Patients infected with the COVID-19 virus develop severe pneumonia, which typically results in death. Radiological data show that the disease involves interstitial lung involvement, lung opacities, bilateral ground-glass opacities, and patchy opacities. This study aimed to improve COVID-19 diagnosis via radiological chest X-ray (CXR) image analysis, making a substantial contribution to the development of a mobile application that efficiently identifies COVID-19, saving medical professionals time and resources. It also allows for timely preventative interventions by using more than 18000 CXR lung images and the MobileNetV2 convolutional neural network (CNN) architecture. The MobileNetV2 deep-learning model performances were evaluated

The main reason for the emergence of a deepfake (deep learning and fake) term is the evolution in artificial intelligence techniques, especially deep learning. Deep learning algorithms, which auto-solve problems when giving large sets of data, are used to swap faces in digital media to create fake media with a realistic appearance. To increase the accuracy of distinguishing a real video from fake one, a new model has been developed based on deep learning and noise residuals. By using Steganalysis Rich Model (SRM) filters, we can gather a low-level noise map that is used as input to a light Convolution neural network (CNN) to classify a real face from fake one. The results of our work show that the training accuracy of the CNN model

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

Language is an important means through which one can construct one's social world. Accordingly, the way we view ourselves and the world is basically formed by language use whereby identities, relations, and values are constructed and maintained. Most discourse analysts consider narrative not only the locus of construction and enactment of identity, but also a distinguished genre for its analysis.The present study is concerned with how identity can poetically be informed, hence exploring the way black poets use language when reflecting their identity and culture. The poem, right on: white america by the black American poetess Sonia Sanchez, is chosen to be analyzed based on Simpson's stylistic model (2004). In this model, the ana

The research aims to study the elements of attraction in television advertising and determine their relationship to consumer preferences, the extent to which the sample (250) watches television advertisements, and the motives for viewing. Males and females, and the statistical program spss was used to analyze the results using percentages, arithmetic mean, correlation coefficients ch2, Pearson’s coefficient and Alpha Cronbach for the stability of the test, and the research came to the conclusion that the percentage of watching television advertisements is good motivated by the identification of new goods and services in the market, as advertisements sometimes achieve consumer preferences, and the most prominent The forms that a

Recurrent strokes can be devastating, often resulting in severe disability or death. However, nearly 90% of the causes of recurrent stroke are modifiable, which means recurrent strokes can be averted by controlling risk factors, which are mainly behavioral and metabolic in nature. Thus, it shows that from the previous works that recurrent stroke prediction model could help in minimizing the possibility of getting recurrent stroke. Previous works have shown promising results in predicting first-time stroke cases with machine learning approaches. However, there are limited works on recurrent stroke prediction using machine learning methods. Hence, this work is proposed to perform an empirical analysis and to investigate machine learning al

The deep learning algorithm has recently achieved a lot of success, especially in the field of computer vision. This research aims to describe the classification method applied to the dataset of multiple types of images (Synthetic Aperture Radar (SAR) images and non-SAR images). In such a classification, transfer learning was used followed by fine-tuning methods. Besides, pre-trained architectures were used on the known image database ImageNet. The model VGG16 was indeed used as a feature extractor and a new classifier was trained based on extracted features.The input data mainly focused on the dataset consist of five classes including the SAR images class (houses) and the non-SAR images classes (Cats, Dogs, Horses, and Humans). The Conv

The sound effects in TV dramas achievements have become very important not only in terms of function and implementation, but at a greater and wider level in terms of artistic and aesthetic values, which are produced and employed in the most important world artistic achievements of drama, using the latest and most prominent technologies and equipment and according to the expressive and dramatic values expressed by these modern digital sound effects. Therefore, the researcher chose the aesthetic effect of digital sound effects in television drama to identify the aesthetic aspects provided by digital sound effects by employing them and their accompaniment for the image.

The researcher, therefor, divided this study into the methodolo