<span>Blood donation is the main source of blood resources in the blood banks which is required in the hospitals for everyday operations and blood compensation for the patients. In special cases, the patients require fresh blood for compensation such as in the case of major operations and similar situations. Moreover, plasma transfusions are vital in the current pandemic of coronavirus disease (COVID-19). In this paper, we have proposed a donation system that manages the appointments between the donors and the patient in the case of fresh blood donation is required. The website is designed using the Bootstrap technology to provide suitable access using the PC or the smart phones web browser. The website contains large database

In data mining, classification is a form of data analysis that can be used to extract models describing important data classes. Two of the well known algorithms used in data mining classification are Backpropagation Neural Network (BNN) and Naïve Bayesian (NB). This paper investigates the performance of these two classification methods using the Car Evaluation dataset. Two models were built for both algorithms and the results were compared. Our experimental results indicated that the BNN classifier yield higher accuracy as compared to the NB classifier but it is less efficient because it is time-consuming and difficult to analyze due to its black-box implementation.

The study deals with an analysis of the contents of the publications of the campaign (Together to defeat Corona), which was established by the United Nations Development Program in Iraq in the face of the Covid 19 virus.The research problem raises a main question:What are the implications of the campaign (Together to defeat Corona) of the United Nations Development Program (Iraq office) in addressing the Covid-19 virus in Iraq?From this main question, several sub-questions emerged, which were answered by this study in its chapters and investigations, including regarding the contents of advertisements, photos and videos for the publications of the (Together to Defeat Corona) campaign for the United Nations Iraq Office on their Facebook pageA

Attacking a transferred data over a network is frequently happened millions time a day. To address this problem, a secure scheme is proposed which is securing a transferred data over a network. The proposed scheme uses two techniques to guarantee a secure transferring for a message. The message is encrypted as a first step, and then it is hided in a video cover.  The proposed encrypting technique is RC4 stream cipher algorithm in order to increase the message's confidentiality, as well as improving the least significant bit embedding algorithm (LSB) by adding an additional layer of security. The improvement of the LSB method comes by replacing the adopted sequential selection by a random selection manner of the frames and the pixels wit

Vaccination is a vital cornerstone of public health, which has saved countless lives throughout history. Therefore, achieving high vaccination uptake rates is essential for successful vaccination programs. Unfortunately, vaccine uptake has been hindered by deferent factors and challenges. The objective of this study is to assess COVID-19 vaccine uptake and associated factors among the general population.

Coronavirus diseases 2021 (COVID-19) on going situation in Iraq is characterized in this paper. The pandemic handling by the government and the difficulties of public health measures enforcement in Iraq. Estimation of the COVID-19 data set was performed. Iraq is endangered to the pandemic, like the rest of the world besides sharing borders with hotspot neighbouring country Iran. The government of Iraq launched proactive measures in an attempt to prevent the viral spread. Nevertheless, reports of new cases keep escalating leaving the public health officials racing to take more firm constriction to face the pandemic. The paper bring forth the current COVID-19 scenario in Iraq, the government measures towards the public health challenges, and

An Intelligent Internet of Things network based on an Artificial Intelligent System, can substantially control and reduce the congestion effects in the network. In this paper, an artificial intelligent system is proposed for eliminating the congestion effects in traffic load in an Intelligent Internet of Things network based on a deep learning Convolutional Recurrent Neural Network with a modified Element-wise Attention Gate. The invisible layer of the modified Element-wise Attention Gate structure has self-feedback to increase its long short-term memory. The artificial intelligent system is implemented for next step ahead traffic estimation and clustering the network. In the proposed architecture, each sensing node is adaptive and able to