The herein research was carried out in order to identified the presence of bacteria in cervix and uterine lumen in Iraqi cattle during the different estrus phase with focusing on Protus and E coli. Estrus phases were determined by the structures which found on ovary (follicular growth for pro-estrus, mature growing follicle for estrus, hemorrhagic corpus luteam for meta-estrus and active corpus luteam for di-eatrus). Forty cervical swabs (ten for each estrus phase) and forty uterine swabs (ten for each estrus phase) were taken from macroscopically healthy reproductive animals after slaughtering and cultivated on nutrient agar and blood agar, the bacterial isolation were identified with biochemical teats. The present study found that

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.

Shadow removal is crucial for robot and machine vision as the accuracy of object detection is greatly influenced by the uncertainty and ambiguity of the visual scene. In this paper, we introduce a new algorithm for shadow detection and removal based on different shapes, orientations, and spatial extents of Gaussian equations. Here, the contrast information of the visual scene is utilized for shadow detection and removal through five consecutive processing stages. In the first stage, contrast filtering is performed to obtain the contrast information of the image. The second stage involves a normalization process that suppresses noise and generates a balanced intensity at a specific position compared to the neighboring intensit

Activity test of the inhibitors purified from barley and broad beans crop proved the inhibition activity against 6 types of rots Pencillium ssp and Aspergellusflavus and Aspergillus niger and Fusarium solani and Fusarium semitectum and Mucor with three concentrations 0.1 and 0.2 and 0.3 mg/ml, where the inhibitor purified from the second peak of broad beans proved that it had a higher inhibition activity against the growth of test rots which were 53.75 and 62.5 and 78.5 and 76.25 and 84 and 18.8% respectively, at 0.3 mg/ ml followed by the first peak of the inhibitor purified from broad beans the inhibition activity were 43.75 and 50 and 62.96 and 75 and 80 and 12.5 then the inhibitor purified from barley in which the inhibition activity

The present calculation covers the building shield during irradiation process and under water storage of three milion curries Cobalt-60 radiation source the calculation results in design requirement of 8m depth of water in the source stoeage pool

     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

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

      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 minimu

Alzheimer’s disease (AD) is an age-related progressive and neurodegenerative disorder, which is characterized by loss of memory and cognitive decline. It is the main cause of disability among older people. The rapid increase in the number of people living with AD and other forms of dementia due to the aging population represents a major challenge to health and social care systems worldwide. Degeneration of brain cells due to AD starts many years before the clinical manifestations become clear. Early diagnosis of AD will contribute to the development of effective treatments that could slow, stop, or prevent significant cognitive decline. Consequently, early diagnosis of AD may also be valuable in detecting patients with dementia who have n