Background: The frontal sinus area can be used as a diagnostic aid to recognize mouth breather subjects. The aims of this study were to determine the gender difference in each group, to compare the frontal sinus area between mouth breather and nasal breather group, and to verify the presence of correlation between the frontal sinus area and the cephalometric skeletal measurements used in this study. Materials and Methods: Cephalometric radiographs were taken for 60 adults (30 mouth breathers and 30 nasal breathers) age range (18-25), for each group 15 males and 15 females, in the orthodontic clinic in the college of Dentistry at Baghdad University. The control group (nasal breather) with skeletal class I and ANB angle ranged between 2-4º,

Background:

Foreign body inhalation is a life threating event in children and it is common in our country ,which is  a daily practice of Thoracic .It  can lead to morbidity even mortality in the hands of untrained or not well- trained doctors.

Aim:

Is to report a case of missed foreign body inhaled 15-years back, which is uncommonly reported in the literatures and to compare it with other studies reporting similar cases.

Methods:

The details, presentation, clinical findings, radiological appearance and the successful removal by a rigid bronchoscope under general

This study focuses on determining the heterogeneity of Yamama Formation and its spatial distribution in south of Iraq using three indices namely, Coefficient of Variation, Lorenz Coefficient, and Dykstra – Parsons Coefficient. The porosity and permeability values from eleven wells in south of Iraq (Basra and Maysan oil fields) are used for computing heterogeneity indices. Ordinary kriging technique is used to interpolate the computed indices and to show the spatial distribution of these indices over the study area. Results indicated that the average values of Lorenz and Dykstra – Parsons Indices are 0.73 and 0.86, respectively which refer to the extremely heterogeneity nature of Yamama Formation in the study area.The spatial distribu

The performance of a condenser in a domestic refrigerator system without wires and a condenser with a novel design consisted of number of loops as elliptical shape is investigated experimentally in this work. The experiment was conducted with a refrigerator designed to work with HFC134a, under no load and with loads of (1.5,3 and 12 liters of water). In particular, the effects of shape change of the condenser were very important in heat transfer enhancement and reduce of the frictional loss as a result of reducing the pressure drop in the condenser. The results shown that compressor work decreases with elliptical condenser about (8.6% to 11.3%), and then the power consumption decreases also. The performance of household refrigerator with

 

This work implements the face recognition system based on two stages, the first stage is feature extraction stage and the second stage is the classification stage. The feature extraction stage consists of Self-Organizing Maps (SOM) in a hierarchical format in conjunction with Gabor Filters and local image sampling. Different types of SOM’s were used and a comparison between the results from these SOM’s was given.

The next stage is the classification stage, and consists of self-organizing map neural network; the goal of this stage is to find the similar image to the input image. The proposal method algorithm implemented by using C++ packages, this work is successful classifier for a face database consist of 20

Due to the lack of vehicle-to-infrastructure (V2I) communication in the existing transportation systems, traffic light detection and recognition is essential for advanced driver assistant systems (ADAS) and road infrastructure surveys. Additionally, autonomous vehicles have the potential to change urban transportation by making it safe, economical, sustainable, congestion-free, and transportable in other ways. Because of their limitations, traditional traffic light detection and recognition algorithms are not able to recognize traffic lights as effectively as deep learning-based techniques, which take a lot of time and effort to develop. The main aim of this research is to propose a traffic light detection and recognition model based on

Face recognition is a type of biometric software application that can identify a specific
individual in a digital image by analyzing and comparing patterns. It is the process of
identifying an individual using their facial features and expressions.
In this paper we proposed a face recognition system using Stationary Wavelet Transform
(SWT) with Neural Network, the SWT are applied into five levels for feature facial
extraction with probabilistic Neural Network (PNN) , the system produced good results
and then we improved the system by using two manner in Neural Network (PNN) and
Support Vector Machine(SVM) so we find that the system performance is more better
after using SVM where the result shows the performance o

This paper includes the estimation of the scale parameter of weighted Rayleigh distribution using well-known methods of estimation (classical and Bayesian). The proposed estimators were compared using Monte Carlo simulation based on mean squared error (MSE) criteria. Then, all the results of simulation and comparisons were demonstrated in tables. 

An Auto Crop method is used for detection and extraction signature, logo and stamp from the document image. This method improves the performance of security system based on signature, logo and stamp images as well as it is extracted images from the original document image and keeping the content information of cropped images. An Auto Crop method reduces the time cost associated with document contents recognition. This method consists of preprocessing, feature extraction and classification. The HSL color space is used to extract color features from cropped image. The k-Nearest Neighbors (KNN) classifier is used for classification. 

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se