Background and objective: Viral Hepatitis Type B&C is serious public health challenge throughout the world.Hepatitis B and C viruses still remain to be the major causes of chronic hepatitis.It is estimated that around 350-400 million people in the world are chronic carriers of HBV, which represents approximately 7% of the total populationwhereas infection with HCV is found in approximately 3% of the world population, which represents 160 million people. Hepatitis B infection has a wide range of seroprevalence in the Mediterranean countries ranging from intermediate (=>2% ) to high prevalence ( =>7%). World Health Organization estimated a prevalence rate for HCV infection of about 4.6% in Eastern Mediterranean in 1999. During the eightieths

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

Detection and classification of animals is a major challenge that is facing the researchers. There are five classes of vertebrate animals, namely the Mammals, Amphibians, Reptiles, Birds, and Fish, and each type includes many thousands of different animals. In this paper, we propose a new model based on the training of deep convolutional neural networks (CNN) to detect and classify two classes of vertebrate animals (Mammals and Reptiles). Deep CNNs are the state of the art in image recognition and are known for their high learning capacity, accuracy, and robustness to typical object recognition challenges. The dataset of this system contains 6000 images, including 4800 images for training. The proposed algorithm was tested by using 1200

Deep learning convolution neural network has been widely used to recognize or classify voice. Various techniques have been used together with convolution neural network to prepare voice data before the training process in developing the classification model. However, not all model can produce good classification accuracy as there are many types of voice or speech. Classification of Arabic alphabet pronunciation is a one of the types of voice and accurate pronunciation is required in the learning of the Qur’an reading. Thus, the technique to process the pronunciation and training of the processed data requires specific approach. To overcome this issue, a method based on padding and deep learning convolution neural network is proposed to

Cassava, a significant crop in Africa, Asia, and South America, is a staple food for millions. However, classifying cassava species using conventional color, texture, and shape features is inefficient, as cassava leaves exhibit similarities across different types, including toxic and non-toxic varieties. This research aims to overcome the limitations of traditional classification methods by employing deep learning techniques with pre-trained AlexNet as the feature extractor to accurately classify four types of cassava: Gajah, Manggu, Kapok, and Beracun. The dataset was collected from local farms in Lamongan Indonesia. To collect images with agricultural research experts, the dataset consists of 1,400 images, and each type of cassava has

      Twitter popularity has increasingly grown in the last few years, influencing life’s social, political, and business aspects. People would leave their tweets on social media about an event, and simultaneously inquire to see other people's experiences and whether they had a positive/negative opinion about that event. Sentiment Analysis can be used to obtain this categorization. Product reviews, events, and other topics from all users that comprise unstructured text comments are gathered and categorized as good, harmful, or neutral using sentiment analysis. Such issues are called polarity classifications. This study aims to use Twitter data about OK cuisine reviews obtained from the Amazon website and compare the effectiveness

Background: Problem-based learning is becoming increasingly adopted in universities of higher education throughout the world.
Objective: This study is carried out to evaluate the application of this innovative approach in teaching in Hadramout University College of Medicine (HUCOM) in Yemen.
Methods: A cross-sectional study was designed to collect data from students through a well structured questionnaire. Students were classified into two groups: students in preclinical
phase (1-3 levels) and students in clinical phase (4-6 level). 
Results: Out of 372 students participated in the study; 19% in the preclinical phase know about PBL before joining the college while only 16% of students in clinical phas

The majority of real-world problems involve not only finding the optimal solution, but also this solution must satisfy one or more constraints. Differential evolution (DE) algorithm with constraints handling has been proposed to solve one of the most fundamental problems in cellular network design. This proposed method has been applied to solve the radio network planning (RNP) in the forthcoming 5G Long Term Evolution (5G LTE) wireless cellular network, that satisfies both deployment cost and energy savings by reducing the number of deployed micro base stations (BSs) in an area of interest. Practically, this has been implemented using constrained strategy that must guarantee good coverage for the users as well. Three differential evolution

Abstract: Stars whose initial masses are between (0.89 - 8.0) M☉ go through an Asymptotic Giant Branch (AGB) phase at the end of their life. Which have been evolved from the main sequence phase through Asymptotic Giant Branch (AGB). The calculations were done by adopted Synthetic Model showed the following results: 1- Mass loss on the AGB phase consists of two phases for period (P <500) days and for (P>500) days; 2- the mass loss rate exponentially increases with the pulsation periods; 3- The expansion velocity VAGB for our stars are calculated according to the three assumptions; 4- the terminal velocity depends on several factors likes metallicity and luminosity. The calculations indicated that a super wind phase (S.W) developed on the A