Abstract

          The  cultural contrast is considered of the essential subjects in the field of administration of international businesses which have great and effective role for representation in one social system for the individuals have various belongings, and the nursing performance is considered  an essential basic for health care, thus  a discussion for two variables have  done to formulate the intellectual frame for this research which are( the cultural contrast and the nursing performance). depended a questionnaire as a research tool by(90) he/ she nurses of the Iraqis and the arrivals( the

 To expedite the learning process, a group of algorithms known as parallel machine learning algorithmscan be executed simultaneously on several computers or processors. As data grows in both size andcomplexity, and as businesses seek efficient ways to mine that data for insights, algorithms like thesewill become increasingly crucial. Data parallelism, model parallelism, and hybrid techniques are justsome of the methods described in this article for speeding up machine learning algorithms. We alsocover the benefits and threats associated with parallel machine learning, such as data splitting,communication, and scalability. We compare how well various methods perform on a variety ofmachine learning tasks and datasets, and we talk abo

<p>Combating the COVID-19 epidemic has emerged as one of the most promising healthcare the world's challenges have ever seen. COVID-19 cases must be accurately and quickly diagnosed to receive proper medical treatment and limit the pandemic. Imaging approaches for chest radiography have been proven in order to be more successful in detecting coronavirus than the (RT-PCR) approach. Transfer knowledge is more suited to categorize patterns in medical pictures since the number of available medical images is limited. This paper illustrates a convolutional neural network (CNN) and recurrent neural network (RNN) hybrid architecture for the diagnosis of COVID-19 from chest X-rays. The deep transfer methods used were VGG19, DenseNet121

Botnet detection develops a challenging problem in numerous fields such as order, cybersecurity, law, finance, healthcare, and so on. The botnet signifies the group of co-operated Internet connected devices controlled by cyber criminals for starting co-ordinated attacks and applying various malicious events. While the botnet is seamlessly dynamic with developing counter-measures projected by both network and host-based detection techniques, the convention techniques are failed to attain sufficient safety to botnet threats. Thus, machine learning approaches are established for detecting and classifying botnets for cybersecurity. This article presents a novel dragonfly algorithm with multi-class support vector machines enabled botnet

     The research aimed to identify the extent to which the media offices in the ministries depend on the website of the Iraqi Council of Ministers as a source of information. Such goal includes a set of sub-goals: Knowing the reasons for media offices' reliance on the information provided by the Cabinet's website; and revealing the benefit derived from relying on the Cabinet's website.

The researcher used the survey method to screen and comprehend the extent to which the media offices in the ministries depend on the website of the Iraqi Council of Ministers as a source of information.

The mechanism of comprehensive account of the limits of research wa

Diabetes is one of the increasing chronic diseases, affecting millions of people around the earth. Diabetes diagnosis, its prediction, proper cure, and management are compulsory. Machine learning-based prediction techniques for diabetes data analysis can help in the early detection and prediction of the disease and its consequences such as hypo/hyperglycemia. In this paper, we explored the diabetes dataset collected from the medical records of one thousand Iraqi patients. We applied three classifiers, the multilayer perceptron, the KNN and the Random Forest. We involved two experiments: the first experiment used all 12 features of the dataset. The Random Forest outperforms others with 98.8% accuracy. The second experiment used only five att

A collection of 118 specimens of Iraqi phasianid birds belong to four species was examined
for haematozoa. Results show that 21.2% of them were infected with one or more of four
species of blood parasites; Haemoproteus danilewskyi, H. santosdiasi, Plasmodium sp. and
microfilaria. Haemoproteus danilewskyi is reported here for the first time in Iraq.

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

Deep Learning Techniques For Skull Stripping of Brain MR Images