Browsing by Author "Abdulkader, Sarah N."

Filter results by typing the first few letters
Now showing 1 - 2 of 2
  • Thumbnail Image
    Item
    Brain computer interfacing: Applications and
    (Elsevier, 2015-07) Mostafa, Mostafa-Sami M.; Atia, Ayman; Abdulkader, Sarah N.
    Brain computer interface technology represents a highly growing field of research with application systems. Its contributions in medical fields range from prevention to neuronal rehabil- itation for serious injuries. Mind reading and remote communication have their unique fingerprint in numerous fields such as educational, self-regulation, production, marketing, security as well as games and entertainment. It creates a mutual understanding between users and the surrounding sys- tems. This paper shows the application areas that could benefit from brain waves in facilitating or achieving their goals. We also discuss major usability and technical challenges that face brain sig- nals utilization in various components of BCI system. Different solutions that aim to limit and decrease their effects have also been reviewed. - 2015 Production and hosting by Elsevier B.V. on behalf of Faculty of Computers and Information,
  • Thumbnail Image
    Item
    Real-time recognition of American sign language using long- short term memory neural network and hand detection
    (Institute of Advanced Engineering and Science (IAES), 2023-01) Abdulhamied, Reham Mohamed; Nasr, Mona M.; Abdulkader, Sarah N.
    Sign language recognition is very important for deaf and mute people because it has many facilities for them, it converts hand gestures into text or speech. It also helps deaf and mute people to communicate and express mutual feelings. This paper's goal is to estimate sign language using action detection by predicting what action is being demonstrated at any given time without forcing the user to wear any external devices. We captured user signs with a webcam. For example; if we signed “thank you”, it will take the entire set of frames for that action to determine what sign is being demonstrated. The long short-term memory (LSTM) model is used to produce a real-time sign language detection and prediction flow. We also applied dropout layers for both training and testing dataset to handle overfitting in deep learning models which made a good improvement for the final result accuracy. We achieved a 99.35% accuracy after training and implementing the model which allows the deaf and mute communicate more easily with society