Search results for "cluster analysis."
showing 10 items of 805 documents
Probabilistic Transition-Based Approach for Detecting Application-Layer DDoS Attacks in Encrypted Software-Defined Networks
2017
With the emergence of cloud computing, many attacks, including Distributed Denial-of-Service (DDoS) attacks, have changed their direction towards cloud environment. In particular, DDoS attacks have changed in scale, methods, and targets and become more complex by using advantages provided by cloud computing. Modern cloud computing environments can benefit from moving towards Software-Defined Networking (SDN) technology, which allows network engineers and administrators to respond quickly to the changing business requirements. In this paper, we propose an approach for detecting application-layer DDoS attacks in cloud environment with SDN. The algorithm is applied to statistics extracted from…
Semisupervised kernel orthonormalized partial least squares
2012
This paper presents a semisupervised kernel orthonormalized partial least squares (SS-KOPLS) algorithm for non-linear feature extraction. The proposed method finds projections that minimize the least squares regression error in Hilbert spaces and incorporates the wealth of unlabeled information to deal with small size labeled datasets. The method relies on combining a standard RBF kernel using labeled information, and a generative kernel learned by clustering all available data. The positive definiteness of the kernels is proven, and the structure and information content of the derived kernels is studied. The effectiveness of the proposed method is successfully illustrated in standard UCI d…
Applying logistic regression to relevance feedback in image retrieval systems
2007
This paper deals with the problem of image retrieval from large image databases. A particularly interesting problem is the retrieval of all images which are similar to one in the user's mind, taking into account his/her feedback which is expressed as positive or negative preferences for the images that the system progressively shows during the search. Here we present a novel algorithm for the incorporation of user preferences in an image retrieval system based exclusively on the visual content of the image, which is stored as a vector of low-level features. The algorithm considers the probability of an image belonging to the set of those sought by the user, and models the logit of this prob…
Unsupervised clustering method for pattern recognition in IIF images
2017
Autoimmune diseases are a family of more than 80 chronic, and often disabling, illnesses that develop when underlying defects in the immune system lead the body to attack its own organs, tissues, and cells. Diagnosis of autoimmune pathologies is based on research and identification of antinuclear antibodies (ANA) through indirect immunofluorescence (IIF) method and is performed by analyzing patterns and fluorescence intensity. We propose here a method to automatically classify the centromere pattern based on the grouping of centromeres on the cells through a clustering K-means algorithm. The described method was tested on a public database (MIVIA). The results of the test showed an Accuracy…
An algorithm for earthquakes clustering based on maximum likelihood
2007
In this paper we propose a clustering technique set up to separate and find out the two main components of seismicity: the background seismicity and the triggered one. We suppose that a seismic catalogue is the realization of a non homogeneous space-time Poisson clustered process, with a different parametrization for the intensity function of the Poisson-type component and of the clustered (triggered) component. The method here proposed assigns each earthquake to the cluster of earthquakes, or to the set of independent events, according to the increment to the likelihood function, computed using the conditional intensity function estimated by maximum likelihood methods and iteratively chang…
Restricted Neighborhood Search Clustering Revisited: An Evolutionary Computation Perspective
2013
Protein-protein interaction networks have been broadly studied in the last few years, in order to understand the behavior of proteins inside the cell. Proteins interacting with each other often share common biological functions or they participate in the same biological process. Thus, discovering protein complexes made of groups of proteins strictly related, can be useful to predict protein functions. Clustering techniques have been widely employed to detect significative biological complexes. In this paper, we integrate one of the most popular network clustering techniques, namely the Restricted Neighborhood Search Clustering (RNSC), with evolutionary computation. The two cost functions in…
Automatic detection of cervical cells in Pap-smear images using polar transform and k-means segmentation
2016
We introduce a novel method of cell detection and segmentation based on a polar transformation. The method assumes that the seed point of each candidate is placed inside the nucleus. The polar representation, built around the seed, is segmented using k-means clustering into one candidate-nucleus cluster, one candidate-cytoplasm cluster and up to three miscellaneous clusters, representing background or surrounding objects that are not part of the candidate cell. For assessing the natural number of clusters, the silhouette method is used. In the segmented polar representation, a number of parameters can be conveniently observed and evaluated as fuzzy memberships to the non-cell class, out of …
First-Episode Psychosis Patients Who Deteriorated in the Premorbid Period Do Not Have Higher Polygenic Risk Scores Than Others: A Cluster Analysis of…
2023
Abstract Cluster studies identified a subgroup of patients with psychosis whose premorbid adjustment deteriorates before the onset, which may reflect variation in genetic influence. However, other studies reported a complex relationship between distinctive patterns of cannabis use and cognitive and premorbid impairment that is worthy of consideration. We examined whether: (1) premorbid social functioning (PSF) and premorbid academic functioning (PAF) in childhood and adolescence and current intellectual quotient (IQ) define different clusters in 802 first-episode of psychosis (FEP) patients; resulting clusters vary in (2) polygenic risk scores (PRSs) for schizophrenia (SCZ_PRS), bipolar dis…
Dynamics of supramolecular associative polymer networks at the interplay of chain entanglement, transient chain association, and chain‐sticker cluste…
2019
The dynamic mechanical properties of supramolecular associative polymer networks depend on the average number of entanglements along the network‐forming chains, Nₑ, and on their content of associative groups, f. In addition, there may be further influence by aggregation of the associative groups into clusters, which, in turn, is influenced by the chemical structure of these groups, and again by Nₑ and f of the polymer. Therefore, the effects of these parameters are interdependent. To conceptually understand this interdependency, we study model networks in which (a) Nₑ, (b) f, and (c) the chemical structure of the associative groups are varied systematically. Each network is probed by rheolo…
From Bowls to Capsules: Assembly of Hexanuclear Ni II Rings Tailored by Alkali Cations
2020
An anionic hexanuclear NiII metallamacrocycle with endo and exo linking sites has been employed as a building block to generate a series of capsules and bowls of nanometric size. The supramolecular arrangement of the {Ni6 } rings was tailored by the size of the alkali cations, showing the transition from {Ni6 -M2 -Ni6 } capsules (M=LiI and NaI ) to {Ni6 -M} bowls (M=KI and CsI ). The alkyl co-cations are determinant to stabilize the assemblies by means of CH⋅⋅⋅π interactions on the exo side of the metallamacrocycles. The effect on the topology of the supramolecular assemblies of the cation size, cation charge, Et3 NH+ or Me4 N+ counter cations has been analyzed. Magnetic measurements reveal…