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Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
149
DIAGNOSIS OF HIV-AIDS BY ADOPTING MULTI-LAYER MAMDANI FUZZY SOFT
EXPERT SYSTEM
K. Parveen1, S. Y. Siddiqui2 and M. Daud 3
1,3Department of Computer Science, University of Engineering and Technology (UET), Lahore
2Minhaj University Lahore, Pakistan
1Email of Corresponding author : kausarnawaz6@gmail.com
ABSTRACT: A Multi-layered Mamdani Fuzzy Soft Expert System is planned to diagnose HIV-
AIDS. The Proposed DHA-MLMFES System is an Automatic diagnose of HIV-AIDS exploitation
Multi-layer Mamdani Fuzzy Soft Expert System (DHA-ML-MFES) knowledgeable System, will
classify the various stages of HIV-AIDS like No-HIV-AIDS, HIV Stage 1, 2, 3 and Aids. The Expert
System has seven input variables at layer-I and four input variables at layer-II. At layer-I input,
variables are headache, fatigue, aching muscles, sore throat, swollen lymph nodes, red rashes, fever
that detects output condition of HIV-AIDS, infection, and different downside. The additional input
variables at layer-II are PCR+, PCR+ P24+, P24+ Ab+ (ELISA), Ab+ Wb-inderminate that verify the
output condition of HIV-AIDS like No-HIV-AIDS, HIV stage I, II, III or AIDS and alternative
infection and downside. Results display investigation of the accuracy of the outcomes up to 94 percent
of exploitation planned DHA- Multi-layer Mamdani Fuzzy Soft Expert Structure to set up the
advanced HIV-AIDS technique with help of physician assumptions and compile it to the medical Lab
of Roscommon University Hospital, Ireland.
Keywords: Context-HIV-AID, DHA-ML-MFES, Fuzzy Expert System.
(Received 16.03.2022 Accepted 19.07.2022)
INTRODUCTION
Sickness examination could be a critical part of
the field of clinical and human services offices. An
unseemly investigation of sickness, as a rule, winds up in
ill-advised treatment that prompts complexities of
confusion and in the long run to death (Haire, et. al.,
2014). What are the most important witness as well as
evidence of the un-wellness and intensity of symptoms on
the organs? Once this is often resolved, appropriate
treatment is supervised to gleam distress and heals.
Facing implement this expeditiously at the correct time is
difficult and desires an excessive amount of information
concerning the illness and history of the patient. It’s
essential to research the illness at the correct time and
report its conditions (Sastry, et. al., 2017).
HIV-AIDS could be a non-inheritable
immunological disorder syndrome, AIDS is a chronic,
doubtless serious condition caused by the human
immunological disorder virus (HIV) (Abdulkadir et. al.,
2011) . By damaging your immune system, HIV
interferes along with your body's ability to fight the
organisms that cause illness, thus it should cause death if
it's not diagnosed at the correct time (Yogev, et. al.,
2001) . These are varied reasons that may defect the
condition. The reason behind HIV could be a sexually
transmitted infection (STI). It may also be unfolded by
contact with infected blood or from mother to kid
throughout pregnancy, parturition, or breast-feeding.
While not medication, it should take years before HIV
weakens your immune system to the purpose that you just
have AIDS (Idowu, et. al., ,2008) , (Osunyomi, et. al.,
2015). There's no cure for
HIV/AIDS, however, there are medications that
may dramatically slow the progression of the illness.
These medicines have reduced AIDS deaths in several
developed nations. There are several matches of
reasoning that have traversed. Other momentous
phenomenal inspections and completely different blood
tests are conducted for HIV-AIDS (Rasschaert,et. al.,
2011).
When the take a look at PCR+ the most
important test is HIV-AIDS Surface If the PCR+ takes a
look at results positive then others test PCR+ P24+, P24+
Ab+ (ELISA), Ab+ Weber indreminte should be
conducted to examine the Stage of HIV-AIDS. If HIV-
AIDS is severe it causes health problems (Oh, et. al.,
2005), (Monjok, et. al., 2009), (Spicer, et. al., 2010). The
planned ADHA-Multi-layer Mamdani Fuzzy Inference
System is predicated on those outcomes. They were
completely distinctive knowledge-based approach; a few
of them are supported machine learning, applied math,
knowledge abstraction, call the web, and knowledgeable
system. Knowledgeable system techniques are used for a
previous couple of generations in the therapeutic inquiry.
