An adaptive clustering method for surface detection
Adaptive Automata Community Detection and Clustering – A generic methodology –
... 1 Introduction The current effort on Complexity Theory and its formal- ism, able to cover a wide area in many aspects of Science, allows today to make relevant links between social, bio- logical and physical systems [5]. ...
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Adaptive clustering with feature ranking for DDoS attacks detection
... Table I shows the clustering result from MGKM with nine features. The value of each variable of each cluster centroid is listed in Table I. Cluster 1 and cluster 2 are normal phases. These two clusters have no ...
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Adaptive Clustering with Feature Ranking for DDoS Attacks Detection
... Table I shows the clustering result from MGKM with nine features. The value of each variable of each cluster centroid is listed in Table I. Cluster 1 and cluster 2 are normal phases. These two clusters have no ...
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A Repeated Sampling and Clustering Method for Intrusion Detection
... intrusion detection and ensure network ...intrusion detection algorithms. We propose a data-flow adaptive method for intrusion detection based on searching through high-dimensional ...
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A new adaptive response surface method for reliability analysis
... 1. Introduction 1.1. Problem statement In mechanical structures, consideration of uncertainties in modeling is a growing topic because it provides valuable informa- tion in industrial applications. Indeed, sensitivity, ...
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Metallic Surface Coating Defect Detection Using Firefly Based Adaptive Thresholding and Level Set Method
... detect surface defects on titanium coated steel surfaces with varied size through the use of image processing ...of surface defects present on coating surface. For defect detection, Firefly ...
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Comparitive Analysis of Clustering Method for Road Distress Detection
... objects. Clustering is a widely used exploratory data analysis tool that has been successfully applied to biology, social science, information retrieval, signal processing, and many other fields (for example, ...
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A hybrid unsupervised clustering-based anomaly detection method
... IDS, clustering techniques are utilized for finding anomalies in unlabeled ...of clustering algorithms is to separate the given unlabeled data into clusters that achieve high inner similarity and outer ...
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An Online Anomaly-Detection Neural Networks-based Clustering for Adaptive Intrusion Detection Systems
... an adaptive design of intrusion detection systems which offers the capability of detecting known and novel attacks and being updated according to new trends of data patterns provided by security experts in ...
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Brain Image Tumor Detection Using Weiner Filtering and Adaptive Clustering
... tumor detection and segmentation in magnetic resonance imaging (MRI) is foremost in bio medical because it presents information related to anatomical structures as good as advantage abnormal tissues essential to ...
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Creating dialect sub corpora by clustering: a case in Japanese for an adaptive method
... Keywords: dialect, Japanese, clustering, adaptation of language model 1. Introduction In what follows we propose a pipeline through which to derive clusters for dialects, given a body of ‘mixed’ cor- pus composed ...
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A Fast Fraud Detection Approach using Clustering Based Method
... the clustering has proven itself a constant applied solution for detecting ...fraud. Clustering process groups the data in such a way so that highly similar data come under one ...paper, clustering ...
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Community detection model based on incremental EM clustering method
... Community detection is a challenging research problem with broad ...EM method-IEM for community ...The method is more efficient than previous NL-EM, making use of a new incremental approach which is ...
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A Novel Fuzzy Clustering Method for Outlier Detection in Data Mining
... d Max d Max , the above membership function (exp.6) will generate values closer to one(1) for smaller distances (d ji ) and a membership value of zero for the maximum distance. If the distance of a data point is zero ...
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Robust Cell Detection Using Adaptive Fuzzy C Means Clustering and Classification
... means clustering (FCM) for accurate automatic Ki- 67 counting for NET and to localize both tumor and non- tumor ...non-fuzzy clustering algorithms, FCM is less sensitive to noise and give better results for ...
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AN ADAPTIVE GRID-BASED METHOD FOR CLUSTERING MULTI- DIMENSIONAL ONLINE DATA STREAMS
... for clustering analysis are maintained in a grid ...During clustering algorithm, considering unsporadic grids in the grids list instead of the possible grids saves computing time, and space of the ...
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Brain Tumor image Segmentation using Adaptive clustering and Level set Method
... EDGE DETECTION METHOD There are two main edge based segmentation methods- gray histogram and gradient based method [9] ...Edge detection is a term in image processing and computer vision, it ...
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Use of Statistical Outlier Detection Method in Adaptive Evolutionary Algorithms
... [email protected] ABSTRACT In this paper, the issue of adapting probabilities for Evolutionary Algorithm (EA) search operators is revisited. A framework is devised for distinguishing between measurements of ...
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Robust Rock Detection and Clustering with Surface Analysis for Robotic Rock Breaking Systems
... The results from the experiments are gathered into Table I. The pre-processing and the post-processing were conducted using the same parameters with each algorithm. The results indicate great improvement over the ...
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Detection of Diseases on Cotton Leaves Using K Mean Clustering Method
... © 2015, IRJET.NET- All Rights Reserved Page 430 So we conclude that disease detection using K-Mean Clustering method using Euclidean distance is the best methods to disease detection on cotton ...
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