In recent years, predicting heart disease has become one of the most demanding tasks in medicine. In modern times, one person dies from heart disease every minute. Within the field of healthcare, data science is critical for analyzing large amounts of data. Because predicting heart disease is such a difficult task, it is necessary to automate the process in order to prevent the dangers connected with it and to assist health professionals in accurately and rapidly diagnosing heart disease. In this article, an efficient machine learning-based diagnosis system has been developed for the diagnosis of heart disease. The system is designed using machine learning classifiers such as Support Vector Machine (SVM), Nave Bayes (NB), and K-Ne

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

In this paper, a handwritten digit classification system is proposed based on the Discrete Wavelet Transform and Spike Neural Network. The system consists of three stages. The first stage is for preprocessing the data and the second stage is for feature extraction, which is based on Discrete Wavelet Transform (DWT). The third stage is for classification and is based on a Spiking Neural Network (SNN). To evaluate the system, two standard databases are used: the MADBase database and the MNIST database. The proposed system achieved a high classification accuracy rate with 99.1% for the MADBase database and 99.9% for the MNIST database

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

As a result of the growth of economic, demographic and building activities in Iraq, that necessitates carrying out geotechnical investigations for the dune sand to study behavior of footings resting on these soils. To determine these properties and to assess the suitability of these materials for resting shallow foundation on it, an extensive laboratory testing program was carried out. Chemical tests were carried out to evaluate any possible effects of the mineralogical composition of the soil on behavior of foundation rested on dune sands.
Collapse tests were also conducted to trace any collapse potential. Loading tests were carried out for optimum water content and different shapes of footing. Loading test recommends manufacturing o

    The present work provides a list of all amphibians and reptiles recorded from Iraq up to 2014. It includes 115 species (105 species of reptiles and 10 species of amphibians) dating back to 25 families (20 families of reptiles and 5 families of amphibians). Conservation status of each species was mentioned.

     Governmental establishments are maintaining historical data for job applicants for future analysis of predication, improvement of benefits, profits, and development of organizations and institutions. In e-government, a decision can be made about job seekers after mining in their information that will lead to a beneficial insight. This paper proposes the development and implementation of an applicant's appropriate job prediction system to suit his or her skills using web content classification algorithms (Logit Boost, j48, PART, Hoeffding Tree, Naive Bayes). Furthermore, the results of the classification algorithms are compared based on data sets called "job classification data" sets. Experimental results indicate

n this work, it have earthquakes data been analytized as a result of ground water injection attendant to dig wells and oil, which affects the pressure pores in the rock and is according to cause a medium-sized earthquake that have already occurred more than 20 times. according to the publication of the Department of Attenba, the dangers of earthquakes and movement flooring in Guy-Greenbrie-United area of one of United States of America cities, where these data represent a monitor magnitudes of earthquakes from September 2008 the September 2011, we conducted statistical analysis and create a model for earthquakes flooring that was chosen for four dis-tributions and results i the optima reveal the best distribution is the Burr X11 then Gen