SlideShare une entreprise Scribd logo
1  sur  4
Télécharger pour lire hors ligne
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 04 Issue: 02 December 2015 Pages:68-71
ISSN: 2278-2397
68
A Study on the Exposures of Rag- Pickers using
Induced Neutrosophic Cognitive Relational Maps
S.Johnson Savarimuthu1
, D.Yuvageswary2
1 2
P.G. and Research Department of Mathematics, St. Joseph’s College, Cuddalore, India
Email: johnson22970@gmail.com, yuva.dinagaran@gmail.com
Abstract-In this paper, using a new Fuzzy bimodal called Induced
NeutrosophicCognitive Relational Maps (INCRM) we analyse the
Socio-Economic problem faced by Rag-Pickers. Based on the study,
conclusions and some remedial measures are stated.
Keywords-NCM, NRM, NCRM, INCRM, fixed point, limit cycle,
hidden pattern.
I. INTRODUCTION
L.A. Zadeh(1965) introduced the fuzzy model. one in the.
Fuzzy model is a mathematical which deals with neural
networks and fuzzy logics. Generally fuzzy model helps to deal
the uncertainties which are associated with human cognitive
thinking.Among many fuzzy models, in this paper we are
interested inFuzzy Cognitive Maps (FCM), Fuzzy Relational
Maps (FRM).Praveenprakash (2010) has introduceda bimodal
called Fuzzy Cognitive Relation Maps (FCRM)bimodal. In this
paper, a new bimodal called Induced NCRM is introduced to
analyse the Socio-Economic problem of Rag-Pickers.
II. PRELIMINARIES
A. Definition
Let S = S1∪S2, where S1and S2 are nonempty disjoint sets, then
we call Sas a biset.
B. Definition
A matrix M = M1∪ M2 where M1 is anm × n matrix and M2 is a
p×s matrix, then M is called a bimatrix.
C. Definition
A Neutrosophic Cognitive Relation Maps (NCRM) is a
directed fuzzy bigraphit has nodes which deals with concepts
like policies and edges as causal relationships. In a NCRM the
pair of associated nodes is called as binodes.
D. Definition
Consider the binodes, {C1
’
C2
’
….. Cn
’
} of the FCM and {D1
...Dr}, {R1….Rs} of the FRM for the NCRM bimodal.The
directed fuzzy graph is drawn by using the edge biweighte𝑖𝑗
𝑡
=
{0, 1, -1, I}; 1≤ t ≤ 2.It is defined by e𝑖𝑗
1
∪e𝑘𝑠
2
in bimatrix where
e𝑖𝑗
1
is the weight of the edge CiCj and e𝑘𝑠
2
is the directed edge of
DkRs. Here M is the connection bimatrixof the new NCRM
bimodal.
E. Definition
The new NCRMs with edge biweight{1, 0, -1, I} are called
simple NCRMs. An NCRM which has a feedback is the
representation of cyclesi.e., the casual relations between the
nodes is in cyclic way, and thenNCRM is called a
dynamicalbisystem.
F. Definition
The biedgese𝑖𝑗 = (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) take the values in fuzzy casual
biinterval [-1,1] ∪ [-1,1].
i) e𝑖𝑗= 0 indicates no causality between the binodes.
ii) e𝑖𝑗>0 implies that both e𝑖𝑗
1
>0 and e𝑘𝑠
2
>0 ; indicates increase
in the binodesimplies increase in the binodes.
iii) e𝑖𝑗< 0 implies that both e𝑖𝑗
1
<0 and e𝑘𝑠
2
< 0 ; similarly
decrease in the binodesimpliesdecrease in the binodes.
However unlike the FCM and FRM model we can have the
following possibilities other than that of e𝑖𝑗 = 0, e𝑖𝑗>0 and e𝑖𝑗<
0.
i) e𝑖𝑗 = (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) can be such that (e𝑖𝑗
1
) = 0 and (e𝑘𝑠
2
)>0. No
relation.
ii) e𝑖𝑗= (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) also for (e𝑖𝑗
1
) = 0 and (e𝑘𝑠
2
) < 0.
iii) e𝑖𝑗 = (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) we can have (e𝑖𝑗
1
) ≤ 0 and (e𝑘𝑠
2
) > 0
iv) Ine𝑖𝑗 = (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) we can have (e𝑖𝑗
1
) < 0 and (e𝑘𝑠
2
) =0
v) In e𝑖𝑗 = (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) we can have (e𝑖𝑗
1
) > 0 and (e𝑘𝑠
2
) =0
vi)In e𝑖𝑗= (e𝑖𝑗
1
) ∪ (e𝑘𝑠
2
) we can have (e𝑖𝑗
1
) > 0 and (e𝑘𝑠
2
) <0
Thus in the case of NCRM we can have 9 possibilitieswhich is
useful for solving the problem in accurate way.
a) Application of this bimodal to the problem of rag pickers
Because of the changes in packaging and lifestyles, waste has
definitely increased. Over a decade there was not much plastic
packaging. So the scale of waste has changed which results to
the fact of land contamination. Of course in India it is in
common practice of not to separating the waste. Nowadays
electronic waste such as batteries, gadgets waste containing
mercury, broken glasses, medicine bottles, syringe, knives and
vegetable peelersare thrown in same bins. Thus unsanitary
working condition leads them to serious infections and
wounds.
b) Adaptation of the problem to FCRM Bimodal
The linguistic questionnaire was transformed7 main attributes
of problems faced by the rag pickers and 8 attributes as the
cause of it which acts as catalyst.
The attributes are
C1: Left orphans due to parents/family members death.
C2: School dropouts/ no Money to pay school fees/ill-
treatment bythe teachers.
C3: Rag picking as a source of income and livelihood.
C4: Bad Company and Bad habits.
C5: Parents in jail.
C6: Quarrel at home with parents or family members.
C7: Poverty and unemployment’s due to parents/family
memberdeath.
D1: Quarrel at home / ill treatment.
D2: School dropout.
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 04 Issue: 02 December 2015 Pages: 68-71
ISSN: 2278-2397
69
D3: Rag picking as an independent Profession.
D4: Poverty and seeks self-respect.
D5: Take up to all bad habits due to several reasons likebad
company, for sleep, etc., so become drug addicts.
D6: No hygiene/ no knowledge about hygiene / about
thehazardous waste.
D7: Run away from the family.
They deal with
R1: Father a drunkard / No parents / parents in jail.
R2: Enjoy independence /self-support.
R3: No motivation by teacher in school.
