Change Data Capture on OLTP Staging Area for Nearly Real Time Data Warehouse Base on Database Trigger.

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System Progress Estimation in Time based Coordinated Checkpointing Protocols Authors : P. K. Suri, Meenu Satiza

1-6

Adaptive Learning for Algorithm Selection in Classification Authors : Nitin Pise, Parag Kulkarni

7-12

Routing Protocol for Mobile Nodes in Wireless Sensor Network Authors : Bhagyashri Bansode, Rajesh Ingle

13-16

32-Bit NxN Matrix Multiplication: Performance Evaluation for Altera FPGA, i5 Clarkdale, and Atom Pineview-D Intel General Purpose Processors

Authors : Izzeldin Ibrahim Mohd, Chay Chin Fatt, Muhammad N. Marsono

17-23

Recognizing and Interpreting Sign Language Gesture for Human Robot Interaction Authors : Shekhar Singh, Akshat Jain, Deepak Kumar

24-31

Change Data Capture on OLTP Staging Area for Nearly Real Time Data Warehouse base on Database Trigger

Authors : I Made Sukarsa , Ni Wayan Wisswani, K. Gd. Darma Putra, Linawati

32-37

Decision Support System for Admission in Engineering Colleges based on Entrance Exam Marks Authors : Miren Tanna

38-41

A Genetic Algorithm based Fuzzy C Mean Clustering Model for Segmenting Microarray Images Authors : Biju V G, Mythili P

42-48

System Progress Estimation in Time based Coordinated

Checkpointing Protocols

P. K. Suri

Dean, Research and

Development; Chairman, CSE/IT/MCA, HCTM Technical Campus, Kaithal,Haryana, India

Meenu Satiza

HCTM Technical Campus

ABSTRACT

A mobile computing system consists of mobile and stationary nodes. Checkpointing is an efficient fault tolerant technique used in distributed systems. Checkpointing in mobile systems faces many new challenges such as low wireless bandwidth, frequent disconnections and lack of stable storage on mobile nodes. Coordinated Checkpointing that minimizes the number of processes to take useless checkpoints is a suitable approach to introduce fault tolerance in such systems. The time-based checkpointing protocol eliminates communication overheads by avoiding extra control messages and useless checkpoints. Such protocols directly accesses stable storage when checkpoints are saved. In this paper a new probabilistic approach for evaluation of the system progress is devised which is suitable for the mobile distribution applications. The system behavior is observed by varying some system parameters such as fault rate, clock drift rate, saved checkpoint time, checkpoint intervals. A validation regarding system progress is made via a simulation technique. The simulation results show that the proposed probabilistic model is well suited for the mobile computing systems.

General Terms

Checkpointing, System progress, Simulation

Keywords

Distributed system, fault tolerance, time-based checkpointing System progress, consistent checkpoint

1.

INTRODUCTION

Checkpointing is a major technique of fault tolerance system in which state of a process has to be saved in stable storage so that the process can be restarted in case of fault. There are two main categories of checkpointing techniques: (i) coordinated and (ii) uncoordinated checkpointing. In coordinated checkpointing, the processes send the control messages to their dependent processes to save their states at the same time. This results a global consistent state from which the system recovers when a fault occur in the system. In uncoordinated checkpointing, the processes save their states independently. In this type of protocol, during fault occurrence processes rollback to a point of recovery. Recently new type of time based coordinated checkpointing techniques have been introduced which avoid extra coordination messages among dependent processes. The time based approach is based on loosely synchronized timers. The Timer information is piggybacked along application messages. System performance of the time based checkpointing protocols depends on the

application and system’s characteristics such as checkpoint intervals, save checkpoint time, resynchronize time, clock drifts. We proposed a probabilistic model for the system

progress with particular system parametric values. This model shows that how system operations can affect the system performance and the simulation results shows the states at which system perform well with the particular values of defined parameters. A simulation model is also developed to validate the system progress.

1.1

Related work

In 1985, Chandy and Lamport [1] proposed a global snapshot algorithm for distributed systems. The global state is achieved by coordinating all the processors and logging the channel state at the time of checkpointing. Special messages called markers are used for coordination and for identifying the messages originating at different checkpointing intervals. In 1987, Koo-Toueg [5] proposed a two phase Minimum-process Blocking Scheme for distributed system. The consequence of algorithm is a consistent global checkpointing state that involves only the participating processes and prevent live lock problem (A single failure can cause an infinite rollbacks)

In 1996 Ravi Prakash and Mukesh Singhal [11] presented a synchronous non-blocking snapshot collection algorithm for mobile systems that does not force every node to take a local snapshot. They had also proposed a minimal rollback/recovery algorithm in which the computation at a node is rolled back only if it depends on operations that have been undone due to the failure of node(s).

In 1996 N. Neves and W.K.Fuches [9] presented a time based checkpointing protocol which eliminate communication overhead present in traditional checkpointing protocols. The checkpointing protocol was implemented on CM5 and their performance was compared using several applications. In 2001 Guohong Cao and Mukesh Singhal [4] had introduced the concept of “mutable checkpoint” which is neither a tentative checkpoint nor a permanent checkpoint. To design efficient checkpointing algorithms for mobile computing systems the mutable checkpoints can be saved anywhere e.g. the main memory or local disk of MHs. In this way taking a mutable checkpoint avoids the overhead of transferring large amounts of data to the stable storage at MSSs over the wireless network.

In 2002 Chi-Yi Lin et. al. [7] proposed an improved time based checkpointing protocol by integrating the improved timer synchronization technique. The mechanism of time synchronization utilizes the accurate timer in MSSs as an absolute reference. The timers in fixed hosts (MSSs) are more reliable than those in MHs.

In 2003 Chi-Yi Lin and Sy-Yen Kuo[8] had proposed an efficient time-based non-blocking checkpointing protocol. The protocol reduces the no. of checkpoints transmitted over wireless link. The protocol use synchronized timer to indirectly coordinate the creation of checkpoints. In the

system each process takes a soft checkpoint first which is saved in main memory of mobile hosts. If the process is irrelevant to initiator it can be discarded otherwise will be saved in the local disk at a later time as hard checkpoint. As a result the number of disk accesses in mobile hosts can be reduced. The advantage of using time based approach improves the need of explicit coordination message.

In 2006 Men Chaoguang [2] proposed a two-phase time based checkpointing strategy. This eliminates orphan and in-transit messages. In this strategy, the issues of time - based adaptive checkpoint strategy was evaluated which describes about all processes need not to block their computation work and also not to log all messages. In proposed strategy the inconsistency issues are also discussed.

In 2007 Awasthi and Kumar [6] had proposed a probabilistic approach based on keeping track of direct dependencies of processes. Initiator MSS collects the direct dependency vectors of all processes and sends the checkpoint request to all dependent MSSs. This step was taken to reduce the time to collect the coordinated checkpoint. It would also reduce the number of useless checkpoints and the blocking of the processes. The buffering of selective messages at the receiver end and exact dependencies among processes had maintained. Hence the useless checkpoint requests and the number of duplicate checkpoint requests get reduced.

