The traditional view in science, especially in mathematics, is to avoid uncertainty at all levels at any cost. Thus
”being uncertain” is regarded as ”being unscientific”. But unfortunately in real life most of the information that
we have to deal with is mostly uncertain.

One of the paradigm shifts in science and mathematics in this century is to accept uncertainty as part of science
and the desire to be able to deal with it,as there is very little left out in the practical real world for scientific and
mathematical processing without this acceptance!

One of the earliest successful attempts in this directions is the development of the Theories of Probability and
Statistics. However, both of them have their own natural limitations. Another successful attempt again in the
same direction is the so called Fuzzy Set Theory, introduced by Zadeh.

Observing that every fuzzy subset of a set X itself requires a specific real number between/including 0 and 1
to be associated with each element of X, which is not always possible in several of the practical applications,
Zadeh himself introduced, in 1971, the so called level 2 fuzzy subsets of a set X, and in 1974, the so called type 2
fuzzy subsets of a set X, as means to handle even more inexact/uncertain information. In fact, the above concepts
were further generalized by Zadeh himself, to level k and type k fuzzy subsets of a set X respectively, for any
positive integer k bigger than or equals 2.

Note that an ordinary fuzzy subset of a set X is a special case of both level 2 and type 2 fuzzy subsets of X.
Also, an interval valued fuzzy subset, again introduced by Zadeh himself, of any set X is also a special case of
type 2 fuzzy subset of X.

Now both level 2 and type 2 fuzzy subsets of any set X have very interesting applications in several areas of
both Mathematics and Computer Science. The aim of this Mini symposium is to bring together all those researchers
working in various applications of both level 2 and type 2 fuzzy sets/Logic and their special cases namely,
ordinary fuzzy/interval valued fuzzy subsets, in different areas of both Mathematics (in Algebra, Analysis and
Topology), Computer Science (in Logic, (Relational,Object) DBMS, Image Processing, Data Mining,
Decision Making, etc.), Medicine (in Bioinformatics etc..) and Economics (in Econometrics etc.).