The study suggests designing a weighting model for iris features and selection of the
best ones to show the effect of weighting and selection process on system performance.
The search introduces a new weighting and fusion algorithm depends on the i
nter and intra
class differences and the fuzzy logic. The output of the algorithm is the feature’s weight of
the selected features. The designed system consists of four stages which are iris
segmentation, feature extraction, feature weighting_selection_fusion model
implementation and recognition. System suggests using region descriptors for defining the
center and radius of iris region, then the iris is cropped and transformed into the polar
coordinates via rotation and selection of radius-size pixels of fixed window from center to
circumference. Feature extraction stage is done by wavelet vertical details and the
statistical metrics of 1st and 2nd derivative of normalized iris image. At weighting and
fusion step the best features are selected and fused for classification stage which is done by
distance classifier. The algorithm is applied on CASIA database which consists of iris
images related to 250 persons. It achieved 100% segmentation precision and 98.7%
recognition rate. The results show that segmentation algorithm is robust against
illumination and rotation variations and occlusion by eye lash and lid, and the
weighting_selection_fusion algorithm enhances the system performance.
In this research we introduce
a regularization based feature selection algorithm to benefit from
sparsity and feature grouping properties and incorporate it into the
medical image classification task. Using this group sparsity (GS)
method, the wh
ole group of features are either selected or removed.
The basic idea in GS is to delete features that do not affect the
retrieval process, instead of keeping them and giving these features
small weights. Therefore, GS improves system by increasing
accuracy of the results, plus reducing space and time requirements
needed by the system.
This paper introduces a new approach to extract palm print features
and select the best ones. The paper also studies the effectiveness of
the selection process on speed and performance of system.
Student dropout is a serious problem in education, there are many factors that can influence student dropout so it is not an easy issue to resolve. The scope of this research is to examine the accuracy of the ensemble techniques for predicting the st
udent dropout, particularly for primary school students in the Syrian Arab Republic.
The new classifier is designed based on the ensemble techniques “Stacking” and application of techniques Feature Selection where the database suffers from the problem of imbalance.
This new classifier has been compared with individual ones by using the Cross-Validation technique, the study concluded that the proposed classifier is the best among the others that have been compared to predict the student dropout.
