DESC:FORM 2 THE PATENTS ACT, 1970 (39 of 1970) & THE PATENT RULES, 2003 COMPLETE SPECIFICATION (See Section 10 and Rule 13) Title of invention: PARALLELIZATION TECHNIQUES FOR VARIABLE SELECTION AND PREDICTIVE MODELS GENERATION AND ITS APPLICATIONS Applicant: Tata Consultancy Services Limited A company Incorporated in India under the Companies Act, 1956 Having address: Nirmal Building, 9th Floor, Nariman Point, Mumbai 400021, Maharashtra, India The following specification particularly describes the embodiments and the manner in which it is to be performed. CROSS-REFERENCE TO RELATED APPLICATIONS AND PRIORITY The present application claims priority to Indian provisional specification (Title: SYSTEM AND METHOD FOR FEATURE SELECTION AND GENERATION OF PREDICTIVE REGRESSION MODELS) Application No. (201621020879), filed in India on June 17, 2016. TECHNICAL FIELD The disclosure herein generally relate to search optimization techniques, and, more particularly, to parallelizing variables selection and modelling method based on prediction and its applications. BACKGROUND Advancements in various sciences such as physical, life, social sciences etc., have generated large amounts of data and there is great interest to make use of these data for the creation of new knowledge, as it is expected to improve the quality of human life. The quest for the new knowledge that includes insights, rules, alerts, predictive models etc. and its associated positive impact on humanity, have created an urgent need for the development of efficient data analytics techniques and technologies such as high performance computing, cloud computing etc., which can handle large amounts of data. Variable selection methods are one such data analytics approach that is applied to the selection of a subset of variables (X) from a large pool of variables based on various statistics measures. The selected variables can be used for the development of prediction models for a dependent variable (Y), when used with modelling techniques such as multiple linear regression, nonlinear regression, etc. The variables selection can be accomplished using a random or exhaustive search technique. The random approach, includes heuristic methods such as ant colony, particle swarm optimization, genetic algorithm, and the like; however, these methods cannot guarantee an optimal solution as they fail to explore the complete problem (variable) space. Unlike a random approach, the exhaustive search approach, evaluates each possible combination and thus provides the best solution; however, it is a computationally hard problem, thus limiting its applications to the selection of smaller subsets. Predictive regression model generation, in principle involves the following three critical steps: a) data division, b) optimal features/variable selection from a large pool of structural features and c) model generation from the selected optimal features using regression techniques. Data quality and the efficiency of the above three steps determine robustness of the predictive models and their applications/business impact. For example, late stages failures of drug candidates can be addressed using reliable and easily applicable predictive ADMET models [Absorption, Distribution, Metabolism, Excretion and Toxicity]. As these computational models rationalize experimental observations, offer potential for virtual screening applications and consequently can help in reducing time and cost of the drug discovery and development process. The generation of predictive ADMET models based on structural features of drugs and drug candidates typically involves three critical steps, discussed earlier. The variable selection step enables researchers to a) derive rules/alerts that can be used for improving research outcomes b) provide the best sub-set of variables to generate robust predictive models that are applicable in virtual screening of drug candidates even before they are produced in laboratory. Thus, efficiency of the above steps have significant business impact across different domains. SUMMARY Embodiments of the present disclosure present technological improvements as solutions to one or more of the above-mentioned technical problems recognized by the inventors in conventional systems. For example, in one aspect, a processor implemented method for performing parallelization techniques for generating predictive models and variable selection and its applications is provided. The method comprising: defining, by one or more processors, (i) an input data comprising a set of independent variables, a set of dependent variables, and a subset size, (ii) a set of control parameters comprising maximum allowed inter correlation coefficient of independent variables R_int, and a minimum or maximum objective function threshold ?(R?