Khalid M. O. Nahar¹, Ammar Almomani^2,3,*, Nahlah Shatnawi¹, Mohammad Alauthman⁴

Intelligent Automation & Soft Computing, Vol.37, No.2, pp. 2037-2057, 2023, DOI:10.32604/iasc.2023.038235

Abstract This study presents a novel and innovative approach to automatically translating Arabic Sign Language (ATSL) into spoken Arabic. The proposed solution utilizes a deep learning-based classification approach and the transfer learning technique to retrain 12 image recognition models. The image-based translation method maps sign language gestures to corresponding letters or words using distance measures and classification as a machine learning technique. The results show that the proposed model is more accurate and faster than traditional image-based models in classifying Arabic-language signs, with a translation accuracy of 93.7%. This research makes a significant contribution to the field of ATSL. It offers… More >

Ayesha Siddiqa¹, Syed Abbas Zilqurnain Naqvi¹, Muhammad Ahsan¹, Allah Ditta², Hani Alquhayz³, M. A. Khan⁴, Muhammad Adnan Khan^5,*

CMC-Computers, Materials & Continua, Vol.70, No.3, pp. 5519-5536, 2022, DOI:10.32604/cmc.2022.021666

Abstract Due to unforeseen climate change, complicated chronic diseases, and mutation of viruses’ hospital administration’s top challenge is to know about the Length of stay (LOS) of different diseased patients in the hospitals. Hospital management does not exactly know when the existing patient leaves the hospital; this information could be crucial for hospital management. It could allow them to take more patients for admission. As a result, hospitals face many problems managing available resources and new patients in getting entries for their prompt treatment. Therefore, a robust model needs to be designed to help hospital administration predict patients’ LOS to resolve… More >