R4: No place to sleep in night.
R5: Problem given by Police.
R6: Malaria / typhoid.
R7: Scabies / hepatics / skin ailment due to rag picking.
R8: Government and public have taken no steps to manage
waste.
We get the synaptic connecting bimatrixM as follows:
c) Analysis using FCRM Bimodal
First we work for the result for NCRM with initialvectorc1 in
NCM component of NCRM bimodal as ONstate saying that
Left orphans due to parents/family members death as
initializing attribute and the remaing in OFF state and the
initial vector d1 in NRM Component, ofNCRM bimodal as ON
state saying that Quarrel at home is initializing attribute. The
symbol stands forthresholding and it means that values
greater than or equal to1are replaced by 1.
Let I1 = (1 0 0 0 0 0 0) U (1 0 0 0 0 0 0 0)
The effect of I1 on the dynamical system M is
I1M = (1 0 0 0 0 0 0)M1 U (1 0 0 00 0 0 0)M2
(0 10 I 0 01) U (1 00 0 0000)
==> (0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0) M2
T
(01 0I 0 01) U(1 00 I1 00) = I2
Now I1 ≠I2. Hence we precede further to get thelimit point as
follows
I2M = (1+I2
0 I 0 0 2I+1 1+I )M1 U (2+I2
0 0 I 0 1 0
0)M2
(1 0 I 0 0 1 1)U (1 0 0 I 0 1 0 0)
==>(1 0 I 0 0 1 1) U (1 0 0 I 0 1 0 0) M2
T
(1 0 I 0 0 1 1) U (1 00 I 1 0 0) =I3
Continuing this process and updating we get,
I3M = (2+I2
1 I I0 1+2I 2+I) U (1 0 0 I 0 1 0 0)
(1 1 I I 0 0 0) U (1 0 0 I 1 0 0) =I4
I4M = (2+I2
1 I I0 1+3I 2+I) U (2+I2
0 0 I 0 1 0 0)
(1 1 II 0 1 1) U (1 0 0 I 1 0 0) =I5 =I4
Table 1:Collection of Limit points for different input vectors.
No Input
Vector
Limit Points
1 (1 0 0 0 0 0 0) (1 1 I I 0 1 1) U (1 0 0 I 1 0 0) (1 0 0 I 0 1 0 0)
2 (0 1 0 0 0 0 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1
1)
3 (0 0 1 0 0 0 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1
1)
4 (0 0 0 1 0 0 0) (1 1 I I 0 1 1) U (I 0 0 1 I 0 0) (I 0 0 1 0 I 0 0)
5 (0 0 0 0 1 0 0) (1 1 I I 0 1 1) U (1 0 0 I 1 0 0) (1 0 0 I 0 1 0
0)
6 (0 0 0 0 0 1 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1 1)
7 (0 0 0 0 0 0 1) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1
1)
d) Induced NCRM Bimodal
It is found that in the analysis of NCRMbimodal, the expert
have to initialize any vector in ON state i.e. with 1 and the
remaining as 0 which will result to many 1’s in the resultant
vector. To avoid such situation and to show accuracy we are
interested in induced NCRM bimodal.So here we adopt the
method which will give importance to each vector by
initializing them by keeping in ON state and we pass it over to
dynamical system. Secondly the impact on all attributes i.e.
using the induced path we will find the combined graph.
Finally the idea for detecting the relations and influences
between the nodes to find the equilibrium state, Let us take the
connection bimatrix of NCRM bimodal which has both NCM
and NRM components and let I1 be the initial input bivector. In
I1 assign any component to ON state then pass the vector to
adjacent matrix which is just multiplying the vector to the
matrix M. Then the value for NCM, the components which is
greater or equal to 1 is replaced with 1and all with 0. While in
the case of NRM, components will be given ON state for two
highest values and all in OFF state by giving values 1 and 0
respectively
The symbol is the representation of tresholding vector.
We will result to many new bivectors chose one from new
bivector which has highest number of 1’s and pass to the
connection matrix. For each process we have resultant vectors,
and I2 is the vector which has maximum number of 1’s just by
choosing the first occurring vector. Repeating the same
procedure until limit cycle is reached. Now we work for the
result for INCRM bimodal with initial vector c1 in NCM
component of NCRM bimodal as ON state saying that Left
C1 C2 C3 C4 C5 C6 C7
C1 0 1 0 I 0 0 1
C2 1 0 0 0 0 I 1
C3 0 0 0 0 0 1 0
C4 I 0 1 0 0 1 1
C5 0 1 0 I 0 0 1
C6 1 0 0 0 0 0 0
C7 0 0 0 0 0 1 0
R1 R2 R
3
R
4
R
5
R
6
R
7
R8
D1 1 0 0 0 0 0 0 0
D2 0 0 1 0 0 0 0 0
D3 0 1 0 0 1 0 0 1
D4 I 0 0 1 0 0 0 0
D5 1 0 0 0 0 1 0 0
D6 0 0 0 0 1 0 1 I
D7 0 0 1 0 0 0 1 0
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 04 Issue: 02 December 2015 Pages: 68-71
ISSN: 2278-2397
70
orphans due to parents/family members death as initializing
attribute and in NRM, component of NCRM bimodal as ON
state saying that Quarrel at home / ill treatmentis initializing
attribute. The symbol stands for thresholding and it
means that, values 1 and more than 1 are replaced by 1.
Let the initial state vector be I1 = (1 0 0 0 0 0 0) U(1 0 0 0
0 0 0 0).
The effect of I1 on the dynamical system M is
I1M = (1 0 0 0 0 0 0)M1 U (1 0 0 0 0 0 0 0)M2
(0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0)
==> (0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0) M2
T
(0 1 0 I 0 0 1) U (1 0 0 I 1 0 0) =I1
’
Let I1’ = IC1’ UIR1’
From I1’ it is observed that the new bivectors are:
I1
(1)
= (0 0 0 0 0 0 0) U(1 0 0 0 0 0 0)
I1
(2)
= (0 1 0 0 0 0 0) U(0 0 0 0 0 0 0)
I1
(3)
= (0 0 0 I 0 0 0) U(0 0 0 I 0 0 0)
I1
(4)
= (0 0 0 0 0 0 0) U(0 0 0 0 1 0 0)
I1
(5)
= (0 0 0 0 0 0 1) U(0 0 0 0 0 0 0)
Now let us find the new input vector I2.
I1
(1)
M= (0 0 0 0 0 0 0)M1 U(1 0 0 0 0 0 0)M2
==>(0 0 0 0 0 0 0) U(1 0 0 0 0 0 0 0) M2
T
(0 0 0 0 0 0 0)U (1 0 0 I 1 0 0)
Row sum is: (0, 2)
I1
(2)
M = (0 1 0 0 0 0 0)M1 U(0 0 0 0 0 0 0)M2
(1 0 0 0 0 I 1) U(0 0 0 0 0 0 0 0)
==> (1 0 0 0 0 I 1) U(0 0 0 0 0 0 0 0) M2
T
(1 0 0 0 0 I 1) U(0 0 0 0 0 0 0)
Row sum is: (2+I, 0)
I1
(3)
M = (0 0 0 I 0 0 0)M1 U(0 0 0 I 0 0 0)M2
(I 0 I0 0 I I) U(I 0 0 I 0 0 0 0)
==>(I 0 I 0 0 I I) U(I 0 0 I 0 0 0 0)M2
T
(I 0 I 0 0 I I) U(I 0 0 I I 0 0)
