زهراء فاضل عبد الحسين الدباغ

We present a new approach for structural similarity for assessing gray scale image quality. The structural similarity measure SSIM, proposed in 2004, has been widely used. It is based on statistical similarity between the two images. However, SSIM can produce confusing results in some cases where it may give a non-trivial amount of similarity while the two images are quite different. Also, it fails to detect similarity at low peak signal to noise ratio (PSRN). In this paper, we present a novel image similarity measure, named (BR-FS-ISM), by using fuzzy soft Matrix-theoretic technique based on binary relation- fuzzy soft set. The proposed method has been tested under Gaussian noise. Simulation results show that the proposed measure BR-FS-ISM outperforms statistical similarity SSIM by ability to detect similarity under very low PSNR. The average difference is about 10dB.

In this work, we present a new ideas of n-ary relation fuzzy soft set by joining the characteristics of n-ary relation with the parametrization of the soft set and fuzzy soft set, and constructs the new types named n-ary relation fuzzy soft set, which are more functional to make practical and theoretical ideas or studies in the fuzzy soft set theory. We center around the fundamental operations for the n-ary relation fuzzy soft set and study their properties. This new idea advantageous and contributes in some scientific applications, we will present the applications in future.

In this work, we present a new idea of n-ary relation soft set by joining the characteristics of n-ary relation with the parametrization of the soft set, and constructs the new type named n-ary relation soft set, which is more functional to make practical and theoretical ideas or studies in the soft set theory. We center around the fundamental operations for the n-ary relation soft set and study their properties, and applied examples of n-ary relation soft set were presented. This new idea advantageous and contributes in some scientific applications, I will introduce these applications in next articles.

In this work, we present a new ideas of n-ary multi fuzzy soft set, n-ary fuzzy soft matrix and n-ary multi fuzzy soft matrix by joining the characteristics of n-ary relation with the parametrization of multi fuzzy soft set, and constructs the new types named n-ary multi fuzzy soft set and n-ary multi fuzzy soft matrix, also n-ary fuzzy soft matrix is introduced, we center around the fundamental operations and properties for the n-ary multi soft set, n-ary fuzzy soft matrix and n-ary multi fuzzy soft matrix with applied examples. This two new kind of fuzzy soft matrix are more functional, advantageous and contributes in some scientific applications, we will introduce the applications in future.