_cri) pertaining to the set of independent variables, and (iii) a block size and a grid size pertaining to a graphics processing unit (GPU), the block size and grid size are indicative of number of execution threads to be executed in one or more blocks in a grid; calculating, by the one or more processors, one or more inter correlation coefficients of each pair of independent variables from the set of independent variables; ranking, using a lexicographical technique, one or more subsets of independent variables from the set of independent variables; assigning the one or more ranked subsets to corresponding one or more execution threads of the GPU; executing, in parallel, in each of the one or more execution threads specific to the one or more blocks: generating, using an unranking technique, an initial set of subsets from the one or more assigned subsets, and recursively generating, based on the initial set of subsets, a next set of subsets based on the one or more assigned subsets, wherein when the initial set of subsets is being generated, a comparison of an inter correlation coefficient (R_a) across independent variables in a subset of the initial set is performed; performing a comparison of (i) an inter correlation coefficient (R_a) of (a) an updated independent variable in a next subset of a subsequent set and (b) one or more independent variables in the same next subset and (ii) the maximum allowed inter correlation coefficient of independent variables R_int, wherein the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets; and generating, based on the comparison, one or more predictive models for each of the subset. In an embodiment, when (R_a) is less than R_int, the one or more predictive models are generated for each of the subset. In an embodiment, the method may further comprise calculating model correlation coefficient R_m based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables. In an embodiment, the method may further comprise performing a comparison of R_m and ?(R?_cri); and updating value of ?(R?_cri) based on the comparison of R_m and ?(R?_cri). In an embodiment, the method may further comprise determining, based on the comparison, an optimal subset from each corresponding execution thread from each block to obtain one or more optimal subsets, each of the one or more optimal subsets comprises one or more independent variables; and synchronizing, for the grid and block, the one or more optimal subsets from each of the corresponding execution threads specific to each block to obtain a best subset from the synchronized one or more optimal subsets for variable selection. In another aspect, a system for performing parallelization techniques for generating predictive models and variable selection and its applications is provided. The system comprising: a memory storing instructions; one or more communication interfaces; and one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to: define (i) an input data comprising a set of independent variables, a set of dependent variables, and a subset size, (ii) a set of control parameters comprising maximum allowed inter correlation coefficient of independent variables R_int, and a minimum or maximum objective function threshold ?(R?_cri) pertaining to the set of independent variables, and (iii) a block size and a grid size pertaining to a graphics processing unit (GPU), the block size and grid size are indicative of number of execution threads to be executed in one or more blocks in a grid; calculate one or more inter correlation coefficients of each pair of independent variables from the set of independent variables; rank, using a lexicographical technique, one or more subsets of independent variables from the set of independent variables; assign the one or more ranked subsets to corresponding one or more execution threads of the GPU; execute, in parallel, in each of the one or more execution threads specific to the one or more blocks: generate, using an unranking technique, an initial set of subsets from the one or more assigned subsets, and recursively generating, based on the initial set of subsets, a next set of subsets based on the one or more assigned subsets, wherein when the initial set of subsets is being generated, a comparison of an inter correlation coefficient (R_a) across independent variables in a subset of the initial set is performed; perform a comparison of (i) an inter correlation coefficient (R_a) of (a) an updated independent variable in a next subset of a subsequent set and (b) one or more independent variables in the same next subset and (ii) the maximum allowed inter correlation coefficient of independent variables R_int, wherein the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets; and generate, based on the comparison, one or more predictive models for each of the subset. In an embodiment, when (R_a) is less than R_int, the one or more predictive models are generated for each of the subset. In an embodiment, the one or more hardware processors are may be further configured by instructions to calculate model correlation coefficient R_m based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables. In an embodiment, the one or more hardware processors are may be further configured by instructions to perform a comparison of R_m and ?(R?_cri); and update value of ?(R?_cri) based on the comparison of R_m and ?(R?_cri). In an embodiment, the one or more hardware processors are may be further configured by instructions to determine, based on the comparison, an optimal subset from each corresponding execution thread from each block to obtain one or more optimal subsets, each of the one or more optimal subsets comprises one or more independent variables; and synchronize, for the grid and block, the one or more optimal subsets from each of the corresponding execution thread specific to each block to obtain a best subset from the synchronized one or more optimal subsets for variable selection. In yet another aspect, one or more non-transitory machine readable information storage mediums comprising one or more instructions is provided. The one or more instructions which when executed by one or more hardware processors causes performing parallelization techniques for generating predictive models and variable selection and its applications by defining, by one or more processors, (i) an input data comprising a set of independent variables, a set of dependent variables, and a subset size, (ii) a set of control parameters comprising maximum allowed inter correlation coefficient of independent variables R_int, and a minimum or maximum objective function threshold ?