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\title{Computation Model for Identifying Types of Diabetics using Multi-Selection Criteria Evaluation and K-Nearest Neighbor}
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             \author[1]{Towhidul  Islam}

             \affil[1]{  Southern university Bangladesh}

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\date{\small \em Received: 14 December 2017 Accepted: 31 December 2017 Published: 15 January 2018}

\maketitle


\begin{abstract}
        


The main purpose of this topic is to develop a dynamic model of a Diabetes solution system. Diabetes mellitus is a chronic disease caused by inherited and/or acquired deficiency in production of insulin by the pancreas or by the ineffectiveness of the insulin produced such a deficiency results in increased concentrations of glucose in the blood. Which in turn damage many of the body systems in particular the blood vessels and nerves. Diabetes mellitus, often simply referred to as diabetes, is a group of metabolic diseases in which a person has high blood sugar, either because the body does not produce enough insulin, or because cells do not respond to the insulin that is produced. This high blood sugar produces the classical symptoms of polyuria (frequent urination), polydipsia (increased thirst) and polyphonic (increased hunger).diabetes is data mining based notification systems. This system developed is main purposed the people easily treatment for accommodation. This system helps finds diabetes what type of diabetes type1, type2, & no diabetes easily provide this system. In this work, at first identity all the dependent variable or data to classify the suitable from unsuitable location. Then I have classified the data using Multi Criteria Evaluation System (MCES)[2].MCES helped the data set to be properly design and manipulated the system and KNearest Neighbor helped the diabetes range[1]. The main purpose of applying this identification is diabetes level. The concept of basically helped to build knowledge base. Most important of this topics collecting the real data for diabetes information.

