Modeling, in general logic

refers to the establishment of a description of a system in mathematical terms,

which describes the behaviour of the original organism. Such a design of

mathematical representation is called a mathematical model, of the physical system.

In numerous realistic disciplines such as medicine, engineering and ?nance,

amongst others, modeling and investigating lifespan data is essential.

Researchers in mathematics

are in the habit of dividing the universe into two parts: mathematics, and

everything else,that is, the rest of the world, sometimes called “the real

world”. As soon as you practice mathematics to know a situationin the actual world,and

then feasibly practice it totake an action or event of forecast the future, together

the actual world condition and the resultant mathematics methods are taken seriously.The

circumstances and the queries related with them can be any extent from enormous

to tiny. The enormous ones may lead to lifetime careers for those who study

them deeply and special curricula or whole university departments may be set up

to prepare people for such careers. Bioorganism, hormones study, medical

imaging, and cryptography are some such examples. At the another end of the extent,

there are slight circumstances and equivalent interrogations, although they may

be of great importance to the individuals involved: planning a trip, scheduling

the time-table, man requirtment methods, or bidding in an auction. Whether the problem is enormous or

tiny, the procedure of “interface” between the mathematics and the physical world

is the same: the actual circumstances frequently has numerous sides that you

can’t take all into account, so you choose which characteristics are most

significant and retain those. At this instant, you have an perfect description

of the actual condition, which you can then interpret into mathematical

relations. Now you have a mathematical model of the idealized question. Then

you relate your mathematical characters and facts to the model, and gain

exciting understandings, examples, designs, formulas, and algorithms. You

decode all this back into the actual situation, and you assurance to have a

model for the idealized question. But you have to check back: the results are practical,

the answers are reasonable, the consequences are acceptable? If so, then we

have the mathematical model for the actual world problem, If not, take another

look at the choices you made at the beginning, and try again. This entire

process is called mathematical modeling.

The objectives of the mathematical modelling are increasing

scienti?c realizing over measurable expression of present information of a

system (as well as displaying what we know, this may also show up what we do

not know), examination the e?ect of variations in a system, aid result producing,

including tactical decisions and strategic decisions. When learning models, it

is useful to identify wide categories of models. Classi?cation of distinctmodels

into these categories expresses us systematically some of the fundamentals of

their structure.One separation among models is built on the kind of consequence

they forecast. Deterministic model sign are random variation, and so always

forecast the identical consequence from a specified starting point. On the

other hand, the model may be more statistical in nature and so may forecast the

distribution of probable outcomes. Such models are said to be stochastic.

An additional method of differentiate among the kinds

of models is to reflect the level of understanding on which the model is based.

A model which uses a huge volume of theoretic information commonly defines what

occurs atone level in the hierarchy by considering procedures at lower stages,

these are called mechanistic models, since they take interpretation of the

mechanisms over which variations happen. In empirical models, nointerpretation

is taken of the mechanism by which variations to the system occur. As an

alternative, it is only renowned that they do occur, and the model trys to

interpretation quantitatively for variations related with various circumstances.

In this

research work, we were developed various fuzzy mathematical model based on

different distributions to investigate

the effect of oxytocin.The hind paw with drawl latency (HWL) in mature mice increased with the increase of oxytocin concentration.

The changes in HWL after a microinjection of dissimilar doses of oxytocin

consider as random variable following fuzzy generalized gamma distribution and

fuzzy log logistic distribution.

The

changes in cardiac output and stroke volume after the administration of

oxytocin taken as a random variable follows the generalized Rayleigh

distribution. Fuzzy mathematical model was established to measure the

haemodynamic effects of oxytocin by using generalized Rayleigh distribution.

The oxytocin concentration in salaivary samples following intranasal oxytocin

administration fix as random variable follows the Rayleigh distribution in

fuzzy environment.

Clinically,

oxytcoin infusion is used for induction and augmentation of labor and for the

prevention and treatment of postpartum hemorrhage (PPH). The maternal heart

rate after the study medication treated as a random variable follows the Nadarajah and Haghighi distribution,

Exponentiated

Nadarajah and Haghighi

distribution in fuzzy state.

Fuzzy

mathematical models were established by using various distribuiton such as

generalized gamma distribution, log-logistic distribution, Rayleigh

distribution, generalized Rayleigh distribution, Nadarajah

and Haghighi (NH) distribution and Exponentiated Nadarajah and Haghighi distribution

(ENH). We compute the fuzzy mean, variance, survival or reliablity and hazard

rate values to analyze the effect of oxytocin. These theoritical outcomes offer

the enthusiastic information of the effects of oxytocin and it will be useful to the medical field for

further experimental works.