In 2008 Suchistmita Chinara and Santanu Kumar Rath [3] had proposed an energy efficient mobility adaptive distributed clustering algorithm for Mobile ad-hoc Network. In which a better cluster stability and a low maintenance overhead is achieved by volunteer and non volunteer cluster heads. The proposed algorithm is divided into parts like cluster formation, energy consumption model, cluster maintenance. The objective of algorithm is to minimize the re affiliation rate (A changing situation of member node search for another head is called re affiliation) .The simulation Experiment compare the ID of members. A high ID member act as cluster head and cluster maintenance overhead is reduced from time to time.

In 2011 Anil Panghal, Sharda Panghal, Mukesh Rana [10] presented a comprehensive study of the existing techniques namely Checkpoint-based recovery and Log-based recovery. Based on the study they conclude that Log-based recovery techniques which combine checkpointing and logging of nondeterministic events during pre-failure execution are suitable for systems that frequently interact with the outside world. They also conclude that communication-induced checkpointing reduces the message overhead if implemented along with checkpoint staggering can prove to be the best method for recovery in distributed systems

2.

PROBLEM FORMULATION

In this paper a number of time based checkpointing protocols are analyzed. In [9] Neves and Fuchs had given the concept of timers to reduce the communication overhead. They made the following assumptions:

(a) The processes involved in checkpointing have loosely synchronized clocks.

(b) All the processes are approximately synchronized and have a deviation from real time in their local clock timers. The local clock drift rate between the processes

being assumed as ρ.

(c) The timer will terminate at most (2ρT/ (1-ρ2) ≈ 2ρT) seconds apart. Here T is the initial timer value. Normally

drift rate ρ attains values between 10-5

sec. to 10-8 sec.

(d) The clocks will show a maximum drift of 2NρT after N checkpoint interval.

Consider the following figure1. in which P1 and P2 are two processes. The message M1 is sent from process P1 to P2 in its Nth checkpoint interval and also message M2 is being sent in same interval of P1 to (N+1)th interval of P2. Let some fault arrives in timeline of Process P2.

It is observed that checkpoint N+1 is saved in P2 before the fault occurrence. Now P2 has yet to receive M2. But P2 has no information of message M2. Such situations can be handled by resending unacknowledged messages again.

According to Neves the more time is wasted in storing checkpoints and the processes has to block its execution for a long time which is an impractical situation. Such inconsistencies of in-transit or orphan messages can be handled by using time based checkpointing approach where the messages are now being sent along with timers.

According to Men Chaoguang approach [2] the orphan messages can be eliminated by using communication induced approach and in-transit messages can be stored in message logging queue. The following figure 2. Illustrates the above situations.

Consider P1 and P2 are two processes. T1 and T2 are their

timers respectively. Let MD = D + 2 ρ T be the maximum

deviation between the timer of two processes = T1– T2 .tmax is the maximum delivery time at which process P2 should get the message M1. tmin is the minimum delivery time of message M4. ED = MD – tmin is the effective deviation in which the processes cannot send or receive the message .M2 and M3 lies in effective deviation and they arise the inconsistency due to orphan and in-transit messages. To handle orphan message M3 a communication induced checkpoint is placed before the delivery of message M3 and in-transit message M2 can be retrieved from message logging queue.

It is observed that the parametric values ρ, T, D, tmin, tmax,

fault rate λ, Saved checkpoint point time S, time (t) at which fault occurs affects the mobile distributed system performance.

In this paper a probabilistic model is developed in which the system performance is evaluated by varying various system parametric values.

P1

P2

M1 M2

N N+1

N N+1

Fault arrived

3.

SYSTEM PROGRESS EVALUATION

3.1

Probabilistic model development

When faults occur in the system, resynchronization is made.

Here the system’s progress is defined as the ratio of

constructive computational work to the total work during a given interval of time.

In order to perform a simulation experiment on distributed

system a random sample of time t1,t2,t3………..tn is

generated by transforming n uniform random numbers

u1,u2,u3……un in the interval (0,1).Where λ is the positive

constant depending on characteristics of distributed system [12]. The general term of time tk is

tk = –(1/λ)*logeuk where k Є [1,n] Let ts = time to store checkpoint, tmin be the minimum checkpoint delivery time, tmax be the maximum checkpointing delivery time, Tdiff be the maximum difference between timers of different processes, L be the length of checkpoint intervals before resynchronization, ρ be the clock drift rate between the processes, fr be the fault rate, Tw be the probabilistic wasted time of fault occurrence ,Twr be the probabilistic wasted time of fault occurrence between resynchronization, tr be the resynchronization time.

Let T1, T2, T3………Tnmax are n checkpoint intervals between resynchronization. When fault not occurs then this time will be equal to maximum number of checkpoint intervals nmax.

The system progress is evaluated by developing following probabilistic model.

Here ts ≤ (Tdiff + 2*nmax*L* ρ – tmin) (ts + tmin – Tdiff)/ (2*L*ρ) ≤ nmax nmax = ceil ((ts + tmin –Tdiff)/(2*L*ρ)

Let Tcons be the time interval during which constructive computational work is done. Where Tcons is given as

Tcons = L – ts – tk where k Є [1,n]

The probability density function of occurrence of fault is given as

Let Ir = Expected number of intervals between resynchronization = Probability of happening fault during any Interval less than nmax + Probability of happening no fault during any interval less than nmax.

In Fig. 3 a set of nmax checkpoints numbers ({1, 2, 3 ……k, k+1…nmax}) are considered on the time line of the process.

The probability of a fault occurring in the kth checkpoint interval Pr[k] in last resynchronization process is given as

Pr[k] = e – fr* L*k– e – fr* L*(k+1) (nmax – 1)

Ir =

∑

k * Pr[k] + nmax * e – fr* L*k k =1

Ir = (1 – e – fr* L*nmax)/ (e fr* L– 1)

Probability of wasted time of occurrence of fault Tw and the wasted time of occurrence between resynchronization Twr is given as

Tw = ((e – fr*Tcons)*( – fr * Tcons – 1 ) + 1)/(fr*(1 – e – fr*Tcons

))

The Probability of wasted time of occurrence between resynchronization Twr is given as

Twr = (1 – e – fr* L*nmax)*( ts +Tw) + e – fr* L*nmax * tr Let probability of total time between resynchronization is Tr .

Where Tr = Ir * L + Twr

Let TCW be the Probability of time used in constructive work between resynchronization

TCW = Ir*Tcons

The System Progress (SP say) of a process = TCW/Tr

The System Progress of all the processes = ∑ TCW/Tr

The System Progress of the complete system having n processes = (∑ TCW/Tr)/n.