Row sum is: (4I, 3I)
I1
(4)
M = (0 0 0 0 0 0 0)M1 U(0 0 0 0 1 0 0)M2
==> (0 0 0 0 0 0 0) U(1 0 0 0 0 1 0 0)M2
T
(0 0 0 0 0 0 0) U(1 0 0 I 1 0 0)
Row sum is: (0, 2+I)
I1
(5)
M = (0 0 0 0 0 0 1)M1 U(0 0 0 0 0 0 0)M2
(0 0 0 0 0 1 0) U(0 0 0 0 0 0 0 0)
==> (0 0 0 0 0 1 0) U(0 0 0 0 0 0 0 0)M2
T
(0 0 0 0 0 1 0) U(0 0 0 0 0 0 0)
Row sum is: (1, 0)
Hence the new input vector I2 i:
(1 0 0 0 0 I 1) U (1 0 0 I 1 0 0)
The effect of I2 on M is
I2M = (1 0 0 0 0 I 1)M1 U (1 0 0 I 1 0 0)M2
= (I 1 0 I 0 1 1) U (2+I2
0 0 I 0 1 0 0)
(I 1 0 I 0 1 1) U(1 0 0 I 0 1 0 0)
==> (I 1 0 I 0 1 1) U(1 0 0 I 0 1 0 0)M2
T
(I 1 0 I 01 1)U(1 0 0 I 1 0 0) = I2’
Let I2’= IC2’ IR2’
The new bivectors are:
I2
(1)
= (I 0 0 0 0 0 0) U IR2’
I2
(2)
= (0 1 0 0 0 0 0) U IR2’
I2
(3)
= (0 0 0 I 0 0 0) U IR2’
I2
(4)
= (0 0 0 0 0 0 0) U IR2’
I1
(5)
= (0 0 0 0 0 1 0) U IR2’
I2
(6)
= (0 0 0 0 0 0 1) U IR2’
The effects of I2
(1)
, I2
(2)
, I2
(3)
, I2
(4)
, I1
(5)
, I2
(6)
on M we gt the row
sum as (3I, 2), (2+I, 0), (4I, 3I), (0, 2+I), (1, 0) and (1, 0)
respectively.
Therefore the new input vector is:
I3= (1 0 0 0 0 I 1) U (1 0 0 I 1 0 0)= I2
Therefore the limit point is
(1 0 0 0 0 I 1) U (1 0 0 I 1 0 0)
Table 2:The set of limit points corresponding to different input bivectors
No Input
Bivector
Limit Points Induced Path
1 (1 0 0 0 0
0 0) ∪
(1 0 0 0 0
0 0)
(1 0 0 0 0 I 1) ∪ (1 0
0 I 1 0 0)
(1 0 0 I 0 1 0 0)
(C1=>C2=>C2) ∪
(C1=>C1=>C1)
2 (0 1 0 0 0
0 0) ∪
(0 1 0 0 0
0 0)
(0 1 0 I 0 0 1) ∪ (0 0
1 0 0 1 1)
(0 1 1 0 1 0 1 1)
(C2=>C1=>C1) ∪
(C2=>C7=>C6=>C6)
3 (0 0 1 0 0
0 0) ∪
(0 0 1 0 0
0 0)
(1 0 0 0 0 I 1) ∪ (0 0
1 0 0 0 0)
(0 1 0 0 1 0 0 1)
(C3=>C6=>C1=>C2=>C2)
∪
(C3=>C3=>C3)
4 (0 0 0 1 0
0 0) ∪
(0 0 0 1 0
0 0)
(0 1 0 I 0 0 1) ∪ (1 0
0 I 0 0)
(1 0 0 I 0 1 0 0)
(C4=>C3=>C1=>C1)
∪
(C4=> =>C1=>C1))
5 (0 0 0 0 1
0 0) ∪
(0 0 0 0 1
0 0)
(1 0 0 0 0 I 1) ∪ (1 0 0
I 1 0 0) (1 0 0 0 0 1 0
0)
(C5=>C2=>C2) ∪
(C5=>C1=>C1)
6 (0 0 0 0 0
1 0) ∪
(0 0 0 0 0
1 0)
(0 1 0 I 0 0 1) ∪ (0 0
1 0 0 1 1)
(0 0 0 0 1 0 1 I)
(C6=>C1=>C2=>C2)
∪
(C6=>C6=>C6)
7 (0 0 0 0 0
0 1) ∪
(0 0 0 0 0
0 1)
(0 0 0 0 0 I 1) ∪ (0 0
1 0 0 1 1)
(0 1 1 0 1 0 1 1)
(C7=>C6=>C1=>C2=>C2)
∪
(C7=>C6=>C6)
III. CONCLUSION
We analyzed the problems of rag pickers using the induced
NCRM bimodal.For different inputs, by merging we get a
combined graph which is shown in Figure given
below.Weobserve that the nodes C1 and C2are reachable from
all the nodes and there are many paths through the node C6 and
C1 respectively. Similarly, the nodes D1 and D6hasmore paths.
This reveals that C1 and C2 are the main cause and C6 is the
second prime cause for the mentioned problem. That is, Left
orphans due to parents/family members death (C1); School
dropouts (C2); Quarrel at home with parents or family
members (C6). Likewise no hygiene/ no knowledge about the
hazardous waste (D6); Problem given by Police (R5); Malaria /
typhoid (R6); Scabies / hepatics / skin ailment due to rag
picking (R7); Government and public have taken no steps to
manage waste(R8). These are related to the reasons and for
such cases they enter to the profession of Rag picking.
Integrated Intelligent Research (IIR) International Journal of Computing Algorithm
Volume: 04 Issue: 02 December 2015 Pages: 68-71
ISSN: 2278-2397
71
Figure:1
IV. REMEDIAL MEASURES
Steps must be taken to retain children in schools.Public must
be provided with civic sense not to dump hazardous waste
were rag pickers do the rag picking. As individuals, we have to
control our wastes also it is advisable to use color bins for
biodegradable and recyclable wastesGovernment/ NGO’s
should provide proper masks and gloves.Also, it is adequate to
invest in new wastedisposing technologies so that this issue
can deal effectively.Government should take strong step both
to prevent the rag pickers around the hospital zone and also
thehospital authorities not to dumb the dangerous and
hazardous wastes which are reachable by the rag picker.
REFERENCES
[1] Vasantha, W.B. Elumalai, P. Mary John (2004), “The Analysis of health
hazards faced by rag pickers in Chennai city”, National conference on
Mathematical modeling and Analysis, BITS Pilani.
[2] Cornelius T.L. (1999), Fuzzy theory and Systems, Techniques and
Applications, I - IV, (Academic Press Inc. New York).
[3] Kosko B. (1986), Fuzzy Cognitive Maps, Int. Journal of Man-Machine
studies, 24, 65-75.
[4] Kosko B. (1988) Bi-directional Associative Memories, IEEE
Transactions on Systems, Man and Cybernetics, SMC-18:49-60.
[5] K. Thirusangu, P. Elumalai and A. Praveenprakash (2012), “A
bidirectional associative fuzzy cognitive dynamical system”, Indian
Journal of Science and Technology, Vol. 5, 2333-2340.
[6] Pathinathan, Thirusangu and Mary John (2005), Cause for school
dropouts- A fuzzy approach, ActaCienciaIndica, XXXI M, (4) 1279-
1299.
[7] A. Praveen Prakash, (2010), Analysis of the problems faced by the rural
disadvantaged persons with disabilities – Using new fuzzy bimodals,
Ph.D., Thesis, University of Madras.
[8] VasanthaKandasamy and M. Mary John (2002), “Fuzzy Analysis to
Study the Pollution and the Disease Caused by Hazardous Waste from
Textile Industries”, Ultra Sci, 14 248-251.
[9] VasanthaKandasamy and SmarandacheFlorentin (2003), Fuzzy Cognitive
Maps and Neutrosophic Cognitive Maps, Xi-quan, Phoenix.
[10] W. B. VasanthaKandasamyFlorentinSmarandache (2004) Fuzzy
Relational Maps and Neutrosophic Relational Maps.
[11] L. A. Zadeh (1965) "Fuzzy sets", Information and Control 8 (3) 338–353.
[12] Axelord, R. (ed.) (1976), Structure of Decision: The Cognitive Maps of
Political Elites, Princeton Univ. Press, New Jersey.

