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An Adaptive Strategy of Genetic Operators in Genetic Engineering

Abdul

An Adaptive Strategy of Genetic Operators in Genetic Engineering

Manjula S1
Lecturer
DOS in Computer Science
Davangere University,
Davangere-02, INDIA
manjula.shamarao@gmail.com
Shivamurthaiah M2
Lecturer,
DOS in Computer Science
Davangere University,
Davangere-02, INDIA
shivamurthaiah@gmail.com


Abstract- The genetic engineering is also known as “Genetic Modification”. The direct manipulation of an organism’s genome is done by using Genetic algorithm an organism that is generated through genetic engineering is considered to be a Genetically Modified Organism (GMO). Transgenic organisms are able to express foreign genes because the genetic code .Genetic Engineering is implemented in the form of genetic programming technique which is designed with the help of artificial intelligence concepts and methods which performs a user defined tasks. This genetic programming is a set of instruction and fitness function to measure how well a computer has performed a task. The functions which are used in genetic programming is represented in the form of tree structure; The main purpose of genetic algorithm is to create an offspring from existing population and this task can be performed with the help of genetic operators; the main operators used in evolutionary algorithm such as genetic programming are crossover and mutation. This paper is mainly focusing on the crossover and mutation operators; which has different types and operation systems. The cross over is applied on an individual by simply switching one of its nodes with another individual in the population. Mutation affects an individual in the population, it can replace a whole node in the selected individual or it can replace just node’s information. Mutation can happen with any of the individual randomly, while crossing of chromosomes with unexpected resultant values. The selection of individual for crossing is based on the fitness of each individual in the population.

Keywords- Crossover, DNA, Encoding, Fitness, Genetic algorithm, Genetic engineering, Genetic Operators, Genetic Programming, Initialization, Mutation, Offspring, Selection, Transgenic.

INTRODUCTION

The recent trend of computer science is mainly focusing on genetic computing, which is the part of “Soft Computing”, it refers to a collection of computational techniques in computer science, Artificial Intelligence, Machine learning and some engineering disciplines. The Genetic Computing is implemented in the year 1975 John Halland first with the help of “Genetic Algorithm” introduced GA in AI System, an effective GA representation and meaningful fitness evaluation are the keys of the successive GA applications[1]. The appeal of GA’s comes from their simplicity and elegance as robust search algorithms. The GA population based search and optimization method that mimics the process of natural evaluation. The two main concepts of natural evaluation which are natural selection and genetic dynamics. The genetic algorithm is a part of evolutionary computing which is rapidly growing area of AI. GA’s are inspired by Darwin’s theory about evaluation-“Survival of the fittest”, With respect to biological background Charles Darwin has formulated the fundamental principles of natural selection as a main evolutionary tool. In 1865, George Mendel discovered these hereditary principles by experiments he carried out on persons with the help of experimental results Morgan found that Chromosomes are the carriers of hereditary information and that genes representing the hereditary factor were lined upon chromosomes. Each chromosome is built of DNA. The biological terminology used in evolutionary computation, the gene code; it represents the characteristics of an individual and these gene can take different value or alleles. This set of alleles is represented by gene pool. This gene pool can determine all the different possible variations for further generations. The diversity of the individuals in the population is determined by the size of the gene pool. Most living organisms store their genome (is the set all the genes of specific species) on several chromosomes. But in GA to simplify the representation all the genes are usually stored on the same chromosomes. The word genotype to describe the set of its genes. The phenotype describes the physical aspect of an individual. The process of decoding a genotype to produce the phenotype is known as morpho genesis. The biological terms are also used in GA with representing chromosomes as strings gene as feature/character. Genomes as guesses, solutions, collection of genes. Darwin also stated that the survival of organisms can be maintained to the process of reproduction, crossover and mutation. Darwin’s concept of evolution is adapted to computational algorithms to find solution to a problem called objective function in natural fashion. A solution generated by GA is called a chromosome, while collection of chromosome is referred as a population. These chromosomes are composed from genes and its value can be either in numerical, binary symbols or characters depending on the type of problem which needs to be solved. The selection of a good chromosome is based on fitness function, so that it can generate a suitable form of solution for a given problem. This fitness function is also used to select a best chromosome for reproduction of new offspring through crossover operation and mutation state value. The chromosome which has higher fitness value will have greater probability of being selected for creating next generation [2].

  1. STEP-BY STEP IMPLEMENTATION OF GENETIC ALGORITHM

Step 1: Random selection of individual so that we can create a set of initial population for further steps.

Step 2: These selection of individual is base on fitness value, if the chromosome fitness value is above the fixed fitness rate. Then the particular chromosome or individual is considered as valid chromosome for further operation.

Step 3: The randomly selected chromosome need to be determine with this we need to fix the mutation rate and cross over rate value.

Step 4: After determining the chromosome need to determine each chromosome with random values.

Step 5: The initialized random values are used to calculate objective functions. This can be calculated with given genetic equation.

Step 6: The fitness value is derived with the help of mathematical equation, fit[i] =1/ (1+fit_object) by taking object function from previous step.

Step 7: The probability for each chromosomes is formulated by P[i] =fitness[i]/total fitness value of each chromosome.

Step 8: For selection of chromosome we should compute cumulative probability, it is a sum of all probability value that is each chromosome from last step that       is        ,C[i]=P[1]+P[2]+P[3]+…..+P[i].           After calculating cumulative probability the selection process of chromosomes is done by using roulette-wheel methodology is known as chromosomes selection.

Step 9: Crossover- crossing; i.e. randomly select a position in the parent1 chromosome then exchanging the position value of parent 1 with the same position of parent2 chromosomes. This process of exchanging these position values with the parent chromosome is known as crossing techniques. The parent chromosome which will mate randomly selected and the number mate chromosomes controlled using crossover_rate (pc) parameter

Step 10: Mutation- Number of chromosomes that have mutations in a population is determined by the mutation rate parameter. This mutation process is done by replacing the gene at random position with a new value for this to happen a must calculate. The total length of gene in the population, but this mutation is not mandatory for all offspring generation.

Step 11: The random number R[i] is greater than P[i] (probability value and its chromosomes. And similar than P [i+1] (probability of i+chromosomes) then select chromosome [i+1] as a chromosome in the new population for the next generation. P[i] <R[i] <P [i+1] New chromosome [1] =chromosome [i+1] This represents generation of new chromosome.