3.2 Validation of system progress

To confirm the correctness of system progress evaluation, system progress validation is implemented to more detailed confidence level of simulation. Here in the simulation technique to achieve the validation having a better confidence level ,first 1000 runs of simulation experiment are made from 10 samples with 100 checkpoint intervals then

2000,3000,……..10000 runs are made and then average value of system progress, their standard deviation (say SD), upper ( say UL) , lower (say LL) confidence limit of system progress is computed. Further corresponding interval of interest Tcons and then corresponding optimal system progress is evaluated for the system by using the variation among the parameters

say λ, ρ, ts, fr, L. The used simulation technique follows as

Let’s take n independent samples of time interval Length L and according to such n samples values of System progress SP1,SP2,SP3………SPn .Then their mean and

P1

P2

M1

tmax

T2

T1

M2

M3

M4

tmin

ED MD

Fig.2 Elimination of inconsistent state

Fig 3. Fault arrival in kth checkpoint interval

L 2

1 k k+1

Fault Waste

standard deviation σ are evaluated .The sample mean of all

System progresses are to be evaluated by using formula: SPmean = ∑ SPi/ n

The variance σ2

can be estimated as :

σ2

est = (1/ (n-1))*∑(SPk– SPmean) 2

The general relationship between the parameters is given as Pr { –t ≤ SPmean≤ + t} = 1 –α

where t is the tolerance on either side of mean within which the estimated to fall within probability 1– α. The normal

density function is Φ(y) = y1-α/2, the upper confidence limit UL and lower confidence limit LL of System progress can be obtained respectively.

y

Φ(y) =

∫

(1/√2Π)* e– (z2/2)

*dz –α

Where z = (√n) *(SPmean – )/σest UL = SPmean + (y1-α/2 * σest) / (√n) LL = SPmean– (y1-α/2*σest) / (√n) y1-α/2 = 2.58 (99% confidence level)

The interval (UL - LL) will contain the true mean with a specific certain experimental confidence value [12].

3.2

Simulation results

Simulation result shows that the System Performance is affected by the various factors such as number of checkpoint intervals, Clock drift rate of processors, Fault rate of processors, Time of saving checkpoints .In our simulation experiment such variations of factors against System Progress are shown in tabular as well as graphical form.

3.2.1

Checkpoint interval vs. system progress

The following table expresses the parametric values used in proposed model. The first column shows when varying values of Fault rate (Table 4), Drift rate (Table 5), Checkpoint intervals (Table 2) and second and third column shows the corresponding other variable names and their particular values.

Table 1: Parametric values of System model

According to these values the system progress is evaluated and respective graphs are drawn. First according to increasing values of checkpoint intervals (L) corresponding decreasing values of system progress is evaluated i.e. obviously as number of checkpoint intervals are increased corresponding system progress get decreased (Fig 4) i.e. The system progress is affected by number of checkpoints

Table 2. Checkpoint intervals vs. System Progress

Checkpoint Intervals( L)

System Progress SP

100 0.9925

10100 0.950282 20100 0.902832 30100 0.857017 40100 0.81285 50100 0.770316

Fig 4: Checkpoint intervals vs. System Progress

3.2.2

Saved Checkpoint time vs. system progress

Table 3. describes as time to save checkpoints get increased the system progress get decreased.

This is illustrated in Fig.5 which is obviously true as the time to save checkpoint get increased the system progress will decrease.

Table 3. Saved Checkpoint time vs. System Progress

Saved checkpoint Time (ts)

System Progress (SP)

1 0.98183

2 0.981554

3 0.981278

4 0.980999

5 0.980723

6 0.980441

7 0.980166

8 0.979887

9 0.979609

10 0.979331

11 0.979053

12 0.978775

13 0.978498

14 0.978221

15 0.977942

SYSTEM PROGRESS EVALUATION Used System parameters Variable tr 0.1 State Tdiff 0.01

tmin 0.001

fr L 3600

Fault rate ts 0.7

ρ 0.000001

ρ fr 0.00001

Drift rate L 3600

ts 0.7

L fr 0.00001

Ckeckpoint

Interval ρ 0.000001

Fig 5: Checkpoint intervals vs. System Progress

3.2.3

Fault Rate vs. system progress

This subsection describes how fault rate affects the system progress.

Table 4. describes as fault rate get increased The system progress get decreased.This is illustrated in Fig.6

Table 4. Fault Rate vs. system progress

Fault Rate fr

System Progress SP 1.00E-16 0.999803 1.00E-15 0.999741 1.00E-14 0.999767 1.00E-13 0.999783 1.00E-12 0.999802 1.00E-11 0.999761 1.00E-10 0.999726 1.00E-09 0.999765 1.00E-08 0.999658 1.00E-07 0.999625

Fig 6 Fault Rate vs. System Progress

3.2.4

Drift Rate vs. system progress

This subsection describes how drift rate affects the system progress. For low value of drift rate the system progress is high little bit .The System Progress of non blocking protocol is not much affected for different values of drift rate the System .Table 5. and Fig.6 illustrates this.

Table 5. Drift Rate vs. System progress

Drift Rate ρ System Progress SP

0.1 0.994184 0.01 0.993871 1.00E-03 0.993605 1.00E-04 0.983866 1.00E-05 0.993416 1.00E-06 0.994208 1.00E-07 0.993747 1.00E-08 0.994071 1.00E-09 0.993928 1.00E-10 0.994053

Fig 7 Drift Rate vs. System Progress

3.2.5

System progress validation

In Table 6. the first column, first entry illustrates that 10 samples of checkpoint interval of length 100 are taken and corresponding System progress of 100,200,…..1000 checkpoint intervals gets evaluated, their average is shown in second column (i.e. 0.99520).The third and fourth column shows their standard deviation, upper and lower confidence limit respectively. Similarly System Progress of other samples

having checkpoint intervals 2000, 3000 …10000 are validated

Similar validation can be applied to other system parameters. The difference between upper and lower confidence limit should be less than 2*Tolerance value. Here tolerance value is 0.001 for 99% confidence

Table 6. System progress validation

Sample No.

System Progress average

σest

Upper Confidence. Limit

Lower Confidence Limit 1000 _{0.99520 0.00114 0.9961 0.99427} 2000 0.99180 0.00141 0.9929 0.99065 3000 0.98702 0.00146 0.9882 0.98583 4000 _{0.98215 0.00014 0.9833 0.98095} 5000 0.97727 0.00014 0.9784 0.97606 6000 0.97238 0.00147 0.9735 0.97117 7000 _{0.96750 0.00147 0.9687 0.96629} 8000 0.96263 0.00147 0.9638 0.96143 9000 0.95777 0.00146 0.9589 0.95658 10000 0.95293 0.00146 0.9541 0.95174

4.

CONCLUSION

In this paper the problem of arrival of fault is efficiently discussed. A probabilistic model is developed for evaluation of System progress of the processes along with a particular set of parameters. It is observed that the System Progress is evaluated by introducing the time generated by negative exponential distribution function. The system Progress gets optimizes on particular values of system parameters. A validation regarding the System progress on the basis of set of parameter checkpoint interval length (L) value is derived .Such validation can be evaluated regarding the other set of parameters such as drift rate, fault rate, saved checkpoint time

.

5.

ACKNOWLEDGMENTS

Sincere thanks to HCTM Technical Campus Management Kaithal-136027, Haryana, India for their constant encouragement.

6.

