OSAMA REZQ SHAHIN

The Arabic Sign Language has endorsed outstanding research achievements for identifying gestures and hand signs using the deep learning methodology. The term "forms of communication" refers to the actions used by hearing-impaired people to communicate. These actions are difficult for ordinary people to comprehend. The recognition of Arabic Sign Language (ArSL) has become a difficult study subject due to variations in Arabic Sign Language (ArSL) from one territory to another and then within states.

Significant developments occurred in numerous industries and fields during the digital age (1997–2006). One industry that has seen similar changes is the healthcare sector. Big data has primarily come from the healthcare sector since the 1990s. The outcomes of data analysis and dissemination have boosted healthcare and awareness. Perspectives and insights are the outcomes. The most important public health issue has been identified as diabetes and its repercussions. Based on patient medical imaging and records gathered, various techniques have been used to forecast diabetic complications.

With the tremendous increase in web technologies, many people are conveying and expressing their feelings through social media. These data collections, called big data, can be of high value to institutions, governments, and researchers if analyzed and interpreted as well. Most of the studies on sentiment analysis have been performed for the English language and few of these works focus on Arabic. Further, these studies rarely focus on colloquialisms, and no research was carried out on Twitter data related to Saudi universities.

As people all over the world are vulnerable to be affected by the COVID-19 virus, the automatic detection of such a virus is an important concern. The paper aims to detect and classify corona virus using machine learning. To spot and identify corona virus in CT-Lung screening and Computer-Aided diagnosis (CAD) system is projected to distinguish and classifies the COVID-19. By utilizing the clinical specimens obtained from the corona-infected patients with the help of some machine learning techniques like Decision Tree, Support Vector Machine, K-means clustering, and Radial Basis Function.

The production of olives in Saudi Arabia, which accounts for around 6% of worldwide output, is regarded as one of the best in the world. Because olive trees are rain-fed and produced using conventional methods, yields vary greatly each year, which is made worse by viral illnesses and climate change. Therefore, it is necessary to identify plant illnesses early on. Farmers diagnose plant illnesses using conventional visual assessment or laboratory analysis. Diagnosing illnesses affecting olive leaves has been improved with deep learning (DL).