Step 12: Extraction of best chromosomes from generated chromosomes. Figure 1 represents the gene flow with respect to flow chart of GA we are mainly focusing on initialization & evaluation, selection, crossover and mutation operation.

  • GENETIC COMPUTING
  1. Encoding: Process of converting the biological chromosomes into computational chromosomes to perform mathematical operations by initializing random numbers to solve problem with GA. It is purely depend on the problem, here we are focusing on 4 different of encoding system.
  2. Binary
  3. Permutation
  4. Value
  5. Tree

A.1 Binary Encoding: It is most common, it is mainly because in binary encoding every chromosome is a string of 0 and 1 bit configuration. this gives many possible chromosomes even with small number of alleles. This encoding is often not natural for may problem and sometimes correction must be made after crossover and/or mutation.

Chrome A: 1 0 1 1 0 0 0 0 1 1 0 1

Chrome B: 0 1 1 1 1 1 1 0 0 0 0 0

A.2 Permutation Encoding: This encoding methodology is mainly used in ordering type  of  problems  example   TSP(Travelling    Salesman Problem)[6].       In      permutation      encoding,      every chromosome is a string of numbers which represents a sequence of numbers as chromosome values. Even in this problem, for some types of cross over and mutation corrections    must    be     made   to    live     chromosome consistently.

Chrome A: 1 5 8 2 9

Chrome B: 9 7 6 4 3

 

A.3 Value Encoding: Value encoding technique is usually implemented in complicated problems, where use of binary encoding for this type of problem would be very difficult. this uses correct value encoding technique in problem solving with complicate values such as; real number, form numbers, characters which are connected to problem. This encoding is often necessary to develop some new cross over and mutation specific for the problem.

Chrome A (1.258 6.765 8.119 Chrome B (left), (right), (forward) Chrome C ABCJEFGHIONPQSUV.

A.4 Tree encoding: A tree encoding is mainly used for valuing programs/expressions for GP. In this tree encoding every chromosome is tree of some object such as commands/functions used in programming language LISP is often used to this, because program in it are represented in the form and can be easily parsed as a tree. So the crossover and mutation can be done relatively easily.


 

 

 

  1. Initialization and Evaluation:

 

B.1 Initialization: To get the knowledge of both, consider of an example of an application that  uses GA to solve the problem of combination, consider an equation a+2b+c=10. To find the value of a,b,c we are using GA, the main objective of this problem is  minimizing value  if function f(x). where f(x)=(a+2b+c)-10. Since there is a 3 variables in the equation, we can compose chromosome as follows. To   speed   up  the   computation  we   can   distinct   the initialization values to variable as used integer values between 0 and 10. To solve this problem we define 3 different number of chromosomes[3] In population, then we   generate   random   values   of    gene   a,b,c   for    3chromosomes.

Chrome[1]=[a;b;c]=[5;2;3]

Chrome[2]=[a,b,c]=[8,6,4]

Chrome[3]=[a,b,c]=[7,9,1]

B.2Evaluation: We compute the objective function value for each chromosome produced in initialization step. This can be evaluated by implementing the initialized value into an given equation. a+2b+c=10èf(x)=(a+2b+c)-10.

F_obj[1]=abs((5+2*2+3)-10)

=abs((5+4+3)-10)

=abs(12-10) =2

F-obj[2]=abs((5+2*6+4)-10) =abs((8+12+4)-10)=14

F-obj[3]=abs((7+2*9+4)-10)

=abs((7+18+1)-10)=16

The object function value is calculated, the fittest chromosomes have higher probability to be selected for the production for the next generation. To compute the probability of fitness, first we need to compute fitness of each chromosomes. To avoid division by zero problem, the E_obj value will be added by default value 1.

Fitness [1]=1/(F_obj[1]+1)=1/(2+1)=1/3=0.333

Fitness [2]=1/(F_obj[2]+1)=1/(14+1)=1/15=0.0666

Fitness [3]=1/(F_obj[3]+1)=1/(16+1)=1/17=0.058823

Total fitness of all the 3 chromosomes.

Total=0.333+0.0666+0.058=0.457

The probability of each chromosome is calculated by;

P[i] =Fitness[i]/Total.

P[i]=Fitness[i]/Total.

P[1]=0.333/0.457=0.7286

P[1]=0.066/0.457=0.1444

P[1]=0.058/0.457=0.1269

Fig 3: Chromosome Probability value

From the above probability, chromosome 1 has the highest fitness and also highest probability, which is to be selected for next generation chromosome.

  1. Roulett Wheel Selection: The selection process where implementing Roulett_wheel; for that we should compute the cumulative probability values. C[1]=0.7286

C[2]=0.7286+0.1.444+0.1269=0.873

C[3]=0.7286+0.1444+0.1269=0.9999

After calculating the cumulative of selection process using Roulette_wheel can be done. The process is to generate random number R in range 0-1. R[1]=0.486

R[2]=0.1199

R[3]=0.586

If the random number R[1] is greater than P[2] and smaller than P[1].

P[2]<R[1]<P[1]è0.1444<0.486<0.7286

Then select chromosome 1as a chromosome in new population for nest generation/offspring. i.e. new chromosome[0]=chromosome[1]

  1. Crossover: In GA crossover is a genetic operator used to vary the programming of a chromosomes from 1 generation to next generation. The crossover is a process of taking more than 1 parent solutions and producing child solutions from them the pseudo code for the crossover process is as follows:

Crossover Algorithm:

Begin Kç0;

While(K<population) do

R(K)çrandom[0-1];

If (R[K]<PC) then Select chromosome[K] as parent else

discard the chromosome and goto next; end;

//if loop end

K=K=1

end; //while loop end

end; //end if begin

In this paper we are mainly focusing on one-point crossover, two-point crossover, cut and splice crossover and uniform & half uniform crossover, 3 parent crossover.

D.1: 1-point crossover: A single crossover point on both parents organism string is selected. All data beyond that point in either organism string is swapped between the two parent organisms. the resulting organisms are the children/offspring.

D.2: 2-point crossover: In this, 2 points to be selected on the parent organism’s string. Everything between the 2-points is swapped between the parent organisms, rendering two child parents.

Fig 5: 2-Point Crossover D.3 Cut & Splice:

Another crossover variant, this approach results in a change in length of the children string. The reason for this difference is that each parent string has a separate of crossover point.

