 Open Access
 Total Downloads : 197
 Authors : Sunita V. Lahane, M. U. Kharat
 Paper ID : IJERTV3IS040559
 Volume & Issue : Volume 03, Issue 04 (April 2014)
 Published (First Online): 29042014
 ISSN (Online) : 22780181
 Publisher Name : IJERT
 License: This work is licensed under a Creative Commons Attribution 4.0 International License
MultiLevel Analysis System Education Data Based Meta Learning Classification
Sunita V. Lahane
PhD Student, Department of Computer Science,
Sant Gadge Baba Amravati University, Amravati, M.S. India
Dr. M. U. Kharat
Professor, Department of Computer Engineering, MET Bhujbal Knowledge City,
Nashik, M.S. India
Abstract Present paper is designed to validate the capacities of data mining techniques in context of higher education proposing a datamining model for higher education system in the university. In this research work, classification technique is used to estimate students performance. Since there are many techniques that are used for data classification, hence ANN method is used here. By this technique, ANN, which defines about the students selection for college academic system. It provides prior information about the student selection and identification of college the dropouts who need special attention and allow the teacher to provide appropriate advising/ counselling.
Keywords Meta Learning; ANN; Seat Selection System; Classification.

INTRODUCTION
The introduction of information technology in several fields has lead the large number of data storage in different formats like records, files, documents, images, sound, videos, technical data and many new data formats. The data collected from different applications need proper method of extracting knowledge from large sources for better assessment making. Knowledge discovery in databases (KDD), often called data mining, aims at the discovery of useful information from large collections of data [1] [2].
The capability of to predict/classify a students performance is very important in high dimensional educational environments. A very promising arena to achieve this objective is the use of Data Mining (DM) technique [3] [4]. In fact, one in every of the foremost useful DM tasks in e learning is classification. There are totally different instructional objectives for mistreatment classification, the duvet potential student teams with similar characteristics and reactions to a selected pedagogic strategy, to discover students misuse or gameplaying, to cluster students UN agency are hintdriven or failuredriven and notice common misconceptions that students possess, to spot learners with low motivation and notice remedial actions to lower dropout rates
[5] [6]. To predict/classify students when using intelligent tutoring systems, etc. and there are different types of classification methods and artificial intelligent algorithms that have been applied to predict student outcome, marks or scores [7]. Calculating students grades from check scores using neural networks; predicting student educational success (classes that area unit booming or not) mistreatment discriminant operate analysis. Classifying students mistreatment genetic algorithms to predict their final grade; predicting a students educational success using completelydifferent data processing methods; predicting a students marks (pass and fail classes) using regression techniques [8] [9].
Educational data mining methods typically take issue from strategies from the broader data mining literature, in expressly exploiting the multiple levels of purposeful hierarchy in academic information. Strategies from the psychology literature unit usually integrated with strategies from the machine learning and data mining literature to realize this goal
[11] [12] [17]. Education is a vital component for the betterment and progress of a rustic. It permits the individuals of a rustic civilized and well unnatural. Mining in academic atmosphere is named academic data processing, concern with developing new strategies to get information from academic information to investigate students trends and behaviors towards education [13] [14] [18]. Lack of deep and enough information in higher academic system could prevents system management to realize quality objectives, data processing methodology will facilitate bridging this data gaps in education system [15].In the field of data mining data about how students choose to use educational high dimensional software, it may be valuable an instantaneously consider data at the keystroke level, answer level, session level, student level, classroom level, and school level. Issues of time, sequence, and context also play significant roles in the study of educational high dimensional data [16]. This metaanalysis summarizes evidence from randomized educations matching the effect of educational involvements in which VPs were used either as another method or as preservative to usual programmer versus intercessions based on more methods that are outofdate [19]. We accomplished also an assessment of the efficacy of VPs when used to accomplish results oriented to clinical reasoning or outcomes addressed to communication skills and ethical reasoning [20].
The paper is divided into different sections where Section II reviews some of the related work carried out on Meta learning classifier. Section III Describes developed meta learning architecture for Educational data selection system. Section IV gives implementation details. Section V shows results of meta learning process. Section VI concludes the paper and draws direction to future work.

