new formula. Applying proposed method decreases the complexity of FCM algor...

The classification of the electrocardiogram (ECG) into different patho-physiological disease categories is a complex pattern recognition task. In this paper, we propose a scheme to integrate fuzzy c-means (FCM) clustering, principal component analysis (PCA) and neural networks (NN) for ECG beat classification. The PCA is used to decompose ECG signals into weighted sum of basic components that a...