Splice D.4 Uniform and half-uniform: Uniform crossover uses a fixed mixing ratio between two parents; in this crossover it enables the parent chromosome to contribute the gene level rather than the segment level. if the maximum ratio is 0.5; the offspring has approximately half of the genes from the first parent and other half of from second parent. Although crossover point can be randomly chosen.

In half-uniform crossover, scheme exactly half of the non matching bits are swapped. thus the first we must calculate the number of differing bits, the number is divided by two, the resulting number is how many of the bits that do not match between two parents will be swapped.

D.5: 3-parent crossover:

In this technique, a child is derived from 3 parents, the bits are randomly chosen; each bit of first parent is checked with bit of second parent whether they are same, if same then the bit is taken for the offspring, otherwise the bit taken for offspring.

Parent 1:  1 1 0 1 0 0 0 1 0

parent 2:  0 1 1 0 0 1 0 0 1

parent 3:  1 1 0 1 1 0 1 0 1

Resulting offspring= 1 1 0 1 0 0 0 0 1

  1. Mutation:  It is a GO used to maintain genetic diversity from 1 generation of the other population of GA chromosome to the next[7]. in biological aspects, mutation alters one or more gene value in a chromosome from its initial state, by this resultant solution is entirely change from previous solution, hence GA can come to better solution by using mutation.

Different types of mutations are:

  1. Bit string mutation:
  2. Flip bit mutation
  3. Boundary
  4. Non uniform
  5. Uniform
  6. Gaussian

E.1 Bit string mutation: The mutation of bit stream ensure through bit flips at random positions.         ex: 1 0 1 0 0 1 0                                                                                                                                                            1 0 1 0 1 1 0

The probability of a mutation of bit is 1/L, where l-is a length of binary vector. Thus a mutation state of 1/mutation is reached.

E.2 Flip bit: This mutation operator takes the chosen genome and inverts the bits.

E.3 Boundary: It replaces the genome with either lower or upper bound randomly. This can be used for integer and flod genes.

E.4 Non-uniform: The probability that amount of mutation will go to zero with next generation increased by using non uniform mutation operator. It keeps the population from stagnating in the early stages of the evolution. it tunes solution in the later stages of evolution.

E.5 Uniform: It replaces the value of the chromosome gene with a uniform random value selected between the user specified upper and lower bounds for that gene.

E.6 Gaussian: This operator adds a unit Gaussian distributed random value to the chosen gene. if it falls outside of the user specified lower or upper bounds for that gene, the new gene value is clipped.

IMPLEMENTATION & RESULT

As per the algorithm code has been designed with the help of MATLAB application and result is represented in the form of graph.

  1. The coding represents initialization technique as a sample

function [pop]=initialise(popsize, stringlength, fun); pop=round(rand(popsize, stringlength+2)); pop(:,

stringlength+1)=sum(2.9(size(pop(:,1:stringlength),2)−1

:−1:0).

*pop(:,1:stringlength))*(b−a)/(2.9stringlength−1)+a;

pop(;, stringlength+2)=fun(pop(;, stringlength+1));

end

  1. The coding represents selection technique as a sample

function [newpop]=roulette(oldpop); totalfit=sum(oldpop(:,stringlength+2)); prob=oldpop(:,stringlength+2) / totalfit; prob=cumsum(prob); rns=sort(rand(popsize,1));

fitin=1; newin=1;

while newin<=popsize

if (rns(newin)<prob(fitin)) newpop(newin,:)=oldpop(fitin,:); newin=newin+1; else

fitin=fitin+1;

end

end

  1. The coding represents crossover technique as a sample

function [child1, child2]=crossover(parent1, parent2, pc);

if (rand<pc)

cpoint=round(rand*(stringlength−2))+1;

child1=[parent1(:,1:cpoint)

parent2(:,cpoint1+1:stringlength)];

child2=[parent2(:,1:cpoint)

parent1(:,cpoint1+1:stringlength)];

child1(:,

stringlength+1)=sum(2.9(size(child1(:,1:stringlength),2)

−1:−1:0).

*child1(:,:stringlength))*(b−a)/(2.9stringlength−1)+a;

child2(:,

stringlength+1)=sum(2.9(size(child2(:,1:stringlength),2)

−1:−1:0).

*child2(:,1:stringlength))*(b−a)/(2.9stringlength−1)+a;

child1(:, stringlength+2)=fun(child1(:, stringlength+1));

child2(:, stringlength+2)=fun(child2(:, stringlength+1));

else

child1=parent1;

child2=parent2;

end

end

  1. The coding represents Mutation technique as a sample

function [child]=mutation(parent, pm):

if (rand<pm)

mpoint=round(rand*(stringlength−1))+1;

child=parent;

child[mpoint]=abs(parent[mpoint]−1);

child(:,                                            stringlength+1)=sum(2.9

(size(child(:,1:stringlength),2)−1:−1:0).

*child(:,1:stringlength))*(b−a)/ (2.9stringlength−1)+a;

child(:, stringlength+2)=fun(child(:, stringlength+1));

else

child=parent;

end

end

 

Fig 8: Graphical representation of Fitness Value from Population Group

Figure 8 represents the graph of valid persons is the group of population which has been initialized for the genetic operation, the above implementation code represents the how creation of child will takes place, to create a child first we need to initialize the group of population, This can be evaluated by implementing the initialized value into an given equation. a+2b+c=10èf(x)=(a+2b+c)-10. By using this equation we need to find the Functional object for each and every person in the population, once it has been calculated we need to compute the probability of fitness, first we need to compute fitness of each chromosomes. To avoid division by zero problem, the E_obj value will be added by default value 1. This detail mathematical implementation is shown in 3.2 Initialization and Evaluation, based on this we have implemented the MATLAB code where it represents the initialization program, selection of population program, crossover program and mutation program, the graph represents the valid persons which has been selected based on fitness value, this graph is also representing best value & mean value of the entire generation. By keeping these valid fellows we are implementing cross over technique.

