Investigation of medical images have major consequence in the field of treatment.in this work ,MR images have been used to distinguish the normal brain from brain with Alzheimer disease .Texture is an native property of all surfaces it contains important facts about the structural organization of the surfaces and their connections neighboring area. In direction to classify texture must be segmented into a number of section that has the similar properties, for this purpose we used k- means algorithm  with GLCM for  feature extraction ,finally we used k-nearest neighbor algorithm to distinguish between normal and abnormal brain

A number of research initiatives have recently been launched around the world regarding the conceptualization, specification, design and development principles of the future use of credit cards, storing secret information on them, while most time we use them for online payment. In addition, if it has enough money, we can pay for what we need at any time. Therefore, the goal of this proposed research is to use data mining techniques to predict credit card payment next month. Our proposed system contains five steps: (a) find the suitable database from the internet because this database is not available in Iraq, (b) pre-process the credit card database based on person correlation matrix to determine which feature is less correlated with other to remove it and reduce the time of prediction, (c) split pre-processing database into two parts training and testing dataset, (d) apply TreeNet prediction data mining techniques (TPDMT) on training dataset to test if we need payment next month or do not, find the optimal tree. TreeNet based on Boosting Machine usually makes the predictor to use Decision Trees (DTs). (e ) Finally, pass the testing dataset on the optimal tree results from TPDMT, then using the five measures related to confusion matrix to evaluate the results including “Accuracy (AC), recall or true positive rate (TP), precision (P), F-measure (considers both precision and recall) and Fb”.

Software-Defined Networks (SDN) It is a centralized control structure in the network that opens up new possibilities that did not exist before. The significant characteristic of this innovative approach is the focus on the capability of proposing networks of high dynamicity and programmability to transform the intelligence of underlying systems to the networks via controllers. The main issue of the SDN approach is found in its security, mainly due to its central-controlling architecture since the entire network is controlled from a central point. This makes it very vulnerable to single-point failure. In this paper, a fully Distributed SDN controller is proposed for solving the one point failure which exists within the single SDN controller. In general, the concept involves forming cluster of distributed controllers whereby each controller controls its domain and can thereby share the load within the network. The experimental results of the proposed system show an increase and enhancement in the performance of the network. The single-point failure issues have been overcome. The throughput of the proposed system increased with 20% while the packet loss rate was minimize with 33%.

When talking about the fundamentals of writing research papers, we find that keywords are still present in most research papers, but that does not mean that they exist in all of them, we can find papers that do not contain keywords. Keywords are those words or phrases that accurately reflect the content of the research paper. Keywords are an exact abbreviation of what the research carries in its content. The right keywords may increase the chance of finding the article or research paper and chances of reaching more people who should reach them. The importance of keywords and the essence of the research and address is mainly to attract these highly specialized and highly influential writers in their fields and who specialize in reading what holds the appropriate characteristics but they do not read and cannot read everything. In this paper, we extract new keywords by suggesting a set of words, these words were suggested according to the many mentioned in the researches with multiple disciplines in the field of computer. In our system, we take a number of words (as many as specified in the program) that come before the proposed words and consider it as new keywords. This system proved to be effective in finding keywords that correspond to some extent with the keywords developed by the author in his research.

<p>Techniques of data mining that used in the medical diagnosis a number of diseases like cancer, diabetes, stroke, and heart disease. The great importance emerging fields for providing diagnosis and a profounder understanding of medical data, its coms from Data mining in medical field .researcher attempts to solve real world health problems in the prognosis and treatment of diseases, by using Healthcare data mining. In this research, the algorithm of k-means is used for grouping medical data, the problem of k-means is to find optimal centers of clusters so, and fuzzy logic is used to get optimal centers of clusters.</p>