The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) syst...
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The applications such as the remote communication and the control system are in critically integrated arrangement. The controlling of these network is specified by supervisory control and data acquisition (SCADA) systems. This study discusses about the attack prediction and classification process by using an enhanced model of machine learning technology. The attack types are classified by the optimal selection of features extracted from the sensor data. In this, the features are labelled and cluster between the matrixes are extracted. These cluster forms the initial processing of attack identification which prevents the mismatched result. This clustering of data is performed by mean-shift clustering algorithm. From that clustered data, the features that are irrelevant for classification process is identified and suppressed by using the genetically seeded flora optimisation algorithm. In this optimisation process, the flora seeds are selected genetically to select best features. Then, from that optimally selected clustered data, the relevancy vector is predicted and the types are classified. The classification process is performed by the Boltzmann machine learning algorithm. The classified results of the proposed method for testing SCADA dataset are analysed and the performance metrics are evaluated and compared with the state-of-the-art methods.
The presence of noise in images of degraded documents limits the direct application of segmentation approaches and can lead to the presence of a number of different artifacts in the final segmented image. A possible s...
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The presence of noise in images of degraded documents limits the direct application of segmentation approaches and can lead to the presence of a number of different artifacts in the final segmented image. A possible solution is the integration of a pre-filtering step which may improve the segmentation quality through the reduction of such noise. This study demonstrated that combining the mean-shift clustering algorithm and the tensor-driven diffusion process into a joint iterative framework produced promising results. For instance, this framework generates segmented images with reduced edge and background artifacts when compared to results obtained after applying each method separately. This improvement is explained by the mutual interaction of global and local information, introduced, respectively, by the mean-shift and the anisotropic diffusion. Another point of note is that the anisotropic diffusion process smoothed images while preserving edge continuities. The convergence of this framework was defined automatically under a stopping criterion not previously defined when the diffusion process was applied alone. To obtain a fast convergence, the common framework utilizes the speedup algorithm of the Fukunaga and Hostetler mean-shift formulation already proposed by Lebourgeois et al. (International Conference on Document Analysis and Recognition (ICDAR), pp 52-56, 2013). This new variant of the mean-shiftalgorithm produced similar results to the original one, but ran faster due to the application of the integral volume. The first application of this framework was document ink bleed-through removal where noise is stemmed from the interference of the verso side on the recto side, thus perturbing the legibility of the original text. Other categories of images could also be subjected to the proposed framework application.
In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, w...
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In a centralized cooperative spectrum sensing (CSS) system, it is vulnerable to malicious users (MUs) sending fraudulent sensing data, which can severely degrade the performance of CSS system. To solve this problem, we propose sensing data fusion schemes based on K-medoids and mean-shift clustering algorithms to resist the MUs sending fraudulent sensing data in this paper. The cognitive users (CUs) send their local energy vector (EVs) to the fusion center which fuses these EVs as an EV with robustness by the proposed data fusion method. Specifically, this method takes a Medoids of all EVs as an initial value and searches for a high-density EV by iteratively as a representative statistical feature which is robust to malicious EVs from MUs. It does not need to distinguish MUs from CUs in the whole CSS process and considers constraints imposed by the CSS system such as the lack of information of PU and the number of MUs. Furthermore, we propose a global decision framework based on fast K-medoids or mean-shift clustering algorithm, which is unaware of the distributions of primary user (PU) signal and environment noise. It is worth noting that this framework can avoid the derivation of threshold. The simulation results reflect the robustness of our proposed CSS scheme.
Pathological examination of microscopic image of Pap smear slide remains the main method for cervical cancer diagnosis. The accurate segmentation and classification of images are two important phases of the analysis. ...
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Pathological examination of microscopic image of Pap smear slide remains the main method for cervical cancer diagnosis. The accurate segmentation and classification of images are two important phases of the analysis. Firstly, the mean-shift clustering algorithm is applied to obtain regions of interest (ROI) for cell nuclei segmentation. Then the flexible mathematical morphology is applied to split overlapped cell nuclei for better accuracy and robustness. For classification of the images, features based on shape, textural features based on color space and Gabor features are extracted and put together to obtain better classification performance. The optimal feature set is obtained by chain-like agent genetic algorithm (CAGA), P-value and maximum relevance-minimum multicollinearity (MRmMC). The proposed segmentation and classification methods were tested on 362 cervical Pap smear images. Experimental results showed that the cervical cell nuclei can be segmented by the proposed segmentation method with high effective segmentation results (Sensitivity: 94.25% 1.03% and Specificity 93.45% 1.14%). The feature selection method based on CAGA with Gabor features has the highest classification performance for normal, uninvolved and abnormal images (more than 96% accuracy). The proposed method can automatically and effectively segment cell nuclei of microscopic images. From the experimental results, Gabor features and feature selection based on CAGA are apparently helpful for improving the performance of classification. (C) 2018 Elsevier Ltd. All rights reserved.
The most important view of image segmentation is a process of analyzing image in to individual pixel values for each boundary. All the more definitely, segmentation is the strategy of doling out a mark to each and eac...
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The most important view of image segmentation is a process of analyzing image in to individual pixel values for each boundary. All the more definitely, segmentation is the strategy of doling out a mark to each and each pixel in a photograph to such an extent that pixels with the equivalent name share positive traits. This article implements an image segmentation method based on mean-shift clustering algorithm with a Gaussian Mixture Models. meanshift is a non-parametric feature-space analysis technique for locating the maxima of a density function, a so-called mode-seeking algorithm. Application domains include cluster analysis in computer vision and image processing. The proposed method is used to evaluated in terms of accuracy, precision and actual solution. For this analysis produced accurate image with minimum time period. This new technique is used to analyze the varies lung nodules by means of lowering the noise the usage of computerized segmentation algorithm in specific region. (c) 2021 Elsevier Ltd. All rights reserved. Selection and peer-review under responsibility of the scientific committee of the International Conference on Advances in Materials Research-2019.
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