Mutation can happen randomly with respect to old chromosome and it will create new chromosome with mutation resultants, it is explain by considering an example problem & code has been implemented and resultant is shown below; consider binary population, old chrome with 4 individual each of length 8:

old chrome = [ 0 0 0 0 0 1 1 1

1 0 0 0 1 0 0 1

0 0 1 0 1 0 0 0

1 1 0 1 1 0 1 1]

By calling mutation(parent, pm) where pm represents the threshold value of mutation parameter and program has been executed with relevant resultant value

New chrome = [0 0 1 0 0 1 1 1

1 1 0 0 0 0 0 1

0 0 0 0 1 0 0 0

1 1 0 1 1 0 1 1]

The complement of binary string is obtained by applying mutation and probability. Here mutation ([1 0 10 1 1 1 0], 1) and resultant value is 0 1 0 1 0 0 0 1

CONCLUSION

Genetic algorithms are a very powerful tool, allowing searching for solutions when there are no other feasible means to do so. The algorithm is easy to produce and simple to understand and enhancements easily introduced. This gives the algorithm much flexibility. This paper presents the optimal solution for a genetic computational problem. It has demonstrated with the help of mathematical equations and accurate results have been extracted. This Genetic Computation can be implemented with the help of MATLAB or any other applications.

REFERENCES

  • An Introduction to Genetic Algorithms: MELANIE MITCHELL
  • A Fast and Elitist Multiobjective Genetic Algorithm: NSGA-II: Kalyanmoy Deb, Amrit Pratap, Sameer Agarwal, T.Meyarivan;

IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, VOL.6, NO.2, APRIL 2002

  • Genetic Algorithm for Solving Simple Mathematical Equality Problem: Denny Hermawanto; Indonesian Institute of Sciences (LIPI), INDONESIA.
  • GENETIC ALGORITHMS: kumara Sastry, David Goldberg; University of Illinois, USA.
  • ARTIFICIAL INTELLIGENCE: R.Goebel, J.Siekmann and W.Wahlster.
  • Introduction to Genetic Programming: Matthew Walker.
  • ARTIFICIAL INTELLIGENCE AND SOFT COMPUTING: Begavioral and Cognitive Modelling of the Human Brain; Amit Konar, Jadavpur University, Calcutta, India.
  • Soft Computing : S N Sivanandam S N Deepa

 

A Restoration Strategy to Extract a Definit Image from Noisy Image in Medical Image Processing System

Abdul

            A Restoration Strategy to Extract a Definit Image                     from Noisy Image in Medical Image Processing System

                                                              Manjula S1, Shivamurthaiah M2

                                          #DOS in Computer Science, Davangere University Davangere-02

                                                                                           1manjula.shamarao@gmail.com

                                                                                               2shivamurthaiah@gmail.com

Abstract— In the past two decades image processing technology is playing vital role in the current area of research, this image processing technique is not only implemented in the field of computer technology but also in the field of business statistics, Defence Research and Development, Forensic labs, Medical image processing, etc. The imaging technology in medicine made the doctor to see the interior portion of the body for easy diagnosis, image processing techniques developed for analyzing remote sensing data may be modified to analyze the output of medical imaging systems to get best advantage to analyze symptoms of the patients with ease. This paper is mainly concentrating on commonly encountered problem that is image restoration for medical imaging application. Image restoration is a pre-processing method that suppresses a known degradation. The restoration technique can be defined as the estimation of the original image or ideal image from the observed one by the effective inversion of degradation phenomena through which the scene was imaged”. This restoration of image technique will be implemented on the noisy image which is generated in medical images like X-ray, CT, MRI, PET and SPECT have minute information about heart, brain, nerves, etc. These images are corrupted during transmission; Removal of noise is an essential and challengeable operation in image processing, before performing any process, image must be first restored. The main purpose of restoration is to reduce noise which is present in image, this can be done by introducing a new methods such as; Medical image smoothing based on Minimum Wiener (mean square error) filtering, Nearest neighbour method. The main purpose of implementing this filtering technique is to get clear image from the noisy image/blurred image.

KeywordsDegradation, Expectation, Fourier Transformation Image Processing, Image Smoothing, Minimum Wiener Filtering, Nearest neighbourhood method, Reconstruction, Restoration.

INTRODUCTION

The removal of noise is an essential and challengeable operation in image processing. Before performing any process, images must be first restored. Images may be corrupted by noise during image acquisition and transmission. Noise and blurring effects always corrupts any recorded image. Impulse noise reduction is an active area of research in image processing. With low computational complexity, a good noise filter is required to satisfy two criteria namely, suppressing the noise and preventing the useful information in the signal. It is more effective in terms of eliminating impulse noise and preserving edges and fine details of digital image. Hence the first and foremost step before the image processing procedure is the restoration of the image by removal of noises in the images. The noise removal is to suppress the noise. The filter can be applied effectively to reduce heavy noise. In order to preserve the details as much as possible, the noise is removed step by step procedure. In any image denoising algorithm, it is very important that the denoising process has no blurring effect on the image and makes no changes or relocation to image edges. There are various methods for image denoising using simple filters, such as; average filter, median filter and Gaussian filters, are some of the common techniques employed for image denoising. These filters reduce noise at the cost of smoothing the image and hence softening the edges. This paper is mainly focusing on two different restoration techniques; Wiener Filter and Nearest neighbour Filter. This paper represents two new approaches that address two problems that are common in image restoration context for medical image processing.

 

  1. The problem of regularized image restoration when no prior information about the original image and the noise available. To overcome this type of issues a new paradigm is adopted according to which the required information is extracted from the available data at the previous iteration step. i.e. the partially          restored          image          at          each          step.
  2. Consider the problem of not knowing exactly the point spread function of degradation system. The theory of constraint totals least squares and maximum a posterior estimation is used to derive town on linear filters that address this problem.
  3. IMAGE RESTORATION
  4. The resolution of an image can be further improved by image restoration, which aims on recovering the original scene from the degraded image observation. By these means, image restoration estimates an inverse filter to compensate for image degradations, including random noise and blurring. Since this process is an ill posed problem, there is no unique solution and a small amount of noise can result in large reconstruction errors. Therefore, restoration methods aim at the modelling the degradation by using a priori knowledge of original scene and Point Spread Function (PSF) as well as noise. The factors the limits the resolution of medical equipment is the fact that they spread the energy of a perfect point source. This blurring phenomenon is captured by point spread function which characterizes each imaging device. Another limiting factor is sensor or photon limited noise that appears in most imaging modalities. Image degradation process can be adequately modelled by a linear space invariant blur and random additive noise. Then the degradation model is described by this mathematical equation which represents degraded and noisy image. G= Hf + η  Or G(x,y) = h(x,y) * f(x,y) +η(x,y)

F(x,y) : Original image; g(x,y): degraded and noisy image;

h(x,y): degradation function; η(x,y): additive noise. This degraded image can be restored by using following mathematical equation; for this process inverse filtering techniques are used to extract reconstructed image F^(x,y) = hR(x,y) * g(x,y)

F^(x,y) : Restored image; g(x,y): degraded and noisy image; hR(x,y): inverse filter

Mathematically, image restoration is an inverse problem in which „f‟ has to be found from the knowledge of „g‟ and „H‟. Image restoration is an ill-posed inverse problem because in most real life application „H‟ has many very small Eigen values. Thus when the inverse of „H‟ is taken to find „f‟, these Eigen values result in the amplification of the high frequency noise in „g‟ and consequently a every noise estimates the obtained image. Regularization is a mathematical approach which has been used in image restoration to ameliorate the effects of the ill-conditioned nature of „H‟. According to this approach, a prior knowledge about the image „f‟ and the noise is used in addition to the available data. The trade-off between fidelity to the data and satisfaction of the prior constraints is controlled by the regularization parameters.