(R?_cri) pertaining to the set of independent variables, and (iii) a block size and a grid size pertaining to a graphics processing unit (GPU), the block size and grid size are indicative of number of execution threads to be executed in one or more blocks in a grid; calculating, by the one or more processors, one or more inter correlation coefficients of each pair of independent variables from the set of independent variables; ranking, using a lexicographical technique, one or more subsets of independent variables from the set of independent variables; assigning the one or more ranked subsets to corresponding one or more execution threads of the GPU; executing, in parallel, in each of the one or more execution threads specific to the one or more blocks: generating, using an unranking technique, an initial set of subsets from the one or more assigned subsets, and recursively generating, based on the initial set of subsets, a next set of subsets based on the one or more assigned subsets, wherein when the initial set of subsets is being generated, a comparison of an inter correlation coefficient (R_a) across independent variables in a subset of the initial set is performed; performing a comparison of (i) an inter correlation coefficient (R_a) of (a) an updated independent variable in a next subset of a subsequent set and (b) one or more independent variables in the same next subset and (ii) the maximum allowed inter correlation coefficient of independent variables R_int, wherein the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets; and generating, based on the comparison, one or more predictive models for each of the subset. In an embodiment, when (R_a) is less than R_int, the one or more predictive models are generated for each of the subset. In an embodiment, the one or more instructions which when executed by the one or more hardware processors further cause calculating model correlation coefficient R_m based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables. In an embodiment, the one or more instructions which when executed by the one or more hardware processors further cause performing a comparison of R_m and ?(R?_cri); and updating value of ?(R?_cri) based on the comparison of R_m and ?(R?_cri). In an embodiment, the one or more instructions which when executed by the one or more hardware processors further cause determining, based on the comparison, an optimal subset from each corresponding execution thread from each block to obtain one or more optimal subsets, each of the one or more optimal subsets comprises one or more independent variables; and synchronizing, for the grid and block, the one or more optimal subsets from each of the corresponding execution thread specific to each block to obtain a best subset from the synchronized one or more optimal subsets for variable selection. It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed. BRIEF DESCRIPTION OF THE DRAWINGS The accompanying drawings, which are incorporated in and constitute a part of this disclosure, illustrate exemplary embodiments and, together with the description, serve to explain the disclosed principles: FIG. 1 illustrates an exemplary block diagram of a system for parallelization of variables selection and generating predictive models thereof for its applications in accordance with an embodiment of the present disclosure. FIG. 2 illustrates an exemplary flow diagram of a method for parallelization of variables selection and generating predictive models thereof for its applications in accordance with an embodiment of the present disclosure. FIG. 3 is an exemplary flow diagram illustrating CUDA kernels workflow implemented by the system of FIG. 1 for parallelization of variables selection and generating predictive models thereof and its applications in accordance to an embodiment of the present disclosure. FIG. 4 illustrates an exemplary CUDA global reduction kernel according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF EMBODIMENTS Exemplary embodiments are described with reference to the accompanying drawings. In the figures, the left-most digit(s) of a reference number identifies the figure in which the reference number first appears. Wherever convenient, the same reference numbers are used throughout the drawings to refer to the same or like parts. While examples and features of disclosed principles are described herein, modifications, adaptations, and other implementations are possible without departing from the spirit and scope of the disclosed embodiments. It is intended that the following detailed description be considered as exemplary only, with the true scope and spirit being indicated by the following claims. Referring now to the drawings, and more particularly to FIGS. 1 through 4, where similar reference characters denote corresponding features consistently throughout the