Heart sound is an electric signal affected by some factors during the signal's recording process, which adds unwanted information to the signal. Recently, many studies have been interested in noise removal and signal recovery problems. The first step in signal processing is noise removal; many filters are used and proposed for treating this problem. Here, the Hankel matrix is implemented from a given signal and tries to clean the signal by overcoming unwanted information from the Hankel matrix. The first step is detecting unwanted information by defining a binary operator. This operator is defined under some threshold. The unwanted information replaces by zero, and the wanted information keeping in the estimated matrix. The resulting matrix

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

In the present research we the study the deposition of radioactive elements naturally and particularly radioactive radon gas in parts of the body of organisms which are of direct relevance to human life in the city of Baghdad as the samples which were collected from the bones and skin of some kinds of birds and chicken based on the principle that radioactive elements are concentrated always on the bones. We use of this as the exercise detector impact nuclear (CR-39), using the technology Cylindrical diffusion , the results indicated that the largest concentration of radon found in the bone bird Seagull tapered as it was (625 ± 37) Bq.cm-3, and less concentration of radon gas in the chicken bones of Al-kafeel as it was (105 ± 10) Bq.c

The current study is aimed to identify personality types of the lecturers of Baghdad University as well as identify the prevailing types among the lecturers. In addition, the study aimed to identify the significant difference among personality types (The Enneagram) according to the variables of academic title and scientific major . One hundred and ninety four lecturers were involved in this study. In order to achieve the study's aims, the scale of (Baron& Wagele 1994) was used to measure the nine personality types (The Enneagram) that was translated by( Anwar Omar Ibrahim 2012). Some statistical analysis were used and the result demonstrated that Romantic types was found to be the most prevailing types among the lecturers of Baghdad