Playfair cipher is a substitution scheme. The classical playfair scheme has a limited matrix size  containing only uppercase letters, so it is prone to hackers and cryptanalysis. To increase the resistance of playfair cipher, a new encipherment and decipherment method is proposed in this work, which depends on the permutation and its inverse, respectively. In addition, a modified  key matrix is utilized, which includes capital and small Alphabets, numbers, and 38 special characters collected from ASCII codes. In the proposed method, both substitution and transposition schemes are used, where the first stratum of the cipher is a substitution by using  key matrix and the second stratum is a transposi

Two unsupervised classifiers for optimum multithreshold are presented; fast Otsu and k-means. The unparametric methods produce an efficient procedure to separate the regions (classes) by select optimum levels, either on the gray levels of image histogram (as Otsu classifier), or on the gray levels of image intensities(as k-mean classifier), which are represent threshold values of the classes. In order to compare between the experimental results of these classifiers, the computation time is recorded and the needed iterations for k-means classifier to converge with optimum classes centers. The variation in the recorded computation time for k-means classifier is discussed.

    Manufacturing high-efficiency polymeric materials to moderate fast neutrons by converting them into slow or thermal neutrons. These materials absorb thermal neutrons as well as gamma rays associated with neutrons. Materials of small mass number are used to slow down fast neutrons because neutrons have a high cross-section when they interact with these materials. Materials of high mass number absorb gamma rays. Polyurethane and epoxy were mixed in various ratios to create a blend to serve as neutrons shield, lead (Pb) was then added to the blend at weight percentages of 20%, 30%, 40%, 50%, and 70% to produce a polymer composite.

    Polymeric materials reinforced with lead in various ratios were tested to select the best

The performance quality and searching speed of Block Matching (BM) algorithm are affected by shapes and sizes of the search patterns used in the algorithm. In this paper, Kite Cross Hexagonal Search (KCHS) is proposed. This algorithm uses different search patterns (kite, cross, and hexagonal) to search for the best Motion Vector (MV). In first step, KCHS uses cross search pattern. In second step, it uses one of kite search patterns (up, down, left, or right depending on the first step). In subsequent steps, it uses large/small Hexagonal Search (HS) patterns. This new algorithm is compared with several known fast block matching algorithms. Comparisons are based on search points and Peak Signal to Noise Ratio (PSNR). According to resul

Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

This paper aims to study the quaternary classical continuous optimal control problem consisting of the quaternary nonlinear parabolic boundary value problem, the cost function, and the equality and inequality constraints on the state and the control. Under appropriate hypotheses, it is demonstrated that the quaternary classical continuous optimal control ruling by the quaternary nonlinear parabolic boundary value problem has a quaternary classical continuous optimal control vector that satisfies the equality constraint and inequality state and control constraint. Moreover, mathematical formulation of the quaternary adjoint equations related to the quaternary state equations is discovered, and then the weak form of the quaternary adjoint

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a