figures, there are shown preferred embodiments and these embodiments are described in the context of the following exemplary system and/or method. FIG. 1 illustrates an exemplary block diagram of a system 100 for parallelization of variables selection and generating predictive models thereof for its applications in accordance with an embodiment of the present disclosure. In an embodiment, the device 100 includes one or more processors 104, communication interface device(s) or input/output (I/O) interface(s) 106, and one or more data storage devices or memory 102 operatively coupled to the one or more processors 104. In an embodiment, the one or more devices are Long Term Evolution (LTE) devices (e.g., cellular devices). The one or more processors 104 may be one or more software processing modules and/or hardware processors. In an embodiment, the hardware processors can be implemented as one or more microprocessors, microcomputers, microcontrollers, digital signal processors, central processing units, state machines, logic circuitries, and/or any devices that manipulate signals based on operational instructions. Among other capabilities, the processor(s) is configured to fetch and execute computer-readable instructions stored in the memory. In an embodiment, the device 100 can be implemented in a variety of computing systems, such as laptop computers, notebooks, hand-held devices, workstations, mainframe computers, servers, a network cloud and the like. The I/O interface device(s) 106 can include a variety of software and hardware interfaces, for example, a web interface, a graphical user interface, and the like and can facilitate multiple communications within a wide variety of networks N/W and protocol types, including wired networks, for example, LAN, cable, etc., and wireless networks, such as WLAN, cellular, or satellite. In an embodiment, the I/O interface device(s) can include one or more ports for connecting a number of devices to one another or to another server. The memory 102 may include any computer-readable medium known in the art including, for example, volatile memory, such as static random access memory (SRAM) and dynamic random access memory (DRAM), and/or non-volatile memory, such as read only memory (ROM), erasable programmable ROM, flash memories, hard disks, optical disks, and magnetic tapes. In an embodiment, one or more modules (not shown) of the device 100 can be stored in the memory 102. The memory 102 may further store information pertaining to communication between devices, and a base station (not shown in FIG. 1). FIG. 2, with reference to FIG. 1, illustrates an exemplary flow diagram of a method for parallelization of variables selection and generating predictive models thereof for its applications in accordance with an embodiment of the present disclosure. In an embodiment, the device(s) 100 comprises one or more data storage devices or the memory 102 operatively coupled to the one or more hardware processors 104 and is configured to store instructions for execution of steps of the method by the one or more processors 104. The steps of the method of the present disclosure will now be explained with reference to the components of the device 100 as depicted in FIG. 1, and the flow diagram. In an embodiment of the present disclosure, at step 202, the one or more hardware processors 104 define (i) an input data comprising a set of independent variables, a set of dependent variables, and a subset size, (ii) a set of control parameters comprising maximum allowed inter correlation coefficient of independent variables R_int, and a minimum or maximum objective function threshold ?(R?_cri) pertaining to the set of independent variables, and (iii) a block size and a grid size pertaining to a graphics processing unit (GPU), the block size and grid size are indicative of number of execution threads to be executed in one or more blocks in a grid. In an embodiment of the present disclosure, at step 204, the one or more hardware processors 104 calculate one or more inter correlation coefficients of each pair of independent variables from the set of independent variables. In an embodiment of the present disclosure, at step 206, the one or more hardware processors 104 rank, using a lexicographical technique, one or more subsets of independent variables from the set of independent variables. In an embodiment of the present disclosure, at step 208, the one or more hardware processors 104 assign the one or more ranked subsets to corresponding one or more execution threads of the GPU. In an embodiment of the present disclosure, steps 210, 212, and 214 are executed in parallel in each of the one or more execution threads specific to the one or more blocks. For example, at step 210, the one or more processors generate, using an unranking technique, an initial set of subsets from the one or more assigned subsets, and recursively generating, based on the initial set of subsets, a next set of subsets based on the one or more assigned subsets, wherein when the initial set of subsets is being generated, a comparison of an inter correlation coefficient (R_a) across independent variables in a subset of the initial set is performed. In an embodiment of the present disclosure, at step 212, the one or more hardware processors 104 perform a comparison of (i) an inter correlation coefficient (R_a) of (a) an updated independent variable in a next subset of a