It'll raise symptomatic veracity also cut down the
expenditure.
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
150
Literature Review: Sexually transmitted infection is the
foremost prevalent disease all over the world and United-
States forwarding not purely mature and adult nature, for
all that in sexuality-active-generation. Still, amount to
just 1/4 in a total population has more than sexually
active people, between the 15 to 25 years ages and half of
them are a new patient of human immunodeficiency virus
(Korenromp et. al., 2000) , (Goldie, et. al., 1999)
Between the ages 15 to 25sense participation in
the elite sexually transmitted quota of any adulthood
brace with-in the United-State with prevalence quota
reaching in a few groups and sub-groups of rash epidemic
proportions. The other untreated left sexually transmitted
infections might result in thoughtful confusion such as
infertility, abnormal pain, cancer, or some death case.
Literally in an economical position of life and more in
social life, sexually trans-mitted patients all over the
world additionally more actual than the big toll on
economic life and term of society rates related to
identifying the treatment of sexually trans-mitted persons
(Waters, et. al., ,2016).
The life pharmaceutical rate of sexually
transmitted infection is non-genetic between the age of
15-25 in the 2014 years and they were calculable at 6.5b
dollars. Also Sexually trans-mitted foreboding& fatality,
and its Co-worker ally cost, it is very important to
contemplate the companion between Sexually transmitted
infection and psychological acquisition in a more as
ability injurious to reactions on a psychological and
sexually trans-mitted person for treatment and their
identification(Newman , et. al., 2004), (Hellinger,et. al.,
2006) . Because of the very high cost in price and the co-
worker foreboding & fatality, in every major work in
physical and psychological, More chance of deed a
Sexually transmitted infection in the many/every of the
foremost generous and prompt risk and menace threats to
the health environment and more-being of teenagers. In
such a reality, the regulation of remedy/narcotic
acknowledge sexually transmitted infection a torment and
trouble among teens and their drawn-up the circumstance
of a national Sexually trans-mitted infection hindrance
method/blueprint (Aaron, et. al., 2005) .
So, avert sexually transmitted infection and
human immunodeficiency virus serve as to perform an
indispensable impersonal clinical and common people
health preference. In this documentation, we pavilion to
inaugurate and provide shortly review of genealogy to
teenager’s sexually transmitted infection and Human
Immunodeficiency Virus risk. And also, we pavilion to
deliberate previous take-up threaten and lighten up the
strengths and weaknesses in those threaten in youth ages
(Wu, et. al., 1999) (Kelly, et. al., 1999).
All after, we pavilion to enunciate assignment
and directive for the future inquiry to accord with
crack/disparity with-in the literature and to be research,
although come up with unified method/blueprint for point
targets bionomics of the sexually transmitted infection
epidemic among teenagers in the whole world (Hallett,,
et. al., 2011). Numerous sexual partners, intermittent
sexual rendezvous, and a little bit small pervasiveness of
dependable condoms utilize and be increase the chance of
sexually transmitted infection and Human
Immunodeficiency Virus risk (Brown, et. al., 2000)
.There has been more generous investigation enunciates
the federation between sexually transmitted infection and
Human Immunodeficiency Virus risk. The first objection
deals with and engages in a pro specialist for classified to
determine and establish the genealogy of this sexual-risk
demeanor (Venkatesh et. al., 2011).
Massive torso and body of inquiry have
enthusiastic/zealous to the real task. Propose scrutiny
scheme the great and effective idea for the event of
competent action to mediation that looks for to vitiate
sexual risk conduct through customizing important
precedent. We have recorded the observational precedent
to youth’s sexual risk nature and the procurement of
sexually transmitted virus with the help of the structure
(Meng, et. al., 2013) , (Stover, et. al., ,2016). This may be
utilized to be pinched from multiple endless studies
committing youth’s results &samples and delineating a
numerous diversify of race in the neuter. Marked
teenagers and their co-workers with very high sexual-risk
attitude have very high sexually transmitted
infection(Abbas,, et. al., 2011) ,(Cambiano,et. al., 2011) .
