Several correlations have been proposed for bubble point pressure, however, the correlations could not predict bubble point pressure accurately over the wide range of operating conditions. This study presents Artificial Neural Network (ANN) model for predicting the bubble point pressure especially for oil fields in Iraq. The most affecting parameters were used as the input layer to the network. Those were reservoir temperature, oil gravity, solution gas-oil ratio and gas relative density. The model was developed using 104 real data points collected from Iraqi reservoirs. The data was divided into two groups: the first was used to train the ANN model, and the second was used to test the model to evaluate their accuracy and trend stability

Autism Spectrum Disorder, also known as ASD, is a neurodevelopmental disease that impairs speech, social interaction, and behavior. Machine learning is a field of artificial intelligence that focuses on creating algorithms that can learn patterns and make ASD classification based on input data. The results of using machine learning algorithms to categorize ASD have been inconsistent. More research is needed to improve the accuracy of the classification of ASD. To address this, deep learning such as 1D CNN has been proposed as an alternative for the classification of ASD detection. The proposed techniques are evaluated on publicly available three different ASD datasets (children, Adults, and adolescents). Results strongly suggest that 1D

Whenever, the Internet of Things (IoT) applications and devices increased, the capability of the its access frequently stressed. That can lead a significant bottleneck problem for network performance in different layers of an end point to end point (P2P) communication route. So, an appropriate characteristic (i.e., classification) of the time changing traffic prediction has been used to solve this issue. Nevertheless, stills remain at great an open defy. Due to of the most of the presenting solutions depend on machine learning (ML) methods, that though give high calculation cost, where they are not taking into account the fine-accurately flow classification of the IoT devices is needed. Therefore, this paper presents a new model bas

Purpose: The diagnosis and determine the level of balance between the time available for life and work with the doctors in the hospitals of t the six hospitals in the City of Medicine.

Design / methodology / Approach: It has been relying on ready-scale, to make sure the diagnosis and determine the level of balance between the time available for life and work, where they were distributed on Form 42 doctors in the six hospitals in the City of Medicine, were analyzed by software (Nvivo and SPSS v.22).

Results: The results showed that there is a good level of balance between the time available for life and work with the doctors.

Research limitations: The diffi

Surgical site infections are the second most common type of adverse events occurring in hospitalized patients. Surgical antibiotic prophylaxis refers to the use of preoperative and postoperative antibiotics to decrease the incidence of postoperative wound infections. The objective of this study was to evaluate the antibiotic administration pattern for surgical antibiotic prophylaxis and the adherence to American Society of Health-System Pharmacists surgical antibiotic prophylaxis guideline in Medical City Teaching Hospitals/Baghdad. The medical records of one hundred patients who underwent elective surgical procedures were reviewed. Adherence to the recommendations of American society of health‑system pharmacists guideline was ass

Objective(s):  To find out the incidence Rate of abortions in pregnant women Admitted Maternal and pediatric Hospitals at Al-Diwaniyah City and to identify the relationship between the incidence rate of abortion and the associated risk factors that led to the occurrence of abortion.

Methodology: A descriptive study was conducted to identify the Incidence Rate of Abortions and its Associated Factors among Women at AL-Diwaniyah City’s Maternity and pediatric Hospital from 16 September 2020 to 16 March 2021 . The sample study includes (100) pregnant women with abortion out of (3800) pregnant women. The data was collected by means of a questionnaire through a personal intervie

Seven fish species were collected from the drainage network at Al-Madaen region, south of
Baghdad with the aid of a cast net during the period from March to August 1993. These fishes
were infected with 22 parasite species (seven sporozoans, three ciliated protozoans, seven
monogeneans, two nematodes, one acanthocephalan and two crustaceans) and one fungus
species. Among such parasites, Chloromyxum wardi and Cystidicola sp. are reported here for
the first time in Iraq. In addition, 11 new host records are added to the list of parasites of
fishes of Iraq.

      The service system has become a necessity of life in modern cities to be the most basic necessities of modern humans, they constitute a major base, which is based on the sustainability of life in the city and a standard measured through the degree of well-being and progress of civilized peoples and their interaction with the surrounding environment, making the services sector as a need not be an option, whenever the cities widened in population and space whenever provision of services and upgrading the quality and quantity more pressing, which made the subject of the services takes the biggest area of the trends and thinking of urban planners and those who in charge of drawing the cities policies. Consideri

Offline handwritten signature is a type of behavioral biometric-based on an image. Its problem is the accuracy of the verification because once an individual signs, he/she seldom signs the same signature. This is referred to as intra-user variability. This research aims to improve the recognition accuracy of the offline signature. The proposed method is presented by using both signature length normalization and histogram orientation gradient (HOG) for the reason of accuracy improving. In terms of verification, a deep-learning technique using a convolution neural network (CNN) is exploited for building the reference model for a future prediction. Experiments are conducted by utilizing 4,000 genuine as well as 2,000 skilled forged signatu