subsequent set and (b) one or more independent variables in the same next subset and (ii) the maximum allowed inter correlation coefficient of independent variables R_int. In an embodiment of the present disclosure, the updated independent variable in the next subset is obtained upon recursively generating one or more next subsets from the initial set of subsets. In an embodiment of the present disclosure, at step 214, the one or more hardware processors 104 generate one or more predictive models for each of the subset based on the comparison. In an embodiment of the present disclosure, when R_a is less than R_int, the one or more predictive models are generated for each of the subset. In an embodiment of the present disclosure, based on the one or more generated predictive models, the one or more hardware processors 104 are further configured by the instructions to calculate model correlation coefficient R_m based on the one or more generated predictive models and one or more dependent variables from the set of dependent variables. In an embodiment of the present disclosure, the model correlation coefficient R_m comprise standard error, mean square error, variance, and the like. The one or more hardware processors 104 further perform a comparison of the model correlation coefficient R_m and the objective function ?(R?_cri). Based on the comparison between R_m and R_cri, the value of R_cri is updated. In an example embodiment, the value of R_cri is updated when R_mis greater than R_cri. In an embodiment of the present disclosure, the one or more hardware processors are further configured by the instructions to: determine, based on the comparison, an optimal subset from each corresponding execution thread to obtain one or more optimal subsets wherein each of the one or more optimal subsets comprises one or more independent variables, and synchronize, for the grid size, the one or more optimal subsets from each of the corresponding execution thread specific to each block to obtain a best subset from the synchronized one or more optimal subsets for variable selection. FIG. 3, with reference to FIGS. 1-2, is an exemplary flow diagram illustrating CUDA Kernels workflow implemented by the system 100 of FIG. 1 for parallelization of variables selection and generating predictive models thereof and its applications in accordance to an embodiment of the present disclosure. The steps of the method of FIG. 3 will now be explained with reference to the components of the device 100 and the flow diagram as depicted in FIGS. 1-2. In an embodiment of the present disclosure, at step 302, the one or more hardware processors 104 define (i) an input data comprising a set of independent variables, a set of dependent variables, and a subset size, (ii) a set of control parameters comprising maximum allowed inter correlation coefficient of independent variables R_int, and an objective function ?(R?_cri) pertaining to the set of independent variables, and (iii) a block size and a grid size pertaining to a graphics processing unit (GPU), the block size and grid size are indicative of number of execution threads to be executed in one or more blocks in a grid. In an embodiment of the present disclosure, at step 304, the one or more hardware processors 104 calculate one or more inter correlation coefficients of each pair of independent variables from the set of independent variables. In an embodiment of the present disclosure, at step 306, the one or more hardware processors 104 rank, using a lexicographical technique, one or more subsets of independent variables from the set of independent variables. In an embodiment of the present disclosure, at step 308, the one or more hardware processors 104 assign the one or more ranked subsets to corresponding one or more execution threads of the GPU. At step 310, CUDA Kernel is launched, wherein the steps 210-214 of the flow diagram of FIG. 2 are executed in parallel for one or more threads pertaining to one or more blocks. Upon executing the steps 210-214, at step 312, the one or more hardware processors 104 determine, based on the comparison, an optimal subset from each corresponding execution thread to obtain one or more optimal subsets wherein each of the one or more optimal subsets comprises one or more independent variables. At step 314, the one or more hardware processors 104 synchronize, for the grid size, the one or more optimal subsets from each of the corresponding execution thread specific to each block to obtain a best subset from the synchronized one or more optimal subsets for variable selection. In an embodiment of the present disclosure, steps 312, and 314 are executed in parallel in each of the one or more execution threads specific to the one or more blocks depicted in step 310. FIG. 4, with reference to FIGS. 1 through 3, illustrates an exemplary CUDA global reduction Kernel according to an embodiment of the present disclosure. More particularly, FIG. 4 depicts thread identifiers (IDs), shared memory values during CUDA global reduction kernel execution. Below are implementation details of the embodiments of the present disclosure by way of example: The parallelization of variable selection and modeling method based on prediction (VSMP) on graphical processing unit (GPU) is performed using Compute Unified Device Architecture (CUDA) programming language. In this, the host, CPU, calls a CUDA kernel, a function that is executed