\end{abstract}


\keywords{classification diabetes, MCES algorithm-NN algorithm.}

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\let\tabcellsep& 	 	 		 
\section[{Introduction}]{Introduction}\par
iabetes solution is data mining based notification systems. This system developed is main purposed every people easily known diabetes patient. Then what kinds of diabetes type 1,type 2 \& No diabetes lives and easily find out for accommodation. This system helps easily finds out type 1, type 2 \& Nodiabetes identify. This system designed basically Multi Criteria Evaluation Systems (MCES) method used \hyperref[b1]{[2]}. MCES computing the data set to be properly design and manipulated the system. The main purpose of applying this identification is to design a physical level. This system works, at first select different types Symptoms. Example: frequently urination, very thirsty. weight less increased hunger tried and weakness injured dry delay every see present itch bred few see eye blurry vision irritability tingling gum infections etc. Second step in test selection example OGT, FGT, Hbalc, ABF, HDL, Blood etc \hyperref[b3]{[4]}. Each Criteria select based on need then test result provide. Then base test value so result provide type1, type 2 or No diabetes. Next scaling the each criterion diabetes level using K-Nearest Neighbor, K-Nearest Neighbor helped the diabetes range. Then type 1 range sum>=130.55mm \& Hbalc>=6. \hyperref[b4]{5} then type 2 diabetes range sum>=130.55mm \& Hbalc<6.5 then type 2 diabetes and range sum<=130.55mm then no diabetes \hyperref[b5]{[6]}. The main purpose of applying this identification is diabetes level. The concept of basically helped to build knowledge base. Most important of this topics collecting the real data for diabetes information. Standardization of criterion scores particularly as sign the value. All the value defined between two intervals scores o and 1.The maximum value is score 0, the minimum value is score 1, the mid value is score 0.5 and other value in scores 0 and 1 \hyperref[b1]{[2]}.When the patient search symptoms then gives the measurable for each criteria particularly importance of this criteria .Finally, this system select a perfect test result provide then drug suggestion and dose time. 
\section[{II.}]{II.} 
\section[{Diabetes Mellitus}]{Diabetes Mellitus}\par
Diabetes mellitus is a chronic disease caused by inherited and/or acquired deficiency in production of insulin by the pancreas or by the ineffectiveness of the insulin produced such a deficiency results in increased concentrations of glucose in the blood. Which in turn damage many of the body systems in particular the blood vessels and nerves. Diabetes mellitus, often simply referred to as diabetes, is a group of metabolic diseases in which a person has high blood sugar, either because the body does not produce enough insulin, or because cells do not respond to the insulin that is produced \hyperref[b3]{[4]}. 
\section[{a) Because blood glucose very high}]{a) Because blood glucose very high}\par
This high blood sugar produces the classical symptoms of polyuria (frequent urination), polydipsia (increased thirst) and polyphonic (increased hunger: under the design of process natural man and diabetes man \hyperref[b3]{[4]}. 
\section[{b) Symptoms}]{b) Symptoms}\par
Diabetes mellitus is a chronic disease caused by inherited and/or acquired deficiency in production of insulin by the pancreas or by the ineffectiveness of the insulin produced such a deficiency results in increased concentrations of glucose in the blood. Under the diabetes symptoms \hyperref[b3]{[4]}  
\section[{c) Reason for diabetes}]{c) Reason for diabetes}\par
Which in turn damage many of the body systems in particular the blood vessels and nerves. Under the Diabetes reason for diabetes \hyperref[b5]{[6]}. ? Family father mother near relative to diabetes ineffective ? Weight very high ? Do not physical exercise and hardworking ? Longtime Cotswold medicine use 
\section[{d) Conditions that cause diabetes}]{d) Conditions that cause diabetes}\par
Diabetes mellitus is a chronic disease caused by inherited and/or acquired deficiency in production of insulin by the pancreas or by the ineffectiveness of the following conditions \hyperref[b5]{[6]}.? Physical thickness ? Pregnant ? Injured ? Shock ? Surgery ? Mental contrariety\par
e) Principle forms of diabetes i. Type 1 diabetes: insulin dependent in which the pancreas fails to produce the insulin which is essential for survival this form develops most frequently in children and adolescents but is being increasingly noted later in life. Type 1 diabetes, formerly called juvenile diabetes or insulindependent diabetes, is usually first diagnosed in children, teenagers, or young adults. In this form of diabetes, the beta cells of the pancreas no longer make insulin because the body's immune system has attacked and destroyed them. Treatment for type 1 diabetes includes taking insulin shots or using an insulin pump, making wise food choices, exercising regularly, taking aspirin daily (for some), and controlling blood pressure and cholesterol \hyperref[b5]{[6]}. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVIII Issue IV Version I  
\section[{III. MCES (Multi Criteria Evaluation System)}]{III. MCES (Multi Criteria Evaluation System)}\par
MCES is basically called decision making process. MCES provides a framework for exploring solution to decision making problem, which may be poorly defined. It is a method for combining data according to their importance in making a given decision. At a conceptual level, MCES method involve qualitative or quantitative weighting, scoring or ranking of criteria to reflect their importance to either a single or a multiple set of objectives .The main advantage of MCES is that they make it possible to consider a large number of data, relations an objectives which are generally present in a specific real world policy problem, so that the problem at hand can be studied in a multi dimensional fashion. Perhaps the simplest MCES is the weighted linear summation system .The steps involved in applying this system a diabetes solution system is illustrated in Figure \hyperref[fig_0]{01and} can be described as follows.\par
Step 1\par
At first select Symptoms and different test of selection criteria. 
\section[{Step 2}]{Step 2}\par
Standardization criterion scores of their measurable. Most MCES analysis, especially those using quantitative and mixed data sources, require some form of standardization of the scales of measurement used by the data layers. This is necessary to facilitate the comparison of factors measures using different units and scales of measurement. 
\section[{Step 3}]{Step 3}\par
Allocation weighted of their each criterion. This is done by adding weighted to reflect the importance of each criteria. A high level of importance done maximum of weighted and low level of importance done minimum of weighted.\par
Step 4\par
