Voice studies are one of the sensory studies of their first association with auditory taste, which broadcasts its connection to the most sensory point contained in the physical corners, and in order to satisfy our conscience from these fundamental voices, we searched for in modern contexts, high proportions related to the words of Muhammadiyah, so we are looking for (events In the hadiths of the Prophet (peace be upon him), to settle our journey when the true adhkaar received from the Prophet, peace be upon him, and to control us start a journey through which we repeat between the cities of audio repetitions, sometimes we find ourselves have stood at the entrance of what corresponds to the sounds between the title of the Hadith and its t

The research tackles an important subject, namely, the light text and how it works well in the Arab television programs. The methodological framework of the research     presents the research problem stated in the following question: How can the text be used and what is its impact in the Arab TV programs? The importance of this research is that it deals with the subject of light text and its impact on Arab television programs.

This study is useful to the workers and scholars in the field of lighting as well as the goal of the research in (studying of the employment of light text in Arab television programs).

The limits of research were manifested in the study of the light text and how to make use of it

In the current study, a novel approach for separating ethanol-water mixture by microbubble distillation technology was investigated. Traditional distillation processes require large amounts of energy to raise the liquid to its boiling point to effect removal of volatile components. The concept of microbubble distillation by comparison is to heat the gas phase rather than the liquid phase to achieve separation. The removal of ethanol from the thermally sensitive fermentation broths was taken as a case of study. Consequently the results were then compared with those which could be obtained under equilibrium conditions expected in an “ideal” distillation unit. Microbubble distillation has achieved vapour compositions higher than th

After the outbreak of COVID-19, immediately it converted from epidemic to pandemic. Radiologic images of CT and X-ray have been widely used to detect COVID-19 disease through observing infrahilar opacity in the lungs. Deep learning has gained popularity in diagnosing many health diseases including COVID-19 and its rapid spreading necessitates the adoption of deep learning in identifying COVID-19 cases. In this study, a deep learning model, based on some principles has been proposed for automatic detection of COVID-19 from X-ray images. The SimpNet architecture has been adopted in our study and trained with X-ray images. The model was evaluated on both binary (COVID-19 and No-findings) classification and multi-class (COVID-19, No-findings

 An oil spill is a leakage of pipelines, vessels, oil rigs, or tankers that leads to the release of petroleum products into the marine environment or on land that happened naturally or due to human action, which resulted in severe damages and financial loss. Satellite imagery is one of the powerful tools currently utilized for capturing and getting vital information from the Earth's surface. But the complexity and the vast amount of data make it challenging and time-consuming for humans to process. However, with the advancement of deep learning techniques, the processes are now computerized for finding vital information using real-time satellite images. This paper applied three deep-learning algorithms for satellite image classification

Optimum perforation location selection is an important study to improve well production and hence in the reservoir development process, especially for unconventional high-pressure formations such as the formations under study. Reservoir geomechanics is one of the key factors to find optimal perforation location. This study aims to detect optimum perforation location by investigating the changes in geomechanical properties and wellbore stress for high-pressure formations and studying the difference in different stress type behaviors between normal and abnormal formations. The calculations are achieved by building one-dimensional mechanical earth model using the data of four deep abnormal wells located in Southern Iraqi oil fields. The magni

Lung cancer is the most common dangerous disease that, if treated late, can lead to death. It is more likely to be treated if successfully discovered at an early stage before it worsens. Distinguishing the size, shape, and location of lymphatic nodes can identify the spread of the disease around these nodes. Thus, identifying lung cancer at the early stage is remarkably helpful for doctors. Lung cancer can be diagnosed successfully by expert doctors; however, their limited experience may lead to misdiagnosis and cause medical issues in patients. In the line of computer-assisted systems, many methods and strategies can be used to predict the cancer malignancy level that plays a significant role to provide precise abnormality detectio

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

Breast cancer is a heterogeneous disease characterized by molecular complexity. This research utilized three genetic expression profiles—gene expression, deoxyribonucleic acid (DNA) methylation, and micro ribonucleic acid (miRNA) expression—to deepen the understanding of breast cancer biology and contribute to the development of a reliable survival rate prediction model. During the preprocessing phase, principal component analysis (PCA) was applied to reduce the dimensionality of each dataset before computing consensus features across the three omics datasets. By integrating these datasets with the consensus features, the model's ability to uncover deep connections within the data was significantly improved. The proposed multimodal deep