II.1NEED OF IMAGE RESTORATION: In most of the application, denoising the image is fundamental to subsequent image processing operations, various techniques of image processing such as edge enhancement, edge detection, object recognition, image segmentation, object tracking etc., do not perform well in noisy environment. Therefore, image restoration is applied as pre-processing steps. The purpose of various image restoration methods is to smooth out the noisy pixels while maintaining edge features so that there is no adverse effect of noise removal technique on image.

METHODS OF RESTORATION

Image restoration is usually the first step of the entire image processing process; it increases the quality of the image by getting rid of noisy pixels. The restoration of an actually degraded image can be done by writing algorithm, which go on for identifying noisy pixel in the entire image. This paper is mainly focusing the two different methods through mathematical implementation.

III.1 IMAGE RESTORATION USING NEAREST

NEIGHBOURHOOD METHOD

Image restoration using nearest neighbour method need to find out the mean value of all neighbours which come in a mask/window/filter of matrix size 3×3, 5×5, 7×7 and 21×21, by using this mask/window/filter calculated pixel value will be generated which represents the probability of occurrence of each pixel value. The resultant output image is better reconstructed image than the degraded/noisy image. During restoration, the high frequency information of given degraded image is estimated from its low frequency information based on artificial noise. For the restoration problem, a number of techniques are designed corresponding to various versions of the blurring function. The main problem of this technique is, if we want to apply this filter/mask/window to edge pixel values, then we need to add some extra pixels to the image matrix by using padding techniques. This technique will make your image matrix is fully covered with zero value in all the sides of the image matrix and then mask/filter/window will be travelled along each pixel value and we can generate the resultant pixel to the particular position/pixel value that particular pixel value which need to be reconstructed is called “Hot Spot”. The mathematical representation using mask of M x N size matrix for reconstruction of the pixel f(x,y) of m x n size image matrix can be written as

0 <=  x <=  m-1 & 0 <= y <= n-1

RH denotes the set of pixel/coordinate values converted by mask. The process of performing this restoration by using nearest neighbour filtering, the following steps need to be implemented.

Position the mask over the current pixel such that hotspot f(x,y) coincide with mask pixel values.Form all products of filter elements with the corresponding elements in the neighbourhood.

Add up the products and store it at current position in the output image.

These steps must be repeated for every pixel in the image. To handle image borders, there is a problem in applying a filter at the edge of the image where the mask partly falls outside the image. This problem can be overcome by using padding technique and mirroring technique, where these two techniques will introduces the extra pixels at the border of the image and another technique is ignoring edges.

Padding:

The input image matrix padded with zero values at the border. This increases the size of input image before applying mask. This gives us all the values for mask overlapping and returns an output image of the same size as the original, but this may cause introducing unwanted artificial values around edges. Consider the hotspot pixel position as f(1,1) with value 4 and apply mask to hotspot which is the border value of image, hence we are applying padding technique and the resultant value is generated, then replace original image value with the generated/reconstructed pixel values in the output matrix values.

0 0 0 0 0
0 4 7 5 0 1 1 1 5 7 5
0 6 20 6 0 * 1 / 9  1 1 1 =   6 20 6
0 1 2 4 0 1 1 1 1 2 4
0 0 0 0 0

Mirroring: A mirror image of the known image is created with the border as mirroring axis, copy the first and last rows and columns so that the mask is overlapped fully to the input image, here in this method the output image size is as same as input image size. Consider the example input image with mirror values of first and last rows and columns values are entered with mirror technique, consider the hotspot pixel position f(1,1) with value 4 and apply mask to the hotspot position which is the border value of image, hence we are applying mirroring technique and the resultant value is generated, then replace original image value with the generated/reconstructed pixel values in the output matrix values.

4 4 7 5 5
4 7 5 5 1 1 1 7 7 5
6 6 20 6 6 * 1 / 9 1 1 1 =   6 20 6
1 1 2 4 4 1 1 1 1 2 4
1 1 2 4 4

Ignoring edges: Applying the mask to only that pixel in the image for which the mask lies fully within the image. That means mask is applied to all pixels in the image except for edges which results in an output image that is smaller than that of input image. It may lead to loops of significant amount of information. Consider the hotspot pixel f(2,2) with value 20 which need to reconstructed by using mask and this technique is only applicable for this centre position of the matrix and remaining edge pixel will be neglected, hence those values will remain same without any mask implementation.

4 7 5 1 1 1 4 7 5
6 20 6 * 1 / 9 1 1 1 =   6 7 6
1 2 4 1 1 1 1 2 4

Image Reconstruction Algorithm by using Nearest Neighbourhood Technique.

Step 1: Read an input image by using imread () function in MATLAB environment.

Step 2: Display the Original image which has been read.

Step 3: Consider a pixel say f(x,y) as a hotspot and identify its nearest neighbours by implementing 8-neighbours for chess_board distance and neighbour for city_block distance.

Step 4: If the input image has a large matrix element then extract relevant values based on 8/4 neighbour method which has holds the hotspot position/values. i.e. f(x,y)

Step 5: Then calculate the mean value of all the neighbours of sub-matrix by applying suitable mask/filter.

Step 6: The approximate mean value will be generated with the help in step 5, that resultant value is g(x,y).

Step 7: Replace the pixel at f(x,y) with the value obtained in step 6. i.e. g(x,y).

Step 8: Finally display the reconstructed image.

Wiener filter:

Wiener filter restores the image in the presence of blur as well as noise. Consider above derived CLSF equation, if x is zero then CLST fitter is same as wiener fitter. The recovered image is represented as,

f^(x,y)=hR(x,y)*g(x,y)

f^(x,y)-inverse filter.

g(x,y)-degraded & noise image.

multiplying g(x,y) on both the side for above equation f^(x,y) g(x,y)=hR(x,y)*g(x,y) g(x,y) taking equation on both sides.

