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The biggest concern of Network is security. Intro find the tricks and tools of the Attackers. Data Mining techniques automatically learn the pattern of the tuples and Intelligent decision are made. Supervised learning methods finds the attack based on previous knowledge and unknown attacks are detected by using Unsupervised learning. Dos, Probe and Normal data are correctly detected by maximum Data...
A novel feature extraction method for hand gesture recognition from sequences of image frames is described and tested. The proposed method employs higher order local autocorrelation (HLAC) features for feature extraction. The features are extracted using different masks from Grey-scale images for characterising hands image texture with respect to the possible position, and the product of the pixels...
In the current engross world, traffic overflow is a common problem for the metropolises. In spite of increasing the size of transportation systems and prompting the public transportation may increase the traffic overflow. This kind of traffic overflow problem cannot be solved manually. Today the traffic data has been entered and erupted the time of huge transportation of the data. Hence it is important...
Existing extended one-versus-rest multi-label support vector machine (OVR-ESVM) adopting non-linear kernel is seriously restricted by excessive training time when it is applied to large-scale data set. In order to overcome this problem, we improve the OVR-ESVM by introducing the principle of approximate extreme points and new approximate ranking loss to construct a novel extended OVR-ESVM using approximate...
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning...
The dramatic increase in the network traffic data has become a major concern in security systems. Intrusion detection systems (TDSs), as common widely used security systems for communication networks, are not an exception. An IDS monitors the network traffic to detect attacks through classifying the network traffic data into normal and abnormal classes. Due to the high dimensionality of the network...
Multi-label learning is popular in current research of machine learning areas, and there have already been many methods using label relationship to solve multi-label problems. However, the meaning of their relationship is not so obvious that it's hard for us to know the fact among labels. Besides, with the development of multi-label learning, hierarchical multi-label classification is a new research...
Automatic image annotation methods automatically assign labels to images in order to facilitate tasks such as image retrieval, search, organizing and management. Incorrect labels may negatively influence the search results so image annotation should be as accurate as possible. Labels pertaining to objects or to whole scenes are commonly used for image annotation, and precision is especially important...
In this paper, we investigate neural network ensemble (NNE) classifier and its application to multi-spectral image classification. The effectiveness of the NNE classifier is demonstrated on SPOT multi-spectral image data. Compared with standard classifiers, such as Bayes maximum-likelihood classifier, k-NN classifier, it has shown that the NNE classifier can have better performance on multi-spectral...
A sign language recognition system is an attempt to bring the speech and the hearing impaired community closer to more regular and convenient forms of communication. Thus, this system requires to recognize the gestures from a sign language and convert them to a form easily understood by the hearing. The model that has been proposed in this paper recognizes static images of the signed alphabets in...
HEVC (High Efficiency Video Coding) achieves cutting edge encoding efficiency and outperforms previous standards, such as the H.264/AVC. One of the key contributions to the improvement is the intra-frame coding that employs abundant coding unit (CU) sizes. However finding the optimal CU size is computationally expensive. To alleviate the intra encoding complexity and facilitate the real-time implementation,...
Network protocol classification plays an important role in modern network security and fine-grained management architectures. The state-of-the-art network protocol classification methods aim to take the advantages of flow statistical features and machine learning techniques. However the classification performance is severely affected by limited supervised information and unknown network protocols...
Biometric systems are becoming important since they provide efficient and more reliable means of human identity verification. Gait Recognition has created much interest in computer vision society over the last few years. In this paper, we have presented a Gait based human identification system using skeleton data acquired by using Microsoft Kinect sensor. The sensor acts as a digital eye which takes...
The widely known classifier chains method for multi-label classification, which is based on the binary relevance (BR) method, overcomes the disadvantages of BR and achieves higher predictive performance, but still retains important advantages of BR, most importantly low time complexity. Nevertheless, despite its advantages, it is clear that a randomly arranged chain can be poorly ordered. We overcome...
With the rapid development of network information technology, the text is as a basic information carrier and begins to present exponential growth. The existing text classification methods haven't got information from the vast amounts of information resources timely and accurately. In order to solve the problem, the paper puts forward a new method about text categorization. It is a KNN algorithm based...
When steganalysis performed on heterogeneous images made up by different resampled images and raw single-sampled images, the difference of statistical properties between which can caused “mismatch” between training and testing images in steganalytic classifier. Therefore, the detection performance of the classifier decreases. The problem above limits the application of the existing steganalysis algorithms...
In this paper, we study neural network ensembles (NNE) classifier with regularized negative correlation learning (RNCL) and its application to pattern classification. In RNCL algorithm, the regularization parameter is used to control the trade off between mean square error and regularization, and to improve the ensemble's generalization ability. We propose an automatic RNCL algorithm based on gradient...
Approaches to imbalanced classification problem usually focus on rebalancing the class sizes, neglecting the effect of hidden structure within the majority class. The aim of this paper is to highlight the effect of sub-clusters within the majority class on detecting minority class instances, and handle imbalanced classification by learning the structure in the data. We propose a decomposition based...
Multi-label classification has attracted much attention in recent years due to various applications in real world. There have been many algorithms to deal with this problem. However, there is no algorithm that simultaneously exploits the locality in the instance space and label space which plays an important role in generating a satisfactory model. In this paper we present such an algorithm. It utilizes...
In this paper, a ensemble learning classification algorithm based on the novel feature selection method is proposed. The feature selection method takes full account of the discrimination and class information of each feature by calculating the scores. Specially, the scores are fused for getting a weight for each feature. We select the significant features according to the weights. The result of feature...
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