Publications Internationales

Permanent URI for this collectionhttps://dspace.univ-boumerdes.dz/handle/123456789/13

Browse

Author: search.filters.author.Hamami, L.Has files: Yes

Search Results

Now showing 1 - 3 of 3
  • No Thumbnail Available
    Item
    Osteoporosis assessment using Multilayer Perceptron neural networks
    (IEEE, 2012) Harrar, Khaled; Hamami, L.; Akkoul, S.; Lespessailles, E.
    The objective of this paper is to investigate the effectiveness of a Multilayer Perceptron (MLP) to discriminate subjects with and without osteoporosis using a set of five parameters characterizing the quality of the bone structure. These parameters include Age, Bone mineral content (BMC), Bone mineral density (BMD), fractal Hurst exponent (Hmean) and coocurrence texture feature (CoEn). The purpose of the study is to detect the potential usefulness of the combination of different features to increase the classification rate of 2 populations composed of osteporotic patients and control subjects. k-fold Cross Validation (CV) was used in order to assess the accuracy and reliability of the neural network validation. Compared to other methods MLP-based analysis provides an accurate and reliable platform for osteoporosis prediction. Moreover, the results show that the combination of the five features provides better performance in terms of discrimination of the subjects
  • No Thumbnail Available
    Item
  • No Thumbnail Available
    Item
    A wavelet optimization approach for ECG signal classification
    (2012) Daamouche, Abdelhamid; Hamami, L.; Alajlan, N.; Melgani, F.
    Wavelets have proved particularly effective for extracting discriminative features in ECG signal classification. In this paper, we show that wavelet performances in terms of classification accuracy can be pushed further by customizing them for the considered classification task. A novel approach for generating the wavelet that best represents the ECG beats in terms of discrimination capability is proposed. It makes use of the polyphase representation of the wavelet filter bank and formulates the design problem within a particle swarm optimization (PSO) framework. Experimental results conducted on the benchmark MIT/BIH arrhythmia database with the state-of-the-art support vector machine (SVM) classifier confirm the superiority in terms of classification accuracy and stability of the proposed method over standard wavelets (i.e., Daubechies and Symlet wavelets)