RELATED WORK
Fabrizio Consorti et al. [21] have proposed a metaanalysis technique to performed the Effect Size (ES) from randomized studies comparing the effect of educational interventions in
which Virtual patients (VPs) were used either as an alternative method or additive to usual curriculum versus interventions based on more traditional methods. In this proposed methods, twelve randomized controlled studies were retrieved, assessing 25 different outcomes. Under a randomeffect model, meta analysis showed a clear positive pooled overall effect for VPs compared to other educational methods (Odds Ratio: 2.39; 95% C.I. 1.48O3.84). A positive effect has been documented both when VPs have been used as an additive resource (O.R.: 2.55; C.I. 1.36O4.79) and when they have been compared as an alternative to a more traditional method (O.R.: 2.19; 1.06O4.52). When grouped for type of outcome, the pooled ES for studies addressing communication skills and ethical reasoning was lower than for clinical reasoning outcome.
Lien Vanderkerken et al. [22] have proposed a technique for vocal challenging behavior (VCB) forms a common problem in individuals with autistic disorder. In this proposed methods, they evaluated the effectiveness of several psychosocial interventions applied to decrease VCB in individuals with autistic disorder, they conducted a meta analysis of singlecase experiments (SCEs). The overall treatment effect was large and statistically significant. However, the effect varied significantly over the included studies and participants. Examining this variance, evidence was found for a moderator effect of VCB type and intervention type, with, on average, the largest effects for interventions used to reduce VCB including stereotypical VCB and for interventions containing both antecedent and consequence components. Age, gender, primary treatment setting, publication year, and study quality did not significantly moderate the intervention effect.
RamÃ³n Sagarna et al. [23] have proposed a fundamental question in the field of approximation algorithms, for a given problem instance, is the selection of the best (or a suitable) algorithm with regard to some performance criteria. They proposed multidimensional Bayesian network (mBN) classifiers as a relatively simple, yet wellprincipled, approach fr helping to solve this problem. Precisely, they proposed an algorithm selection decision problem into the elucidation of the nondominated subset of algorithms, which contains the best. This formulation could be used in different ways to elucidate the main problem, each of which could be tackled with an mBN classifier. They illustrated the feasibility of the approach for reallife scenarios with a case study in the context of Search Based Software Test Data Generation (SBSTDG). A set of five SBSTDG generators was considered and the aim was to assist a hypothetical test engineer in elucidating good generators to fulfil the branch testing of a given programmed.
Ningning Zhao et al. [24] have presented a study to explore the relationship between family socioeconomic status and mathematics performance on the base of a multilevel analysis involving a large sample of Chinese primary school students. A weak relationship was found between socioeconomic status and performance in the Chinese context. The relationship does not follow a linear, but a quadratic curve, implying that students from a disadvantaged family and higher socioeconomic background have a higher probability to attain higher mathematics scores. This could be explained because of Chinese cultural beliefs about education, exams and social class mobility. Moreover, the aggregated socioeconomic status at the school level seems to moderate in
the relation between individual SES and academic performance. This suggests that individuals from a disadvantaged family was achieved higher in the school with a higher family socioeconomic status than students who were enrolled in schools with a lower and average family socioeconomic status.
Alejandro PeÃ±aAyala et al. [25] have pursued a twofold goal, the first was to preserved and enhanced the chronicles of recent educational data mining (EDM) advances development; the second was to organized, analyzed, and discussed the content of the review based on the outcomes produced by a data mining (DM) approach. Thus, as result of the selection and analysis of 240 EDM works, an EDM work profile was compiled to describe 222 EDM approaches and 18 tools. A profile of the EDM works was organized as a raw database, which was transformed into an adhoc database suitable to be mined. As result of the execution of statistical and clustering processes, a set of educational functionalities was found, a realistic pattern of EDM approaches was discovered, and two patterns of valueinstances to depict EDM approaches based on descriptive and predictive models were identified. The review concludes with a snapshot of the surveyed EDM works, and provides an analysis of the EDM strengths, weakness, opportunities, and threats, whose factors represent, in a sense, future work to be fulfilled.
Research Objective
This metaanalysis was designed to answer questions about the impact of instructional technology on postsecondary students achievement and attitudes as both a combined collection of studies, and as two subcollections of studies: 1) no technology in the control condition; and 2) some technology in the control condition. In addition, it looked at a set of moderator variables: 1) levels of education; 2) subject matter; 3) classroom/blended learning; 4) difference between treatment and control in technology use; and 5) pedagogical uses of technology.

PROBLEM DEFINITION AND CONTRIBUTION OF THE PAPER
A classical supervised classification problem consists of finding a function which, taking a set of random feature variables as arguments, predicts the value of a one dimensional discrete random class variable. There exist scenarios, however, where more than one class variable may arise, so the extension of the classical problem to the multidimensional class variable case is increasingly earning the attention of the research community.