Finally, applying the MCES method. An MCES method may then multiply theses standardized scores by the weights for each of the data layers in stage 1 and sum these to allocate a score to each pixel on the output map. Further evaluation of the results may be carried out by ranking the values in the results map and reclassifying the ranked map to show the top ranked correct symptoms or Test. This test easily indicates then kinds of diabetes easily provide. K-nearest neighbor (K-nn) algorithm is a branch of supervised learning \hyperref[b0]{[1]}. Now-a-days it is being applying in various fields of data and information processing irrespective of science, commerce and arts. In the context of machine learning, K-nn is considered an effective data classification technique based on adjacent developed examples of sample space. The value of K is always positive and an object is classified by considering the greater number of choice of its neighbors. The neighbors are chosen from data set which is best fit for correct classifications and Euclidean distance helps to measure the overall distances. Here every occurrence correlates to points in sample space or within populations. Generally distance or similarity between instances or objects is easy if the data sets are numeric or integer. A very typical formula to calculate distances is Euclidian distances formula as follows: However it is very essential to bear in mind that all the instances at sample space must be same scale. As for example income will compare with income not the height of the human beings. 
\section[{Mu lti C rite r ia E v a lu a tio n S ys te m (MC E S ).}]{Mu lti C rite r ia E v a lu a tio n S ys te m (MC E S ).}\par
For qualitative data the distance measurement process will be different and it is important to consider that the instances are same or not. At this stage the qualitative objects are measured by allocating Boolean values to each object. It might be possible to converts to instances between which distance can be identified by some techniques. As for example color, temperature, age, height etc. Text and character has identified as one instance per word with the frequency start from 0, 1, 2???????????.n. 
\section[{b) The classifications process of K-nn as follows}]{b) The classifications process of K-nn as follows}\par
The two main steps of K-nn must follow are: 1. Training 
\section[{Predictions}]{Predictions}\par
Training means to get information from all sample spaces and populations. To accomplish this work we need to have the idea about the all instances and objects. In this sense it is very important to bear in mind that data set must be in same class. The qualitative and quantitative data measurement will be different. The predictions will manage by considering the predefined methods. 
\section[{i. The k-nn Algorithm}]{i. The k-nn Algorithm}\par
The total algorithmic steps are as follows: 1. Parameter selections (int m, int n). m=0, n=1, 2, 3?????????.n. ?(f) = ? w ij (s1-f s2 ) 2\par
Where w ij is the similarity between examples i and j. And f i and f j are the predictions for example i and j. In the figure above we see that the small circle belongs three different color dots where the black one is the pivotal element and based on that point we will calculate the green and other two green and red points. According to this figure we have to predict the green points as a K nearest neighbors. The neighbors are very closest to the pivotal point. 
\section[{d) Organization of The Process}]{d) Organization of The Process}\par
Now it is important to build the process how Knn may organized in reality or the time line. To manage the proper training area we have to shorten the area or to select the appropriate area. When we are able to fix the sample area for computation, it will help us to reduce the computational complexity for entire process. n= indicate the nearest value. m= categories of the neighbors. In the figure above we see that there are two categories of neighbors. One data set indicate by plus (+) sign and other is small hole. 
\section[{IV.}]{IV.} 
\section[{Data Analysis}]{Data Analysis}\par
A Data analysis is integrates hardware, software, and data for capturing, managing, analyzing, and displaying all forms of data referenced information. Data analysis allows us to view, understand, interpret, and visualize data in many ways that reveal relationships, patterns, and trends in the form of measurement, globes, reports, and charts. Its can be integrated into any enterprise information system framework. The integration of data which may have been obtained from various sources, computerized at various scales, and based upon different projection systems, is a complex task and remains a major challenge. In a general sense, the term describes any information system that integrates stores, analyzes, shares, and displays data information for informing decision making. Finally, its can produced different types information is combined relation each other.\par
Diabetes mellitus is characterized by recurrent or persistent hyperglycemia, and is diagnosed by demonstrating any one of the following as shown in the table \hyperref[b0]{(1)} which is diabetes diagnostic criteria. Fasting plasma glucose level ? 7.0 mmol/l (126 mg/dl) Plasma glucose ? 11.1 mmol/l (200 mg/dl) two hours after a 75 g oral glucose load as in a glucose tolerance test Symptoms of hyperglycemia and casual plasma glucose ? 11.1 mmol/l (200 mg/dl) Glycated hemoglobin (Hb A1C) ? 6.5\% \hyperref[b6]{[7]}. 
\section[{Global Journal of Computer Science and Technology}]{Global Journal of Computer Science and Technology}\par
Volume XVIII Issue IV Version I  Step 2 Secondly, scaling the each criteria particularly and assign the value or (measurable). Showing bellow:\par
Table  {\ref 2}: Scaling the each criteria particularly and assign the value\par
Step 3 Standardization of criterion scores particularly distances value. All the value defined between two intervals scores o and 1.The maximum value is score 0, the minimum value is score 1, the mid value is score 0.5 and other value in scores 0 and 1. This score create the based on equation (This equation only one location). Showing bellow: Thirsty= (thirsty \textunderscore max-thirsty \textunderscore value)*1/( thirsty \textunderscore maxthirsty \textunderscore min); Weakness= (weak \textunderscore max-weak \textunderscore value)*1/( weak \textunderscore maxweak \textunderscore min);\par
. OGT= (ogt \textunderscore max-ogt \textunderscore value)*1/( ogt \textunderscore max-ogt \textunderscore min); Hbalc= (hbalc \textunderscore max-hbalc \textunderscore value)*1/( hbalc \textunderscore maxhbalc \textunderscore min);  V. 
\section[{CONCLUSION}]{CONCLUSION}\par
The main goal of Computation Model for Identifying Types of Diabetics Using Multi-Selection Criteria Evaluation and K-Nearest Neighbor algorithm is to get best algorithms that describe given data from multiple aspects. There are different diabetic's symptoms classification algorithm that can be used for the identification of diabetes disease among patients. In this paper two classification techniques (MSCE, K-NN) are applied to predict the diabetes disease in patients. The algorithms are very necessary for intend an automatic classification tools. In our study first the two techniques were first filtered by using the computing time in which MCES helped the data set to be properly design and manipulated the system and K-Nearest Neighbor helped the diabetes range. The main purpose of applying this identification is diabetes level.\begin{figure}[htbp]
\noindent\textbf{1}\includegraphics[]{image-2.png}
\caption{\label{fig_0}Fig. 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-3.png}
\caption{\label{fig_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{563}\includegraphics[]{image-4.png}
\caption{\label{fig_2}5 . 6 .Fig. 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4}\includegraphics[]{image-5.png}
\caption{\label{fig_3}Fig. 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{}\includegraphics[]{image-6.png}
\caption{\label{fig_4}}\end{figure}
  \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.85\textwidth}}
? Frequently urination\\
? Very thirsty.\\
? Weight less\\
? Increased hunger\\
? Tried and weakness\\
? Injured dry delay\\
? Every see present itch bred\\
? Few see eye\\
? Blurry vision\\
? Irritability\\
? Tingling\\
? Gum infections\end{longtable} \par
 