E[f^(x,y) g(x,y)]=E(hR(x,y)*g(x,y) g(x,y)] E[f^(x,y) g(x,y)]=hR(x,y)*E[g(x,y) g(x,y)]

(since h is not statistical function as fixed expectation) We know that

E[(f^(x,y) g(x,y)]=rfg(x,y)  (cross correlation)

E[g(x,y) g(x,y)]=rgg(x,y) (auto correlation)

Substitute these values in the previous equation.

rfg(x,y)  = hR(x,y) * rgg(x,y)

Apply Fourier transform for above equation.

F[rfg(x,y)]=F[hR(x,y)*rgg(x,y)].

F[hR(x,y)] . F[rgg(x,y)]

Where F[rfg(x,y)]=Sfg(u,v) =>power spectral density.

F(hR(x,y)]=hR(u,v)

F(rgg(x,y)]=Sgg(u,v)=PSD

Substitute these values to the FT function Sf^g(u,v) = HR(u,v) Sgg(u,v)

HR(u,v) =  Sf^g(u,v)
Sgg(u,v)
if f^= f,
à 1
HR(u.v) =   Sfg(u,v)
Sgg(u,v)

The presence of noise, degradation model is g(x,y)=f(x,y)*h(x,y)+n(x,y)

multiply f(x,y)

g(x,y)*f(x,y) = f(x,y)*h(x,y)*f(x,y)+n(x,y)*f(x,y)

Taking expectation on both side

E[g(x,y)*f(x,y)] = E[f(x,y) f(x,y)]*h(x,y)+E[n(x,y) f(x,y)]

E[n(x,y) f(x,y)]=0 (noise is not correlated to input)

E[f(x,y) g(x,y)]=rfg(x,y)=cross correlation

E[f(x,y) f(x,y)]=rff(x,y)=auto correlation

rfg(x,y)=rff(x,y)*h(x,y) + 0.

Taking Fourier transform on both sides

F[rfg(x,y)]=Frff(x,y)*h(x,y)]

  • F[rff(x,y)]*F[h(x,y)]

H(u,v) changes H*(u,v) to adjust FT

S fg (u,v)=S ff (u,v) H*(u,v) à 2

Consider the degraded function g(x,y)=f(x,y)*h(x,y)+n(x,y)

Multiply g(x,y) on both side

g(x,y) g(x,y)=g(x,y) [f(x,y)*h(x,y)+n(x,y)]

  • [f(x,y)*h(x,y)+n(x,y)] [f(x,y)*h(x,y)+n(x,y)]
  • f(x,y) f(x,y)*h(x,y) h(x,y)+ n(x,y) n(x,y)+2[f(x,y)*h(x,y)+n(x,y)]

Taking expectation on both side

E[g(x,y) g(x,y)]=E[f(x,y) f(x,y)*h(x,y) h(x,y)]+E[n(x,y) n(x,y)]+2E[f(x,y)*h(x,y)+n(x,y)+n(x,y)]

rgg(x,y) = rff(x,y)*h2(x,y)+rnn(x,y)+0 à2E[f(x,y)*h(x,y)+n(x,y)]=0

àBy implementing FT

Sgg(x,y)=Sff(u,v)*|H(u,v)|2 +Snn(u,v) à3

Substitute the value of equation 2 and 3 in equation 1

HR(u,v) =  Sfg(u,v)  = H*(u,v) Sff(u,v)
Sfg(u,v) |H(u,v)|2 Sff(u,v)+Snn(u,v)

The derived equation for Wiener filter

  1. CONCLUSIONS

This algorithm provides one of the good way of image restoration from noisy image; it gives the smooth image by the technique of Wiener filter nearest neighbour method.

ACKNOWLEDGMENT

We thank each and every person who helped us directly or indirectly, we also thank who reads this paper and gives the input for us, we took number of papers and books as our reference, for them our lots of thanks.

REFERENCES

  • IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 12, NO. 12, DECEMBER 2003, Marius Lysaker, Arvid Lundervold, and Xue-Cheng Tai
  • Fundamentals of Biomedical Image Processing, Thomas M. Deserno
  • “IEEE Transactions on Medical Imaging 2006;25(11):1405-9” 4,

Mills-Peninsula Health Services, Burlingame, CA 9401

  • Digital Image Processing, Rafael C.Gonzalez and Richard E Woods
  • Digital Image Processing and MATLAB, Vipul Sing
  • ieee.org

STRESS

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STRESS

Stress  is a most common word heard from people belonging to 16 to 60 years of age.It is always considered as an abnormal state of mind by common people and they are unaware of the exact meaning  ofstress.

According to psychology, stress is a feeling of strain and pressure. It is a type of psychological pain. Small amount of stress may be desirable and even healthy. Stress is considered as positive as it helps in improving performance of an individual by motivation.For example: athletic performances.

REASONS FOR STRESS:

When our mental state does not copeup with the situational demands emotionally, we get strained and stress hormones are released in the body.We can consider stress as a fight against unfavorable or pressurized situations by our body.People usually think stress is a new invention of recent years wherein it is in existence from early human ages.

The stress of early age and modern age is completely different.They had stress of securing their lives from predators, man eating animals, venom of snakes etc., because their lives wereinsecure and they had to lead their life in the forests with the animals as their fellow beings.

The stress may be caused through internal or external factors. Causes of stress will be different from person to person. The author  has tried to list out a few common reasons :

Being unhappy in job,Chronicillness, Expectations,Attachments, Anxiety, Competition, Social obligations.

How to overcome the stress, its impacts, and preventive measures will be discussed in the upcoming editions of the blog.

BHARATHI N S

 Assistant professor

                                               Department of commerce and management

EMPLOYEE RELATION

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                                    EMPLOYEE RELATION

ARTICLE BY: SUPRIYA.G

  ASST. PROF

  CITY COLLEGE

“Happy employees are productive employees” successful business knows how to manage relationship to build lasting employee satisfaction.

Employees are important- the most important part of any business is its people. No business can run effective without them. But people don’t work in a vacuum, they need to communicate and work with others.

Employee need to manage relationship in the workplace to keep the business functioning smoothly, avoid problems and make sure they are performing at their best.