REFERENCES

[1] Chandy K.M. and Lamport L. “Distributed Snapshots:

Determining Global States of Distributed Systems” ACM

Transactions Computer systems vol. 3, no.1. pp. 63-75, Feb.1985

[2] Chaoguang M., Yunlong Z. and Wenbin Y., “A two -phase time-based consistent checkpointing strategy,” in

Proc. ITNG’06 3rd IEEE International Conference on

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Distributed Systems, vol. 12, no. 2, pp. 157-172, February 2001

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Checkpointing Protocol for Mobile Computing System”

in Proc of the 2002 IEEE Pacific Rim International

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[12]“ System simulation with digital computer” by Narsingh Deo

Adaptive Learning for Algorithm Selection in

Classification

Nitin Pise

Research Scholar

Department of Computer Engg. & IT College of Engineering, Pune, India

Parag Kulkarni

Phd, Adjunct Professor Department of Computer Engg. & IT

College of Engineering, Pune, India

ABSTRACT

No learner is generally better than another learner. If a learner performs better than another learner on some learning situations, then the first learner usually performs worse than the second learner on other situations. In other words, no single learning algorithm can perform well and uniformly outperform other algorithms over all learning or data mining tasks. There is an increasing number of algorithms and practices that can be used for the very same application. With the explosion of available learning algorithms, a method for helping user selecting the most appropriate algorithm or combination of algorithms to solve a problem is becoming increasingly important. In this paper we are using meta-learning to relate the performance of machine meta-learning algorithms on the different datasets. The paper concludes by proposing the system which can learn dynamically as per the given data.

General Terms

Machine Learning, Pattern Classification

Keywords

Learning algorithms, Dataset characteristics, algorithm selection

1. INTRODUCTION

The knowledge discovery [3] is an iterative process. The analyst must select the right model for the task he is going to perform, and within it, the right model or algorithm, where the special morphological characteristics of the problem must always be considered. The algorithm is then invoked and its output is evaluated. If the evaluations results are poor, the process is repeated with new selections. A plethora of commercial and prototype systems with a variety of models

and algorithms exist at the analyst’s disposal. However, the selection among them is left to the analyst. The machine learning field has been evolving for a long time and has given us a variety of models and algorithms to perform the classification, e.g. decision trees, neural networks, support vector machines [4], rule inducers, nearest neighbor etc. The analyst must select among them the ones that better match the morphology and the special characteristics of the problem at hand. This selection is one of the most difficult problems since there is no model or algorithm that performs better than all others independently of the particular problem characteristics. A wrong choice of model can have a more severe impact: A hypothesis appropriate for the problem at hand might be ignored because it is not contained in the

model’s search space.

There is an increasing number of algorithms and practices that can be used for the very same application. Extensive research

has been performed to develop appropriate machine learning techniques for different data mining tasks, and has led to a proliferation of different learning algorithms. However, previous work has shown that no learner is generally better than another learner. If a learner performs better than another learner on some learning situations, then the first learner usually performs worse than the second learner on other situations [5]. In other words, no single learning algorithm can perform well compared to the other algorithms and outperform other algorithms over all classification tasks. This has been confirmed by the “no free lunch theorems” [6]. The major reasons are that a learning algorithm has different performances in processing different datasets and that different variety of ‘inductive bias’ [7]. In real-world applications, the users need to select an appropriate learning algorithm according to the classification task that is to be performed [8],[9]. If we select the algorithm inappropriately, it results in a slow convergence or may lead to a sub-optimal local minimum. Meta-learning has been proposed to deal with the issues of algorithm selection [10]. One of the aims of meta-learning is to help or assist the user to determine the most suitable learning algorithm(s) for the problem at hand. The task of meta-learning is to find functions that map datasets to predicted data mining performance (e.g., predictive accuracies, execution time, etc.). To this end meta-learning uses a set of attributes, called meta-attributes, to represent the characteristics of classification tasks, and search for the correlations between these attributes and the performance of learning algorithms. Instead of executing all learning algorithms to obtain the optimal one, meta-learning is performed on the meta-data characterizing the data mining tasks. The effectiveness of meta-learning is largely dependent on the description of tasks (i.e., meta-attributes).

Ensemble methods are learning algorithms that construct a set of classifiers and then classify new data points by taking a vote of their predictions. Combining classifiers or studying methods for constructing good ensembles of classifiers to achieve higher accuracy is an important research topic [1] [2]. The drawback of ensemble learning is that in order for ensemble learning to be computationally efficient, approximation of posterior needs to have a simple factorial structure. This means that most dependence between various parameters cannot be estimated. It is difficult to measure correlation between classifiers from different types of learners. Also there are learning time and memory constraints. Learned concept is difficult to understand. So we are trying to propose adaptive learning. We need to propose algorithm for selection of methods for classification task. The datasets are identified and we are trying to map to learning algorithms or methods. We need to generate adaptive

function. Adaptive learning will be built on the top of ensemble methods.

2. RELATED WORKS

Several algorithm selection systems and strategies have been proposed previously [3][10][11][12]. STATLOG [14] extracts various characteristics from a set of datasets. Then it combines these characteristics with the performance of the algorithms. Rules are generated to guide inducer selection based on the dataset characteristics. This method is based on the morphological similarity between the new dataset and existing collection of datasets. When a new dataset is presented, it compares the characteristics of the new dataset to the collection of the old datasets. This costs a lot of time. Predictive clustering trees for ranking are proposed in [15]. It uses relational descriptions of the tasks. The relative performance of the algorithms on a given dataset is predicted for a given relational dataset description. Results are not very good, with most relative errors over 1.0 which are worse than default prediction. Data Mining Advisor (DMA) [16] is a system that already has a set of algorithms and a collection of training datasets. The performance of the algorithms for every subset in the training datasets is known. When the user presents a new dataset, DMA first finds a similar subset in the training datasets. Then it retrieves information about the performance of algorithms and ranks the algorithms and gives the appropriate recommendation. Our approach is inspired by the above method used in [16].

Most work in this area is aimed at relating properties of data to the effect of learning algorithms, including several large scale studies such as the STATLOG (Michie et al., 1994) and METAL (METAL-consortium, 2003) projects. We will use

this term in a broader sense, referring both to ‘manual’

analysis of learner performance, by querying, and automatic model building, by applying learning algorithms over large collections of meta-data. An instance based learning algorithm (K-nearest neighbor) was used to determine which training datasets are closest to a test dataset based on similarity of features, and then to predict the ranking of each algorithm based on the performance of the neighboring datasets.

3. LEARNING ALGORITHMS AND

DATASET CHARACTERISTICS

In general there are two families of algorithms, the statistical, which are best implemented by an experienced analyst since they require a lot of technical skills and specific assumptions and the data mining tools, which do not require much model specification but they offer little diagnostic tools. Each family has reliable and well-tested algorithms that can be used for prediction. In the case of the classification task [11], the most frequent encountered algorithms are logistic regression (LR), decision tree and decision rules, neural network (NN) and discriminant analysis (DA). In the case of regression, multiple linear regression (MLR), classification & regression trees (CART) and neural networks have been used extensively.

In the classification task the error rate is defined straightforwardly as the percentage of the misclassified cases in the observed versus predicted contingency table. When NNs are used to predict a scalar quantity, the square of the correlation for the predicted outcome with the target response is analogous to the r-square measure of MLR. Therefore the error rate can be defined in the prediction task as:

Error rate = 1 - correlation2 (observed, predicted)

In both tasks, error rate varies from zero to one, with one indicating bad performance of the model and zero the best possible performance.

