in this work, we define a fuzzy soft set theory and its related properties. we then define fuzzy soft aggregation operator that allows constructing more efficient decision making method. finally, we give an example which shows that the method can be successfully applied to many problems that contain uncertainties.

Soft tissue reconstruction of a circumferentially degloved finger is major challenge. A 64-year-old male patient with underlying diabetes and hypertension presented biopsy-proven squamous cell carcinoma involving his entire finger. The entirety was to obtain safety margin. reconstructed using bilateral free hypothenar flap, the recovered without any complications.

Since the introduction of the concepts by Vladimir, a large and increasing number of researchers have worked on the algorithmic and the theoretical analysis of SVM, merging concepts from disciplines as distant as statistics, functional analysis, optimization, and machine learning. The soft margin classifier was introduced few years later by Cortes and Vapnik [1], and in 1995 the algorithm was e...

This paper proposes a new residual convolutional neural network (CNN) architecture for single image depth estimation. Compared with existing deep CNN based methods, our method achieves much better results with fewer training examples and model parameters. The advantages of our method come from the usage of dilated convolution, skip connection architecture and soft-weight-sum inference. Experime...

In this paper we present a new formulation of the Support Vector Machine for classifying data. It is based on development of ideas from methods of total least squares, in which error in measured data is incorporated in the model design. The new formulation studied is similar to the soft margin SVM, but has to be solved using nonlinear optimization rather than quadratic programming. Initial resu...

in this paper, based in the l ukasiewicz logic, the definition offuzzifying soft neighborhood structure and fuzzifying soft continuity areintroduced. also, the fuzzifying soft proximity spaces which are ageneralizations of the classical soft proximity spaces are given. severaltheorems on classical soft proximities are special cases of the theorems weprove in this paper.

Hard and soft classifiers are two important groups of techniques for classification problems. Logistic regression and Support Vector Machines are typical examples of soft and hard classifiers respectively. The essential difference between these two groups is whether one needs to estimate the class conditional probability for the classification task or not. In particular, soft classifiers predic...