EFFICIENT EDUCATIONAL DATA CLASSIFICATION THROUGH PROPOSED FEATURE EXTRACTION ALGORITHM WITH
ANN CLASSIFIER
The ultimate target of this research is to design and develop a technique for email classification using naÃ¯ve bayes classifier. The naÃ¯ve bayes algorithm spam filtering is a probabilistic classification technique of email filtering which is based on bayes theorem with naÃ¯ve independence assumptions. Let us consider each of the email can be
illustrated by a set of features (attributes) an ,
where 1 n N . Filtering of spam mails with naÃ¯ve bayes by considering of all features is very difficult also, it need more time. In order solve this problem in this paper; we propose an efficient algorithm to select the significant features from the
available to filter the spam in efficient manner. The overall model of the proposedmail spam classification system is
given in the following figure 1 and each part of the framework is elucidated concisely in the following sections.
A. Artificial Neural Network
Fig. 1: overall model of the proposedhigh dimensional classification system
with various input and output layer weights and we update the
In this paper, we use the artificial neural network for classification. Here, we training the artificial neural network
weights of the neural network with the help of recently developed optimization algorithm [1]. The following figure represents the structure of artificial neural network.
Fig. 2 shows that the general architecture of artificial neural network
Normally, an artificial neural network has an input layer, an output layer, with one or more hidden layers in between the input and output layer. ANN is an artificial intelligence technique that is used for generating training data set and
testing the applied input data. From the above figure
I1, I 2 , I3…I K are the input data which is pass through the
input layer and output layer finally the ANN gives the
output Yi from the output layer Oi . The set of inputs are given
to the input layer, which consists set of weights

The basis function
W1,W2 ,W3…WK and the result from the input layer Bi is
given to the hidden layer, which also consists set of weights W1,W2 ,W3…WM to calculate the output of artificial
neural network.
B Assigning Weights to Input Layer and Hidden Layer
Normally, we initialize the input layer weight as randomly in the range of 0 to 1 and we calculate the output. In this paper, we use the ANN algorithm to choose the input layer
K
Bi I k wk
k 1

Activation function
Ai 1
1 exp Bi
(2)
(3)
and hidden layer weights. The ANN algorithm works based on the echolocation behaviors of ANNs. From the ANN algorithm, we get the values of many characteristics of ANNs
such as velocity vi , position pi , frequency f ,
From the eq. (2) where Bi indicates basis function of ith
solution and the symbol I k indicate the input and the symbol wk represents the weight of the input layer. From the
min
f max wavelength i , rate of pulse ri and loudness A0 from which we select the position pi of the ANN as the weights of the input and the hidden layer of the artificial neural network. The dimension D (attributes) of the ANNs position pi is decided based on the number of input layer K and number hidden layer M of the artificial neural network. The
equation where (3) Ai indicates activation function of ith solution.
2) Output Calculation for Output Layers: The ANN provides the input for hidden layer from the output of the input layer and the hidden layer process that data and donates the result to the output layer. The following equation (4) represents the calculation of hidden layer.
dimension of the each ANNs position (number of solution for
H h
(4)
each iteration) is calculated by the following eq.1.
D K M
(1)
Oi A
p

wh
Based on he number of training data of the artificial neural network, the algorithm finalizes the NN population N BP . The NN algorithm generates N BP
From the equation (4) where Oi indicates output function of ith solution which is calculated in the hidden layer of the ANN. From the equation (4) Ah indicates the activation
number of solutions at D dimensions randomly then the NN
algorithm initializes the velocity vi for each NNs position pi subsequently it also initialize the value of frequency f min , f max , i , rate of pulse ri and loudness A0 .
function output and wh
layer.
Algorithm procedure
indicates the weight of the hidden
After the initialization of every parameter of the NN algorithm, the next step is to assign weights to input layer and hidden layer from the dimension D of the NNs position. The initial K number of attributes is assigned for input layer weights and next N number of attributes is assigned for hidden layer weights.

Training of Artificial Neural Network
Once the weights are assigned to input layer and hidden layer, the training of neural network gets start. The training of the artificial neural network has two main function the first one is, the input layer get the input and process it and gives output to hidden layer to process (i.e. hidden layer gets the input from the output of input layer). The second function done in the hidden layer, the hidden layer process the input from the input layer and gives the output to the output layer. Finally, the result of the ANN is get from the output layer.

