The purpose of this study is to assess the performance of government units (Iraqi hospitals) by trying to determine the possibility of the current governmental accounting system to provide information on the performance reports of government units. In order to improve the efficiency of hospital resources management, the services provided by the hospital should be subject to performance measurement and evaluation The importance of the health sector in the provision of services, in order to raise the efficiency of the performance of services provided by government units has reached the researcher a set of conclusions, the most important

1. The financial reports produced by the accounting s

This paper shows an approach for Electromyography (ECG) signal processing based on linear and nonlinear adaptive filtering using Recursive Least Square (RLS) algorithm to remove two kinds of noise that affected the ECG signal. These are the High Frequency Noise (HFN) and Low Frequency Noise (LFN). Simulation is performed in Matlab. The ECG, HFN and LFN signals used in this study were downloaded from ftp://ftp.ieee.org/uploads/press/rangayyan/, and then the filtering process was obtained by using adaptive finite impulse response (FIR) that illustrated better results than infinite impulse response (IIR) filters did.

Several Intrusion Detection Systems (IDS) have been proposed in the current decade. Most datasets which associate with intrusion detection dataset suffer from an imbalance class problem. This problem limits the performance of classifier for minority classes. This paper has presented a novel class imbalance processing technology for large scale multiclass dataset, referred to as BMCD. Our algorithm is based on adapting the Synthetic Minority Over-Sampling Technique (SMOTE) with multiclass dataset to improve the detection rate of minority classes while ensuring efficiency. In this work we have been combined five individual CICIDS2017 dataset to create one multiclass dataset which contains several types of attacks. To prove the eff

In this paper, we used four classification methods to classify objects and compareamong these methods, these are K Nearest Neighbor's (KNN), Stochastic Gradient Descentlearning (SGD), Logistic Regression Algorithm(LR), and Multi-Layer Perceptron (MLP). Weused MCOCO dataset for classification and detection the objects, these dataset image wererandomly divided into training and testing datasets at a ratio of 7:3, respectively. In randomlyselect training and testing dataset images, converted the color images to the gray level, thenenhancement these gray images using the histogram equalization method, resize (20 x 20) fordataset image. Principal component analysis (PCA) was used for feature extraction, andfinally apply four classification metho

As a result of the exacerbation of the problem of water pollution, research was directed towards studying the treatment using ceramic membranes, which proved to be highly effective in treating all water sources. The research aims to study the possibility of preparing a new type of ceramic membranes from Syrian zeolite that was not previously used in this field. In this research, ceramic membranes were prepared from Syrian raw zeolite in several stages. Zeolite sample was characterized, grinded, mixed with boric acid, pressed to form desks, treated thermally according to experiment program, finally coated with silver nanoparticles. Specifications of prepared membranes were determined according to reference methods, effectiveness of prepar

          The primary objective of this paper is to improve a biometric authentication and classification model using the ear as a distinct part of the face since it is unchanged with time and unaffected by facial expressions. The proposed model is a new scenario for enhancing ear recognition accuracy via modifying the AdaBoost algorithm to optimize adaptive learning. To overcome the limitation of image illumination, occlusion, and problems of image registration, the Scale-invariant feature transform technique was used to extract features. Various consecutive phases were used to improve classification accuracy. These phases are image acquisition, preprocessing, filtering, smoothing, and feature extraction. To assess the proposed

Modern statistical techniques offer a range of methodologies for modelling time series data, with conditional and unconditional approaches providing complementary insights that enhance overall model accuracy. This article introduced a modified ARIMA model employing conditional and unconditional parameter estimates. The methodology for the new model based on novel methods is provided. The prediction process, one and two steps ahead, is covered in detail, and a novel algorithm is presented. The best model is picked based on various measurement criteria, such as coefficient of determination (R2), root mean squared error (RMSE), and mean absolute scaled error (MASE). The suggested model is applied to a monthly petrol sales dataset (Jan