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Articles by Eimad Eldin Abdu Ali Abusham
Total Records ( 2 ) for Eimad Eldin Abdu Ali Abusham
  Kalaiarasi Sonai Muthu Anbananthen , Fabian Chan Huan Pheng , Subhacini Subramaniam , Shohel Sayeed and Eimad Eldin Abdu Ali Abusham
  Fidelity and comprehensibility are the common measures used in the evaluation of rules extracted from neural networks. However, these two measures are found to be inverse relations of one another. Since the needs of comprehensibility or fidelity may vary depending on the user or application, this paper presented a significance based rule extraction algorithm that allows a user set parameter to scale between the desired degree of fidelity and comprehensibility of the rules extracted. A detailed explanation and example application of this algorithm is presented as well as experimental results on several neural network ensembles.
  Eimad Eldin Abdu Ali Abusham and Wong Eng Kiong
  A new face recognition method, based on the local non-linear mapping, is proposed in this study. Face images are typically acquired in frontal views and often illuminated by a frontal light source. Unfortunately, recognition performance is found to significantly degrade when the face recognition systems are presented with patterns that go beyond from these controlled conditions. Face images acquired under uncontrolled conditions have been proven to be highly complex and are non-linear in nature; thus, the linear methods fail to capture the non-linear nature of the variations. The proposed method in this study is known as the Non-linear Principal Component Embedding (NPCE) which is aimed to solve the limitation of both linear and non-linear methods by extracting discriminant linear features from highly non-linear features; the method can be viewed as a linear approximation which preserves the local configurations of the nearest neighbours. The NPCE automatically learns the local neighbourhood characteristic and discovers the compact linear subspace which optimally preserves the intrinsic manifold structure; a principal component is then carried out onto low dimensional embedding with reference to the variance of the data. To validate the proposed method, Carnegie Mellon University Pose, Illumination and Expression (CMU-PIE) database was used. Experiments conducted in this research revealed the efficiency of the proposed method in face recognition as follows: (1) extract discriminant linear features from highly non-linear features based on the local mapping and (2) Runtime speed is improved as face feature values are reduced in the embedding space. The proposed method achieves a better recognition performance in the comparison with both the linear and non-linear methods.
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