Since the COVID-19 pandemic alarm was made by the severe acute respiratory syndrome (SARS)-coronavirus (CoV) 2, several institutions and agencies have pursued to clarify the viral virulence and infectivity. The fast propagation of this virus leads to an unprecedented rise in the number of cases worldwide. COVID-19 virus is exceptionally contagious that spreads through droplets, respiratory secretions, and direct contact. The enveloped, single-stranded RNA virus has a specific envelop region called (S) region encoding (S protein) that specifically binds to the host cell receptor. Viral infection requires receptors' participation on the host cell membrane's surface, a  key- step for the viral invasion of susceptible cells.

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Background: The long term survival of dental implants is evaluated by the amount of crestal bone loss around the implants. Some initial loss of bone around dental implants is generally expected. There is reason to believe that reflecting a mucoperiosteal flap promotes crestal bone loss in the initial phase after an implant has been inserted. The surgical placement of a dental implant fixture is constantly changing and in recent years, there has been some interest in developing techniques that minimize the invasive nature of the procedure, with flapless implant surgery being advocated. The purpose of this study was to compare the radiographic level of the peri- implant bone after implant placement between traditional flapped surgery and f

The proliferation of many editing programs based on artificial intelligence techniques has contributed to the emergence of deepfake technology. Deepfakes are committed to fabricating and falsifying facts by making a person do actions or say words that he never did or said. So that developing an algorithm for deepfakes detection is very important to discriminate real from fake media. Convolutional neural networks (CNNs) are among the most complex classifiers, but choosing the nature of the data fed to these networks is extremely important. For this reason, we capture fine texture details of input data frames using 16 Gabor filters indifferent directions and then feed them to a binary CNN classifier instead of using the red-green-blue

Analyzing sentiment and emotions in Arabic texts on social networking sites has gained wide interest from researchers. It has been an active research topic in recent years due to its importance in analyzing reviewers' opinions. The Iraqi dialect is one of the Arabic dialects used in social networking sites, characterized by its complexity and, therefore, the difficulty of analyzing sentiment. This work presents a hybrid deep learning model consisting of a Convolution Neural Network (CNN) and the Gated Recurrent Units (GRU) to analyze sentiment and emotions in Iraqi texts. Three Iraqi datasets (Iraqi Arab Emotions Data Set (IAEDS), Annotated Corpus of Mesopotamian-Iraqi Dialect (ACMID), and Iraqi Arabic Dataset (IAD)) col

Some new norms need to be adapted due to COVID-19 pandemic period where people need to wear masks, wash their hands frequently, maintain social distancing, and avoid going out unless necessary. Therefore, educational institutions were closed to minimize the spread of COVID-19. As a result of this, online education was adapted to substitute face-to-face learning. Therefore, this study aimed to assess the Malaysian university students’ adaptation to the new norms, knowledge and practices toward COVID-19, besides, their attitudes toward online learning. A convenient sampling technique was used to recruit 500 Malaysian university students from January to February 2021 through social media. For data collection, all students

Early detection of brain tumors is critical for enhancing treatment options and extending patient survival. Magnetic resonance imaging (MRI) scanning gives more detailed information, such as greater contrast and clarity than any other scanning method. Manually dividing brain tumors from many MRI images collected in clinical practice for cancer diagnosis is a tough and time-consuming task. Tumors and MRI scans of the brain can be discovered using algorithms and machine learning technologies, making the process easier for doctors because MRI images can appear healthy when the person may have a tumor or be malignant. Recently, deep learning techniques based on deep convolutional neural networks have been used to analyze med

HM Al-Dabbas, RA Azeez, AE Ali, Iraqi Journal of Science, 2023

With the rapid development of smart devices, people's lives have become easier, especially for visually disabled or special-needs people. The new achievements in the fields of machine learning and deep learning let people identify and recognise the surrounding environment. In this study, the efficiency and high performance of deep learning architecture are used to build an image classification system in both indoor and outdoor environments. The proposed methodology starts with collecting two datasets (indoor and outdoor) from different separate datasets. In the second step, the collected dataset is split into training, validation, and test sets. The pre-trained GoogleNet and MobileNet-V2 models are trained using the indoor and outdoor se