Facial identification is one of the biometrical approaches implemented for identifying any facial image with the use of the basic properties of that face. In this paper we proposes a new improved approach for face detection based on coding eyes by using Open CV's Viola-Jones algorithm which removes the falsely detected faces depending on coding eyes. The Haar training module in Open CV is an implementation of the Viola-Jones framework, the training algorithm takes as input a training group of positive and negative images, and generates strong features in the format of an XML file which is capable of subsequently being utilized for detecting the wanted face and eyes in images, the integral image is used to speed up Haar-like features calc

ABSTRACT Purpose: The determination of standard scores and levels for some mental skills by researchers is of great importance, especially if it matches the target research sample, Method: as the researchers used the descriptive approach in the survey method, and the researchers chose the sample of youth players for clubs for the season (2022-2021), numbering (127) players, and the researchers identified the scale and procedures and applied it to the research sample, Results: obtained the results that were processed, extracted grades and standard levels, and then interpreted them and obtained conclusions, Conclusion: the most important of which are: The standard levels of mental skills reached the results of the sample studied within the

Fourty  -tow   Libyan     patients  with  hydatidosis,  which  were

referred to by the physician for the detection of hydatid cyst by X - rays, Ultrasound and CT-Scan.  The  infection rate  in  females and males was(69% )and (31% )respectively .The highest rate 69% was in the liver, followed by the lung( 23.8%), the brain (4.8%) and kidney

(2.4%).

A total of  42 serum  samples were gathered from Libyan patients infected with hydatidosis, 33 serum samples from patients cases with other parasitic diseases than hydatidosis and 30 serum samples from healthy normal controls and were tested by Dot-ELIZA utilizing antigen B from sheep hy

     The diseases presence in various species of fruits are the crucial parameter of economic composition and degradation of the cultivation industry around the world. The proposed pear fruit disease identification neural network (PFDINN) frame-work to identify three types of pear diseases was presented in this work. The major phases of the presented frame-work were as the following: (1) the infected area in the pear fruit was detected by using the algorithm of K-means clustering. (2) hybrid statistical features were computed over the segmented pear image and combined to form one descriptor. (3) Feed forward neural network (FFNN), which depends on three learning algorithms of back propagation (BP) training, namely Sca

Mobile-based human emotion recognition is very challenging subject, most of the approaches suggested and built in this field utilized various contexts that can be derived from the external sensors and the smartphone, but these approaches suffer from different obstacles and challenges. The proposed system integrated human speech signal and heart rate, in one system, to leverage the accuracy of the human emotion recognition. The proposed system is designed to recognize four human emotions; angry, happy, sad and normal. In this system, the smartphone is used to   record user speech and send it to a server. The smartwatch, fixed on user wrist, is used to measure user heart rate while the user is speaking and send it, via Bluetooth,