• Conference Object  

      Comparative analysis of artificial neural network models: Application in bankruptcy prediction 

      Charalambous, Chris; Charitou, Andreas; Kaourou, Froso (IEEE, 1999)
      This study compares the predictive performance of three neural network methods, namely the Learning Vector Quantization, Radial Basis Function, the Feedforward network that uses the conjugate gradient optimization algorithm, ...
    • Article  

      Determination of the number of errors in DFT codes subject to low-level quantization noise 

      Takos, G.; Hadjicostis, Christoforos N. (2008)
      This paper analyzes the effects of quantization or other low-level noise on the error correcting capability of a popular class of real-number Bose-Chaudhuri-Hocquenghem (BCH) codes known as discrete Fourier transform (DFT) ...
    • Conference Object  

      The effect of color correction of endoscopy images for quantitative analysis in endometrium 

      Neophytou, Michael S.; Pattichis, Constantinos S.; Tanos, Vasilios; Pattichis, Marios S.; Kyriacou, Efthyvoulos C.; Koutsouris, Demetrios Dionysios (2005)
      The objective of this study was to develop a standardized protocol for the capturing and analysis of endoscopy digital images for subsequent use in a Computer Aided Diagnosis (CAD) system in gynaecological cancer. Images ...
    • Conference Object  

      Hybrid neural network electromyographic system: Incorporating the WISARD net 

      Pattichis, Constantinos S.; Schizas, Christos N.; Sergiou, A.; Schnorrenberg, F. (IEEE, 1994)
      Clinical electromyography (EMG) provides useful information for the diagnosis of neuromuscular disorders. The utility of artificial neural networks trained with the backpropagation, the Kohonen's self-organizing feature ...
    • Conference Object  

      New technique for the classification and decomposition of EMG signals 

      Christodoulou, Christodoulos I.; Pattichis, Constantinos S. (IEEE, 1995)
      The shapes and firing rates of motor unit action potentials (MUAPs) in an electromyographic (EMG) signal provide an important source of information for the diagnosis of neuromuscular disorders. In order to extract this ...
    • Conference Object  

      Non-stationary texture segmentation using an AM-FM model 

      Pattichis, Marios S.; Christodoulou, Christodoulos I.; Pattichis, Constantinos S.; Bovik, Alan Conrad (1997)
      We present a novel method for segmenting non-stationary textures. Our approach uses a multidimensional AM-FM representation for the texture, and provides the FM features to an SOFM-LVQ neural network system that performs ...