The research aims to identify the level of awareness of student teachers in the behavioral disorders and autism specialization about the diagnosing Autism Spectrum Disorder and Social (Pragmatic) Communication Disorder according to some variables. The study was conducted on a sample of (113) student teachers. The researcher employed the awareness scale of a teacher-screening questionnaire for autism spectrum disorder and social pragmatic communication disorder. The results showed that the average of teachers in the total degree of awareness of autism spectrum disorder and social communication have recorded a moderate degree. As for the awareness of autism spectrum disorder was high. Then, the awareness of social communication disorder wa

Researchers are increasingly using multimodal biometrics to strengthen the security of biometric applications. In this study, a strong multimodal human identification model was developed to address the growing problem of spoofing attacks in biometric security systems. Through the use of metaheuristic optimization methods, such as the Genetic Algorithm(GA), Ant Colony Optimization(ACO), and Particle Swarm Optimization (PSO) for feature selection, this unique model incorporates three biometric modalities: face, iris, and fingerprint. Image pre-processing, feature extraction, critical image feature selection, and multibiometric recognition are the four main steps in the workflow of the system. To determine its performance, the model wa

Boltzmann mach ine neural network bas been used to recognize the Arabic speech.  Fast Fourier transl(>lmation algorithm has been used t() extract speciral 'features from an a caustic signal .

The  spectral  feature size is reduced by series of operations in

order to make it salable as input for a neural network which is used as a recogni zer by Boltzmann Machine Neural  network which has been used as a recognizer for phonemes . A training set consist of a number of Arabic phoneme repesentations, is used to train lhe neuntl network.

The neural network recognized Arabic. After Boltzmann Machine Neura l    network   training  the  system   with 

The recognition of handwritten numerals has many applications in automatic identification and cognition. This research contains three experimented scenarios to recognize the handwritten English (i.e. Arabic) numerals. In the first scenario the bilinear interpolation of the image is used, while in the second scenario and after the bilinear interpolation is being applied, the Sobel operators are applied on the resulted interpolated image. In the third scenario which represents the last one, the effect of normalization of image dimensions is tested. 550 images of handwritten numerals were tested. Three types of tests were conducted for each scenario namely: trained-set test, not-trained-set test and comprehensive-set test. Depending on the

Human action recognition has gained popularity because of its wide applicability, such as in patient monitoring systems, surveillance systems, and a wide diversity of systems that contain interactions between people and electrical devices, including human computer interfaces. The proposed method includes sequential stages of object segmentation, feature extraction, action detection and then action recognition. Effective results of human actions using different features of unconstrained videos was a challenging task due to camera motion, cluttered background, occlusions, complexity of human movements, and variety of same actions performed by distinct subjects. Thus, the proposed method overcomes such problems by using the fusion of featur

Activity recognition (AR) is a new interesting and challenging research area with many applications (e.g. healthcare, security, and event detection). Basically, activity recognition (e.g. identifying user’s physical activity) is more likely to be considered as a classification problem. In this paper, a combination of 7 classification methods is employed and experimented on accelerometer data collected via smartphones, and compared for best performance. The dataset is collected from 59 individuals who performed 6 different activities (i.e. walk, jog, sit, stand, upstairs, and downstairs). The total number of dataset instances is 5418 with 46 labeled features. The results show that the proposed method of ensemble boost-based classif

In the 1970s, the world knew the long-tailed nesokia Nesokia bunnii (Khajuria, 1981) (Rodentia, Muridae) from the Mesopotamian marshes of Garden of Eden in Southern Iraq. This distinct rodent was known from only five voucher specimens collected at the confluence of Tigris and Euphrates Rivers in southern Iraq while its occurrence in Southwestern Iran had
never been reported. In the 1990s, a large extent of its natural habitat was catastrophically desiccated and the animal was last seen in the 1970s. Since then, the status of this elusive rodent was shrouded in mystery. In 2007, an extraordinary photograph of a carcass of this species came to the light from Hawizeh Marsh which was interpreted as concrete evidence of the species’ pers

Background: Treatment modalities of acromegaly and disease control impact differently on glucose homeostasis and lipid changes, and consequently on cardiometabolic risk. Aim: To investigate the possible association of lipid profile changes with the glycemic control status in acromegaly patients treated with octreotide LAR. Methods: This cross-sectional study included 52 Iraqi patients with acromegaly treated with octreotide LAR and not using statins. Demographic, anthropometric, and clinical data were collected, as well as the duration of Octreotide LAR administration. The glycemic state was assessed and classified as DM, prediabetes, or normal. Plasma levels of triglycerides, LDL cholesterol, HDL cholesterol, and non-HDL were evalu