اهتفاقثو اهرخفو ةملأا ةيأ ةيوه نم أزجتي لا اءزج يرامعملا ثارتلا لكشي . ةيخيراتلا ةرامعلا رصانع نا يف اهؤانبو اهميمصت مت دق ةيملاسلإا تابلطتمل يويحو لاعف لكشب ةباجتسلاا ىلع ةرداق نوكت ثيحب يضاملا ةينيدلاو ةيسفنلاو ةيعامتجلااو ةيئيبلاو ةيداملا سانلا . رضاحلا تقولا يف نويرامعملا نوسدنهملا موقي رصانعلا ذخأب اوذخأي ام ةداعو ميمصتلا عيراشم يف اهلاخدا و ةيملاسلإا ةيخيراتلا ينابملا نم ةفلتخملا نيوكت ر...

Horizontal collaborative clustering is such a clustering method that carries clustering on one data set describing a pattern set in one feature space with collaborative introducing of outer partition information obtained by clustering on another data set but describing the same pattern set in another feature space. In order to implement the collaborative clustering, horizontal collaborative fuz...

This paper addresses the issue of image segmentation by clustering in the domain of image processing. The clustering algorithm taken account here is the Fuzzy C-Means which is widely adopted in this field. Bacterial Foraging Optimization Algorithm is an optimal algorithm inspired by the foraging behavior of E.coli. For the purpose to reinforce the global search capability of FCM, the Bacterial ...

A robust validity index for fuzzy c-means (FCM) algorithm is proposed in this paper. The purpose of fuzzy clustering is to partition a given set of training data into several different clusters that can then be modeled by fuzzy theory. The FCM algorithm has become the most widely used method in fuzzy clustering. Although, there are some successful applications of FCM have been proposed, a disad...

The 'kernel method' has attracted great attention with the development of support vector machine (SVM) and has been studied in a general way. In this paper, this 'method' is extended to the well-known fuzzy c-means (FCM) and possibilistic c-means (PCM) algorithms. It is realized by substitution of a kernel-induced distance metric for the original Euclidean distance, and the corresponding algori...

Medical image segmentation demands an efficient and robust segmentation algorithm against noise. The conventional fuzzy c-means algorithm is an efficient clustering algorithm that is used in medical image segmentation. But FCM is highly vulnerable to noise since it uses only intensity values for clustering the images. This paper aims to develop a novel and efficient fuzzy spatial c-means cluste...

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...