Honeywords are fake passwords that serve as an accompaniment to the real password, which is called a “sugarword.” The honeyword system is an effective password cracking detection system designed to easily detect password cracking in order to improve the security of hashed passwords. For every user, the password file of the honeyword system will have one real hashed password accompanied by numerous fake hashed passwords. If an intruder steals the password file from the system and successfully cracks the passwords while attempting to log in to users’ accounts, the honeyword system will detect this attempt through the honeychecker. A honeychecker is an auxiliary server that distinguishes the real password from the fake passwords and t

This study was conducted in the plant protection department/ College of Agriculture/ University of Baghdad to evaluate the efficiency of physical agents ozone, ultraviolet radiation, microwave for destroying afla produced in corn seeds. An isolate af A.flavus producing Aflatoxin B1 was obtained from plant protection dept. college of Agric. University of Baghdad. Results showed destroy toxin AFLA B1 the effect of radiation microwave in the media of Japex degree 80 and 100 c^o 57.14% and 85.71%, respectively, and for 20 sec, compared to the treatment comparison 0.00% as found significant differences were apparent between transactions and the treatment of comparison, as and notes the existence of a significant dif

The research aims to know the concept of politic behavior as one of the important behaviours in the different fields and sectors. It is considered to be part of the organizatial work to face the expected  risks. It includes two group of factors personal (self –monitors, locus of control ,expectation s of success, perceived job alternatives)and organizational(promotion ,division resources,role  ambiguity ,democratic decision)studied by the researcher  in the frame of the relationship with the variable  of display  continuous trust matain which includes two variable (build trust mantain, display  trust continuouness)through applied frame by random sample consists of (90)employee  at Farouq State

Lowpass spatial filters are adopted to match the noise statistics of the degradation seeking
good quality smoothed images. This study imply different size and shape of smoothing
windows. The study shows that using a window square frame shape gives good quality
smoothing and at the same time preserving a certain level of high frequency components in
comparsion with standard smoothing filters.

Medical image segmentation is one of the most actively studied fields in the past few decades, as the development of modern imaging modalities such as magnetic resonance imaging (MRI) and computed tomography (CT), physicians and technicians nowadays have to process the increasing number and size of medical images. Therefore, efficient and accurate computational segmentation algorithms become necessary to extract the desired information from these large data sets. Moreover, sophisticated segmentation algorithms can help the physicians delineate better the anatomical structures presented in the input images, enhance the accuracy of medical diagnosis and facilitate the best treatment planning. Many of the proposed algorithms could perform w

Image retrieval is used in searching for images from images database. In this paper, content – based image retrieval (CBIR) using four feature extraction techniques has been achieved. The four techniques are colored histogram features technique, properties features technique, gray level co- occurrence matrix (GLCM) statistical features technique and hybrid technique. The features are extracted from the data base images and query (test) images in order to find the similarity measure. The similarity-based matching is very important in CBIR, so, three types of similarity measure are used, normalized Mahalanobis distance, Euclidean distance and Manhattan distance. A comparison between them has been implemented. From the results, it is conclud

In this paper two main stages for image classification has been presented. Training stage consists of collecting images of interest, and apply BOVW on these images (features extraction and description using SIFT, and vocabulary generation), while testing stage classifies a new unlabeled image using nearest neighbor classification method for features descriptor. Supervised bag of visual words gives good result that are present clearly in the experimental part where unlabeled images are classified although small number of images are used in the training process.