The development of science and studies has led to the creation of many modern means and technologies that focused and directed their interests on enhancing security due to the increased need for high degrees of security and protection for individuals and societies. Hence identification using a person's vital characteristics is an important privacy topic for governments, businesses and individuals. A lot of biometric features such as fingerprint, facial measurements, acid, palm, gait, fingernails and iris have been studied and used among all the biometrics, in particular, the iris gets the attention because it has unique advantages as the iris pattern is unique and does not change over time, providing the required accuracy and stability in verification systems. This feature is impossible to modify without risk. When identifying with the iris of the eye, the discrimination system only needs to compare the data of the characteristics of the iris of the person to be tested to determine the individual's identity, so the iris is extracted only from the images taken. Determining correct iris segmentation methods is the most important stage in the verification system, including determining the limbic boundaries of the iris and pupil, whether there is an effect of eyelids and shadows, and not exaggerating centralization that reduces the effectiveness of the iris recognition system. There are many techniques for subtracting the iris from the captured image. This paper presents the architecture of biometric systems that use iris to distinguish people and a recent survey of iris segmentation methods used in recent research, discusses methods and algorithms used for this purpose, presents datasets and the accuracy of each method, and compares the performance of each method used in previous studies
A methodology has been proposed for estimating the nonlinear effects in radio tracts of receiving and transmitting devices in radio-electronic means of mobile communication systems, based on using the nonlinear transfer functions of the higher-order Volterra series. A procedure has been devised for obtaining the output responses from a nonlinear non-inertia circuit under the harmonious input action using a method for determining the transfer functions of higher orders obtained on the basis of the transfer functions of lower orders. We have derived the analytical expressions for the output responses from a nonlinear system of different orders for three inputs for the case of representing a nonlinear system in the form of a nonlinear non-inertia circuit. The values of the transfer functions of higher orders for a nonlinear non-inertia circuit were determined by using a state variable method. This paper demonstrates the derivation of analytical expressions to calculate a harmonic coefficient based on the second and third harmonics using the nonlinear higher-orders transfer functions of a nonlinear non-inertia circuit. It has been shown that the use of the nonlinear transfer functions to the fifth order inclusive allows a more accurate assessment of nonlinear effects in the form of the harmonious and intermodulation distortions in the radio tracts of radio-electronic means of mobile systems. The outlined technique for determining the nonlinear transfer functions is invariant to the topology of a nonlinear electrical circuit, as well as to the quantity and type of nonlinear elements. Existing estimation procedures of electromagnetic compatibility related to the problems of calculating intermodulation interference can be improved by the introduction of the determined magnitudes of influence products. The proposed methodology makes it possible to evaluate the set of nonlinear effects in the problems related to electromagnetic compatibility in the groups of radio-electronic means with the accuracy required by users
The Syriac language is one of the ancient Semitic languages that appeared in the first century AD. It is currently used in a number of cities in Iraq, Turkey, and others. In this research paper, we tried to apply the work of Ali and Mahmood 2020 on the letters and words in the Syriac language to find a new encoding for them and increase the possibility of reading the message by other people.
Classification of network traffic is an important topic for network management, traffic routing, safe traffic discrimination, and better service delivery. Traffic examination is the entire process of examining traffic data, from intercepting traffic data to discovering patterns, relationships, misconfigurations, and anomalies in a network. Between them, traffic classification is a sub-domain of this field, the purpose of which is to classify network traffic into predefined classes such as usual or abnormal traffic and application type. Most Internet applications encrypt data during traffic, and classifying encrypted data during traffic is not possible with traditional methods. Statistical and intelligence methods can find and model traffic patterns that can be categorized based on statistical characteristics. These methods help determine the type of traffic and protect user privacy at the same time. To classify encrypted traffic from end to end, this paper proposes using (XGboost) algorithms, finding the highest parameters using Bayesian optimization, and comparing the proposed model with machine learning algorithms (Nearest Neighbor, Logistic Regression, Decision Trees, Naive Bayes, Multilayer Neural Networks) to classify traffic from end to end. Network traffic has two classifications: whether the traffic is encrypted or not, and the target application. The research results showed the possibility of classifying dual and multiple traffic with high accuracy. The proposed model has a higher classification accuracy than the other models, and finding the optimal parameters increases the model accuracy.
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