Contenu connexe

Similaire à A Study on the Exposures of Rag- Pickers using Induced Neutrosophic Cognitive Relational Maps

A mathematical model of movement in virtual reality through thoughts
A mathematical model of movement in virtual reality through thoughts A mathematical model of movement in virtual reality through thoughts
A mathematical model of movement in virtual reality through thoughts IJECEIAES
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptdungtran126845
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptsami97008
 
IntroductiontoCompChem_2009 computational chemistry .ppt
IntroductiontoCompChem_2009 computational chemistry .pptIntroductiontoCompChem_2009 computational chemistry .ppt
IntroductiontoCompChem_2009 computational chemistry .pptDrSyedZulqarnainHaid
 
COPUTATIONAL CHEMISTRY.ppt
COPUTATIONAL CHEMISTRY.pptCOPUTATIONAL CHEMISTRY.ppt
COPUTATIONAL CHEMISTRY.pptDrKandasamy1
 
IntroductiontoCompChemistry-basic introduc
IntroductiontoCompChemistry-basic introducIntroductiontoCompChemistry-basic introduc
IntroductiontoCompChemistry-basic introducJaved Iqbal
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptAnandKumar279666
 
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...Katie Robinson
 
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...ijcnes
 
Farmer's Suicides in India: A Neuro-Fuzzy Analysis
Farmer's Suicides in India: A Neuro-Fuzzy AnalysisFarmer's Suicides in India: A Neuro-Fuzzy Analysis
Farmer's Suicides in India: A Neuro-Fuzzy AnalysisMangaiK4
 
M2 Internship report rare-earth nickelates
M2 Internship report rare-earth nickelatesM2 Internship report rare-earth nickelates
M2 Internship report rare-earth nickelatesYiteng Dang
 
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...IOSR Journals
 
Mm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsMm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsEellekwameowusu
 
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...MangaiK4
 
NEST 2012 Question Paper
NEST 2012 Question PaperNEST 2012 Question Paper
NEST 2012 Question PaperEneutron
 
IRJET- Optimization of 1-Bit ALU using Ternary Logic
IRJET- Optimization of 1-Bit ALU using Ternary LogicIRJET- Optimization of 1-Bit ALU using Ternary Logic
IRJET- Optimization of 1-Bit ALU using Ternary LogicIRJET Journal
 
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASE
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASEBINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASE
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASEIJCSEA Journal
 
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...IRJET Journal
 

Similaire à A Study on the Exposures of Rag- Pickers using Induced Neutrosophic Cognitive Relational Maps (20)

A mathematical model of movement in virtual reality through thoughts
A mathematical model of movement in virtual reality through thoughts A mathematical model of movement in virtual reality through thoughts
A mathematical model of movement in virtual reality through thoughts
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.ppt
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.ppt
 
IntroductiontoCompChem_2009 computational chemistry .ppt
IntroductiontoCompChem_2009 computational chemistry .pptIntroductiontoCompChem_2009 computational chemistry .ppt
IntroductiontoCompChem_2009 computational chemistry .ppt
 
COPUTATIONAL CHEMISTRY.ppt
COPUTATIONAL CHEMISTRY.pptCOPUTATIONAL CHEMISTRY.ppt
COPUTATIONAL CHEMISTRY.ppt
 
IntroductiontoCompChemistry-basic introduc
IntroductiontoCompChemistry-basic introducIntroductiontoCompChemistry-basic introduc
IntroductiontoCompChemistry-basic introduc
 
IntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.pptIntroductiontoCompChem_2009.ppt
IntroductiontoCompChem_2009.ppt
 
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...
ARL Formulas Of MA Control Chart With Zero-Inflated Binomial Model When Under...
 
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...
Using New Triangular Fuzzy Cognitive Maps (TRFCM) to Analyze Causes of Divorc...
 
MT2
MT2MT2
MT2
 
International Journal of Engineering Inventions (IJEI)
International Journal of Engineering Inventions (IJEI)International Journal of Engineering Inventions (IJEI)
International Journal of Engineering Inventions (IJEI)
 
Farmer's Suicides in India: A Neuro-Fuzzy Analysis
Farmer's Suicides in India: A Neuro-Fuzzy AnalysisFarmer's Suicides in India: A Neuro-Fuzzy Analysis
Farmer's Suicides in India: A Neuro-Fuzzy Analysis
 
M2 Internship report rare-earth nickelates
M2 Internship report rare-earth nickelatesM2 Internship report rare-earth nickelates
M2 Internship report rare-earth nickelates
 
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...
Parametric Sensitivity Analysis of a Mathematical Model of Two Interacting Po...
 
Mm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithmsMm chap08 -_lossy_compression_algorithms
Mm chap08 -_lossy_compression_algorithms
 
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...
An Analysis of Risk Factors of Breast Cancer Using Interval Weighted Fuzzy Co...
 
NEST 2012 Question Paper
NEST 2012 Question PaperNEST 2012 Question Paper
NEST 2012 Question Paper
 
IRJET- Optimization of 1-Bit ALU using Ternary Logic
IRJET- Optimization of 1-Bit ALU using Ternary LogicIRJET- Optimization of 1-Bit ALU using Ternary Logic
IRJET- Optimization of 1-Bit ALU using Ternary Logic
 
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASE
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASEBINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASE
BINARY TREE SORT IS MORE ROBUST THAN QUICK SORT IN AVERAGE CASE
 
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...
Finite Element Solution On Effects Of Viscous Dissipation & Diffusion Thermo ...
 

Plus de MangaiK4

Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...
Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...
Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...MangaiK4
 
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...MangaiK4
 
High Speed Low-Power Viterbi Decoder Using Trellis Code Modulation
High Speed Low-Power Viterbi Decoder Using Trellis Code ModulationHigh Speed Low-Power Viterbi Decoder Using Trellis Code Modulation
High Speed Low-Power Viterbi Decoder Using Trellis Code ModulationMangaiK4
 
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...MangaiK4
 
Relation Extraction using Hybrid Approach and an Ensemble Algorithm
Relation Extraction using Hybrid Approach and an Ensemble AlgorithmRelation Extraction using Hybrid Approach and an Ensemble Algorithm
Relation Extraction using Hybrid Approach and an Ensemble AlgorithmMangaiK4
 
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy Logic
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy LogicSaturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy Logic
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy LogicMangaiK4
 
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...MangaiK4
 
Image Based Password using RSA Algorithm
Image Based Password using RSA AlgorithmImage Based Password using RSA Algorithm
Image Based Password using RSA AlgorithmMangaiK4
 
Evolution of Cryptographic Algorithms and Performance Parameters
Evolution of Cryptographic Algorithms and Performance ParametersEvolution of Cryptographic Algorithms and Performance Parameters
Evolution of Cryptographic Algorithms and Performance ParametersMangaiK4
 
A Framework for Desktop Virtual Reality Application for Education
A Framework for Desktop Virtual Reality Application for EducationA Framework for Desktop Virtual Reality Application for Education
A Framework for Desktop Virtual Reality Application for EducationMangaiK4
 
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...MangaiK4
 
Smart Security For Data Sharing In Cloud Computing
Smart Security For Data Sharing In Cloud ComputingSmart Security For Data Sharing In Cloud Computing
Smart Security For Data Sharing In Cloud ComputingMangaiK4
 
SPSS:An Effective Tool to Compute Learning Outcomes in Acadamics
SPSS:An Effective Tool to Compute Learning Outcomes in AcadamicsSPSS:An Effective Tool to Compute Learning Outcomes in Acadamics
SPSS:An Effective Tool to Compute Learning Outcomes in AcadamicsMangaiK4
 
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approach
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining ApproachBugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approach
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining ApproachMangaiK4
 
Motion Object Detection Using BGS Technique
Motion Object Detection Using BGS TechniqueMotion Object Detection Using BGS Technique
Motion Object Detection Using BGS TechniqueMangaiK4
 
Encroachment in Data Processing using Big Data Technology
Encroachment in Data Processing using Big Data TechnologyEncroachment in Data Processing using Big Data Technology
Encroachment in Data Processing using Big Data TechnologyMangaiK4
 
Performance Evaluation of Hybrid Method for Securing and Compressing Images
Performance Evaluation of Hybrid Method for Securing and Compressing ImagesPerformance Evaluation of Hybrid Method for Securing and Compressing Images
Performance Evaluation of Hybrid Method for Securing and Compressing ImagesMangaiK4
 
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...Performance Evaluation of Image Based Authentication using Illusion-Pin for S...
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...MangaiK4
 
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...MangaiK4
 
A Study on Coverage Criteria Based Test Case Reduction Techniques
A Study on Coverage Criteria Based Test Case Reduction TechniquesA Study on Coverage Criteria Based Test Case Reduction Techniques
A Study on Coverage Criteria Based Test Case Reduction TechniquesMangaiK4
 

Plus de MangaiK4 (20)

Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...
Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...
Application of Ancient Indian Agricultural Practices in Cloud Computing Envir...
 