The dataset characteristics are related with the type of problem. In the case of the classification task the number of classes, the entropy of the classes and the percent of the mode category of the class can be used as useful indicators. The relevant ones for the regression task might be the mean value of the dependent variable, the median, the mode, the standard deviation, skewness and kurtosis. Some database measures include the number of the records, the percent of the original dataset used for training and for testing, the number of missing values and the percent of incomplete records. Also useful information lies on the total number of variables. For the categorical variables of the database, the number of dimensions in homogeneity analysis and the average gain of the first and second Eigen values of homogeneity analysis as well as the average attribute entropy are the corresponding statistics. For the continuous variables, the average mean value, the average 5% trimmed mean, the median, the variance, the standard deviation, the range, the inter-quartile

range, skewness, kurtosis and the Huber’s M-estimator are some of the useful statistics that can be applied to capture the information on the data set.

The determinant of the correlation matrix is an indicator of the interdependency of the attributes on the data set. The average correlation, as it is captured by Crobach-α reliability coefficient, may be still an important statistic. By applying principal component analysis on the numerical variables of the data set, the first and second largest Eigen values can be observed.

If the data set for a classification task has categorical explanatory variables, then the average information gain and the noise to signal ratio are two useful information measures, while the average Goodman and Kruskal tau and the average chi-square significance value are two statistical indicators. Also in the case of continuous explanatory variables, Wilks’ lambda and the canonical correlation of the first discrimination function may be measures for the discriminating power within the data set.

By comparing a numeric with a nominal variable with the

student’s t-test, two important statistics are produced to indicate the degree of their relation, namely Eta squared and the Significance of the F-test.

Table 1. DCT dataset properties [17]

Nr_Attributes Nr_num_attributes Nr_sym_attributes Nr_examples Nr_classes MissingValues_Total MissingValues_relative Mean_Absolute_Skew MStatistic MeanKurtosis NumAttrsWithOutliers MstatDF

MstatChiSq SDRatio

WiksLambda Fract

Cancor BarlettStatistic Class Entropy Mutual Information Joint Entropy Eqivalent_nr_of_attrs Entropy Attributes NoiseSignalRatio

4. PROPOSED METHOD

Here we are considering properties of scenarios. We need to classify learning scenario. We are extracting features of input data or datasets. We are using the concept of meta-learning. Meta-learning relates algorithms to their area of expertise using specific problem characteristics. The idea of meta-learning is to learn about classifiers or meta-learning algorithms, in terms of the kind of data for which they actually perform well. Using dataset characteristics, which are called meta-features; one predicts the performance results of individual learning algorithms. These features are divided into several categories:

 Sample or general features: Here we need to find out the number of classes, the number of attributes, the number of categorical attributes, the number of samples or instances etc.

 Statistical features: Here we require to find canonical discriminant, correlations, skew, kurtosis etc.

 Information theoretic features: Here we need to extract class entropy, signal to noise ratio etc. We are proposing adaptive methodology. Different thoughts can be considered, e.g. parameters such as the input data, learning methods, learning policies, learning methods combination. Here there can be a single learner or multiple learners. Also we can use simple voting or averaging while combining the performance of the different learners.

5. EXPERIMENTS

5.1

Experimental Descriptions

Here we need to map the dataset’s characteristics to the performance of the algorithm. We are capturing the

knowledge about the algorithms’ from experiments. Here we are calculating the algorithms’ accuracy on each dataset.

After the experiments, accuracy of each algorithm corresponding to every dataset is saved in the knowledge base for the future use. The Ranking procedure is shown in Figure 1.

Given a new dataset, we use k-NN [7] to find out the most similar dataset in the knowledge base with the new one. K-Nearest Neighbor learning is the most basic instance-based method. The nearest neighbors of an instance are defined in terms of the standard Euclidean distance. Let an arbitrary instance x be described by the feature vector

<a1 (x), a2 (x), --- an(x) >

Where ar (x) denotes the value of the rth attribute of instance x. Then the distance between two instances xi and xj isdefined to be d (xi- xj),

d (xi-xj) = √ (∑(ar (xi) –ar (xj ) )) 2

Here r varies from 1 to n in summation. 24 characteristics are

used to compare the two dataset’s similarities. A distance

function that based on the characteristics of the two datasets is used to find the most similar neighbors, whose performance is expected to be similar or relevant to the new dataset. The recommended ranking of the new dataset is built by

aggregating the learning algorithms’ performance on the similar datasets. The knowledge base KB stores the dataset’s characteristics and the learning algorithms’ performance

k Similar Datasets

Ranking of learning

algorithms for the

new dataset

Result: Recommended

learning algorithm

Decision

Making

New Dataset

Calculate Dataset

Characteristics

Characteristics of the

new dataset

k-NN

Calculate Dataset

Characteristics

Knowledge base

(Learning

algorithms’

performance &

Dataset

Characteristics)

Fig 1: The Ranking of Learning Algorithms

6. RESULTS AND DISCUSSIONS

Here we have used Adult Dataset [13]. The dataset Adult has following features:

 48842 instances

 14 attributes (6 continuous, 8 nominal)

 Contains information on adults such as age, gender, ethnicity, martial status, education, native country, etc.

 The instances are classified into either “Salary >50K” or “Salary <= 50K”

Table 2 shows the ranking of eight algorithms used on Adult Dataset from UCI Repository. The table shows highest rank to LogitBoost algorithm, then to J48, oneR and finally lowest rank is given to ZeroR algorithm.

Table 2. Ranking of different algorithms on Adult Dataset

Algorithm Rank

LogitBoost 1

J48 2

OneR 3

DecisionStump 4

IB1 5

IBK 6

NaiveBayes 7

ZeroR 8

Table 3. Correctly & Incorrectly Classified Instances for Adult Dataset

Algorithm % of

Correct classified instances

% of Incorrect classified instances

LogitBoost 84.68 15.32

ZeroR 76.07 23.93

Fig. 2: % Classified instances with top ranked algorithm LogitBoost on Adult Dataset

Figure 2 shows percentage of classified instances with the top ranked algorithm called LogitBoost on Adult Dataset. Here 84.68 % instances are correctly classified.

Figure 3 shows percentage of classified instances with the lowest ranked algorithm called ZeroR on Adult Dataset. Here 76.07 % instances are correctly classified.

Fig. 3: % Classified instances with lowest ranked algorithm ZeroR on Adult Dataset

7. CONCLUSIONS AND FUTURE WORK

In this paper, we present our preliminary work on using meta-learning method for helping user effectively to select the most appropriate learning algorithms and give the ranking recommendation automatically. It will assist both novice and expert users. Ranking system can reduce the searching space, give him/her the recommendation and guide the user to select the most suited algorithms. Thus the system will assist to learn adaptively using the experiences from the past data. In the future work, we will investigate more on our proposed method and test extensively on other datasets. Meta Learning helps improve results over the basic algorithms. Using Meta Characteristics on the Adult dataset to determine an appropriate algorithm, almost 85% correct classification is achieved for LogitBoost algorithm. So out of eight algorithms LogitBoost algorithm is recommended to the user.

8. ACKNOWLEDGMENTS

Our thanks to the experts who have contributed towards development of the different algorithms and made them available to the users.

9. REFERENCES

[1] Kuncheva, L, Bezdek J., and Duin, R. 2001 Decision Templates for Multiple Classifier Fusion: An Experimental Comparison, Pattern Recognition. 34, (2), pp.299-314, 2001.

