Some experiments need to know the extent of their usefulness to continue providing them or not. This is done through the fuzzy regression discontinuous model, where the Epanechnikov Kernel and Triangular Kernel were used to estimate the model by generating data from the Monte Carlo experiment and comparing the results obtained. It was found that the. Epanechnikov Kernel has a least mean squared error.

Voice over Internet Protocol (VoIP) is important technology that’s rapidly growing in the wireless networks. The Quality of Service (QoS) and Capacity are two of the most important issues that still need to be researched on wireless VoIP. The main aim of this paper is to analysis the performance of the VoIP application in wireless networks, with respect to different transport layer protocols and audio codec. Two scenarios used in the simulation stage. In the first scenario VoIP with codec G.711 transmitted over User Datagram Protocol (UDP), Stream Control Transmission Protocol (SCTP), and Real-Time Transport Protocol (RTP). While, in the second scenario VoIP with codec G.726 transmitted over UDP, SCTP, and RTP protocols. Network simulator

 This paper argues the accuracy of behavior based detection systems, in which the Application Programming Interfaces (API) calls are analyzed and monitored. The work identifies the problems that affecting the accuracy of such detection models. The work was extracted (4744) API call through analyzing. The new approach provides an accurate discriminator and can reveal malicious API in PE malware up to 83.2%. Results of this work evaluated with Discriminant Analysis

    A Simple, rapid and sensitive extractive and spectrophotometric method has been described for the analysis of diphenhyldramine –HCl (DPH) in pure form and in pharmaceutical formulations. The method is based on the formation of chloroform soluble ion-pair  complex with Bromophenol blue(BPB) in a phthalate buffer at pH 3.0.The extracted complex shows maximum absorbance at 410 nm. Beer's law is obeyed in the concentration range 0.2-25.0 µg.ml-1. The molar absorptivity and Sandell's sensitivity for the system being  2.416x104 L.mol-1.cm-1 and 0.012µg.cm-2, respectively. The limit of detection was found to be 0.155 µg.ml-1.           The proposed me

Its well known that understanding human facial expressions is a key component in understanding emotions and finds broad applications in the field of human-computer interaction (HCI), has been a long-standing issue. In this paper, we shed light on the utilisation of a deep convolutional neural network (DCNN) for facial emotion recognition from videos using the TensorFlow machine-learning library from Google. This work was applied to ten emotions from the Amsterdam Dynamic Facial Expression Set-Bath Intensity Variations (ADFES-BIV) dataset and tested using two datasets.

The aim of this paper is to design fast neural networks to approximate periodic functions, that is, design a fully connected networks contains links between all nodes in adjacent layers which can speed up the approximation times, reduce approximation failures, and increase possibility of obtaining the globally optimal approximation. We training suggested network by Levenberg-Marquardt training algorithm then speeding suggested networks by choosing most activation function (transfer function) which having a very fast convergence rate for reasonable size networks.             In all algorithms, the gradient of the performance function (energy function) is used to determine how to

Abstract

          Binary logistic regression model used in data classification and it is the strongest most flexible tool in study cases variable response binary when compared to linear regression. In this research, some classic methods were used to estimate parameters binary logistic regression model, included the maximum likelihood method, minimum chi-square method, weighted least squares, with bayes estimation , to choose the best method of estimation by default values to estimate parameters according two different models of general linear regression models ,and different s