Now today, artificial-intellige nce utilize
employed to diagnose completely other various varieties
of pharmaceutical reports. Intelligent structures were
being refined to resolve pharmaceutical problems. Fuzzy
Logic Structure is an intensely dominant knowledgeable
structure to investigate other many issues and Answers.
Fuzzy Inference System is incredibly helpful wherever
probabilities for ambiguity could appear. It's employed in
many aspects of daily life like automatically designed
structure, computer Sci-Engineering, pharmaceutical
structure, meteorology, and many more (Diffey,et. al.,
2017). Conferred a Fuzzy Inference System for the
investigation for virus infections and associate in nursing
Advance structure for finding victimization on the fuzzy
logical Inference System (Hethcote,et. al., 1994).
Diagnosis of Liver disease victimization Fuzzy Inference
System of idea on indication like physiological reaction,
complete Weakness, temperature, digestive juice, no
feeling of craving, etc. Introduce a pharmaceutical
structure on the idea of a fuzzy logical Inference System
(Umoh, et. al., 2013).
The fuzzy logical Inference System employed
for husbanding and guiding these employs stress of
human and to execute it, and notify by the alarm.
Thiruvenkatasuresh, M. P. and Venkatachalam, V.
introduced a Fuzzy Inference System to identification the
assorted diseases supported initial symptoms. Diagnose
the liver disease in their analysis (Thiruvenkatasuresh,, et.
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
151
al., 2017) . They introduce ―New Hybrid liver disease
identification System supported Genetic formula and
adaptive Network Fuzzy Inference System‖, planned
Associate in nursing skilled system victimization Fuzzy
Inference System to diagnose and monitor infectious
disease (Sculpher, et. al., 2000) .
Soft Computingapproaches like fuzzy system
(Hussain, et. al., 2019),(Fatima et. al., 2013), (Siddiqui,et.
al., ,2020), neural network (Abbas,, et. al., 2020), and
swarm intelligence (Khan, et. al., 2019) ,evolutionary
computing (Khan,et. al., 2015) Island DE (Khan,et. al.,
2018) are the robust candidate in the field of smart heath.
METHODOLOGY
The proposed decision-based system diagnosing
HIV-AIDS Multi-layer Mamdani Fuzzy Inference
System based on mostly very advanced level computing
is interpreted during this section. Figure 1 shows the flow
of planned Automatic diagnoses HIV-Aids Multi-layer
Mamdani Fuzzy Inference Systemmethodology. The
Automatic diagnoses HIV-Aids Multi-layer Mamdani
Fuzzy Inference System has 2 layers as shown in figure
2. In layer-I diagnose the HIV-AIDS (No/Yes)
victimization seven inputs as shown in figure 2.
Figure 1:Proposed Methodology of DHA-ML-MFES
Figure 2:Layers Diagram for Proposed DHA-ML-MFES
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
152
Parameters Headache, Fatigue, Aching Muscles,
sore throat, swollen lymph Nodes, Red Rashes, and Fever
are accustomed to building up an operation shown in the
first table to gauge the standing of HIV-AIDS. When
first-Layer diagnoses HIV-AIDS then second-Layer is
active for diagnoses the stages of the HIV-Aids. Four
input parameters are shown in figure 2. Second-Layer
inputs are shown in Second-Table.
The First-Layer of planned Automatic diagnoses
HIV-Aids Multi-layer Mamdani Fuzzy Inference System
can be written.
Second-Layer of Automatic diagnoses HIV-Aids Multi-layer Mamdani Fuzzy Inference System can be written as
Input Parameters and Ranges: Input parameters were
applied to mathematical numbers that are accustomed to
diagnosing HIV-AIDS. During this search, a total of 11
differing kinds of input parameters are used in the first
and second layers. The same parameters are used in the
first-layer and others of the parameters are utilizing at the
second-layer. These input parameters and their ranges
and semantic sign are shown in table1 and table 2.
Table 1. Layer-I input parameters of Automatic diagnose of HIV-AIDS exploitation Multi-layer Mamdani Fuzzy
Soft Expert System.
