The enhancement of the information technology in many domains has had a positive impact on the healthcare sector. The ability to share medical data is one of the positive outcomes. However, this improvement comes with a number of threats. Although many threat preventive measures have been applied yet, no one can be confident that the system is safe from attacks. Thus, an algorithm needs to assess the damage occurring as a result of an attack before recovering the database. In this work, we present a distributed algorithm that uses hash tables to deal with the “information warfare” problem in healthcare systems. Hash tables astoundingly improve recovery time by delivering swift access to transactions. Moreover, the only needed data is stored within the hash table; this greatly decreases memory and resources exploitation. The proposed algorithm - implemented in Java - is compared with previous works, and demonstrated superior effects.

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The study of automatic indexing and text retrieval methods for language has a long history. Automatic indexing involves extracting words from a document to categorize it based on subject matter and to improve the information retrieval process. Despite extensive research in other languages, there remains limited investigation into automated Arabic text categorization. In this research, the researchers introduce an innovative method to enhance the accuracy of automatic indexing of Arabic texts by incorporating a thesaurus. Their approach extracts new relevant words by referencing thesaurus, which contains words, synonyms, and correlations identified through its construction using a natural language toolkit and a WordNet library.Synonyms with similar meanings that frequently appear together are grouped using a JavaScript Object Notation dictionary. The research results demonstrate a significant improvement in accuracy and efficiency compared to prior studies.