Hajar Dastanpour; Ali Fanian
Abstract
Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently ...
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Today, intrusion detection systems are used in the networks as one of the essential methods to detect new attacks. Usually, these systems deal with a broad set of data and many features. Therefore, selecting proper features and benefitting from previously learned knowledge is suitable for efficiently detecting new attacks. A new graph-based method for online feature selection is proposed in this article to increase the accuracy in detecting attacks. In the proposed method, irrelevant features are first removed by inputting a limited number of instances. Then, features are clustered based on graph theory to reduce the search space. After the arrival of new instances at each stage, new clusters of features are created that may differ from the clusters created in the previous step. Therefore, to find the appropriate clusters, these two clusters are combined to select some relevant features with minimum redundancy. The evaluation results show that the proposed method has better performance, for instance classification with a lesser run time than similar online feature selection methods. The proposed method is also faster with a suitable accuracy in instances classification compared to some offline methods.
K. Nalini Sujantha Bel; I.Shatheesh Sam
Abstract
A novel feature extraction algorithm using Otsu’s Threshold (OT-features) on scrambled images and the Instantaneous Clustering (IC-CBIR) approach is proposed for Content-Based Image Retrieval in cloud computing. Images are stored in the cloud in an encrypted or scrambled form to preserve the privacy ...
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A novel feature extraction algorithm using Otsu’s Threshold (OT-features) on scrambled images and the Instantaneous Clustering (IC-CBIR) approach is proposed for Content-Based Image Retrieval in cloud computing. Images are stored in the cloud in an encrypted or scrambled form to preserve the privacy content of the images. The proposed method extracts the features from the scrambled images using the Otsu’s threshold. Initially, the Otsu’s threshold is estimated from the scrambled image and based on this threshold the image is divided into two classes in the first iteration. Again, the new threshold values are estimated from two classes. The difference between the new threshold and the previous threshold gives two features. This process is repeated for number of iteration to obtain the complete OT-features of the scrambled image. This paper also proposes an instantaneous clustering approach (IC-CBIR) where the image is moved into a cluster as soon as the image is uploaded by the image owner. Therefore while retrieving the images, the images near to a particular cluster are matched instead of matching with a complete set of image features in the dataset which reduces the search time. The performance of the proposed algorithm is being tested using four different types of the dataset such as Corel 10K, Misc, Oxford flower, and INRIA Holidays dataset. The experimental evaluation reveals that the proposed method outperforms better than the traditional CBIR algorithm on encrypted images in terms of precision, time of search and time of index construction.
Sumayia Al-Anazi; Isra Al-Turaiki; Najwa Altwaijry
Abstract
Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ...
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Motif discovery is a challenging problem in bioinformatics. It is an essential step towards understanding gene regulation. Although numerous algorithms and tools have been proposed in the literature, the accuracy of motif finding is still low. In this paper, we tackle the motif discovery problem using ensemble methods. A review and classification of current ensemble motif discovery tools is presented. We then propose our Cluster-based Ensemble Motif Discovery Tool (CEMD) which is based on k-medoids clustering of state-of-art stand-alone motif finding tools. We evaluate the performance of CEMD on benchmark datasets and compare the results to both stand-alone and similar ensemble tools. Experimental results indicate that CEMD has better sensitivity than state-of-art stand-alone tools when dealing with human datasets. CEMD also obtains better values of sensitivity when motifs are implanted in real promoter sequences. As for the comparison of CEMD with ensemble motif discovery tools, results indicate that CEMD achieves better results than MEME-ChIP on all evaluation measures. CEMD shows comparable performance to RSAT peak-motifs and MODSIDE.
R. Mortazavi; S. Jalili
Abstract
In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least ...
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In this paper, we propose an effective microaggregation algorithm to produce a more useful protected data for publishing. Microaggregation is mapped to a clustering problem with known minimum and maximum group size constraints. In this scheme, the goal is to cluster n records into groups of at least k and at most 2k_1 records, such that the sum of the within-group squared error (SSE) is minimized. We propose a local search algorithm which iteratively satisfies the constraints of the optimal solution of the problem. The algorithm solves the problem in O (n2) time. Experimental results on real and synthetic data sets with different distributions demonstrate the effectiveness of the method in producing useful protected data sets.