Mthethwa, FisokuhleNath, AmitabhaOptimization is a vital constituent of machine learning as it brings about efficiency of the analysis process. An optimizer is used to minimize an objective function, which in t
A survey on machine learning in Internet of Things: Algorithms, strategies, and applications 4.2.2 Fuzzy C-means clustering a) Algorithm's principle The Fuzzy C-means algorithm is an extension of the K-mean algorithm by introducing a notion of fuzziness in the definition of the degree of belo...
We propose a novel semi-supervised clustering method called GO Fuzzy c-means, which enables the simultaneous use of biological knowledge and gene expression data in a probabilistic clustering algorithm. Our method is based on the fuzzy c-means clustering algorithm and utilizes the Gene Ontology annot...
The Fuzzy c-means is one of the most popular ongoing area of research among all types of researchers including Computer science, Mathematics and other areas of engineering, as well as all areas of optimization practices. Several problems from various are
Then, fuzzy c-means thresholding is performed to obtain a rough brain mask for each image slice, followed by refinement steps. For slices that contain eye regions, an additional eye removal algorithm is proposed to eliminate eyes from the brain mask. By using the proposed method, accurate ...
The Algorithm Fuzzy c-means (FCM) is a method of clustering which allows one piece of data to belong to two or more clusters. This method (developed by Dunn in 1973 and improved by Bezdek in 1981) is frequently used in pattern recognition. It is based on minimization of the following ...
EIT electrode arrangement, (2) Measurement of gastric impedance Z by using compact gEIT data acquisition system (DAQ), (3) Reconstruction of gastric conductivity distribution σ by using time-difference gauss–newton algorithm and 4) Estimation of gastric volume V by using dual-step fuzzy ...
In addition, an infrared array sensor was deployed to collect data for the lower body. Posture recognition was performed using a fuzzy c-means clustering algorithm. Six types of sleeping body posture were recognized from the combination of the upper and lower body postures. Results The ...
Third, the algorithm uses the information obtained during SVD to classify the neuronal waveforms by means of FCM clustering [16–19]. The unsupervised nature of FCM and its ability to detect clusters of different shapes makes it particularly useful for online sorting because of its robustness to ...
The fuzzy C means clustering (FCM, Fuzzy C-Means) theory is a combination of texture measurement and adaptive laws threshold image segmentation algorithm FCM. Through the experiment, simulation results show that the adaptive threshold method of image segmentation results human visual perception system ...