[2] Dietterich, T. 2002 Ensemble Methods in Machine Learning 1st Int. Workshop on Multiple Classifier Systems, in Lecture Notes in Computer Science, F. Roli and J. Kittler, Eds. Vol. 1857, pp.1-15, 2002.

[3] Alexmandros, K. and Melanie, H. J. 2001 Model Selection via Meta-Learning: A Comparative Study. International Journal on Artificial Intelligence Tools. Vol. 10, No. 4 (2001).

[4] Joachims, T. 1998 Text Categorization with Support Vector Machines: Learning with Many Relevant Features. Proceedings of the European Conference on Machine Learning, Springer.

[5]

Schaffer, C. 1994 Cross-validation, stacking and bi- level stacking: Meta-methods for classification learning,

In

Cheeseman,

P.

and

Oldford

R.W.(eds)

Selecting

Models from Data: Artificial Intelligence and

IV, 51-59.

Correct

Incorrect

Correct

Incorrect

[6] Wolpert, D. 1996 The lack of a Priori Distinctions

between Learning Algorithms, Neural Computation, 8, 1996, 1341-1420.

[7] Mitchell, T. 1997 Machine Learning, McGraw Hill. [8] Brodley, C. E. J.1995 Recursive automatic bias selection

for classifier construction, Machine Learning, 20, 63-94. [9] Schaffer, C. J. 1993 Selecting a Classification Methods

by Cross Validation, Machine Learning, 13, 135-143. [10]Kalousis, A. and Hilario, M. 2000 Model Selection via

Meta-learning: a Comparative study, Proceedings of the 12th International IEEE Conference on Tools with AI, Canada, 214-220.

[11]Koliastasis, D. and Despotis, D. J. 2004 Rules for Comparing Predictive Data Mining Algorithms by Error Rate, OPSEARCH, VOL. 41, No. 3.

[12]Fan, L., Lei M. 2006 Reducing Cognitive Overload by Meta-Learning Assisted Algorithm Selection,

Proceedings of the 5th IEEE International Conference on Cognitive Informatics, pp. 120-125, 2006.

[13]Frank, A. and Asuncion, A. 2010. UCI machine learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

[14]Michie, D. and Spicgelhater, D. 1994 Machine Learning, Neural and Statistical Classification. Elis Horwood Series in Artificial Intelligence, 1994.

[15]Todorvoski, L. and Blockeel, H. 2002 Ranking with Predictive Clustering Trees, Efficient Multi-Relational Data Mining, 2002.

[16]Alexandros, K. and Melanie, H. J. 2001 Model Selection [17]Peng, Y., Flach., P., Soarces C. and Brazdil, P., 2002 Improved Dataset Characterization for Meta-learning, Springer LNCS 2534, pp. 141-152, 2002.

Routing Protocol for Mobile Nodes in Wireless Sensor

Network

Bhagyashri Bansode

Department of Computer Engineering, Pune Institute of Computer Technology, Pune,

Maharashtra, India.

Rajesh Ingle

Phd, Department of Computer Engineering, Pune Institute of Computer Technology, Pune,

Maharashtra, India

ABSTRACT

Wireless sensor network made up of sensor nodes which are fix or mobile. LEACH is clustered based protocol uses time division multiple access. It supports mobile nodes in WSN. Mobile node changes cluster. LEACH wait for two TDMA cycles to update the cluster, within these two cycles mobile

node which changed cluster head, can’t send data to any other

cluster head, it causes packet loss. We propose an adaptive Low Packet Loss Routing protocol which support mobile node with low packet loss. This protocol uses time division multiple access scheduling to reserve the battery of sensor node. We form clusters, each cluster head update cluster after every TDMA cycle to reduce packet loss. The proposed protocol sends data to cluster heads in an efficient manner based on received signal strength. The performance of proposed LPLR protocol is evaluated using NS2.34 on Linux 2.6.23.1.42.fc8 platform. It has been observed that the proposed protocol reduces the packet loss compared to LEACH-Mobile protocol.

Keywords

Cluster based routing, mobility, LEACH-Mobile, WSN

1.INTRODUCTION

A wireless sensor network (WSN) consists of spatially distributed autonomous sensors to monitor physical or environmental conditions, such as temperature, sound, vibration, pressure, humidity, motion or pollutants and to cooperatively pass their data through the network to a main location. Modern networks are bi-directional, also enabling control of sensor activity. The development of wireless sensor networks was motivated by military applications such as battlefield surveillance; today such networks are used in many industrial and consumer applications, such as industrial process monitoring and control, machine health monitoring. WSN consist of mobile or fix sensor nodes. In some cases it consists of hybrid sensor nodes. All nodes sense and send data to server. This increases communication overhead because all nodes are sending data to server. This network containing hundreds or thousands of sensor node and main challenge in WSN is to reduce energy consumption and low packet loss in each sensor node. There are many routing protocols like Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), and Ad hoc On Demand Distance Vector (AODV) [1]. These protocols are supported to WSN but they are not suitable for tiny, low capacity sensor nodes and they require high power consumption. Flat-based multi-hop routing protocols, designed for static WSN [2-6], have also been exploited in WSN mobile nodes. However it not supports to mobility of sensor node

The main challenge in WSN is to minimize energy consumption in each sensor node. Many researchers concentrate on the routing protocol that would consume less power and hence prolong network’s life span. Wireless ad hoc network routing protocols have been proposed for routing protocols in WSN.

Low Energy Adaptive Clustering Hierarchy-Mobile (LEACH-Mobile) [7] is routing protocol which support to WSN which have mobile nodes. LEACH-Mobile supports sensor nodes mobility in WSN by adding membership declaration to LEACH protocol. LEACH-Mobile protocol selects heads randomly and form cluster. Cluster head create Time Division Multiple Access (TDMA) schedule. Nodes sense and send that data to cluster head according to TDMA schedule. Mobility of node is big challenge to maintain cluster. Mobile nodes changes cluster continuously. LEACH-Mobile protocol update cluster after every two cycles of TDMA schedule. Packet loss happened in between two cycles of TDMA schedule. Mobile node which is not near to any cluster cannot send data to any cluster head so it causes packet loss.

Sensor nodes in LEACH-Mobile wait for two consecutive failure TDMA cycles, then cluster head decide that it has moved out of its cluster. During these two TDMA cycles sensor node loss the packets. In LPLR, sensor node does not need to wait for two consecutive TDMA cycles from cluster head to make decision. Cluster head directly decides that member node has moved out of its cluster after one TDMA cycle. The data loss is reduced by sending its data to new cluster head and sends join acknowledgment message to the cluster head.

We proposed a new low packet loss technique with efficient power consumption routing protocol for WSN. This proposed routing protocol called Low Packet Loss Routing Protocol for mobile nodes in wireless sensor network (LPLR Mobile-WSN). In our proposed protocol, the cluster head sends data request message to its members. When the cluster head does not receive data from its members, the packet is considered lost and cluster head delete membership of sensor node from the cluster. On the other hand, when the sensor node does not receive data request message from cluster head it will try to get entry into new cluster to avoid packet loss. Cluster head gives entry in TDMA schedule to incoming nodes from other cluster. Transmitter will send the message according to the received signal strength of data request message from the cluster head

.

