Anomaly detection is still a difficult task. To address this problem, we propose to strengthen DBSCAN algorithm for the data by converting all data to the graph concept frame (CFG). As is well known that the work DBSCAN method used to compile the data set belong to the same species in a while it will be considered in the external behavior of the cluster as a noise or anomalies. It can detect anomalies by DBSCAN algorithm can detect abnormal points that are far from certain set threshold (extremism). However, the abnormalities are not those cases, abnormal and unusual or far from a specific group, There is a type of data that is do not happen repeatedly, but are considered abnormal for the group of known. The analysis showed DBSCAN using the

     Community detection is an important and interesting topic for better understanding and analyzing complex network structures. Detecting hidden partitions in complex networks is proven to be an NP-hard problem that may not be accurately resolved using traditional methods. So it is solved using evolutionary computation methods and modeled in the literature as an optimization problem.  In recent years, many researchers have directed their research efforts toward addressing the problem of community structure detection by developing different algorithms and making use of single-objective optimization methods. In this study, we have continued that research line by improving the Particle Swarm Optimization (PSO) algorithm using a

  This study  focused on a fundamental issue which was represented by ability of Iraqi central bank in facing the difficulty of determining  the optimal ratio of liquidity in the Iraqi banks in terms of the balancing between its obligations to the depositors and borrowers, and liquidate their funds on one hand and the risks on the other hand.the search aimed  for achieving the goals which represented by identifying the possibility of Iraqi banks to apply the regulations rules and  instructions  issued by central bank  of  Iraq in determining  ratio  of  liquidity and  its  appropriate with Iraqi  banks  action to implement  a  reasonable  profit to&

This paper proposes a new method Object Detection in Skin Cancer Image, the minimum
spanning tree Detection descriptor (MST). This ObjectDetection descriptor builds on the
structure of the minimum spanning tree constructed on the targettraining set of Skin Cancer
Images only. The Skin Cancer Image Detection of test objects relies on their distances to the
closest edge of thattree. Our experimentsshow that the Minimum Spanning Tree (MST) performs
especially well in case of Fogginessimage problems and in highNoisespaces for Skin Cancer
Image.
The proposed method of Object Detection Skin Cancer Image wasimplemented and tested on
different Skin Cancer Images. We obtained very good results . The experiment showed that

Objective: Evaluation of women's knowledge about risk factors and early detection of breast cancer at
Ibn Rushd college of education in Baghdad University.
Methodology: The study sample included (184) women in the Ibn Rushd College / University of
Baghdad, whose age ranged between (17-58) years. Data were collected through a structured
questionnaire prepared by the National Cancer Research Center which were answered during a scientific
symposium about breast cancer. The score was calculated by correcting the results of the answer, giving
one score for each correct answer and then estimating the level of knowledge and inputting all data in a
statistical program.
Results: The results showed limited level of women's

Medulloblastomas and ependymomas are the most common malignant brain tumors in children. However genetic abnormalities associated with their development and prognosis remain unclear. Recently two gene fusions, KIAA1549–BRAF and SRGAP3–RAF1 have been detected in a number of brain tumours. We report here our development and validation of RT-RQPCR assays to detect various isoforms of these two fusion genes in formalin fixed paraffin embedded (FFPE) tissues of medulloblastoma and ependymoma. We examined these fusion genes in 44 paediatric brain tumours, 33 medulloblastomas and 11 ependymomas. We detected both fusion transcripts in 8/33, 5/33 SRGAP3 ex10/RAF1 ex10, and 3/33 KIAA1549 ex16/BRAF ex9, meduloblastomas but none in the 11 ep