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.

The unconventional techniques called “the quick look techniques”, have been developed to present well log data calculations, so that they may be scanned easily to identify the zones that warrant a more detailed analysis, these techniques have been generated by service companies at the well site which are among the useful, they provide the elements of information needed for making decisions quickly when time is of essence^. The techniques used in this paper are:

• Apparent resistivity R_wa
• R_xo /R_t

   The above two methods had been used to evaluate Nasiriyah oil field formations (well-NS-3) to discover the hydrocarbon bearing formations. A compu

Background: The prevalence of spinal cord lesions is high in multiple sclerosis particularly in the cervical cord, and their detection can assist in both the diagnosis and follow-up of the patients. For spinal multiple sclerosis, MRI is considered the first line investigation.

Objective: To evaluate the value of sagittal 1.5 Tesla proton density-fast spin echo (PD-FSE) MRI in the detecting and increasing conspicuity of multiple sclerosis lesions in cervical cord in comparison with sagittal T2 fast spin-echo (T2-FSE) MRI.

Patients and Methods: A cross sectional study carried out from 3^rd of January 2017 to 1^st of January 2018 in the MRI department of

Artificial Neural Network (ANN) model's application is widely increased for wastewater treatment plant (WWTP) variables prediction and forecasting which can enable the operators to take appropriate action and maintaining the norms. It is much easier modeling tool for dealing with complex nature WWTP modeling comparing with other traditional mathematical models. ANN technique significance has been considered at present study for the prediction of sequencing batch reactor (SBR) performance based on effluent's (BOD5/COD) ratio after collecting the required historical daily SBR data for two years operation (2015-2016) from Baghdad Mayoralty and Al-Rustamiya WWTP office, Iraq. The prediction was gotten by the application of a feed-forwa

The design of safe pedestrian facilities usually depends on the assessment of pedestrian characteristics and behavior. In this investigation, pedestrian walking speed through the religious occasion have been monitored at three locations, Al- Kadhimiya (Imam AL-Kadim), Najaf and Karbala (Imam AL-Husain) holy shrines. Video captures of the pedestrian through their walking to the two holy shrines have been prepared and analyzed for walking speed, gender, age groups, and clothing tradition. The pedestrian sample size is 468, 501, and 447 for Al- Kadhimiya, Karbala, and Najaf respectively. When the gender is taken into consideration, it can be noted that the walking speed of male and female pedestrian is (0.97, 1.68, and 1.63