What does employee relation mean: the term employee relation refers to a company effort to manage relationship between employers and employee? An organization with a good employee relation program provide fair and consistency treatment to all employees, so they will be committed to their jobs and loyal to their company, such program also aim to prevent and resolve problem arising from situation at work .

Employee relation program are typically part of Human Resource strategy design to ensure the most effective use of people to accomplish the organization mission.

Human resource strategies  of a company use to help them gain and maintain a competitive edge in the market place. Employee relation program focus effective employee isues such as: pay and benefit, supportive work life balance and save working condition.

Let’s think last job we truly loved, was it because we are treated like an important part of the team? We probably had an interest in seeing the business success like stake holder??

Article will be continued…

IMPACT BY GLOBALISATION ON LANGUAGES AND LITERATURE.

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IMPACT BY GLOBALISATION ON LANGUAGES AND LITERATURE.

A PRESENTATION

 

BY,

                                                        Smt Nagashree Arun.

                                                        Assistant Professor,

                                                        Department of English

                                                        CITY COLLEGE, Jaynagar

                                                        Bengaluru

 

 

“Globalization” is a social process, and is readily increasing in today’s world, concentrating more in the social and economic worlds. This increase in globalization has many effects on language, both positive and negative. Although linguistic aspects of globalisation have not received many aspects as other fields, the global face of language is slowly changing, in turn effecting the growth of languages. These effects on language in turn affect the culture of the language in many ways. The impact can either allow languages to speed and dominate or cease to exist.

Language and culture are like contemporaries. Language is in a sense the substance of culture. They serve as an important symbol of social structures, enabling different groups of people to know what ethnic groups they belong to, and what common heritage they share. Without a language, people would lose their cultural as well as geographical identity. In turn language is the bonding force amongst the social structures.

Even today, linguists are not able to give an acute report on the total count of World languages. For them there are around 6,500 different natural languages. The count would exceed 10,000 if considered slangs and dialects of the native languages. Out of these around 6% of  them account for the speech of more than half the world’s population, like Mandarin Chinese, Spanish, Hindi, French, Bengali, Portuguese, Russian, German, Japanese, Arabic, and English. And for about 2000 languages there are around a very limited speakers, fewer than 5000. And around 85% of the world population are well versed in two or more languages.

English is distinguished from other languages by having very significant numbers of non-native speakers, thus making it language most affected by globalization. It’s clear that globalization is making English especially important not just in universities, but in areas such as computing, diplomacy, medicine, shipping, and entertainment.

When we think of global forms of entertainment, we immediately think of the Internet, social media, movies, or television shows.  But, contrary to popular belief, literature also holds an important place in the flow of entertainment. Books today have crossed socio cultural boundaries and borders creating awareness and connecting people worldwide through shared information. Global literature is not a new concept. As new ways emerge of delivering literature to readers worldwide, many scholars are examining the importance of translations on literature, the impact that literature has on culture, and the ways that cultures can transform books.  World literature can be an amazing tool for analyzing globalization because it provides a wonderful example of the ways that information is shared across languages and cultures.

The study of world literature is a powerful tool for global studies because it encompasses so many themes that are important to understanding globalization.  World literature can show us how information is shared between cultures and nations. It provides insight into how cultural artefacts are transformed as they traverse languages and boundaries. It also can help us to understand the ways that new media technologies could be facilitating globalization by creating a public space for the transmission of literature and other information across the globe.

Globalisation definitely affects culture and literature… A person in India can read Dostoyevski or Chekhov in his own language and vice-versa – a Russian student can look up Indian authors on the internet. Another huge progression is online studying. This is opening new doors and opportunities for students globally.

In Asiatic and African countries, globalisation is always associated with, Westernisation and Modernisation. Following this idea globalization is changing the social approach towards the existent cultures and local languages. Adapting western ideologies in contradiction to local cultures or introducing the local cultural flavours globally in English have become the trend.

Aravinda Adiga’s The White Tiger was published in 2008, and before, at the end of that year, it had made its author famous throughout world. This 2008 Booker Prize winner novel studies the contrast between India’s rise as a modern global economic giant and the protagonist, Balram, who comes from rural poverty background. Past six decades have witnessed changes in Indian society, and these changes, many of which are for the better, have overturned the traditional and cultural hierarchies, showcasing the other world that India is more than the land of snake charmers and elephants.

 

The impact of globalization on language and literature is quite significant.  As ideas and beliefs are spread to more parts of the world through information technology and wider access, what has been traditionally defined as “culture” begins to undergo change as newer understandings are integrated into traditional conceptions.  This creates a new vision of what culture envelops and how literature is reflected.  With globalization, it is nearly impossible to stop the spread of ideas, for its very nature brings to light the inter-connectivity of all individuals.  Due to this, the changing conception of literature and culture is almost inevitable in my mind.

 

Languages are the essential medium in which the ability to communicate across culture develops. Knowledge of one or several languages enables us to perceive new horizons, to think globally, and to increase our understanding of ourselves and of our neighbours. Languages are, then, the very lifeline of globalization; without language, there would be no globalization; and vice versa, without globalization, there would be no growth of world languages.

 

EMOTIONAL INTELLIGENCE

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EMOTIONAL INTELLIGENCE

 

AN ARTICLE BY: SEEMA.M G

ASST. PROF

CITY COLLEGE

” Men are not prisoners of fate,but only prisoners of their own minds.They have within themselves the  power to become free at any movement.”

-US PRESIDENT ROOSEVELT

We all have different personalities,different wants and needs and different ways of showing our emotions.navigating through this all takes tact and cleverness-especially if we hope to succeed in life.This is where emotional intelligence becomes important.

WHAT IS EMOTIONAL INTELIGANCE(E.I)?

E.I is the ability to recognize your emotions, understand what they are telling you and realize how your emotions effect people around you. It also involves your perception of others, when you understand how u feel, this allows you to manage relationships more effectively.

WHY E I IS IMPORTANT TO STUDENTS

In the busy shedule of attending classes, tutorials,coaching classes,assingmentsand exams,most of the studentsnot only fail to understand others emotions but forget to take care of their own mental health and emotins!!

Our education has always emphasised on academic results,but is that all we need to  get success in our life?why are students performing very well in schools and called as best students not able to handle the college/peer pressure, is this something which cannot be handle or these students have never been taught about this.

In my coming up article i will be sharing with you few tips to enhance EI.

