The increasing growth of Android phones, due to its openness and popularity also calls for the increase in attacks. A very keen and intellectual study proves that Android users can easily be hacked with the existing single time permission approval system. Once approved, an application can access all the resources requested as permissions, at any time according to the code integrated into it. The permissions shown at installation time totally depends on the user's privacy preference. An application rated riskier by a particular privacy rating software may not be riskier in the eyes of a user who is already decided to install this application. So in order to provide a clear cut idea about how an application will harm him or what are the risks involved in an application compared to the other top rated clean applications in the same category, we propose a security system, which involves a combination of static and dynamic risk analysis of Android applications. The proposed system allows its users to install a riskier application rated by static analyzer, and review it with a help of dynamic analyzer, which monitors application activity handling permissions.

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Unsharp Masking is a popular image processing technique used for improving the sharpness of structures on dental radiographs. However, it produces overshoot artefact and intolerably amplifies noise. On radiographs, the overshoot artefact often resembles the indications of prosthesis misfit, pathosis, and pathological features associated with restorations. A noise- robust alternative to the Unsharp Masking algorithm, termed Gradient-adaptive Nonlinear Sharpening (GNS) which is free from overshoot and discontinuity artefacts, is proposed in this paper. In GNS, the product of the arbitrary scalar termed as ‘scale’ and the difference between the output of the Adaptive Edge Smoothing Filter (AESF) and the input image, weighted by the normalized gradient magnitude is added to the input image. AESF is a locally-adaptive 2D Gaussian smoothing kernel whose variance is directly proportional to the local value of the gradient magnitude. The dataset employed in this paper is downloaded from the Mendeley data repository having annotated panoramic dental radiographs of 116 patients. On 116 dental radiographs, the values of Saturation Evaluation Index (SEI), Sharpness of Ridges (SOR), Edge Model Based Contrast Metric (EMBCM), and Visual Information Fidelity (VIF) exhibited by the Unsharp Masking are 0.0048 ± 0.0021, 4.4 × 1013 ± 3.8 × 1013, 0.2634 ± 0.2732 and 0.9898 ± 0.0122. The values of these quality metrics corresponding to the GNS are 0.0042 ± 0.0017, 2.2 × 1013 ± 1.8 × 1013, 0.5224 ± 0.1825, and 1.0094 ± 0.0094. GNS exhibited lower values of SEI and SOR and higher values of EMBCM and VIF, compared to the Unsharp Masking. Lower values of SEI and SOR, respectively indicate that GNS is free from overshoot artefact and saturation and the quality of edges in the output images of GNS is less affected by noise. Higher values of EMBCM and VIF, respectively confirm that GNS is free from haloes as it produces thin and sharp edges and the sharpened images are of good information fidelity.