on the GPU device. This kernel function is executed on one or more GPU threads that perform same operations on different input data. The total number of GPU threads depends upon the number of blocks (grid) and threads per block, which are launched by the host function. Each thread is identified by its block ID and thread ID (BLOCK_ID, THREAD_ID). Before launching the CUDA VSMP compute kernel the total combinations of subsets of independent variables to be evaluated are assigned among the GPU threads evenly using below expression illustrated by way of example: combinatons_per_thread = (total_combinatons)/(GPU_BLOCK_SIZE ×GPU_GRID_SIZE ) Where, total_combinations= (n¦r),r is the desired subset size. The GPU device can be a Tesla K20 NVIDIA GPU hosted on a 24 Core 1200 MHz Server. Before porting the compute and memory intensive steps on to the GPU the following modifications are performed to achieve desired parallelism in the algorithm: In CUDA programming model the memory latency of local memory is faster than the shared memory, which in turn is faster than global memory. Since the dynamic memory allocations on GPU are done on global memory rather the local memory, feasible array and matrix declarations were made static to reduce the memory latency. The dependent variables are transferred to the device shared memory, as each thread accesses this memory for evaluating one or more built predictive models. In addition the block and grid level optimal subset values are also stored in the shared memory to facilitate the application of parallel reduction to find the best subset among the launched GPU threads. The doubles (double precision) were converted to floats or integers (single precision) to reduce the time taken to compute the arithmetic operations. In CUDA, each thread is divided in to a group of 32 threads called as warps. The threads in each warp run, in parallel, synchronously for each step of the code. Thus, a code having (many) ‘if and else’ or ‘for’ loops creates divergence in the paths of the threads of a particular warp, which are then executed sequentially, resulting in performance bottle necks. As observed, the leave one out cross validation performed after each model generation is time consuming, and also accounts for thread divergence but has little or no effect on the best subset the method finds. Thus, the leave one out validation is eliminated from the algorithm. The subsets to evaluate are arranged lexicographically and assigned to each thread depending on the rank of the subset and index of the thread. Below are illustrative subsets depicted by way of examples. Using blood brain barrier (BBB) data of 88(m, number of observations) drugs and drug-like compounds and 277 descriptors/independent variables derived from chemical structures of the observations employed. The terms “descriptors” and “variables” are synonymous and can be used interchangeably. For example, in the case of selection of for combinations of 4 subset of 277 variables, with grid size 4096 and block size 512, below table 1 depicts details of subsets, rank, and assigned kernels: Table 1 Subset Rank Assigned Kernel (Block ID, Thread ID) 1, 2, 3, 4 0 (0, 0) 1, 2, 3, 5 1 1, 2, 3, 6 2 …. …. …. …. 1, 2, 3, 119 115 (0, 1) …. …. …. …. 274, 275, 276, 277 240027424 (4095, 511) Further, in the current case study a Multiple Linear Regression model was implemented, for predicting the dependent variable, as per below expression depicted by way of example: y ^ =Xß ^=X?(X^' X)?^(-1) X^' y (1) wherein X is a given dataset comprising (n compounds, m descriptors/ independent variables) and y (n compound’s dependent variable). The compute intensive steps as noticed in the above equation are the matrix multiplications and inverse operations. In an embodiment of the present disclosure, different approaches (or techniques) for calculating the matrix inverse may be utilized, for example, using determinants and co-factors, Gauss Jordan Row Elimination, and Strassen Technique, while the approaches determinants and co-factors, and Strassen are recursive methods which are not suitable for the GPU CUDA architecture, returning an address out of bounds error. Thus, a matrix inverse is computed using Gauss Jordan Row elimination, whose compute complexity is ?(n^3 ). Further, the following optimizations are implemented to improve the performance of the code: While evaluating the set of next subsets or combinations recursively the inter-correlation of the independent variables or descriptors is checked only for the updated subset values rather than the whole combination. This reduces the comparison and memory read operations from (r-1)! to (r-1), where r is the subset size. For example, if a current subset is (5, 11, 19, 47) and the next subset to be evaluated is (5, 11, 19, 48), the inter correlation of 48 is checked against 5, 11, 19 to verify if it's below R_int (ex: 0.75) rather than checking 5 against 11, 19, 48; 11 against 19, 48; and 19 against 48. Also, the subset matrix needed for building (or generating) predictive model(s) has been updated only with the values of updated descriptor (48th descriptor in above example). This optimization reduces the memory read and writes from (r*m) to m operations, where m is the number of compounds. Temporary redundant variables were eliminated and the for loops were unrolled to compact the arithmetic operations as illustrated in the below example for ( j=0;j