Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

Extracellular vesicles, specifically microparticles (MPs), are rapidly gaining attention for their capacity to act as biomarkers for diagnosis, prognosis, or responsiveness to therapy in lung disease, in keeping with the concept of precision medicine. However, MP analysis by high-sensitivity flow cytometry (FCM) is complicated by a lack of accurate means for MP enumeration. To address this gap,...

C security has become an increasingly vital field in computer science in response to the proliferation of private sensitive information. The term “Intrusion” refers to any unauthorized access which attempts to compromise confidentiality, integrity and availability of information resources [1] [14] [32]. Traditional intrusion prevention techniques such as firewalls, access control and encryption...

Clustering is an important technique for unsupervised image segmentation. The use of fuzzy c-means clustering can provide more information and better partitions than traditional c-means. In image processing, the ability to reduce the precision of the input data and aggregate similar examples can lead to significant data reduction and correspondingly less execution time. This paper discusses brF...

Clustering algorithms are highly dependent on the features used and the type of the objects in a particular image. By considering object similar surface variations (SSV) as well as the arbitrariness of the fuzzy c-means (FCM) algorithm for pixel location, a fuzzy image segmentation considering object surface similarity (FSOS) algorithm was developed, but it was unable to segment objects having ...

In several papers, clustering has been used for preprocessing datasets before applying classification algorithms in order to enhance classification results. A strong clustered dataset as input to classification algorithms can significantly improve the computation time. This can be particularly useful in “Big Data” where computation time is equally or more important than accuracy. However, there...

In web search applications, queries are submitted to search engines to represent the information needs of users. Discovering the number of diverse user search goals for a query and depicting each goal with some keywords automatically. In the existing work propose a novel approach to infer user search goals by analyzing search engine query logs. First propose a novel approach to infer user searc...

Background removal is an application of image segmentation. There are many methods for image segmentation. In this paper, Fuzzy C-Means (FCM) is used for the image segmentation. In this paper, the clusters centroid is given as input from the histogram of the image. These inputs are updated and passed through FCM algorithm to get segmented images. The segmented images are added to remove the bac...

One of the main drawbacks of the FCM clustering algorithm is that it does not calculate the suitable number of clusters. This paper presents a method to solve this problem, by means of an equalization function (using uniform data) for the FCM functional J. The results for 2 and 3 dimensional data tests are also presented.