Sr #
Input Parameters
Ranges
Semantic Sign
1
Headache
LT < 1.5
Low
B/W 1 - 2
Normal
GT > 1.7
High
2
Fatigue
LT < 1.5
Low
B/W 1 - 3
Normal
GT > 2
High
3
Acting Muscles
LT < 1.5
Low
B/W 1 – 2.5
Normal
GT > 2
High
4
Sore Throat
LT < 1.5
Low
B/W 1 – 2.5
Normal
GT > 2
High
5
Swollen Lymph Nodes
LT < 1.5
Low
B/W 1 – 2.5
Normal
GT > 2
High
6
Red Rashes
LT < 1.5
Low
B/W 1 – 2.5
Normal
GT > 2
High
7
Fever
LT < 98
Low
B/W 97 - 101
Normal
GT > 100
High
Table 2. Layer-2 input variable of Automatic diagnoses of HIV-AIDS exploitation Multi-layer Mamdani Fuzzy
Soft Expert System.
Sr.
Inputs Parameters
Ranges
Semantic Sign
1
PCR+
LT < 3.1
Low
B/W 2 - 5
Normal
GT > 4
High
2
PCR+ P24+
LT < 3.1
Low
B/W 2 - 5
Normal
GT > 4
High
3
P24+,Ab+(ELISA)
LT < 2
Low
B/W 1 - 4
Normal
GT > 3
High
4
Ab+, WB-inderminate
LT < 3.5
Low
B/W 2 - 6
Normal
GT > 5
High
LT=Less-Than, GT=Greater-Than, B/W=Between
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
153
Output Parameters: In this Documentation, multi-
layered architecture is prospective to interpret HIV-
AIDS. If the first-Layer output is yes, then the second
layer is activated. Output parameters for layers 1-2 are
displayed in table 3.
Table 3. Layer 1-2 output parameter of prospective Multi-layer Mamdani Fuzzy Expert System.
Sr,
Output Parameters
Semantic sign
1
Layer-I
HIV-AIDS
Positive
Negative
2
Layer-II
DHIV-AIDS
Positive
Negative
Graphical Representation and Member-ship functions
of prospective structure: Membership conducts the
mechanism proposition curve parameters between 1, 0&
con-jointly implementation of mathematical-relation that
implement mathematical input and output Parameters.
Graphical and mathematical illustration of Automatic
diagnoses of HIV-AIDS exploitation Multi-layer
Mamdani Fuzzy Inference System& Representation of
I/O parameter for the first and second layers is displayed
in table 4.
This documentation for the member-ship
function is created on the discussion with pharmaceutical
examiners from the Medical-Lab Department of
Roscommon University Hospital, Ireland.
Table 4: Inputs and Outputs parameters and Graphical Representation Functions utilize in Automatic diagnose
of HIV-AIDS exploitation Multi-layer Mamdani Fuzzy Soft Expert System.
Sr#
Input Parameters
Member-ship Function
Graphical Representation
1
PCR+=A
2
PCR+, P24+=B
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
154
3
P24+,Ab+(ELISA)=C
4
Ab+,WB-inderminate=I
Looks-up Rules Table: Looks-up rules table for
proposed Automatic diagnose of HIV-AIDS exploitation
Multi-layer Mamdani Fuzzy Inference System Structure
consists of 81 rules. All rules are displayed in table 5
Looks-up rules table creates with the guidance of
pharmaceutical examiners& Medical-Lab Department of
Roscommon University Hospital, Ireland.
Table 5: Looks-up rules Table for Automatic diagnose of HIV-AIDS exploitation Multi-layer Mamdani Fuzzy
Soft Expert System.
