The strong cryptography employed by PGP (Pretty Good Privacy) is one of the best available today. The PGP protocol is a hybrid cryptosystem that combines some of the best features of both conventional and public-key cryptography. This paper aim to improve PGP protocol by combined between the Random Genetic algorithm, NTRU (N-th degree Truncated polynomial Ring Unit) algorithm with PGP protocol stages in order to increase PGP protocol speed, security, and make it more difficult in front of the counterfeiter. This can be achieved by use the Genetic algorithm that only generates the keys according to the Random Genetic equations. The final keys that obtained from Genetic algorithm were observed to be purely random (according to the randomne

In this paper, a differential operator is used to generate a subclass of analytic and univalent functions with positive coefficients. The studied class of the functions includes:  

which is defined in the open unit disk  satisfying the following condition

This leads to the study of properties such as coefficient bounds, Hadamard product, radius of close –to- convexity, inclusive properties, and (n, τ) –neighborhoods for functions belonging to our class.

Software Defined Networking (SDN) with centralized control provides a global view and achieves efficient network resources management. However, using centralized controllers has several limitations related to scalability and performance, especially with the exponential growth of 5G communication. This paper proposes a novel traffic scheduling algorithm to avoid congestion in the control plane. The Packet-In messages received from different 5G devices are classified into two classes: critical and non-critical 5G communication by adopting Dual-Spike Neural Networks (DSNN) classifier and implementing it on a Virtualized Network Function (VNF). Dual spikes identify each class to increase the reliability of the classification

Scheduling Timetables for courses in the big departments in the universities is a very hard problem and is often be solved by many previous works although results are partially optimal. This work implements the principle of an evolutionary algorithm by using genetic theories to solve the timetabling problem to get a random and full optimal timetable with the ability to generate a multi-solution timetable for each stage in the collage. The major idea is to generate course timetables automatically while discovering the area of constraints to get an optimal and flexible schedule with no redundancy through the change of a viable course timetable. The main contribution in this work is indicated by increasing the flexibility of generating opti

n this paper, we formulate three mathematical models using spline functions, such as linear, quadratic and cubic functions to approximate the mathematical model for incoming water to some dams. We will implement this model on dams of both rivers; dams on the Tigris are Mosul and Amara while dams on the Euphrates are Hadetha and Al-Hindya.

In this paper, we develop the Hille and Nehari Type criteria for the oscillation of all solutions to the Fractional Differential Equations involving Conformable fractional derivative. Some new oscillatory criteria are obtained by using the Riccati transformations and comparison technique. We show the validity and effectiveness of our results by providing various examples.

Automatic document summarization technology is evolving and may offer a solution to the problem of information overload. Multi-document summarization is an optimization problem demanding optimizing more than one objective function concurrently. The proposed work considers a balance of two significant objectives: content coverage and diversity while generating a summary from a collection of text documents. Despite the large efforts introduced from several researchers for designing and evaluating performance of many text summarization techniques, their formulations lack the introduction of any model that can give an explicit representation of – coverage and diversity – the two contradictory semantics of any summary. The design of gener

               This paper studies the effect of the solar indices (total sunspot no., solar wind density and coronal mass ejection linear speed) on F₂- layer electron density, as well as, the comparison of seasonal variations of electron density of F₂- layer, N_mF₂, for Mars at local noontime and for maximum and minimum solar cycles.                  

 Results show that there is an inverse correlation between sunspot number, solar wind density, CME linear speed and ionosphere electron density for Mars, at maximum and minimum solar cycles. This correlation is clear when electron density is at minimum value.                          

Background : AgNOR parameters are well known to pathologists as a proliferation marker with advantage over other proliferation markers of being cheaper, simpler and able to assess
proliferation speed as well as state , AgNOR stainability was found to be well preserved in smears kept for up to 2 years , studies have shown that AgNOR values can serve as a useful
prognostic parameter and a marker for tumour progression in different carcinomas (1) This study was conducted to see the importance of AgNOR staining in the peritoneal fluid
cytopathological examination
Patient and Methods: It was a descriptive and prospective study conducted in Department of cytopathology in the medical city and Department of pathol

<p>Analyzing X-rays and computed tomography-scan (CT scan) images using a convolutional neural network (CNN) method is a very interesting subject, especially after coronavirus disease 2019 (COVID-19) pandemic. In this paper, a study is made on 423 patients’ CT scan images from Al-Kadhimiya (Madenat Al Emammain Al Kadhmain) hospital in Baghdad, Iraq, to diagnose if they have COVID or not using CNN. The total data being tested has 15000 CT-scan images chosen in a specific way to give a correct diagnosis. The activation function used in this research is the wavelet function, which differs from CNN activation functions. The convolutional wavelet neural network (CWNN) model proposed in this paper is compared with regular convol