Communication Opportunities Created by the Internet

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Communication Opportunities Created by the Internet

The Internet has become embedded in every aspect of our day-to-day lives, changing the way we interact with others. This insight struck me when I started out in the world of social media. I created my first social network in 2005, when I was finishing college in the United States—it had a political theme. I could already see that social media were on the verge of changing our way of communicating, helping us to share information by opening up a new channel that cuts across conventional ones.

That first attempt did not work out, but I learned from the experience.I get the feeling that in many countries failure is punished too harshly—but the fact is, the only surefire way of avoiding failure is to do nothing at all. I firmly believe that mistakes help you improve; getting it wrong teaches you how to get it right. Creativity, hard work, and a positive attitude will let you achieve any goal.

In 2006, after I moved to Spain, I created Tuenti. Tuenti (which, contrary to widespread belief, has nothing to do with the number 20; it is short for “tu entidad,” the Spanish for “your entity”) is a social communication platform for genuine friends. From the outset, the idea was to keep it simple, relevant, and private. That’s the key to its success.

I think the real value of social media is that you can stay in touch from moment to moment with the people who really matter to you. Social media let you share experiences and information; they get people and ideas in touch instantly, without frontiers. Camaraderie, friendship, and solidarity—social phenomena that have been around for as long as humanity itself—have been freed from the conventional restrictions of space and time and can now thrive in a rich variety of ways.

Out of all the plethora of communication opportunities that the Internet has opened up, I would highlight the emergence of social media and the way they have intricately melded into our daily lives. Social media have changed our personal space, altering the way we interact with our loved ones, our friends, and our sexual partners; they have forced us to rethink even basic daily processes like studying and shopping; they have affected the economy by nurturing the business startup culture and electronic commerce; they have even given us new ways to form broad-based political movements.

 

Published By Hamza

The Internet and Education

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The Internet and Education

The Internet has clearly impacted all levels of education by providing unbounded possibilities for learning. I believe the future of education is a networked future. People can use the Internet to create and share knowledge and develop new ways of teaching and learning that captivate and stimulate students’ imagination at any time, anywhere, using any device. By connecting and empowering students and educators, we can speed up economic growth and enhance the well-being of society throughout the world. We should work together, over a network, to build the global learning society.

The network of networks is an inexhaustible source of information. What’s more, the Internet has enabled users to move away from their former passive role as mere recipients of messages conveyed by conventional media to an active role, choosing what information to receive, how, and when. The information recipient even decides whether or not they want to stay informed.

We have moved on from scattergun mass communication to a pattern where the user proactively selects the information they need.

Students can work interactively with one another, unrestricted by physical or time constraints. Today, you can use the Internet to access libraries, encyclopedias, art galleries, news archives, and other information sources from anywhere in the world: I believe this is a key advantage in the education field. The web is a formidable resource for enhancing the process of building knowledge.

I also believe the Internet is a wonderful tool for learning and practicing other languages—this continues to be a critical issue in many countries, including Spain, and, in a globalized world, calls for special efforts to improve.

The Internet, in addition to its communicative purposes, has become a vital tool for exchanging knowledge and education; it is not just an information source, or a locus where results can be published, it is also a channel for cooperating with other people and groups who are working on related research topics.

To know more click the below link

Communication Opportunities Created by the Internet

Published By Hamza

CREATIVITY

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CREATIVITY

Written by MALINI N

Most of us have heard the story of an intelligent crow that put pebbles into a jar of water, till it could reach water to quench its thirst. The normal thing the crow could have done was to fly all over, till it got water. Instead when its beak could not reach water, it found a way for the water to reach its beak. This is a beautiful example of creativity.

Creativity is all about finding unconventional solutions. Such solutions require imagination and out of box thinking. It involves seeing connections between apparently unconnected things, seeing hidden patterns and evolving solutions. Creativity doesn’t end with imagination. It involves actions. Therefore it is creative people who can think differently and produce new ideas or products.

The best examples for the most creative designs in nature range from a human body to a bird’s nest to a crawling worm. Also nature’s designs are the most efficient designs. Thus, instead of simply looking at some solution for the things around us, it is good to think of creative solutions. Every child is born creative. It is only because of environmental factors that few children evolve more creative than others.
As students, you are in the intense learning phase of your life. It is for this reason that you should acquire a high degree of
creativity. Few steps to nurture your creativity are as follows.

Break away from routine – Think and act differently, question the rules with “why this way and why not the other way”. This leads to inventions.
Add value to existing things – Candles were used as light sources. Thomas Alva Edison discovered light bulb which was far
superior to candles in many ways. He failed 1000 times, but did not give up. Creative people should accept failures as a part of learning.

Chase your curiosity – If you have found something interesting, that excites you but not pursued, start working on it immediately.
Set small creative tasks each day and complete it. This will energise you to take up bigger creative tasks.
Spend time with nature – Get away from the monotony and get into a state of peace. Your imagination works better as nature brings calmness in you.
Acquire good reading habit – This gives you new ideas along with your own. It helps in creating new solutions.
If we do not function creatively, we may loose a lot of opportunities in life.
One of the take-aways is to start asking questions like “Why this way and why not the other way?”. List few more take aways for yourself.
Source : Pramod(2017)

A Daylight Robbery

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A Daylight Robbery

Written by  Dr Vinaya T

The recent outbreak on PSU banks writing off Rs. 2.41 Trillion or Rs. 241,911 crore loans from April 2014 to September 2017 is an eye-opener and an extremely sad case for the Indian banking industry. We know that the Indian banking sector with its robust policies was able to overcome and sustain the effects of 2008 recession which rocked the global economy, but ten years down the line in 2018 we have witnessed some of the miserable cases of daylight robbery by the billionaire jewellery designer Nirav Modi defrauded PNB to the tune of Rs. 11,300 crore and the infamous absconding business tycoon Vijay Mallya who defaulted multiple banks to the tune of a whopping Rs. 9,000 crore poses serious questions to the much celebrated banking system in the country.
The other worrying aspect of this write off which is touted as a routine exercise undermines the corporate political nexus considering the period in which these loans were written off and the high handedness of the elected government in disclosing the borrower wise credit information citing RBI Act hides the major mishandling and inefficiencies in curbing the corrupt practices existing within the banking industry, corporate entities (with easy entry and exit) and the politicians. To conclude the paradox continues to be that the innocent voter receives multiple reminders from the banker to even pay an amount even as little as Rs. 1,000: whereas the influential borrower could flee the country through diplomatic channel by making himself unreachable or by overseas citizenship.