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A new vision-based approach to accomplish automatic detection and speed measuring of vehicles is proposed in this paper. In the proposed method, a cascade classifier, based on Haar features, is trained on frontal view of vehicles and deployed for vehicle detection. A fast and accurate foreground segmentation algorithm is proposed to distinguish moving vehicles from the background and prune detection...
Finding of frequent sub-graphs is an important operation on graphs and it is defined as detection of all sub-graphs that appear frequently in a set of graphs. This paper proposes detection of frequent sub-community graph from n-set of community graph of villages; are useful for characterizing community graph sets, finding difference among groups of community graphs, classifying and clustering of community...
To deal with the inaccuracy and the uniform distribution of the DV-Hop algorithm, it is necessary to put forward a new optimization algorithm based on DV-Hop and RSSI for the two-dimensional planar node location called RSDV-Hop. The algorithm is based on the shortest hop distance with RSSI algorithm and gets the position between the nodes and the anchors according to the difference of signal strength...
Jaundice is the most common condition that requires medical attention in newborns. Although most newborns develop some degree of jaundice, a high level bilirubin puts a newborn at risk of bilirubin encephalopathy and kernicterus which are rare but still occur in Egypt. In this paper, a new weighted rough set framework is introduced for early intervention and prevention of neurological dysfunction...
The K-neighbor query algorithm is an important class of search algorithm in the spatial database, this paper will adopt the K-means algorithm to carry on sorting to the smallest enclosing rectangle in accordance with orientation relationship based on the measurement of distance and pruning strategies of MBR in the traditional K-nearest neighbor query, it can carry on the K-neighbor queries after sorting,...
When data mining techniques are applied to uncertain data, their uncertainty has to be considered to obtain high quality results. Usually, an uncertain object is described by a probability density function, a probability density function is approximated by a large amount of sample points, and the distance between two uncertain objects is expressed by the expected distance. Computing the expected distance...
In this paper, a novel supervised architecture for binary classification based on local modelling and information theory is described. The architecture is composed of two steps: in the first one, a separating borderline between the two classes is piecewise constructed by a set of centroids calculated by a modified clustering algorithm, based on information theory; each of these centroids define a...
This study aims to introduce a new concept of weighted association rule mining. The purpose is to discover cross section relationship among items and extract the unknown patterns. We proposed two algorithms called HWA (O) and HWA (P) based on the concept that greater the difference among items in an association rule, the higher the weight score is. Hierarchical weights in HWA (O) are assigned according...
The cluster analysis deals with the problems of organization of a collection of data objects into clusters based on similarity. It is also known as the unsupervised classification of objects and has found many applications in different areas. An important component of a clustering algorithm is the distance measure which is used to find the similarity between data objects. K-means is one of the most...
Learning Vector Quantization (LVQ) is a popular class of nearest prototype classifiers for multiclass classification. Learning algorithms from this family are widely used because of their intuitively clear learning process and ease of implementation. They run efficiently and in many cases provide state of the art performance. In this paper we propose a modification of the LVQ algorithm that addresses...
In this study, a novel iterative optimization clustering algorithm is proposed by using a manifold distance based dissimilarity metric which can measure the geodesic distance along the manifold and a criterion function which can express the clustering target, that is the samples in the same cluster being somehow more similar than samples in different one. The steps of the algorithm are discussed in...
How to choose a proper number of the neighbors is an important issue of the locally linear embedding algorithm. To investigate this issue, we propose an optimized locally linear embedding algorithm with adaptive neighbors (ANLLE). The ANLLE selects the neighbors with a locally adaptive criterion. In addition, a new data point mapping method that computes the low-dimensional description of the correspondents...
Moving object detection is a very important step in video surveillance. And frame difference algorithms are suitable for these applications. First of all, an automatic threshold calculation method was proposed according to statistic information to obtain moving pixels of video frames. Then moving regions can be formed by morphological operations. At last, the nearest distance of two regions was proposed...
In this paper, based on the discussion of some important issues related to cooperative design, and the analysis for niche technology, a group classifier algorithm and a sharing learning algorithm in a multi-agent cooperative system are put forward. The aim is to use socio-cultural perspectives and niche technology for supporting design reuse and share in a cooperative design system.
Getting a better grasp of computer network security is of great significance to protect the normal operation of network system. Based on rough set (RS), clustering model, security features reduction and clustering algorithm are presented, which provides a basis of network security strategies. Further research is to mine and process the dynamic risks and management of network security. Using the reduction...
Clustering techniques have been used by many intelligent software agents in order to retrieve, filter, and categorize documents available on the World Wide Web. Clustering is also useful in extracting salient features of related Web documents to automatically formulate queries and search for other similar documents on the Web. Traditional clustering algorithms either use a priori knowledge of document...
In this paper, an efficient text classification algorithm for repeating-text information on the e-commerce site can automatically classify and sort the similar string. This algorithm will greatly increase the efficiency and accuracy of audited information. All tests show that for the number of information between 100 and 1000 the algorithm is very efficient, and the 1000 text information(strings)...
This paper focuses on clustering algorithm of many-dimensional objects, where only the distances between objects are used. Centers of classes are found with the aid of neuron-like procedure with lateral inhibition. The result of clustering does not depend on starting conditions. Our algorithm makes it possible to give an clusters that really exist in the empirical data.
Automatic modulation recognition is a topic of interest in many fields including signal surveillance, multi-user detection and radio frequency spectrum monitoring. A major weakness of conventional modulation recognition algorithms is their reliance on high SNR environments and favorable statistics. In this paper an algorithm is developed using elements of cyclo-spectral analysis, ICA and SVM algorithms...
With the dramatic increasing number of available Web services, how to locate the right services is becoming a big challenge in pervasive environments. The Web services discovery mechanism of UDDI based on keywords and simple classification can not meet the current needs. A semantic distance between ontology concepts is defined to calculate the similarity between ontology concepts. Based on the similarity,...
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