[15] is given in Section 3 and the proposed GAFCM is described in detail in Section 4.

Figure 1 Block diagram of microarray image processing.

3.

FUZZY

C

MEAN

(FCM)

ALGORITHM

Let x= xi, i = 1 to N be the pixels of a single microarray spot,

where N is the number of pixels present in the spot. These pixels have to be clustered in two classes BG and FG. Let cj

j=1,2 be the cluster centers of the FG and BG pixels respectively. Each pixel should have membership degrees uij

for each cluster. The pixel is assigned to a particular cluster based on the value of the membership degree function. Hence the algorithm aims at iteratively improving the membership degree function until there is no change in the cluster centers. The sum of the membership values of a pixel belonging to all clusters should satisfy Equation 1.

∑

(1)

The Euclidean distance from a pixel to a cluster center is given by

(2)

The aim of this method is to minimize the absolute value of the difference between the two consecutive objective functions Ft and Ft+1 given by the Equation 3 and 4.

∑

∑

(3)

₍₄₎

Where m is the fuzziness parameter and ε is the error which has to be minimized. Iteratively in each step, the updated membership uij and the cluster centers cj are given by Equations 5 and 6. _∑

(5)

∑

(6)

4. GENETIC ALGORITHM BASED FCM

OPTIMIZATION (GAFCM).

GA is a powerful, stochastic non-linear optimization tool based on the principles of natural selection and evolution [16][17][18][19][20]. To find the optimum fuzzy partitions of a microarray spot signal, a new GA based fuzzy c mean clustering method has been proposed. Clustering using GAFCM can be achieved using the following steps. Here each chromosome in the population of GA encodes a possible partition of image and the goodness of the chromosome is computed by using a fitness function. The technique is described as follows.

A.

Population initialization

The chromosomes are made up of real numbers which represent microarray spot BG and FG pixel intensity centers respectively. These values are randomly initialized by taking all possible intensity values in the search space under evaluation.

B.

Fitness computation

Fitness of a chromosome is calculated in two steps. In the first step membership values of the image data points to the different clusters are computed by using FCM algorithm. In the second step fitness value is computed. This is used as a measure to evaluate the fitness of the chromosome. The membership degree function uij can be computed using the FCM algorithm explained in Section 3. Saha et.al has given a fitness function for the segmentation of satellite images [21][22]. This has been further modified for finding the cluster center of c-DNA microarray spots and is given in Equation 7.

(7)

Where

(8)

(9)

(10)

Ec is same as Equation 4. This is the difference between

two successive objective function values in FCM. This value is to be minimized. Dc is the maximum Euclidean

distance between two cluster centers among all centers. E is the error matrix; Gijis a 2x N reference matrix. The first row of the reference matrix is the one dimensional binary image corresponding to the simulated spot. The second row is the complement of first row. The objective is to maximize the Fit so as to achieve proper clustering. To ensure this E & Ec values has to decrease and Dc has

DNA Microarray

image

Gridding

Automatic spot cropping based on

gridding

Segmentation of spot from

background Red and

Green channel intensity Computation

of gene expression

C.

Selection, Crossover and Mutation

Roulette wheel selection method is applied on the population where, each chromosome receives a number that is proportional to its fitness value. Crossover and Mutation are the two Genetic Operators used for the creation of new Chromosomes. After repeating steps A, B, C for a fixed number of iterations the best cluster centers are selected [23]. The flow chart for performing GAFCM is given in Figure 2

No

Yes

Figure 2 Flow chart of GAFCM algorithm.

5. EVALUATION OF THE PROPOSED

METHOD

To quantify the effectiveness of the proposed approach, simulated as well as real microarray images from the Stanford Microarray Database (SMD) have been used. The spots were gridded and segmented using K-Means, FCM and GAFCM independently for comparison purposes. Simulated microarray images were used for validation and comparison purposes since their gene expressions are known. Spots were simulated with realistic characteristics to ensure that it looks like a true c-DNA image, consisting of more than 1000 spots. Hence a real c-DNA image was used as a template, and its binary version was produced by employing a threshold technique [24]

After converting it into a binary image, the spot area is replaced by random values of mean intensities. In the simulated microarray image the mean intensity value of each spot was predefined, ranging between 0 and 255 for both the R and G channels [24]. BG intensities were replaced by a single intensity value.

The accuracy of any segmentation technique can be evaluated using three parameters. The segmentation matching factor SMF, The coefficient of determination r2 and The concordance correlation Pc. The SMF [25][26][27] for every

binary spot, produced by the clustering algorithm is given by

(11)

Where Aseg is the area of the spot, as determined by the

proposed algorithm and Aact is the actual spot area. A perfect

match is indicated by a 100% score, any score higher than 50% indicates reasonable segmentation where as a score less than 50% indicate poor segmentation. The coefficient of determination r2 [24][28][29] indicates the strength of the linear association between simulated and calculated spots, as well as the proportion of the variance of the calculated data.

∑

∑

(12)

Where Iseg and Iact are the mean intensity value of the

calculated and simulated spots respectively and Imean is the

overall mean spot intensity values of the simulated image. The algorithm that scores r2 value closer to 1 has better performance.

The concordance correlation Pc was calculated using the Equation

̅ ̅

(13)

Start

Crop the spot sub image based on Gridding

Initialize the center encoded population matrix P (K) of size (Nx2)

Select chromosome

Update uij matrix

Calculate cj matrix based on uij

Find Ec, E, Dc and Fit matrix

Selection, Cross over & Mutation

Update the population matrix P (K)

If (iterations= desired value)

Select the best uij, cj & Cluster the spot

pixels into BG & FG

Stop

A

Where A and B are two samples, ̅ ̅ are the mean values, and SA and SB are the standard deviation of the samples. The

higher the Pc value, the better the performance of the

algorithm. Further the proposed algorithm’s performance has been tested in the presence of noise. This was done by corrupting the simulated spot with additive white Gaussian noise whose signal-to-noise ratio (SNR) ranges from 1 to 19 dB [30].

6. RESULTS AND DISSCUSSION

The segmentation ability of KM, FCM and the proposed GAFCM algorithm is made by computing and comparing the SMF r2 and Pc values explained in section 5. The K-Means,

FCM and GAFCM algorithms were applied independently on these images for the classification of the BG and FG pixels. Several microarray images with different FG mean were simulated and spots were randomly selected from these images. The SMF value for the three algorithms is shown in Figure 3 with the original spots, actual boundaries and the results obtained for various methods. It is obvious from the result that GAFCM shows an overall SMF of 98.56% compared to FCM with 97.19% and K-means with 68.78%. The average SMF, r2 and Pc values shown in Table 1 is

obtained from the simulated microarray image shown in Figure 4 before corrupting it with noise.

Table 1 The SMF, r2 _{and P}

c value for a simulated

microarray image before adding noise.

