Omar M. Saad

Stability on multiobjective linear programming problems with fuzzy parameters

This paper deals with multiobjective linear programming problems with fuzzy parameters (FMOLP). For such problems, some stability notions as the solvability set and the stability set of the first kind (SSK1) are defined and characterized. Two algorithms for the determination of the set (SSK1) are proposed. One of these algorithms is applied when the fuzzy multiobjective problem remains linear after introducing the so-called α-level set of the fuzzy numbers, while the other algorithm is applied when the fuzzy multiobjective problem becomes nonlinear. Finally, a numerical example is given to illustrate the theory developed in the paper.

On stability of proper efficient solutions in multiobjective fractional programming problems under fuzziness

In this paper a solution algorithm to fuzzy multiobjective fractional programming problems is suggested. These problems involve fuzzy parameters usually in the right-hand side of the constraints. In order to defuzzify the problem the concept of @a-level set of a fuzzy number is given. For obtaining proper efficient solutions, Geoffrion results are extended to fuzzy multiobjective fractional programming problems. In addition, some stability notions are defined and characterized for the problem of concern. Illustrative numerical examples are presented to clarify the theory and the solution algorithm.

Human Authentication Using Face and Fingerprint Biometrics

Multimodal biometric approaches are growing in importance for personal verification and identification, since they provide better recognition results and hence improve security compared to biometrics based on a single modality. In this paper, we present a multimodal biometric system that is based on the fusion of face and fingerprint biometrics. For face recognition, we employ uniform local binary patterns (ULBP), while minutiae extraction is used for fingerprint recognition. Fusion at matching score level is then applied to enhance recognition performance. In particular, we employ the product rule in our investigation. The final identification is then performed using a nearest neighbour classifier which is fast and effective. Experimental results confirm that our approach achieves excellent recognition performance, and that the fusion approach outperforms biometric identification based on single modalities.

Solving a special class of large-scale fuzzy multiobjective integer linear programming problems

We present a method useful in solving a special class of large-scale multiobjective integer problems depending on the decomposition algorithm. These problems involve fuzzy parameters on the right-hand side of the independent constraints. The presented solution method is based upon a combination of the decomposition algorithm coupled with the weighting method together with the branch-and-bound method. An illustrative numerical example is given to clarify the theory and the method discussed in this paper.

A Robust Algorithm for Enhancement of Remotely Sensed Images Based on Wavelet Transform

In the field of remote sensing, removing noise from images is still a challenging research in image processing. Generally there is no common enhancement approach for noise reduction. Several approaches have been introduced and each has its own assumption, advantages and disadvantages. The speckle noise is usually found in the remote sensing images. This paper proposes an adaptive threshold method for image despeckling based on wavelet transform. The quality of the enhanced images in this paper is measured by the statistical quantity measures: Peak Signal-to-Noise Ratio (PSNR), and Mean Square Error (MSE). Experimental results showed that the proposed method demonstrates an improved denoising performance over related techniques according to increasing of PSNR values and decreasing of MSE values of enhanced images.