https://plomscience.com/journals/index.php/PLOMSAI/issue/feed 2024-05-07T18:45:01-07:00 PLOMS AI [email protected] Open Journal Systems <p><strong>PLOMS AI </strong>is a comprehensive journal aiming to proceed science-society interactions . The open access strategy offers increased vulnerability of the research and help in dissemination of research results, as well. We believe that all accurate scientific results have to be published and disseminated by being freely accessible to all.<br /><br /><strong>PLOMS AI</strong> accepts research in areas related to Artificial Intelligence, Computational Intelligence, and bio inspired related areas. The submitted manuscripts are evaluated on the basis of high ethical standards, accurate methodology, scientific and perceived novelty.<br /><br /><strong>Types of articles:</strong><br /><strong>Original research</strong> that contributes to the base of scientific knowledge <br /><strong>Systematic reviews </strong>whose methods ensure the comprehensive and unbiased sampling of existing literature.<br /><strong>Qualitative research</strong> that adheres to appropriate study design and reporting guidelines.<br /><strong>Other submissions</strong> that describes methods, software, databases, or other tools that if they follow the appropriate reporting guidelines. accepts research in areas related to Artificial Intelligence, Computational Intelligence, and bio inspired related areas. The submitted manuscripts are evaluated on the basis of high ethical standards, accurate methodology, scientific and perceived novelty.</p> https://plomscience.com/journals/index.php/PLOMSAI/article/view/21 2024-05-07T18:45:01-07:00 Sahar K. Hussin [email protected] Omar Mohamed [email protected] Mustafa Mohamed [email protected] Eslam Ahmed [email protected] Omar Mahmoud [email protected]

<p>People who are dumb and deaf have difficulty communicating daily. Artificial intelligence (AI) developments have allowed the breaking down of this communication barrier. As a result of this work, an Arabic sign language (ASLT) letter recognition system has been created. The ASLT recognition system employs BILSTM with a media pipe structure to interpret depth data and enhance the social interaction of hearing-impaired people. Depending on user input, the suggested approach would automatically detect and identify Arabic and hand-sign alphabet letters. The proposed model should have a 98% accuracy rate in identifying ASLT for letters, 96% for words, and 100% for digits from 0 to 9. We conducted comparative research to evaluate our method, and the results showed that it is more accurate at differentiating between static signs than earlier studies using the same dataset.</p>

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