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...
A Study on Sparse Representation and Optimal Algorithms in Intelligent Comput...
 
High Speed Low-Power Viterbi Decoder Using Trellis Code Modulation
High Speed Low-Power Viterbi Decoder Using Trellis Code ModulationHigh Speed Low-Power Viterbi Decoder Using Trellis Code Modulation
High Speed Low-Power Viterbi Decoder Using Trellis Code Modulation
 
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...
A Survey on Selected Algorithm –Evolutionary, Deep Learning, Extreme Learning...
 
Relation Extraction using Hybrid Approach and an Ensemble Algorithm
Relation Extraction using Hybrid Approach and an Ensemble AlgorithmRelation Extraction using Hybrid Approach and an Ensemble Algorithm
Relation Extraction using Hybrid Approach and an Ensemble Algorithm
 
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy Logic
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy LogicSaturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy Logic
Saturation Throughput and Delay Aware Asynchronous Noc Using Fuzzy Logic
 
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...
Brain Tumour Detection Using Neural Network Classifier and k-Means Clustering...
 
Image Based Password using RSA Algorithm
Image Based Password using RSA AlgorithmImage Based Password using RSA Algorithm
Image Based Password using RSA Algorithm
 
Evolution of Cryptographic Algorithms and Performance Parameters
Evolution of Cryptographic Algorithms and Performance ParametersEvolution of Cryptographic Algorithms and Performance Parameters
Evolution of Cryptographic Algorithms and Performance Parameters
 
A Framework for Desktop Virtual Reality Application for Education
A Framework for Desktop Virtual Reality Application for EducationA Framework for Desktop Virtual Reality Application for Education
A Framework for Desktop Virtual Reality Application for Education
 
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...
Finite State Automata as Analyzers of certain Grammar Class Rules in the Khas...
 
Smart Security For Data Sharing In Cloud Computing
Smart Security For Data Sharing In Cloud ComputingSmart Security For Data Sharing In Cloud Computing
Smart Security For Data Sharing In Cloud Computing
 
SPSS:An Effective Tool to Compute Learning Outcomes in Acadamics
SPSS:An Effective Tool to Compute Learning Outcomes in AcadamicsSPSS:An Effective Tool to Compute Learning Outcomes in Acadamics
SPSS:An Effective Tool to Compute Learning Outcomes in Acadamics
 
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approach
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining ApproachBugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approach
BugLoc: Bug Localization in Multi Threaded Application via Graph Mining Approach
 
Motion Object Detection Using BGS Technique
Motion Object Detection Using BGS TechniqueMotion Object Detection Using BGS Technique
Motion Object Detection Using BGS Technique
 
Encroachment in Data Processing using Big Data Technology
Encroachment in Data Processing using Big Data TechnologyEncroachment in Data Processing using Big Data Technology
Encroachment in Data Processing using Big Data Technology
 
Performance Evaluation of Hybrid Method for Securing and Compressing Images
Performance Evaluation of Hybrid Method for Securing and Compressing ImagesPerformance Evaluation of Hybrid Method for Securing and Compressing Images
Performance Evaluation of Hybrid Method for Securing and Compressing Images
 
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...Performance Evaluation of Image Based Authentication using Illusion-Pin for S...
Performance Evaluation of Image Based Authentication using Illusion-Pin for S...
 
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...
Football Game Algorithm Implementation on the Capacitated Vehicle Routing Pro...
 
A Study on Coverage Criteria Based Test Case Reduction Techniques
A Study on Coverage Criteria Based Test Case Reduction TechniquesA Study on Coverage Criteria Based Test Case Reduction Techniques
A Study on Coverage Criteria Based Test Case Reduction Techniques
 

Dernier

Work Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashaWork Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashasashalaycock03
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxraviapr7
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsEugene Lysak
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapitolTechU
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfMohonDas
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfMohonDas
 
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...Nguyen Thanh Tu Collection
 
10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdfJayanti Pande
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17Celine George
 
3.21.24 The Origins of Black Power.pptx
3.21.24  The Origins of Black Power.pptx3.21.24  The Origins of Black Power.pptx
3.21.24 The Origins of Black Power.pptxmary850239
 
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTDR. SNEHA NAIR
 
EBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlEBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlDr. Bruce A. Johnson
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17Celine George
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...M56BOOKSTORE PRODUCT/SERVICE
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxheathfieldcps1
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxraviapr7
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17Celine George
 
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Dr. Asif Anas
 

Dernier (20)

Work Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sashaWork Experience for psp3 portfolio sasha
Work Experience for psp3 portfolio sasha
 
Prescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptxPrescribed medication order and communication skills.pptx
Prescribed medication order and communication skills.pptx
 
The Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George WellsThe Stolen Bacillus by Herbert George Wells
The Stolen Bacillus by Herbert George Wells
 
CapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptxCapTechU Doctoral Presentation -March 2024 slides.pptx
CapTechU Doctoral Presentation -March 2024 slides.pptx
 
Diploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdfDiploma in Nursing Admission Test Question Solution 2023.pdf
Diploma in Nursing Admission Test Question Solution 2023.pdf
 
HED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdfHED Office Sohayok Exam Question Solution 2023.pdf
HED Office Sohayok Exam Question Solution 2023.pdf
 
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
CHUYÊN ĐỀ DẠY THÊM TIẾNG ANH LỚP 11 - GLOBAL SUCCESS - NĂM HỌC 2023-2024 - HK...
 
10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf10 Topics For MBA Project Report [HR].pdf
10 Topics For MBA Project Report [HR].pdf
 
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdfPersonal Resilience in Project Management 2 - TV Edit 1a.pdf
Personal Resilience in Project Management 2 - TV Edit 1a.pdf
 
How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17How to Make a Field read-only in Odoo 17
How to Make a Field read-only in Odoo 17
 
3.21.24 The Origins of Black Power.pptx
3.21.24  The Origins of Black Power.pptx3.21.24  The Origins of Black Power.pptx
3.21.24 The Origins of Black Power.pptx
 
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINTARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
ARTICULAR DISC OF TEMPOROMANDIBULAR JOINT
 
March 2024 Directors Meeting, Division of Student Affairs and Academic Support
March 2024 Directors Meeting, Division of Student Affairs and Academic SupportMarch 2024 Directors Meeting, Division of Student Affairs and Academic Support
March 2024 Directors Meeting, Division of Student Affairs and Academic Support
 
EBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting BlEBUS5423 Data Analytics and Reporting Bl
EBUS5423 Data Analytics and Reporting Bl
 
How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17How to Add Existing Field in One2Many Tree View in Odoo 17
How to Add Existing Field in One2Many Tree View in Odoo 17
 
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...KARNAADA.pptx  made by -  saransh dwivedi ( SD ) -  SHALAKYA TANTRA - ENT - 4...
KARNAADA.pptx made by - saransh dwivedi ( SD ) - SHALAKYA TANTRA - ENT - 4...
 
The basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptxThe basics of sentences session 10pptx.pptx
The basics of sentences session 10pptx.pptx
 
Over the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptxOver the counter (OTC)- Sale, rational use.pptx
Over the counter (OTC)- Sale, rational use.pptx
 
How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17How to Show Error_Warning Messages in Odoo 17
How to Show Error_Warning Messages in Odoo 17
 
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
Unveiling the Intricacies of Leishmania donovani: Structure, Life Cycle, Path...
 

A Study on the Exposures of Rag- Pickers using Induced Neutrosophic Cognitive Relational Maps

  • 1. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 04 Issue: 02 December 2015 Pages:68-71 ISSN: 2278-2397 68 A Study on the Exposures of Rag- Pickers using Induced Neutrosophic Cognitive Relational Maps S.Johnson Savarimuthu1 , D.Yuvageswary2 1 2 P.G. and Research Department of Mathematics, St. Joseph’s College, Cuddalore, India Email: johnson22970@gmail.com, yuva.dinagaran@gmail.com Abstract-In this paper, using a new Fuzzy bimodal called Induced NeutrosophicCognitive Relational Maps (INCRM) we analyse the Socio-Economic problem faced by Rag-Pickers. Based on the study, conclusions and some remedial measures are stated. Keywords-NCM, NRM, NCRM, INCRM, fixed point, limit cycle, hidden pattern. I. INTRODUCTION L.A. Zadeh(1965) introduced the fuzzy model. one in the. Fuzzy model is a mathematical which deals with neural networks and fuzzy logics. Generally fuzzy model helps to deal the uncertainties which are associated with human cognitive thinking.Among many fuzzy models, in this paper we are interested inFuzzy Cognitive Maps (FCM), Fuzzy Relational Maps (FRM).Praveenprakash (2010) has introduceda bimodal called Fuzzy Cognitive Relation Maps (FCRM)bimodal. In this paper, a new bimodal called Induced NCRM is introduced to analyse the Socio-Economic problem of Rag-Pickers. II. PRELIMINARIES A. Definition Let S = S1∪S2, where S1and S2 are nonempty disjoint sets, then we call Sas a biset. B. Definition A matrix M = M1∪ M2 where M1 is anm × n matrix and M2 is a p×s matrix, then M is called a bimatrix. C. Definition A Neutrosophic Cognitive Relation Maps (NCRM) is a directed fuzzy bigraphit has nodes which deals with concepts like policies and edges as causal relationships. In a NCRM the pair of associated nodes is called as binodes. D. Definition Consider the binodes, {C1 ’ C2 ’ ….. Cn ’ } of the FCM and {D1 ...Dr}, {R1….Rs} of the FRM for the NCRM bimodal.The directed fuzzy graph is drawn by using the edge biweighte𝑖𝑗 𝑡 = {0, 1, -1, I}; 1≤ t ≤ 2.It is defined by e𝑖𝑗 1 ∪e𝑘𝑠 2 in bimatrix where e𝑖𝑗 1 is the weight of the edge CiCj and e𝑘𝑠 2 is the directed edge of DkRs. Here M is the connection bimatrixof the new NCRM bimodal. E. Definition The new NCRMs with edge biweight{1, 0, -1, I} are called simple NCRMs. An NCRM which has a feedback is the representation of cyclesi.e., the casual relations between the nodes is in cyclic way, and thenNCRM is called a dynamicalbisystem. F. Definition The biedgese𝑖𝑗 = (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) take the values in fuzzy casual biinterval [-1,1] ∪ [-1,1]. i) e𝑖𝑗= 0 indicates no causality between the binodes. ii) e𝑖𝑗>0 implies that both e𝑖𝑗 1 >0 and e𝑘𝑠 2 >0 ; indicates increase in the binodesimplies increase in the binodes. iii) e𝑖𝑗< 0 implies that both e𝑖𝑗 1 <0 and e𝑘𝑠 2 < 0 ; similarly decrease in the binodesimpliesdecrease in the binodes. However unlike the FCM and FRM model we can have the following possibilities other than that of e𝑖𝑗 = 0, e𝑖𝑗>0 and e𝑖𝑗< 0. i) e𝑖𝑗 = (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) can be such that (e𝑖𝑗 1 ) = 0 and (e𝑘𝑠 2 )>0. No relation. ii) e𝑖𝑗= (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) also for (e𝑖𝑗 1 ) = 0 and (e𝑘𝑠 2 ) < 0. iii) e𝑖𝑗 = (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) we can have (e𝑖𝑗 1 ) ≤ 0 and (e𝑘𝑠 2 ) > 0 iv) Ine𝑖𝑗 = (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) we can have (e𝑖𝑗 1 ) < 0 and (e𝑘𝑠 2 ) =0 v) In e𝑖𝑗 = (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) we can have (e𝑖𝑗 1 ) > 0 and (e𝑘𝑠 2 ) =0 vi)In e𝑖𝑗= (e𝑖𝑗 1 ) ∪ (e𝑘𝑠 2 ) we can have (e𝑖𝑗 1 ) > 0 and (e𝑘𝑠 2 ) <0 Thus in the case of NCRM we can have 9 possibilitieswhich is useful for solving the problem in accurate way. a) Application of this bimodal to the problem of rag pickers Because of the changes in packaging and lifestyles, waste has definitely increased. Over a decade there was not much plastic packaging. So the scale of waste has changed which results to the fact of land contamination. Of course in India it is in common practice of not to separating the waste. Nowadays electronic waste such as batteries, gadgets waste containing mercury, broken glasses, medicine bottles, syringe, knives and vegetable peelersare thrown in same bins. Thus unsanitary working condition leads them to serious infections and wounds. b) Adaptation of the problem to FCRM Bimodal The linguistic questionnaire was transformed7 main attributes of problems faced by the rag pickers and 8 attributes as the cause of it which acts as catalyst. The attributes are C1: Left orphans due to parents/family members death. C2: School dropouts/ no Money to pay school fees/ill- treatment bythe teachers. C3: Rag picking as a source of income and livelihood. C4: Bad Company and Bad habits. C5: Parents in jail. C6: Quarrel at home with parents or family members. C7: Poverty and unemployment’s due to parents/family memberdeath. D1: Quarrel at home / ill treatment. D2: School dropout.