Output Calculation for Hidden Layers: While training the artificial neural network, the input layer has two main sub functions to given the inputs for hidden layers, first one is the basis function and the second one is the activation function or transfer function which is given in the following equations (2), (3).

Input: Row student dataset with M number of attributes
Output: classified students seats
Parameters
NLI K Number of input layers
NLH M Number of hidden layers
D Number of attributes of fetness solution
P Number of NN population
i
p t Position of ith NN at time interval t
i
f t Frequency of ith NN at time interval t
i
r t Pulse rate of ith NN at time interval t
Fi Fitness of ith NN
*
pt Best solution ( NN position) at time interval t
i
pt T Updated position of ith ANN at time interval t+T
wk Weight of input layer
wh Weight of hidden layer
Step1. Get the number of input layers NLI K
Get the number of hidden layer NLH M
Calculate number of attributes (dimension) in fitness solution D K M
Get Number of P
Initialize ANN population p t and velocity v t
Define pulse frequency f t at p t
Step 2. Initialize pulse rate r t and the loudness At
Call fitness Fi
Select best solution pt
Generate new solutions pt T (equations 6, 7 and 8)
Step 3: Assign weights (solution) to input layers wk and hidden layers wh
Call fitness Fi
Step 4. If
Arrange position of the solution based on fitness
Select a best solution p old among them
Generate local best solution based on the best solution (equation 11)
Else
Generate new solution (equations 6, 7 and 8)
tT
tT
Accept that solutions as significant solutions Sp
Else
Step 5: Calculate pulse increment ratio spi (equation 13)
Step 6: Calculate loudness decrement ratio spi (equation 14)
Decrease loudness Asp
A
Arrange position of the significant solution based on fitness Step 7: Select the best solution p* from the significant solutions Sp
i
25. Go to step 12 Subroutine: Fitness
For each solution
1. Calculate basis function B i (equation 2)

Calculate activation function Ai (equation 3)

Calculate output Oi (equation 4)

Calculate fitness Fi (equation 5)
End
i
Update loudness At T and pulse ratio rt T (equations 9 and 10)
i
sp
i
i
tT
i
sp
i
tT
r
i
Increase pulse ratio rsp
Deny that solutions
*
i
i
A rand & Fp Fp
If
i
tT
r rand
*
i
i
i
i
i
i
Begin

Calculation of Error (Fitness): In this section, we evaluate the input data of the neural network and weight of the
Based on the error value of the solution only we update the solutions.
input layer and hidden layer based on the error value of the
1 N
2
output. The error value must be less and it indicates how much the input data or weights of the hidden layer and input layer
Fi disired Oi obtained Oi i1
(5)
N
perfect. Here, error value is calculated based on the mean square error (MSE) value of the each iteration. The following equation helps to find the error (fitness) value of the solution.
The above equation (5) which helps to find the error value of the solution or fitness value of the ANNs position. From the above equation (5) is the total number of training data of
the neural network data and disired Oi that indicates the original output of the given input data and obtained Oi that indicates the obtained neural network output. Likewise, for each training data ANN calculates the fitness value with the available solutions.

Updation of Existing Solution: The solutions are
solutions in that iteration. The solutions from the group 1 and group 2 are combined into single group after the existing solutions are updated. Set of significant solutions Spare
selected from the combined group if the following condition
(12) is satisfied moreover based on the fitness value of the solution we increase the pulse rate and reduce the loudness of the solution.
arranged in ascending order based on the fitness value of
every input data of the ANN and the algorithm selects the first
value ascending order as the best solution p* of that
Sp Ai rand & Fpi Fp*
From the above equation (12) Sp
(12)
represents the
iteration t . The solutions are generated in randomly in the NN algorithm since we update each solutions (positions) of the NN algorithm around the best solution since we found the best
solution p* initially subsequently we update the solution with
significant solutions, which consists set of solutions sp1, sp2 ,…, spn . As per the best solution must has
un qualified and qualified rate for that we change the selected and selected rate of the solutions for each solutions from the
the help of the following equations (6), (7) and (8).
ptT pt vtT
(6)
significant solutions
(13) and (14).
Sp based on the following equations
i i i
vtT vt pt p
(7)
F sp r
i i i *
Selected student ratio spi i i
(13)
fi f min
f max f min
(8)
2
Total selected students ratio sp F spi Ai
(14)
From the equation (6) pt T is represents the new solution, i 2
i
pt represents the existing solution and
vt T
specifies that
After the calculation of selected student ratio
spi
and
i i
v
i
updated velocity. From the equation (7) t
represents the
Total selected ratio spi , we change the selected and Total slected of the significant solutions based on the following
existing velocity and p* denotes best solution. From the
equation (15) and (16).
equation (8) fi specifies that frequency of the new solution,
rspi ri spi
(15)
f min signifies minimum frequency, f max denotes that
Aspi Ai spi
(16)
maximum frequency and implies that random vector that belongs to 0 to 1. At the same time, algorithm updates the
loudness Ai and pulse ratio ri for every iteration with the help
We arrange the significant solutions based on the fitness and we select and store the global best solution p* and corresponds fitness after the modification of pulse ratio and
of following equations (9) and (10).
At 1 At
(9)
loudness of those significant solutions. In further iteration, the value of global solution gets updates when the any solution has the better fitness value than the existing global solution.
i i
r t1 r t 1 exp
t
(10)
i i V. RESULTS
The updated new solutions are generated based on the above equations (9) and (10). The newly updated solutions are assigned as weights of the input layer and hidden layer of the artificial neural network and the ANN again calculates the fitness for each input values.

