In recent years, with the growing size and the importance of computer networks, it is very necessary to provide adequate protection for users data from snooping through the use of one of the protection techniques: encryption, firewall and intrusion detection systems etc. Intrusion detection systems is considered one of the most important components in the computer networks that deal with Network security problems. In this research, we suggested the intrusion detection and classification system through merging Fuzzy logic and Artificial Bee Colony Algorithm. Fuzzy logic has been used to build a classifier which has the ability to distinguish between the behavior of the normal user and behavior of the intruder. The artificial bee colony al

This paper focuses on developing a self-starting numerical approach that can be used for direct integration of higher-order initial value problems of Ordinary Differential Equations. The method is derived from power series approximation with the resulting equations discretized at the selected grid and off-grid points. The method is applied in a block-by-block approach as a numerical integrator of higher-order initial value problems. The basic properties of the block method are investigated to authenticate its performance and then implemented with some tested experiments to validate the accuracy and convergence of the method.

    Semantic segmentation is effective in numerous object classification tasks such as autonomous vehicles and scene understanding. With the advent in the deep learning domain, lots of efforts are seen in applying deep learning algorithms for semantic segmentation. Most of the algorithms gain the required accuracy while compromising on their storage and computational requirements. The work showcases the implementation of Convolutional Neural Network (CNN) using Discrete Cosine Transform (DCT), where DCT exhibit exceptional energy compaction properties. The proposed Adaptive Weight Wiener Filter (AWWF) rearranges the DCT coefficients by truncating the high frequency coefficients. AWWF-DCT model reinstate the convolutional l

The majority of systems dealing with natural language processing (NLP) and artificial intelligence (AI) can assist in making automated and automatically-supported decisions. However, these systems may face challenges and difficulties or find it confusing to identify the required information (characterization) for eliciting a decision by extracting or summarizing relevant information from large text documents or colossal content.   When obtaining these documents online, for instance from social networking or social media, these sites undergo a remarkable increase in the textual content. The main objective of the present study is to conduct a survey and show the latest developments about the implementation of text-mining techniqu

This paper proposes improving the structure of the neural controller based on the identification model for nonlinear systems. The goal of this work is to employ the structure of the Modified Elman Neural Network (MENN) model into the NARMA-L2 structure instead of Multi-Layer Perceptron (MLP) model in order to construct a new hybrid neural structure that can be used as an identifier model and a nonlinear controller for the SISO linear or nonlinear systems. Two learning algorithms are used to adjust the parameters weight of the hybrid neural structure with its serial-parallel configuration; the first one is supervised learning algorithm based Back Propagation Algorithm (BPA) and the second one is an intelligent algorithm n

In this paper we have studied the optical properties of CuBr thin

films.  Different  sample  thicknesses  have  been  prepared  by  using thermal evaporation  technique with 14.4 runlsec as the average deposition rate and 1 00°C as the substrate temperature.

Thin films whose compositions can be expressed by (GeS₂)_100-xGa_x (x=0, 6,12,18) formula were obtained by thermal evaporation technique  of bulk material at a base pressure of ~10^-5 torr. Optical transmission spectra of the films were taken in the range of 300-1100 nm then the optical band gap, tail width of localized states,  refractive index, extinction coefficient were calculated. The optical constants were found to increase at low concentration of Ga (0 to12%) while they decreases with further addition of Ga. The optical band gap was found to change in opposite manner to that of optical constants. The variation in the optical parameters are explained in terms of average bond energy

Objectives To compare the clinical efficacy of microvascular decompression surgery (MVD) and gamma knife radiosurgery (GKR) as a treatment for patients with primary trigeminal neuralgia (TN) and evaluate the outcome regarding pain relief, recurrence, and complications with both modalities of treatment. Patients and Methods A randomized prospective study conducted in SaadAlwitry Neurosciences Hospital, Baghdad, Iraq. Eighty-four patients with TN from January 2016 to January 2018, 45 patients had GKR while 39 patients treated with MVD. The pain evaluated pre-and post-operatively using the Barrow Neurological Institute Pain Intensity scale (BNIPI), visual analog scale (VAS) and Brief Pain Inventory Facial (BPI-Facial) scoring systems. In GKR p

Compression is the reduction in size of data in order to save space or transmission time. For data transmission, compression can be performed on just the data content or on the entire transmission unit (including header data) depending on a number of factors. In this study, we considered the application of an audio compression method by using text coding where audio compression represented via convert audio file to text file for reducing the time to data transfer by communication channel. Approach: we proposed two coding methods are applied to optimizing the solution by using CFG. Results: we test our application by using 4-bit coding algorithm the results of this method show not satisfy then we proposed a new approach to compress audio fil