  • 2. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 04 Issue: 02 December 2015 Pages: 68-71 ISSN: 2278-2397 69 D3: Rag picking as an independent Profession. D4: Poverty and seeks self-respect. D5: Take up to all bad habits due to several reasons likebad company, for sleep, etc., so become drug addicts. D6: No hygiene/ no knowledge about hygiene / about thehazardous waste. D7: Run away from the family. They deal with R1: Father a drunkard / No parents / parents in jail. R2: Enjoy independence /self-support. R3: No motivation by teacher in school. R4: No place to sleep in night. R5: Problem given by Police. R6: Malaria / typhoid. R7: Scabies / hepatics / skin ailment due to rag picking. R8: Government and public have taken no steps to manage waste. We get the synaptic connecting bimatrixM as follows: c) Analysis using FCRM Bimodal First we work for the result for NCRM with initialvectorc1 in NCM component of NCRM bimodal as ONstate saying that Left orphans due to parents/family members death as initializing attribute and the remaing in OFF state and the initial vector d1 in NRM Component, ofNCRM bimodal as ON state saying that Quarrel at home is initializing attribute. The symbol stands forthresholding and it means that values greater than or equal to1are replaced by 1. Let I1 = (1 0 0 0 0 0 0) U (1 0 0 0 0 0 0 0) The effect of I1 on the dynamical system M is I1M = (1 0 0 0 0 0 0)M1 U (1 0 0 00 0 0 0)M2 (0 10 I 0 01) U (1 00 0 0000) ==> (0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0) M2 T (01 0I 0 01) U(1 00 I1 00) = I2 Now I1 ≠I2. Hence we precede further to get thelimit point as follows I2M = (1+I2 0 I 0 0 2I+1 1+I )M1 U (2+I2 0 0 I 0 1 0 0)M2 (1 0 I 0 0 1 1)U (1 0 0 I 0 1 0 0) ==>(1 0 I 0 0 1 1) U (1 0 0 I 0 1 0 0) M2 T (1 0 I 0 0 1 1) U (1 00 I 1 0 0) =I3 Continuing this process and updating we get, I3M = (2+I2 1 I I0 1+2I 2+I) U (1 0 0 I 0 1 0 0) (1 1 I I 0 0 0) U (1 0 0 I 1 0 0) =I4 I4M = (2+I2 1 I I0 1+3I 2+I) U (2+I2 0 0 I 0 1 0 0) (1 1 II 0 1 1) U (1 0 0 I 1 0 0) =I5 =I4 Table 1:Collection of Limit points for different input vectors. No Input Vector Limit Points 1 (1 0 0 0 0 0 0) (1 1 I I 0 1 1) U (1 0 0 I 1 0 0) (1 0 0 I 0 1 0 0) 2 (0 1 0 0 0 0 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1 1) 3 (0 0 1 0 0 0 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1 1) 4 (0 0 0 1 0 0 0) (1 1 I I 0 1 1) U (I 0 0 1 I 0 0) (I 0 0 1 0 I 0 0) 5 (0 0 0 0 1 0 0) (1 1 I I 0 1 1) U (1 0 0 I 1 0 0) (1 0 0 I 0 1 0 0) 6 (0 0 0 0 0 1 0) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1 1) 7 (0 0 0 0 0 0 1) (1 1 I I 0 1 1) U (0 1 1 0 0 1 1) (0 1 1 0 1 0 1 1) d) Induced NCRM Bimodal It is found that in the analysis of NCRMbimodal, the expert have to initialize any vector in ON state i.e. with 1 and the remaining as 0 which will result to many 1’s in the resultant vector. To avoid such situation and to show accuracy we are interested in induced NCRM bimodal.So here we adopt the method which will give importance to each vector by initializing them by keeping in ON state and we pass it over to dynamical system. Secondly the impact on all attributes i.e. using the induced path we will find the combined graph. Finally the idea for detecting the relations and influences between the nodes to find the equilibrium state, Let us take the connection bimatrix of NCRM bimodal which has both NCM and NRM components and let I1 be the initial input bivector. In I1 assign any component to ON state then pass the vector to adjacent matrix which is just multiplying the vector to the matrix M. Then the value for NCM, the components which is greater or equal to 1 is replaced with 1and all with 0. While in the case of NRM, components will be given ON state for two highest values and all in OFF state by giving values 1 and 0 respectively The symbol is the representation of tresholding vector. We will result to many new bivectors chose one from new bivector which has highest number of 1’s and pass to the connection matrix. For each process we have resultant vectors, and I2 is the vector which has maximum number of 1’s just by choosing the first occurring vector. Repeating the same procedure until limit cycle is reached. Now we work for the result for INCRM bimodal with initial vector c1 in NCM component of NCRM bimodal as ON state saying that Left C1 C2 C3 C4 C5 C6 C7 C1 0 1 0 I 0 0 1 C2 1 0 0 0 0 I 1 C3 0 0 0 0 0 1 0 C4 I 0 1 0 0 1 1 C5 0 1 0 I 0 0 1 C6 1 0 0 0 0 0 0 C7 0 0 0 0 0 1 0 R1 R2 R 3 R 4 R 5 R 6 R 7 R8 D1 1 0 0 0 0 0 0 0 D2 0 0 1 0 0 0 0 0 D3 0 1 0 0 1 0 0 1 D4 I 0 0 1 0 0 0 0 D5 1 0 0 0 0 1 0 0 D6 0 0 0 0 1 0 1 I D7 0 0 1 0 0 0 1 0
  • 3. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 04 Issue: 02 December 2015 Pages: 68-71 ISSN: 2278-2397 70 orphans due to parents/family members death as initializing attribute and in NRM, component of NCRM bimodal as ON state saying that Quarrel at home / ill treatmentis initializing attribute. The symbol stands for thresholding and it means that, values 1 and more than 1 are replaced by 1. Let the initial state vector be I1 = (1 0 0 0 0 0 0) U(1 0 0 0 0 0 0 0). The effect of I1 on the dynamical system M is I1M = (1 0 0 0 0 0 0)M1 U (1 0 0 0 0 0 0 0)M2 (0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0) ==> (0 1 0 I 0 0 1) U (1 0 0 0 0 0 0 0) M2 T (0 1 0 I 0 0 1) U (1 0 0 I 1 0 0) =I1 ’ Let I1’ = IC1’ UIR1’ From I1’ it is observed that the new bivectors are: I1 (1) = (0 0 0 0 0 0 0) U(1 0 0 0 0 0 0) I1 (2) = (0 1 0 0 0 0 0) U(0 0 0 0 0 0 0) I1 (3) = (0 0 0 I 0 0 0) U(0 0 0 I 0 0 0) I1 (4) = (0 0 0 0 0 0 0) U(0 0 0 0 1 0 0) I1 (5) = (0 0 0 0 0 0 1) U(0 0 0 0 0 0 0) Now let us find the new input vector I2. I1 (1) M= (0 0 0 0 0 0 0)M1 U(1 0 0 0 0 0 0)M2 ==>(0 0 0 0 0 0 0) U(1 0 0 0 0 0 0 0) M2 T (0 0 0 0 0 0 0)U (1 0 0 I 1 0 0) Row sum is: (0, 2) I1 (2) M = (0 1 0 0 0 0 0)M1 U(0 0 0 0 0 0 0)M2 (1 0 0 0 0 I 1) U(0 0 0 0 0 0 0 0) ==> (1 0 0 0 0 I 1) U(0 0 0 0 0 0 0 0) M2 