https://plomscience.com/journals/index.php/PLOMSAI <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> PLOMS en-US PLOMS AI <p><strong>PLOMS Journals Copyright Statement</strong></p> <p><strong>PLOMS LLC</strong>. grants you a non-exclusive, royalty-free, revocable license to: </p> <ul> <li>Academic Journals licenses all works published under the Creative Commons Attribution 4.0 International License. This license grants anybody the right to reproduce, redistribute, remix, transmit, and modify the work, as long as the original work and source are properly cited.</li> <li>PLOMS LLC. grants you no further rights in respect to this website or its content. </li> </ul> <p>Without the prior consent of PLOMS LLC, this website and its content (in any form or medium) may not be changed or converted in any manner. To avoid doubt, you must not modify, edit, alter, convert, publish, republish, distribute, redistribute, broadcast, rebroadcast, display, or play in public any of the content on this website (in any form or medium) without PLOMS LLC's prior written approval.</p> <p><strong>Permissions</strong></p> <p>Permission to use the copyright content on this website may be obtained by emailing to: </p> <p> <strong>[email protected].</strong></p> <p>PLOMS LLC. takes copyright protection very seriously. If PLOMS LLC. discovers that you have violated the license above by using its copyright materials, PLOMS LLC. may pursue legal action against you, demanding monetary penalties and an injunction to prevent you from using such materials. Additionally, you may be required to pay legal fees.</p> <p>If you become aware of any unauthorized use of PLOMS LLC. copyright content that violates or may violate the license above, please contact :</p> <p><strong>[email protected].</strong></p> <p><strong>Infringing content</strong></p> <p>If you become aware of any content on the website that you feel violates your or another person's copyright, please notify <strong>[email protected]</strong>.</p> https://plomscience.com/journals/index.php/PLOMSAI/article/view/21 <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> Sahar K. Hussin Omar Mohamed Mustafa Mohamed Eslam Ahmed Omar Mahmoud Copyright (c) 2024 PLOMS AI https://creativecommons.org/licenses/by-nc/4.0 2024-05-07 2024-05-07 4 1