Selection of Best Solution: The newly generated

Standardization and Descriptive Analysis

Data Sets: The selected high dimensional data utilized various measures of and different time intervals and session lengths. Hence, the obtained data were not immediately comparable. To solve this issue, the data were standardized. Using this method, we conducted a series of

solutions based on the value of pulse ratio ri by generates the ordinary participantspecific regression analyses, whereby
random value ( rand ). The solutions, which have the pulse ANN was predicted by the condition. That way, the root
ratio, less than random value ri rand then such solutions mean squared errors were estimated. Subsequently, the raw
are under group 1 and other solutions are under group 2. The solutions from the group 1 is updated with the help of following equation (11) and the other solutions (group 2) are updated based on the above equations (6), (7) and (8).
data of each participant were divided by the participants root mean squared error in order to get standardized data. Furthermore, before conducting the metaanalysis, we carried out a descriptive analysis to get more insight into
i
ptT p
old
At
(11)
the data. The obtained frequencies, means, standard
deviations, ranges, and correlations of possible moderators
From the above equation (11)
p old
is the best solution of
and of descriptive variables are presented in Appendix A.
the group 1 as well as the best solution of the group 1 is selected based on the fitness of the solutions among them. The
value of is the random number which selected from the
B. Selected Student Classification Results
From the whole selected set of data, some seats are taken
range of (1 to 1). The value
At is the average loudness of all
for training and part of it are taken for testing purpose and then this procedure is repeated for the whole education
database. The results are evaluated with both the training algorithms on all the combination of sets, but due to space constraints, only some of the results are listed and compared. The specifications of selected students are given in table 1, 2,
3, and 4 with different colleges with two different branches. From the results, NN algorithm proves superior, as it is taking advantage of classification methodology and searching technique for classification
. TABLE 1: RESULTS WITH COLLAGE A WITH 500, 1000 AND 5000 STUDENT DATA.
ANN Algorithm 

Total 
CS 
NCS 
CS 
NCS 
CS 
NCS 

MHSC 
2 
2 
0 
11 
0 
14 
0 
OTSC 
2 
0 
2 
0 
5 
0 
5 
NRISC 
0 
0 
0 
0 
0 
0 
0 
HASC 
1 
0 
1 
0 
1 
0 
1 
MHST 
2 
2 
0 
11 
0 
21 
0 
OTST 
1 
0 
1 
0 
1 
0 
1 
NRIST 
0 
0 
0 
0 
0 
0 
0 
HAST 
0 
0 
0 
0 
0 
0 
0 
MHBC 
3 
3 
0 
13 
0 
25 
0 
OTBC 
2 
0 
2 
0 
2 
0 
2 
NRIBC 
1 
0 
1 
0 
2 
0 
2 
HABC 
0 
0 
0 
0 
0 
0 
0 
TABLE 2: RESULTS WITH COLLAGE B WITH 500, 1000 AND 5000 STUDENT DATA.
ANN Algorithm 