T (1 0 0 0 0 I 1) U(0 0 0 0 0 0 0) Row sum is: (2+I, 0) I1 (3) M = (0 0 0 I 0 0 0)M1 U(0 0 0 I 0 0 0)M2 (I 0 I0 0 I I) U(I 0 0 I 0 0 0 0) ==>(I 0 I 0 0 I I) U(I 0 0 I 0 0 0 0)M2 T (I 0 I 0 0 I I) U(I 0 0 I I 0 0) Row sum is: (4I, 3I) I1 (4) M = (0 0 0 0 0 0 0)M1 U(0 0 0 0 1 0 0)M2 ==> (0 0 0 0 0 0 0) U(1 0 0 0 0 1 0 0)M2 T (0 0 0 0 0 0 0) U(1 0 0 I 1 0 0) Row sum is: (0, 2+I) I1 (5) M = (0 0 0 0 0 0 1)M1 U(0 0 0 0 0 0 0)M2 (0 0 0 0 0 1 0) U(0 0 0 0 0 0 0 0) ==> (0 0 0 0 0 1 0) U(0 0 0 0 0 0 0 0)M2 T (0 0 0 0 0 1 0) U(0 0 0 0 0 0 0) Row sum is: (1, 0) Hence the new input vector I2 i: (1 0 0 0 0 I 1) U (1 0 0 I 1 0 0) The effect of I2 on M is I2M = (1 0 0 0 0 I 1)M1 U (1 0 0 I 1 0 0)M2 = (I 1 0 I 0 1 1) U (2+I2 0 0 I 0 1 0 0) (I 1 0 I 0 1 1) U(1 0 0 I 0 1 0 0) ==> (I 1 0 I 0 1 1) U(1 0 0 I 0 1 0 0)M2 T (I 1 0 I 01 1)U(1 0 0 I 1 0 0) = I2’ Let I2’= IC2’ IR2’ The new bivectors are: I2 (1) = (I 0 0 0 0 0 0) U IR2’ I2 (2) = (0 1 0 0 0 0 0) U IR2’ I2 (3) = (0 0 0 I 0 0 0) U IR2’ I2 (4) = (0 0 0 0 0 0 0) U IR2’ I1 (5) = (0 0 0 0 0 1 0) U IR2’ I2 (6) = (0 0 0 0 0 0 1) U IR2’ The effects of I2 (1) , I2 (2) , I2 (3) , I2 (4) , I1 (5) , I2 (6) on M we gt the row sum as (3I, 2), (2+I, 0), (4I, 3I), (0, 2+I), (1, 0) and (1, 0) respectively. Therefore the new input vector is: I3= (1 0 0 0 0 I 1) U (1 0 0 I 1 0 0)= I2 Therefore the limit point is (1 0 0 0 0 I 1) U (1 0 0 I 1 0 0) Table 2:The set of limit points corresponding to different input bivectors No Input Bivector Limit Points Induced Path 1 (1 0 0 0 0 0 0) ∪ (1 0 0 0 0 0 0) (1 0 0 0 0 I 1) ∪ (1 0 0 I 1 0 0) (1 0 0 I 0 1 0 0) (C1=>C2=>C2) ∪ (C1=>C1=>C1) 2 (0 1 0 0 0 0 0) ∪ (0 1 0 0 0 0 0) (0 1 0 I 0 0 1) ∪ (0 0 1 0 0 1 1) (0 1 1 0 1 0 1 1) (C2=>C1=>C1) ∪ (C2=>C7=>C6=>C6) 3 (0 0 1 0 0 0 0) ∪ (0 0 1 0 0 0 0) (1 0 0 0 0 I 1) ∪ (0 0 1 0 0 0 0) (0 1 0 0 1 0 0 1) (C3=>C6=>C1=>C2=>C2) ∪ (C3=>C3=>C3) 4 (0 0 0 1 0 0 0) ∪ (0 0 0 1 0 0 0) (0 1 0 I 0 0 1) ∪ (1 0 0 I 0 0) (1 0 0 I 0 1 0 0) (C4=>C3=>C1=>C1) ∪ (C4=> =>C1=>C1)) 5 (0 0 0 0 1 0 0) ∪ (0 0 0 0 1 0 0) (1 0 0 0 0 I 1) ∪ (1 0 0 I 1 0 0) (1 0 0 0 0 1 0 0) (C5=>C2=>C2) ∪ (C5=>C1=>C1) 6 (0 0 0 0 0 1 0) ∪ (0 0 0 0 0 1 0) (0 1 0 I 0 0 1) ∪ (0 0 1 0 0 1 1) (0 0 0 0 1 0 1 I) (C6=>C1=>C2=>C2) ∪ (C6=>C6=>C6) 7 (0 0 0 0 0 0 1) ∪ (0 0 0 0 0 0 1) (0 0 0 0 0 I 1) ∪ (0 0 1 0 0 1 1) (0 1 1 0 1 0 1 1) (C7=>C6=>C1=>C2=>C2) ∪ (C7=>C6=>C6) III. CONCLUSION We analyzed the problems of rag pickers using the induced NCRM bimodal.For different inputs, by merging we get a combined graph which is shown in Figure given below.Weobserve that the nodes C1 and C2are reachable from all the nodes and there are many paths through the node C6 and C1 respectively. Similarly, the nodes D1 and D6hasmore paths. This reveals that C1 and C2 are the main cause and C6 is the second prime cause for the mentioned problem. That is, Left orphans due to parents/family members death (C1); School dropouts (C2); Quarrel at home with parents or family members (C6). Likewise no hygiene/ no knowledge about the hazardous waste (D6); Problem given by Police (R5); Malaria / typhoid (R6); Scabies / hepatics / skin ailment due to rag picking (R7); Government and public have taken no steps to manage waste(R8). These are related to the reasons and for such cases they enter to the profession of Rag picking.
  • 4. Integrated Intelligent Research (IIR) International Journal of Computing Algorithm Volume: 04 Issue: 02 December 2015 Pages: 68-71 ISSN: 2278-2397 71 Figure:1 IV. REMEDIAL MEASURES Steps must be taken to retain children in schools.Public must be provided with civic sense not to dump hazardous waste were rag pickers do the rag picking. As individuals, we have to control our wastes also it is advisable to use color bins for biodegradable and recyclable wastesGovernment/ NGO’s should provide proper masks and gloves.Also, it is adequate to invest in new wastedisposing technologies so that this issue can deal effectively.Government should take strong step both to prevent the rag pickers around the hospital zone and also thehospital authorities not to dumb the dangerous and hazardous wastes which are reachable by the rag picker. REFERENCES [1] Vasantha, W.B. Elumalai, P. Mary John (2004), “The Analysis of health hazards faced by rag pickers in Chennai city”, National conference on Mathematical modeling and Analysis, BITS Pilani. [2] Cornelius T.L. (1999), Fuzzy theory and Systems, Techniques and Applications, I - IV, (Academic Press Inc. New York). [3] Kosko B. (1986), Fuzzy Cognitive Maps, Int. Journal of Man-Machine studies, 24, 65-75. [4] Kosko B. (1988) Bi-directional Associative Memories, IEEE Transactions on Systems, Man and Cybernetics, SMC-18:49-60. [5] K. Thirusangu, P. Elumalai and A. Praveenprakash (2012), “A bidirectional associative fuzzy cognitive dynamical system”, Indian Journal of Science and Technology, Vol. 5, 2333-2340. [6] Pathinathan, Thirusangu and Mary John (2005), Cause for school dropouts- A fuzzy approach, ActaCienciaIndica, XXXI M, (4) 1279- 1299. [7] A. Praveen Prakash, (2010), Analysis of the problems faced by the rural disadvantaged persons with disabilities – Using new fuzzy bimodals, Ph.D., Thesis, University of Madras. [8] VasanthaKandasamy and M. Mary John (2002), “Fuzzy Analysis to Study the Pollution and the Disease Caused by Hazardous Waste from Textile Industries”, Ultra Sci, 14 248-251. [9] VasanthaKandasamy and SmarandacheFlorentin (2003), Fuzzy Cognitive Maps and Neutrosophic Cognitive Maps, Xi-quan, Phoenix. [10] W. B. VasanthaKandasamyFlorentinSmarandache (2004) Fuzzy Relational Maps and Neutrosophic Relational Maps. [11] L. A. Zadeh (1965) "Fuzzy sets", Information and Control 8 (3) 338–353. [12] Axelord, R. (ed.) (1976), Structure of Decision: The Cognitive Maps of Political Elites, Princeton Univ. Press, New Jersey.