Sr
PCR+
PCR+,P24+
P24+,Ab+(ELISA)
Ab+,WB indeterminate
Outputs
1
LOW
LOW
LOW
LOW
Negative
2
LOW
LOW
LOW
Normal
Negative
3
LOW
LOW
LOW
High
Positive
4
LOW
LOW
High
Normal
Positive
5
LOW
LOW
High
High
Positive
6
LOW
Normal
LOW
LOW
Negative
7
LOW
High
LOW
High
Positive
8
LOW
High
Normal
LOW
Negative
9
LOW
High
Normal
Normal
Negative
10
Normal
LOW
Normal
LOW
Negative
11
Normal
LOW
High
LOW
Positive
12
Normal
Normal
Normal
Normal
Negative
13
Normal
Normal
High
High
Positive
14
High
LOW
LOW
LOW
Negative
15
High
LOW
LOW
Normal
Negative
Input & output Rule-Based structure: Inputs & outputs
Rules develop/create a critical/important part in the
Fuzzy Inference System. Progress depends on any
expert/knowledgeable structure upon these rule-based
structures. During this study/search, Input &Output rules
are created employing an operation table that is displayed
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
155
in table 6. Projected Inputs &Output rules supported
Automatic diagnoses of HIV-AIDS exploitation Multi-
layer Mamdani Fuzzy Inference System is display in
figure 3 and 4.
Inference Engine: The inference engine is one of the
important particles in any skilled structure. In this
documentation, the Mamdani Inference System is utilized
in the first and second layers.
Figure 3: First-Layer Input/outpointed-section structure rules for Automatic diagnose of HIV-AIDS exploitation
Multi-layer Mamdani Fuzzy Soft Expert System
Figure 4: Second-Layer Input/output inter-section structure rules for Automatic diagnoses of HIV-AIDS
exploitation Multi-layer Mamdani Fuzzy Soft Expert System
De- Fuzzifier: De-Fuzzifier is one of the important parts
of a skilled system. There are different kinds of de-
Fuzzifier. During this analysis center of the mass form of
De-Fuzzifier is employed. Figure 5, that displays the De-
Fuzzifier graphical-view and illustration of first-Layer in
Automatic diagnose of HIV-AIDS exploitation Multi-
layer Mamdani Fuzzy Inter-section Structure. In figures
6a-6d, Graphical-view, or illustration of De-Fuzzifier at
Second-Layer DHIV-AIDS expert System is given
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
156
Figure 5: First-Layer, Surface rules diagram Automatic diagnose of HIV-AIDS exploitation Multi-layer
Mamdani Fuzzy Soft Expert System
Figure 5a: Rule Surface for PCR+ and PCR+P24+
Figure 5b: Rule Surface for PCR+ and
P24+Ab+(ELISA)
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
157
Figure 5c: Rule Surface for P24+Ab+(ELISA) and Ab+,
WB-inderminate
Figure 5d: Rule Surface for PCR+P24+and Ab+ WB-
inderminae
In figure 5, diagnoses for the HIV-AIDS
mistreatment chance supported seven inputs parameters
these inputs are often therefore delicate that you simply
won't even notice them. However, the number of virus in
your blood (viral load) is kind of high at this point. As a
result, the infection spreads a lot of simply throughout
primary infection than during the future stage. In some
individuals, persistent swelling of body fluid nodes
happens throughout this stage. Otherwise, there aren't any
specific signs and symptoms. HIV-AIDS remains within
the body and in infected white blood cells. This stage of
HIV infection typically lasts around 10 years if you're not
receiving antiretroviral medical aid. However generally,
even with this treatment, it lasts for many years. Some
individuals develop a lot of severe sickness abundant
sooner.
Figure 5a shows the HIV-AIDS based on PCR+
and PCR+P24+. Colorson the Surface are represented the
different region stages of HIV-AIDS. Similarly, the
remaining figures5b-5d present HIV-AIDS results by
different parameters. The surface Rules diagram
represents different parameter stages.
RESULTS
Working on creating the best results, we utilized
MATLAB R2019a software. This software is best and
also utilized for 2D & 3D modeling, creating algorithmic-
based rules, precursors, and plenty of alternative ground.
This software also utilizes an associate degree
economical tool for computing the program, information
inquiry, visual imaging, and computing. Working on
creating the best results, inputs/output Diagnosis HIV-
AIDS parameters are utilized. During this documentation,
the planned ADHA-ML-MFIS primarily based skilled
System diagnoses HIV-AIDS, table 7. Display that.
