Voice denoising is the process of removing undesirable voices from the voice signal. Within the environmental noise and after the application of speech recognition system, the discriminative model finds it difficult to recognize the waveform of the voice signal. This is due to the fact that the environmental noise needs to use a suitable filter that does not affect the shaped waveform of the input microphone. This paper plans to build up a procedure for a discriminative model, using infinite impulse response filter (Butterworth filter) and local polynomial approximation (Savitzky-Golay) smoothing filter that is a polynomial regression on the signal values. Signal to noise ratio (SNR) was calculated after filtering to compare the results

In this article, the numerical and approximate solutions for the nonlinear differential equation systems, represented by the epidemic SIR model, are determined. The effective iterative methods, namely the Daftardar-Jafari method (DJM), Temimi-Ansari method (TAM), and the Banach contraction method (BCM), are used to obtain the approximate solutions. The results showed many advantages over other iterative methods, such as Adomian decomposition method (ADM) and the variation iteration method (VIM) which were applied to the non-linear terms of the Adomian polynomial and the Lagrange multiplier, respectively. Furthermore, numerical solutions were obtained by using the fourth-orde Runge-Kutta (RK4), where the maximum remaining errors showed th

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

The aim of this paper is to construct the analysis mathematical model for stream cipher cryptosystems in order to be cryptanalysis using the cryptanalysis tools based on plaintext attack (or part from it) or ciphertext only attack, choosing Brüer generator as study case of nonlinear stream cipher system.
The constructing process includes constructing the linear (or non-linear) equations system of the attacked nonlinear generator. The attacking of stream cipher cryptosystem means solving the equations system and that means finding the initial key values for each combined LFSR. 

The subject of the act and its transformations in the presentation is of great importance at the level of study, analysis and interpretation, and through that the researcher adopted the following title (The Dramatic Act and Its Transformations in the Iraqi Theatrical Show). The play of Black Astronomy was chosen as a sample for analysis, and the aim of the research came in order to achieve a special knowledge of the extent of the transformation that occurred between the dramatic structure of the act and theatrical embodiment and its multiple elements of operation in the Iraqi theatrical shows. It was limited to the show of the Iraqi theater in the year (2020) and included the theoretical framework that accommodated three sections. As for

B Saleem, H Alwan, L Khalid, Journal of Engineering, 2011 - Cited by 2

     This paper focuses on choosing a spatial mixture model with implicitly includes the time to represent the relative risks of COVID-19 pandemic using an appropriate model selection criterion. For this purpose, a more recent criterion so-called the widely Akaike information criterion (WAIC) is used which we believe that its use so limitedly in the context of relative risk modelling. In addition, a graphical method is adopted that is based on a spatial-temporal predictive posterior distribution to select the best model yielding the best predictive accuracy. By applying this model selection criterion, we seek to identify the levels of relative risk, which implicitly represents the determination of the number of the model components o