The first successful implementation of Artificial Neural Networks (ANNs) was published a little over a decade ago. It is time to review the progress that has been made in this research area. This paper provides taxonomy for classifying Field Programmable Gate Arrays (FPGAs) implementation of ANNs. Different implementation techniques and design issues are discussed, such as obtaining a suitable activation function and numerical truncation technique trade-off, the improvement of the learning algorithm to reduce the cost of neuron and in result the total cost and the total speed of the complete ANN. Finally, the implementation of a complete very fast circuit for the pattern of English Digit Numbers NN has four layers of 70 nodes (neurons) o

Background: Obesity is a worldwide challenge and is closely
connected to many metabolic diseases. Two types of
adipose tissue, white adipose tissue (WAT) and brown
adipose tissue (BAT) have been identified. White fat cells
store chemical energy, brown adipocytes defend against
hypothermia, obesity and diabetes.
Objective: To localize and quantify brown adipocytes in
human subcutaneous (S) and visceral (V) adipose tissue by
histology and immunohistochemistry.
Type of the study: A cross –sectional study.
Methods: Adipose tissue was obtained from histopathology
specimens taken from ten patients, of different age, sex and
body mass index (BMI), undergoing surgery for different
pathologies

The study included 176 patients attended to Iben AL-Haithem hospital of ophthalmology in Baghdad city , at age of (14-77) years from the period from September 2016-April 2017, there were 138(78%) patients having a positive culture of ocular bacterial infections. Men were representing 41% while women 59%, the patients with bacterial ocular infection from Baghdad were55% while the internally displaced were 45% , the patients with ocular bacterial infections were representing 20% at mean age of 21.6±0.3y, 26% at 37.5±0.21y , 24% at53.5±0.23 y and 30% at69.5±0.10 y. The results showed 37% of the patients with ocular bacterial infections were having a history of diabetes mellitus and 44% with high blood pressure at mean age of 69.5±0.10

   The di-(2-ethylhexyl) phthalate (DEHP) was extracted using different solvents from plastic blood bag. The extracted product was identified using FT-IR, NMR (1H and 13C), DEPT, COSY, HMBC and HSQC_TOCSY spectrometry. The extracted plasticizer was tested in complex formation with Fe2+ and Cr3+ using UV-visible spectrophotometric method. The migration of the plasticizer from the blood bags to the blood was studied and determined during different storage times depending upon the formation of complexes with Fe2+ and Cr3+, and the change in the concentration of Fe2+ and Cr3+. 

Objective: Detection the level of YKL-40 biochemical marker and vitamin D level in sera of Iraqi uterine cancer
females' patients.
Methodology: This study included 90 female volunteers, 30 of them were healthy volunteers who were
considered as a control group, while sixty serum samples were collected from women patients suffering from
uterine tumors (30 malignant and 30 fibroid benign tumors), benign cases were considered as a disease
control group for malignant tumors. The average age of those females was 30-75 years, which matched the
control group. All the samples were collected from Azady hospital in Kirkuk and the gynecologic department at
Medical City in Baghdad during October /2012 to May /2013. All the serum

Text based-image clustering (TBIC) is an insufficient approach for clustering related web images. It is a challenging task to abstract the visual features of images with the support of textual information in a database. In content-based image clustering (CBIC), image data are clustered on the foundation of specific features like texture, colors, boundaries, shapes. In this paper, an effective CBIC) technique is presented, which uses texture and statistical features of the images. The statistical features or moments of colors (mean, skewness, standard deviation, kurtosis, and variance) are extracted from the images. These features are collected in a one dimension array, and then genetic algorithm (GA) is applied for image clustering.

     With the growth of the use mobile phones, people have become increasingly interested in using Short Message Services (SMS) as the most suitable communications service. The popularity of SMS has also given rise to SMS spam, which refers to any unwanted message sent to a mobile phone as a text. Spam may cause many problems, such as traffic bottlenecks or stealing important users' information. This paper,  presents a new model that extracts seven features from each message before applying a Multiple Linear Regression (MLR) to assign a weight to each of the extracted features. The message features are fed into the Extreme Learning Machine (ELM) to determine whether they are spam or ham. To evaluate the proposed model, the UCI bench

      Data mining has the most important role in healthcare for discovering hidden relationships in big datasets, especially in breast cancer diagnostics, which is the most popular cause of death in the world. In this paper two algorithms are applied that are decision tree and K-Nearest Neighbour for diagnosing Breast Cancer Grad in order to reduce its risk on patients. In decision tree with feature selection, the Gini index gives an accuracy of %87.83, while with entropy, the feature selection gives an accuracy of %86.77. In both cases, Age appeared as the  most effective parameter, particularly when Age<49.5. Whereas  Ki67  appeared as a second effective parameter. Furthermore, K- Nearest Neighbor is based on the minimum err

With the rapid development of computers and network technologies, the security of information in the internet becomes compromise and many threats may affect the integrity of such information. Many researches are focused theirs works on providing solution to this threat. Machine learning and data mining are widely used in anomaly-detection schemes to decide whether or not a malicious activity is taking place on a network. In this paper a hierarchical classification for anomaly based intrusion detection system is proposed. Two levels of features selection and classification are used. In the first level, the global feature vector for detection the basic attacks (DoS, U2R, R2L and Probe) is selected. In the second level, four local feature vect