Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

In this paper ,the problem of point estimation for the two parameters of logistic distribution has been investigated using simulation technique. The rank sampling set estimator method which is one of the Non_Baysian procedure and Lindley approximation estimator method which is one of the Baysian method were used to estimate the parameters of logistic distribution. Comparing between these two mentioned methods by employing mean square error measure and mean absolute percentage error measure .At last simulation technique used to generate many number of samples sizes to compare between these methods.

Segmentation is the process of partition digital images into different parts depending on texture, color, or intensity, and can be used in different fields in order to segment and isolate the area to be partitioned. In this work images of the Moon obtained through observations in Astronomy and space dep. College of science university of Baghdad by ( Toward space telescopes and widespread used of a CCD camera) . Different segmentation methods were used to segment lunar craters. Different celestial objects cause craters when they crash into the surface of the Moon like asteroids and meteorites. Thousands of craters appears on the Moon's surface with ranges in size from meter to many kilometers, it provide insights into the age and ge

CuO nanoparticles were synthesized in two different ways, firstly by precipitation method using copper acetate monohydrate Cu(CO₂CH₁₃)₂·H2O, glacial acetic acid (CH3COOH) and sodium hydroxide(NaOH), and secondly by sol-gel method using copper chloride(CuCl₂), sodium  hydroxide (NaOH) and ethanol (C₂H₆O). Results of scanning electron microscopy (SEM) showed that different CuO nanostructures (spherical and Reef) can be formed using precipitation and sol- gel process, respectively, at which the particle size was found to be less than 2 µm. X-ray diffraction (XRD)manifested that the pure synthesized powder has no inclusions that may exist during preparations. XRD result

Background: Osteoporosis is denoted by low bone mass and microarchitectural breakdown of bone tissue, directing to increased fracture risk and bone fragility. Fractures may lead to a decreased quality of life and increased medical costs. Thus, osteoporosis is widely considered a significant health concern.

Objective. This study aimed to compare quantitative computed tomography (QCT) and dual-energy X-Ray absorptiometry (DXA) to detect osteoporosis in postmenopausal women.

Subjects and Methods. We measured spinal volumetric bone mineral density (BMD) with QCT and areal spinal and hip BMD with DXA in 164 postmenopausal women. We calculated the osteo

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

Background: Unlike normal EEG patterns, the epileptiform abnormal pattern is characterized by different mor phologies such as the high-frequency oscillations (HFOs) of ripples on spikes, spikes and waves, continuous and sporadic spikes, and ploy2 spikes. Several studies have reported that HFOs can be novel biomarkers in human epilepsy study. S) Method: To regenerate and investigate these patterns, we have proposed three large scale brain network models (BNM by linking the neural mass model (NMM) of Stefanescu-Jirsa 2D (S-J 2D) with our own structural con nectivity derived from the realistic biological data, so called, large-scale connectivity connectome. These models include multiple network connectivity of brain regions at different

Self-driving automobiles are prominent in science and technology, which affect social and economic development. Deep learning (DL) is the most common area of study in artificial intelligence (AI). In recent years, deep learning-based solutions have been presented in the field of self-driving cars and have achieved outstanding results. Different studies investigated a variety of significant technologies for autonomous vehicles, including car navigation systems, path planning, environmental perception, as well as car control. End-to-end learning control directly converts sensory data into control commands in autonomous driving. This research aims to identify the most accurate pre-trained Deep Neural Network (DNN) for predicting the steerin