Enhanced Arabic disaster data classification using domain adaptation.

Natural disasters, like pandemics and earthquakes, are some of the main causes of distress and casualties. Governmental crisis management processes are crucial when dealing with these types of problems. Social media platforms are among the main sources of information regarding current events and public opinion.

Improving neural machine translation for low resource languages through non-parallel corpora: a case study of Egyptian dialect to modern standard Arabic translation.

Machine translation for low-resource languages poses significant challenges, primarily due to the limited availability of data. In recent years, unsupervised learning has emerged as a promising approach to overcome this issue by aiming to learn translations between languages without depending on parallel data. A wide range of methods have been proposed in the literature to address this complex problem.

Semantic textual similarity for modern standard and dialectal Arabic using transfer learning.

Semantic Textual Similarity (STS) is the task of identifying the semantic correlation between two sentences of the same or different languages. STS is an important task in natural language processing because it has many applications in different domains such as information retrieval, machine translation, plagiarism detection, document categorization, semantic search, and conversational systems. The availability of STS training and evaluation data resources for some languages such as English has led to good performance systems that achieve above 80% correlation with human judgment.

Integrated clinical and specialty pharmacy practice model for management of patients with multiple sclerosis.

Purpose: An integrated clinical and specialty pharmacy practice model for the management of patients with multiple sclerosis (MS) is described.

Summary: Specialty medications, such as disease-modifying therapies (DMTs) used to treat MS, are costly and typically require special administration, handling, and storage. DMTs are associated with high rates of nonadherence and may have associated safety risks.