احمد جبار عبيد الجنابي

Information on internet and especially on the hosts that attached to World Wide Web are continues increased in size, complexity and contents. Dynamic Web sites authorized admins and users frequently add, modified, deleted and sharing lot of information continuously. The task to analysis the hidden information from this content it's very complex. Public users browse many Web pages contents that hosted in many Web servers data center to find interest contents. Web server logs activities and information about public accesses to these Web pages contents and log users navigation information in text files called log files. Log files are text standard format files which can be differ in format, kinds of information provided and sources of these files. Log files recorded many relevant and irrelevant contents, depend on the architecture level of log file used. Web data mining employee Data mining techniques to analysis, extract and discovering knowledge from Web data. In this paper we are proposed new algorithms to analysis hidden information in log files and support many statistics and analysis content in it, also discovering patterns in log data that were not analysis by some log files analysis tools as well as enhanced path discovering process to analysis URL's that intended in log files entries lines. Information on internet and especially on the hosts that attached to World Wide Web are continues increased in size, complexity and contents. Dynamic Web sites authorized admins and users frequently add, modified, deleted and sharing lot of information continuously. The task to analysis the hidden information from this content it's very complex. Public users browse many Web pages contents that hosted in many Web servers data center to find interest contents. Web server logs activities and information about public accesses to these Web pages contents and log users navigation information in text files called log files. Log files are text standard format files which can be differ in format, kinds of information provided and sources of these files. Log files recorded many relevant and irrelevant contents, depend on the architecture level of log file used. Web data mining employee Data mining techniques to analysis, extract and discovering knowledge from Web data. In this paper we are proposed new algorithms to analysis hidden information in log files and support many statistics and analysis content in it, also discovering patterns in log data that were not analysis by some log files analysis tools as well as enhanced path discovering process to analysis URL's that intended in log files entries lines.

In image processing field there is an attention for detection objects, regions and points then made decision in case found it in a single or collection of images may called test or image data set, for this task we have used an algorithm that used in many computer vision application and also considered very fast by compared to others this algorithm can detect and describe local features for any interest object and extract features or descriptor points from it and compare these features/ descriptor by the features that extracted from origin image, matching process has been done among features and decision made based on similar features found, this algorithm called Speeded Up Robust Features (SURF) algorithm. In this paper we used enhanced Speeded Up Robust Features ""SURF"" algorithm, our model counting the features in either object and origin image in data set, then matching percentage calculated using a metric of counting the size of inlier matching features towards outlier features, Radom Sample Consensus (RANSAC) algorithm has been combined with SURF for eliminated error matching that happen in features, then decision has been given based on that metric if the object is present or not. In case object found Speeded UP Robust Features ""SURF"" algorithm can detect the position of the interest object in origin image by using geometric transform. In this paper we have used our metric and enhanced model to made decision and write result finally for each compared process and also write some information that used for matching procedure finally we can distinguish each calculating percentage and valid strength features matched that used for finding the interest objects under different circumstances

With the growth of World Wide Web and large number Hosts are join continuously to the internet, huge number of access events to Web sites pages were recorded by Servers in log files , many users share, send, post and download lot of things from Web Sites, this manner can be difficult to many organization and Agents in order to monitor and control that, the recorded information and type of analysis used to extract useful knowldege and understanding it become a practical challenges to many researchers. Log files can provided many events information regard to Clients activities, server activities and so on. Many organization employee many log files analysis tools to predict, analysis and monitor users behavior towards site contents .In this paper we proposed algorithms to analysis hidden information contents in Log files and discovering patterns by identified users along with them navigation behaviors then clustering similar users based on different interesting log file content for many Web sites that hosted in Web server. Find statistics for every part in log file command line which are not present in many log files analysis tools are supported here and finally discovering frequent Web sites-Users and user's activities towards those Web sites

Image retrieval is a key challenges in many image database application and still an active field in computer vision application. There are many proposed image retrieval systems that retrieve images based on image contents such as colors, texture, shapes and feature descriptor. The main task for image retrieval system is to create a system that capable to retrieve images that are semantically related to user's query from an image database. When user interest to retrieve images that contain a particular objects instead of retrieve similar images which might not related images to his interesting this is called object based on image retrieval. The goal of Objects-based is to retrieve images based on objects that appear in those query images from large database. In this paper we use enhanced Speeded UP Robust Features (SURF) algorithm as main step to extract features from interested query objects and then checked and matched result to retrieve related images from image dataset. Speeded UP Robust Features (SURF) is a scale and rotation invariant detector and descriptor feature algorithm and was applied successfully in many Image retrieval systems due its robust against different image transformation. Finally the result written in a report and images saved along with user's query time stamp.

Information on Internet and specially on website environment is increasing rapidly day by day and become very huge, this information play an important role for discovering various knowledge in the Web. Web Usage Mining one of the Web Mining algorithm categories that concern with discover and analysis useful information regard to link prediction, users' navigation, customers' behavior, site reorganization, web personalization and frequent access patterns from large web data that logs by Web server side and stored in standard text log file format called log file or Web usage data, this data can also be collected from an organization's database such as NASA. Web Usage Mining is a process of applying Data mining techniques and application to analyze and discover interesting knowledge from the Web. There are several existing research works on log file mining, some concern with web site structure, traversal pattern mining, association rule mining, Web page classification, and general statistics such as amount of time spent on a page. In this paper we will focus on mining the different segments content of Web log data entries in order to discover the hidden information and interesting browsing contents from it, then applying clustering algorithm to find similar groups of Web sites that have common browsing contents.