KM FCM GAFCM

SMF 82.304 98.3447 99.3357

r2 0.80188 0.968114 0.991427

Pc 0.77947 0.968089 0.991424

The segmentation ability of the proposed method in the presence of noise has been studied. To do this, the simulated microarray images were added with additive white Gaussian noise gradually. The SMF, r2 and Pc values of the noisy

images were computed using K-means, FCM and GAFCM algorithm. The SNR value is varied from 1dB to 19 dB. Figure 5 shows the graph of SMF vs SNR for the three algorithms and Table 2 gives the corresponding numerical value. It can be seen from the graph that the difference in the SMF is more for FCM and GAFCM compared with K- means. In the case of GAFCM and FCM even though curves are close, GAFCM segmentationis better than FCM for low and high noise images. The result showsthat the overall SMF value varies from 97.050% to70.551%, 96.807% to 69.645% and 85.418% to 53.940% for GAFCM, FCM and K-means respectively. This reveals that GAFCM is having better SMF value.

The Coefficient of determination (r2) for simulated microarray images for K-means, FCM and GAFCM are shown in Table 3. The graph between r2 and SNR in dB is shown in Figure 6. The method that scores r2 value closer to 1 has better performance. The r2 value of GAFCM is closer to 1 compared to FCM and K-means for low noise images. The variation of r2 for SNR variation from 1 to 19 dB is from 0.7501 to 0.1296, 0.6935 to 0.1079 and 0.2880 to 0.0036 for GAFCM, FCM and K-means respectively.

The concordance correlation (Pc) values obtained for

K-means, FCM and GAFCM are shown in Table 4. Figure 7 shows the graph between Pc and SNR in dB. Higher the values

of Pc the better will be the segmentation value for that

algorithm. From Table 4 it can be seen that the Pc value varies

from 0.7471 to 0.0960, 0.6916 to 0.0796 and 0.2878 to 0.0007 for GAFCM, FCM and K-mean respectively. This clearly indicates that the proposed GAFCM has better segmentation capability for the current application.

Figure 3 Comparison results for seven segmented spots obtained from seven simulated images.

Figure 4 Simulated microarray image used to calculate the gene expression.

SNR(dB)

0 2 4 6 8 10 12 14 16 18 20

SMF

50 60 70 80 90 100

SNR vs GAFCM SNR vs K-Means SNR vs FCM

Figure 5 SMF calculated for simulated image corrupted with additive white Gaussian noise having different levels

of SNR (dB) using K-means, FCM, GAFCM algorithms.

Table 2 The comparison of K-means, FCM, GAFCM algorithm based on segmentation matching factor

(SMF) for simulated microarray images with different levels of additive white Gaussian noise

SNR(dB).

SNR(dB) KM FCM GAFCM

1 53.93972 69.64504 70.55050

3 58.52296 78.66445 79.11223

5 63.03961 84.53164 84.63773

7 67.87467 88.79217 89.11575

9 72.60327 92.44617 92.73175

11 77.90749 92.61146 93.02225

13 81.82369 94.17475 94.70089

15 84.01279 95.58631 96.18429

17 85.22194 96.1873 96.28328

19 85.41774 96.80675 97.05008

Table 3 The comparison of K-means, FCM, GAFCM algorithm based on coefficient of determination (r2) for simulated microarray images

with different levels of additive white Gaussian noise SNR(dB).

SNR(dB) KM FCM GAFCM

1 0.003582 0.107935 0.129569

3 0.002433 0.070657 0.08278

5 0.009682 0.200522 0.217191 7 0.014513 0.380952 0.414809 9 0.034473 0.348032 0.382025 11 0.091063 0.310028 0.361558 13 0.211104 0.35561 0.454974 15 0.273211 0.613217 0.657108 17 0.301239 0.619506 0.728683 19 0.287993 0.693543 0.750119

SNR (dB)

0 2 4 6 8 10 12 14 16 18 20

r

2

0.0 0.2 0.4 0.6 0.8

SNR vs GAFCM SNR vs K-Means SNR vs FCM

Figure 6 r2 calculated for simulated image corrupted with additive white Gaussian noise having different levels of

Table 4 The comparison of K-means, FCM, GAFCM algorithm based on concordance correlation (Pc) for

simulated microarray images with different levels of additive white Gaussian noise SNR (dB).

SNR(dB) KM FCM GAFCM

1 0.0007 0.0796 0.0960

3 0.0003 0.0447 0.0497

5 0.0028 0.1813 0.1977

7 0.0052 0.3601 0.3923

9 0.0190 0.3429 0.3778

11 0.0762 0.2910 0.3412

13 0.2058 0.3551 0.4546

15 0.2730 0.6120 0.6536

17 0.3012 0.6173 0.7257

19 0.2878 0.6916 0.7477

SNR (dB)

0 2 4 6 8 10 12 14 16 18 20

Pc

0.0 0.2 0.4 0.6 0.8

SNR vs GAFCM SNR vs K-Means SNR vs FCM

Figure 7 Pc calculated for simulated image corrupted with

additive white Gaussian noise having different levels of SNR (dB) using K-means, FCM, GAFCM algorithms.

The aim of microarray image processing is to find the gene expression value. The gene expression value is the logarithm mean intensity ratio of red and green channels in a spot. The closeness of the computed gene expression value with the actual value shows the performance of the algorithm. To validate this, several microarray images were simulated and tested. Figure 4 shows one such simulated images and the corresponding result is shown in Table 5. The better the segmentation technique the closer will be the gene expression value with the actual value. Table 5 shows the gene expression value obtained for a microarray simulated image of 16 spots using the three segmentation methods along with their actual values of gene expression.It can be seen that the gene expression value measured is almost close to the actual value in the case of GAFCM compared to FCM and K-Means. This shows that GAFCM algorithm has better scope in microarray image spot segmentation application.

Table 5 Comparison of gene expression values computed using K-means, FCM and GAFCM algorithm.

SPOT No

Gene Expression

KM FCM GAFCM Actual

1 -0.01147 -0.06477 -0.04779 -0.04779 2 0.04617 -0.12034 -0.12034 -0.12034 3 0.03171 -0.09431 -0.09431 -0.09431 4 0.16624 0.08583 0.085828 0.091598 5 -0.12983 -0.19036 -0.17852 -0.17852 6 -0.00411 -0.11734 -0.11734 -0.10333 7 -0.05711 -0.1459 -0.13697 -0.13276 8 0.12509 -0.00511 -0.00511 -0.00386 9 -0.02495 -0.07131 -0.07716 -0.07716 10 -0.04111 -0.09078 -0.09078 -0.09078 11 -0.05853 -0.15023 -0.15023 -0.15023 12 0.06195 0.0167 0.016696 0.016696 13 -0.02509 -0.10586 -0.09059 -0.09059 14 0.03494 -0.04701 -0.04701 -0.04922 15 -0.11408 -0.2259 -0.2259 -0.2259 16 0.0467 -0.07544 -0.0705 -0.02818

7. CONCLUSION

Segmentation is an important part in microarray image processing. The microarray spot segmentation for estimating gene expression using K-means FCM and proposed GAFCM has been done. It is seen that the proposed GAFCM algorithm is more efficient than the FCM and K-means in terms of clustering the signal FG and BG pixels. The errors during segmentation lead to inaccurate calculation of gene expression values in the intensity extraction step. All the above

levels. This can be rectified by using suitable filtering techniques. As our future work, the noise removal has to be addressed to get much smoother image and also an improved clustering algorithm is to be developed so that low signal intensity spots can be segmented more effectively.

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