Browsing by Author "Hassen H."

Now showing items 1-5 of 5

    • Arabic Bank Cheque Words Recognition Using Gabor Features 

      Al-Nuzaili Q.; Al-Maadeed S.; Hassen H.; Hamdi A. ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      Arabic cheque processing is one of the important applications of handwriting recognition. The recognition of Arabic Cheque bank is still awaiting lots of work in its constituent stages, which include pre-processing, feature ...

    • Arabic handwriting recognition using sequential minimal optimization 

      Hassen H.; Al-Maadeed, Somaya ( Institute of Electrical and Electronics Engineers Inc. , 2017 , Conference Paper)
      Due to the variability of writing styles and to other problems related to the nature of Arabic scripts, the recognition of Arabic handwriting is still awaiting accurate results. Segmentation of Arabic handwritten words ...

    • COVID-19 lesion segmentation using lung CT scan images: Comparative study based on active contour models 

      Akbari Y.; Hassen H.; Al-Maadeed, Somaya; Zughaier S.M. ( MDPI , 2021 , Article)
      Pneumonia is a lung infection that threatens all age groups. In this paper, we use CT scans to investigate the effectiveness of active contour models (ACMs) for segmentation of pneumonia caused by the Coronavirus disease ...

    • ROM-based inference method built on deep learning for sleep stage classification 

      AlMeer M.H.; Hassen H.; Nawaz N. ( UIKTEN - Association for Information Communication Technology Education and Science , 2019 , Article)
      We used a classical deep feedforward neural network (DFFNN) for an automatic sleep stage scoring based on a single-channel EEG signal. We used an open-available dataset, randomly selecting one healthy young adult for both ...

    • Unsupervised Technique for Anomaly Detection in Qatar Stock Market 

      Al-Thani H.; Hassen H.; Al-Maadced S.; Fetais N.; Jaoua A. ( Institute of Electrical and Electronics Engineers Inc. , 2018 , Conference Paper)
      The aim of anomaly detection is to find patterns or data points that are not confirming the expected behavior inside the dataset. Techniques from a variety of disciplines like machine learning, statistics, information ...