The complexities of modeling uncertain data in economics, engineering, environmental science, sociology, medical science and many other fields can not be successfully dealt with by classical methods. While probability theory, fuzzy set theory,rough set theory, vague set theory and the interval mathematics are useful approaches to describing uncertainty, each of these theories has its inherent d...

Soft set theory [1] was firstly introduced byMolodtsov in 1999 as a general mathematical tool for dealing with uncertain, fuzzy, not clearly defined objects. He has shown several applications of this theory in solvingmany practical problems in economics, engineering, social science, medical science, and so forth, in [1]. In the recent years, papers about soft sets theory and their applications ...

the hybrid fuzzy differential equations have a wide range of applications in science and engineering. we consider the problem of nding their numerical solutions by using a novel hybrid method based on fuzzy neural network. here neural network is considered as a part of large eld called neural computing or soft computing. the proposed algorithm is illustrated by numerical examples and the resu...

Writing academic texts by novice researchers requires a framework and support by learning how to cite the works of others. However, compared to the studies on other academic writings, studying citations by considering certainty markers has received little attention. The main purpose of this study was to investigate the shifts of certainty markers (hedges and boosters) in pre- and post-citation ...

Soft set theory was initiated by Molodtsov in 1999. In the past years, this theory had been applied to many branches of mathematics, information science and computer science. In 2003, Maji et al. introduced some operations of soft sets and gave some operational rules. Recently, some of these operational rules are pointed out to be not true. Furthermore, Ali et al., in their paper, introduced an...

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.

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.

*Formation of Soft Nanomachines, Core Research for Evolutional Science and Technology, Japan Science and Technology Agency, †Single Molecule Processes Project, International Cooperative Research Project, Japan Science and Technology Agency, ‡Department of Biophysical Engineering, Osaka University, §Soft Biosystem Group, Laboratories for Nanobiology, Graduate School of Frontier Biosciences, Osak...