Rules
PCR+
(L,N,H)
PCR+
P24+
(L,N,H)
P24+,Ab+(ELISA)
(L,N,H))
Ab+,
WB
(L,N,H)
Human
Expert
Decision
Proposed
DHA
Decision
Probability
of
Correctness
(PC)
The
Probability
of Error’s
(Pe=1-PC)
1
L(7.66)
N(116)
H(90)
N(0.5)
Negative
Negative
0.93
0.07
2
L(7.66)
N(107)
L(60.3)
N(0.387)
3
L(8)
N(110)
L(70)
N(0.4)
4
L(13)
N(101)
N(80.3)
N(0.387)
5
L(9.3)
N(116)
L(66.5)
H(0.7)
6
N(30.3)
N(118)
N(85.4)
N(0.5)
7
N(18)
H(128)
H(90.4)
N(0.387)
8
N(30.3)
N(114)
N(88.7)
H(0.72)
9
N(26)
N(132)
L(60.3)
N(0.387)
10
N(30)
N(135)
N(78.9)
H(0.69)
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
158
11
N(28)
H(128)
H(90.4)
N(0.387)
12
N(22)
N(114)
H(88.7)
H(0.72)
13
N(31)
N(127)
N(79.3)
L(0.387)
14
N(16.3)
N(92.9)
H(94.7)
N(0.63)
15
H(48)
H(122)
H(80.3)
N(0.5)
Negative
16
H(42)
N(110)
L(66.5)
L(0.387)
17
H(48)
N(122)
N(80.3)
N(0.5)
18
H(42)
N(110)
L(66.5)
L(0.387)
19
H(60)
N(126)
N(87.4)
L(0.4)
20
H(37.9)
N(97)
L(96.3)
N(0.613)
Positive
21
H(54)
N(120)
H(94)
N(0.422)
Negative
22
H(48)
N(122)
N(80.3)
N(0.5)
23
H(59)
N(110)
L(76.5)
H(0.7)
24
L(8.66)
N(114)
H(90.3)
N(0.5)
25
L(10.6)
N(108)
L(64.3)
L(0.387)
26
L(8.9)
N(119)
L(70.6)
N(0.4)
27
L(13.7)
N(103)
N(86.3)
L(0.387)
28
L(6.3)
N(111)
L(64.5)
H(0.7)
29
L(37.3)
N(95)
H(97.7)
H(0.64)
Positive
30
L(18.9)
N(118)
H(92.4)
L(0.387)
Negative
31
L(32.3)
N(114)
N(81.7)
H(0.72)
32
L(20.3)
N(114)
N(83.4)
N(0.5)
33
L(30.5)
N(135)
L(75.9)
H(0.69)
34
L(28.2)
N(128)
H(93.4)
L(0.387)
35
N(22.1)
N(104)
H(85.7)
H(0.72)
36
H(31.6)
N(121)
L(75.3)
L(0.387)
37
H(29.3)
N(92.9)
L(34.7)
L(0.13)
Figure 6: Layer-II, Lookup diagram for Proposed DHA-MLMFES
Figure 7, shows the accuracy of our proposed
model for diagnosis of HIV-aids using a fuzzy soft expert
system, it indicates that overall accuracy touch 94 percent
which is very important and has a high impact on the
diagnosis system
Figure 7: Layer-II, Lookup diagram for Proposed DHA-MLMFES
Conclusion: The primary focus of our analysis to style
professional System DHA-MLMFES to diagnoses HIV-
AIDS by taken the guidance of pharmaceutical examiners
& Medical-Lab of Roscommon University Hospital,
Ireland. This planned skilled Structure is straight
forwarding and also the best simple to utilize for
Pakistan Journal of Science (Vol. 74 No. 3 September, 2022)
159
each/every pharmaceutical-professionals and non-
pharmaceutical-professionals. Common personnel might
be also diagnosing the advancement of HIV-AIDS by
giving necessary inputs. The accuracy of our proposed
DHA-ML-MFES model for diagnosis of HIV-AIDS
using a fuzzy soft expert system is 94 percent and a miss
rate 6 percent, which is very important and has a high
impact on the diagnosis system.
In the future, this planned Automatic diagnosis
HIV-AIDS Multi-layered Mamdani Fuzzy Expert
Structure given as treatment by utilizing many alternative
advance-techniques together with machine Intelligence
like Neural networks and Neuro-fuzzy.
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