• A Hybrid Feature Selection Method for Complex Diseases SNPs 

      Alzubi, Raid; Ramzan, Naeem; Alzoubi, Hadeel; Amira, Abbes ( Institute of Electrical and Electronics Engineers Inc. , 2017 , Article)
      Machine learning techniques have the potential to revolutionize medical diagnosis. Single Nucleotide Polymorphisms (SNPs) are one of the most important sources of human genome variability; thus, they have been implicated ...
    • Content-based image retrieval with compact deep convolutional features 

      Alzu'bi, Ahmad; Amira, Abbes; Ramzan, Naeem ( Elsevier B.V. , 2017 , Article)
      Convolutional neural networks (CNNs) with deep learning have recently achieved a remarkable success with a superior performance in computer vision applications. Most of CNN-based methods extract image features at the last ...
    • Heterogeneous Implementation of ECG Encryption and Identification on the Zynq SoC 

      Ali, Amine Ait Si; Zhai, Xiaojun; Amira, Abbes; Bensaali, Faycal; Ramzan, Naeem ( Institute of Electrical and Electronics Engineers Inc. , 2016 , Conference Paper)
      This paper presents an innovative and safe connected health solution for human identification. The system consists of the encryption and decryption of ECG signals using the advanced encryption standard (AES) as well as the ...
    • Matching pursuit-based compressive sensing in a wearable biomedical accelerometer fall diagnosis device 

      Gibson, Ryan M.; Amira, Abbes; Ramzan, Naeem; Casaseca-de-la-Higuera, Pablo; Pervez, Zeeshan ( Elsevier Ltd , 2017 , Article)
      There is a significant high fall risk population, where individuals are susceptible to frequent falls and obtaining significant injury, where quick medical response and fall information are critical to providing efficient ...
    • Multiple comparator classifier framework for accelerometer-based fall detection and diagnostic 

      Gibson, Ryan M.; Amira, Abbes; Ramzan, Naeem; Casaseca-de-la-Higuera, Pablo; Pervez, Zeeshan ( Elsevier Ltd , 2016 , Article)
      There are a significant number of high fall risk individuals who are susceptible to falling and sustaining severe injuries. An automatic fall detection and diagnostic system is critical for ensuring a quick response with ...