Background: Biologic mechanisms of the form-function interaction are one of important component of orthodontic diagnosis. The purpose of this study is to search for the statistical associations between natural postural and craniofacial morphologic variables of the head. Materials and methods: The sample comprised natural head posture (NHP) cephalograms of 90 subjects, aged 18 to 25 years. Interpretation of the facial structure was made by using both intracranial and the extra-cranial reference lines in AutoCAD computer program. Results The measures of anteroposterior maxillary position, SNA showed a low negative correlations with the anterior cranial base angulation to true vertical (SN.Ver) and with the cranio-cervical position of the head

The present study was carried out to compare the physicochemical characteristics of eggs of guinea fowl, turkeys and domestic chickens outdoor reared in traditional farms in Baghdad, Iraq. A total of 166 fresh eggs; 32 eggs from guinea fowls (Numida meleagris), 44 eggs from turkeys (Meleagris gallopavo) and 90 eggs from domestic chickens; were collected. Egg weight, percentage of egg components, chemical composition (protein, lipids, and ash), and lipid profile were determined. Results revealed the significant differences in egg weight among studied birds. The average egg weights for guinea fowl, turkey, and indigenous chicken were 48.51 ± 0.72, 52.15 ± 0.74 and 61.24 ± 0.22 g, respectively. No significant differences were found in egg c

NGC 6946 have been observed with BVRI filters, on October 15-18,
2012, with the Newtonian focus of the 1.88m telescope, Kottamia
observatory, of the National Research Institute of Astronomy and
Geophysics, Egypt (NRIAG), then we combine the BVRI filters to
obtain an astronomical image to the spiral galaxy NGC 6946 which
is regarded main source of information to discover the components of
this galaxy, where galaxies are considered the essential element of
the universe. To know the components of NGC 6946, we studied it
with the Variable Precision Rough Sets technique to determine the
contribution of the Bulge, disk, and arms of NGC 6946 according to
different color in the image. From image we can determined th

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

Artificial intelligence (AI) is entering many fields of life nowadays. One of these fields is biometric authentication. Palm print recognition is considered a fundamental aspect of biometric identification systems due to the inherent stability, reliability, and uniqueness of palm print features, coupled with their non-invasive nature. In this paper, we develop an approach to identify individuals from palm print image recognition using Orange software in which a hybrid of AI methods: Deep Learning (DL) and traditional Machine Learning (ML) methods are used to enhance the overall performance metrics. The system comprises of three stages: pre-processing, feature extraction, and feature classification or matching. The SqueezeNet deep le

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog