In this paper, we propose an image filtering technique based on fuzzy logic control to remove impulse noise for low as well as highly corrupted images. The proposed method is based on noise detection, noise removal and edge preservation modules. The main advantage of the proposed technique over the other filtering techniques is its superior noise removal as well as detail preserving capability. Based on the criteria of peak-signal-tonoise-ratio (PSNR), mean square error (MSE), structural similarity index measure (SSIM) and subjective evaluation measure we have found experimentally that the proposed method provides much better performance than the state-of-the-art filters. To analyze the detail preservation capability of the proposed filter sensitivity analysis is performed by changing the detail preservation module to see its effects on the details (texture and edge information) of resultant image. This sensitivity analysis proves experimentally that significant image details have been preserved by the proposed method.
Abstract-In this paper, we present a novel framework -it uses the information in kernel structures of a process -to do run-time analysis of the behavior of an executing program. Our analysis shows that classifying a process as malicious or benign -using the information in the kernel structures of a process -is not only accurate but also has low processing overheads; as a result, this lightweight framework can be incorporated within the kernel of an operating system. To provide a proof-of-concept of our thesis, we design and implement our system as a kernel module in Linux. We perform the time series analysis of 118 parameters of Linux task structures and preprocess them to come up with a minimal features' set of 11 features. Our analysis show that these features have remarkably different values for benign and malicious processes; as a result, a number of classifiers operating on these features provide 93% detection accuracy with 0% false alarm rate within 100 milliseconds. Last but not least, we justify that it is very difficult for a crafty attacker to evade these lowlevel system specific features.
This paper takes Twitter as the framework and intended to propose an optimum approach for classification of Twitter data on the basis of the contextual and lexical aspect of tweets. It is a dire need to have optimum strategies for offensive content detection on social media because it is one of the most primary modes of communication, and any kind of offensive content transmitted through it may harness its benefits and give rise to various cyber-crimes such as cyberbullying and even all content posted during the large even on twitter is not trustworthy. In this research work, various facets of assessing the credibility of user generated content on Twitter has been described, and a novel real-time system to assess the credibility of tweets has been proposed by assigning a score or rating to content on Twitter to indicate its trustworthiness. A comparative study of various classifying techniques in a manner to support scalability has been done and a new solution to the limitations present in already existing techniques has been explored.
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