Total 
CS 
NCS 
CS 
NCS 
CS 
NCS 

MHSC 
2 
2 
0 
10 
0 
13 
0 
OTSC 
1 
0 
1 
0 
4 
0 
4 
NRISC 
0 
0 
0 
0 
0 
0 
0 
HASC 
0 
0 
0 
0 
1 
0 
1 
MHST 
2 
2 
0 
10 
0 
21 
0 
OTST 
1 
0 
1 
0 
1 
0 
1 
NRIST 
0 
0 
0 
0 
0 
0 
0 
HAST 
0 
0 
0 
0 
0 
0 
0 
MHBC 
3 
3 
0 
13 
0 
25 
0 
OTBC 
1 
0 
1 
0 
1 
0 
1 
NRIBC 
1 
0 
1 
0 
2 
0 
2 
HABC 
0 
0 
0 
0 
0 
0 
0 
TABLE 3: RESULTS WITH COLLAGE C WITH 500, 1000 AND 5000 STUDENT DATA.
ANN Algorithm 

Total 
CS 
NCS 
CS 
NCS 
CS 
NCS 

MHSC 
2 
2 
0 
10 
0 
13 
0 
OTSC 
1 
0 
1 
0 
4 
0 
4 
NRISC 
0 
0 
0 
0 
0 
0 
0 
HASC 
0 
0 
0 
0 
1 
0 
1 
MHST 
2 
2 
0 
10 
0 
21 
0 
OTST 
1 
0 
1 
0 
1 
0 
1 
NRIST 
0 
0 
0 
0 
0 
0 
0 
HAST 
0 
0 
0 
0 
0 
0 
0 
MHBC 
3 
3 
0 
12 
0 
25 
0 
OTBC 
1 
0 
1 
0 
1 
0 
1 
NRIBC 
1 
0 
1 
0 
2 
0 
2 
HABC 
0 
0 
0 
0 
0 
0 
0 
TABLE 4: RESULTS WITH COLLAGE D WITH 500, 1000 AND 5000 STUDENT DATA.
ANN Algorithm 

Total 
CS 
NCS 
CS 
NCS 
CS 
NCS 

MHSC 
2 
2 
0 
10 
0 
13 
0 
OTSC 
1 
0 
1 
0 
4 
0 
4 
NRISC 
0 
0 
0 
0 
0 
0 
0 
HASC 
0 
0 
0 
0 
0 
0 
1 
MHST 
2 
2 
0 
10 
0 
20 
0 
OTST 
1 
0 
1 
0 
1 
0 
1 
NRIST 
0 
0 
0 
0 
0 
0 
0 
HAST 
0 
0 
0 
0 
0 
0 
0 
MHBC 
2 
2 
0 
12 
0 
24 
0 
OTBC 
1 
0 
1 
0 
1 
0 
1 
NRIBC 
1 
0 
1 
0 
1 
0 
1 
HABC 
0 
0 
0 
0 
0 
0 
0 
VI. CONCLUSION
In this paper, we have presented an efficient technique to classify the student seats using ANN classifier. Initially, the input student data is given to the feature selection to select the suitable feature for shortlisted classification. The traditional ANN algorithm is taken and the optimized feature space is chosen with the best fitness. Once the best feature space is identified through ANN algorithm, the shortlisted student classification is done using the ANN classifier. The results for the shortlisted student detection are validated through evaluation metrics namely, sensitivity, specificity, accuracy and computation time. For comparative analysis, proposed spam classification is compared with the existing works such as particle swarm optimization and neural network for two datasets. Scalability is one of the features provided by the system where database is spread across the network and only decision tables (small in size) transferred through Meta learning agents are used to classify them.
As a future work, the system can be tested using number of different classification algorithms, So that their features can be combined and may prove useful for other applications student college selection. As features are being combined some of the demerits of different algorithms will also be combined, focus has to be given on this issue also.
ACKNOWLEDGEMENT
Authors thank Dr. V. M. Thakare, P.G. Department of Computer Science, Sant Gadge Baba Amravati University, Amravati, and Maharashtra; for his kind support in providing laboratory infrastructure facility required for the research work.
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Sunita V. Lahane has received B.E. degree in Computer Engineering from Marathvada University, India in 1992,
M.E. degree from Pune University in 2007. She is a registered Ph.D. student of Amravati University. She is currently working as Assistant Professor in Computer Engineering department in MIT, Pune. She has more than 10 years of teaching experience and successfully handles administrative work in MIT, Pune. Her research interest includes Data mining, Business Intelligence &
Aeronautical space research.
Dr. Madan U. Kharat has received his B.E. from Amravati University, India in 1992, M.S. from Devi Ahilya University (Indore), India in 1995 and Ph.D. degree from Amravati University, India in 2006. He has experience of 18 years in academics. He has been working as a Principle of PLIT, Yelgaon, Budhana. His research interest includes Deductive Databases, Data Mining and Computer Networks.