Ahmad Keshavarz

Marzieh Masoumi, Ahmad Keshavarz, Reza Fotohi

Nowadays, the speed up development and use of digital devices such as smartphones have put people at risk of internet crimes. The evidence of present crimes in a computer file can be easily unreachable by changing the prefix of a file or other algorithms. In more complex cases, either file divided into different parts or the parts of a file that has information about the file type are deleted, where the file fragment recognition issue is discussed. The known files are divided into different fragments, and different classification algorithms are used to solve the problems of file fragment recognition. The issue of identifying the type of file fragment due to its importance in cybercrime issues as well as antivirus has been highly emphasized and has been addressed in many articles. Increasing the accuracy in this field on the types of widely used files due to the sensitivity of the subject of recognizing the type of file under study is the main goal of researchers in this field. Failure to identify the correct type of file will lead to deviations of the results and evidence from the main issue or failure to conclude. In this paper, first, the file is divided into different fragments. Then, the file fragment features, which are obtained from Binary Frequency Distribution, are reduced by 2 feature reduction algorithms; Sequential Forward Selection algorithm as well as Sequential Floating Forward Selection algorithm to delete sparse features that result in increased accuracy and speed. Finally, the reduced features are given to 3 Multiclass classifier algorithms, Multilayer Perceptron, Support Vector Machines, and K-Nearest Neighbor for classification and comparison of the results. The proposed recognition algorithm can recognize 6 types of useful files and may distinguish a type of file fragments with higher accuracy than the similar works done.

Mohammad Reza Khosravi, Habib Rostami, Gholam Reza Ahmadi et al.

Remote sensing image processing is so important in geo-sciences. Images which are obtained by different types of sensors might initially be unrecognizable. To make an acceptable visual perception in the images, some pre-processing steps (for removing noises and etc) are preformed which they affect the analysis of images. There are different types of processing according to the types of remote sensing images. The method that we are going to introduce in this paper is to use virtual colors to colorize the gray-scale images of satellite sensors. This approach helps us to have a better analysis on a sample single-band image which has been taken by Landsat-8 (OLI) sensor (as a multi-band sensor with natural color bands, its images' natural color can be compared to synthetic color by our approach). A good feature of this method is the original image reversibility in order to keep the suitable resolution of output images.

Mohammad Reza Khosravi, Mohammad Sharif-Yazd, Mohammad Kazem Moghimi et al.

In interpretation of remote sensing images, it is possible that some images which are supplied by different sensors become incomprehensible. For better visual perception of these images, it is essential to operate series of pre-processing and elementary corrections and then operate a series of main processing steps for more precise analysis on the images. There are several approaches for processing which are depended on the type of remote sensing images. The discussed approach in this article, i.e. image fusion, is the use of natural colors of an optical image for adding color to a grayscale satellite image which gives us the ability for better observation of the HR image of OLI sensor of Landsat-8. This process with emphasis on details of fusion technique has previously been performed; however, we are going to apply the concept of the interpolation process. In fact, we see many important software tools such as ENVI and ERDAS as the most famous remote sensing image processing tools have only classical interpolation techniques (such as bi-linear (BL) and bi-cubic/cubic convolution (CC)). Therefore, ENVI- and ERDAS-based researches in image fusion area and even other fusion researches often dont use new and better interpolators and are mainly concentrated on the fusion algorithms details for achieving a better quality, so we only focus on the interpolation impact on fusion quality in Landsat-8 multispectral images. The important feature of this approach is to use a statistical, adaptive, and edge-guided interpolation method for improving the color quality in the images in practice. Numerical simulations show selecting the suitable interpolation techniques in MRF-based images creates better quality than the classical interpolators.