Diagnosing heart disease has become a very important topic for researchers specializing in artificial intelligence, because intelligence is involved in most diseases, especially after the Corona pandemic, which forced the world to turn to intelligence. Therefore, the basic idea in this research was to shed light on the diagnosis of heart diseases by relying on deep learning of a pre-trained model (Efficient b3) under the premise of using the electrical signals of the electrocardiogram and resample the signal in order to introduce it to the neural network with only trimming processing operations because it is an electrical signal whose parameters cannot be changed. The data set (China Physiological Signal Challenge -cspsc2018) was ad

Historical cultural environments are a repository of values and symbols that pass down across generations through spatial experiences. Despite their intellectual and cultural potential, their role in fostering belonging and identity has declined; they are often viewed as silent landmarks, isolated from lived experiences. This highlights the need for an integrated model that makes spatial experience a stimulating process for reinvigorating the meaning inherent in historical contexts and reconnecting the new generation with their cultural roots. This research aims to explore how cultural meaning in historical contexts can be reactivated through spatial experience. To achieve this, the study proposes a triadic model – physical encoun

Cervical carcinoma represent the second predominant cancer in female and there is a strong correlation between cervical cancer and the infection with high-risk types of HPV and expression the viral oncogenes. EMT is viewed as a vital advance in carcinoma development and ensuing metastasis. To evaluate correlation between the expression of Twist and HPV16 infection in a group of Iraqi patients with cervical carcinoma. A total of forty paraffin blocks included in this study which were divided into 30 sample of cervical cancer infected with HPV16and 10 sample of normal cervical tissues. The samples were subjected to immunohistochemical technique using Anti-Twist2 polyclonal antibody. The obtained data from this study indicate that majority of

Permeability estimation is a vital step in reservoir engineering due to its effect on reservoir's characterization, planning for perforations, and economic efficiency of the reservoirs. The core and well-logging data are the main sources of permeability measuring and calculating respectively. There are multiple methods to predict permeability such as classic, empirical, and geostatistical methods. In this research, two statistical approaches have been applied and compared for permeability prediction: Multiple Linear Regression and Random Forest, given the (M) reservoir interval in the (BH) Oil Field in the northern part of Iraq. The dataset was separated into two subsets: Training and Testing in order to cross-validate the accuracy

Objective: The aims of present study to detect the effectiveness of instruction program of non-pharmacological guideline on blood pressure and laboratory test.

Methodology: A pre-experimental study was conducted in Al-Sader Teaching Hospital   from 8^th of September 2019 to 25^th of May 2020, in order to find out the effectiveness of instruction program concerning non-pharmacological guideline on controlling essential hypertension among patients. A non- probability (purposive sample) of 50 patients with essential hypertension is selected. Those patients are already diagnosed with Essential Hypertension

With the great development in the field of the Internet, the talk about the new media and its implications began, And its interactive services have made the future of media material sometimes participating in it and manufacturing it at other times,
the public is seeking information and choosing the appropriate ones, as well as exchanging messages with the sender after what the role of the receiver is just receiving information only.
This study aims to demonstrate the effects of using digital media in various forms and types to construct the value system of Iraqi society through the identification of the following aims:
Identify the most popular digital media for the Iraqi public in their daily lives on the Internet.
Identify

A three-stage learning algorithm for deep multilayer perceptron (DMLP) with effective weight initialisation based on sparse auto-encoder is proposed in this paper, which aims to overcome difficulties in training deep neural networks with limited training data in high-dimensional feature space. At the first stage, unsupervised learning is adopted using sparse auto-encoder to obtain the initial weights of the feature extraction layers of the DMLP. At the second stage, error back-propagation is used to train the DMLP by fixing the weights obtained at the first stage for its feature extraction layers. At the third stage, all the weights of the DMLP obtained at the second stage are refined by error back-propagation. Network structures an