\caption{\label{tab_0}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{} \par 
\begin{longtable}{P{0.3065217391304348\textwidth}P{0.25\textwidth}P{0.2934782608695652\textwidth}}
\multicolumn{3}{l}{Computation Model for Identifying Types of Diabetics using Multi-Selection Criteria Evaluation and}\\
\tabcellsep K-Nearest Neighbor\tabcellsep \\
ii.\tabcellsep \tabcellsep \\
\tabcellsep Design of Process: Non -Diabetes\tabcellsep Design of Process: diabetes patients\\
\tabcellsep Natural man\tabcellsep Diabetes patients\\
\tabcellsep Pancreas\tabcellsep Pancreas\\
Year 2018\tabcellsep Insulin\tabcellsep Do not insulin\\
10\tabcellsep \tabcellsep \\
\tabcellsep Glucose do\tabcellsep Glucose do\\
\tabcellsep the work\tabcellsep not work\\
\tabcellsep Energy produced\tabcellsep Energy not produced\\
\tabcellsep Health\tabcellsep It is diabetes patients\\
( ) C\tabcellsep \tabcellsep \\
© 2018 Global Journals 1\tabcellsep \tabcellsep \end{longtable} \par
  {\small\itshape [Note: Treatment includes taking diabetes medicines, making wise food choices, exercising regularly, taking aspirin daily (for some), and controlling blood presser. There are several signs and symptoms that indicate a person may have either pre-diabetes or undiagnosed diabetes\hyperref[b5]{[6]}.]} 
\caption{\label{tab_1}}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.48710045662100454\textwidth}P{0.13972602739726026\textwidth}P{0.16495433789954336\textwidth}P{0.05821917808219178\textwidth}}
Condition\tabcellsep 2 hour glucose\tabcellsep Fasting glucose\tabcellsep HbA 1c\\
Unit\tabcellsep mmol/l(mg/dl)\tabcellsep mmol/l(mg/dl)\tabcellsep \%\\
Normal\tabcellsep <7.8 (<140)\tabcellsep <6.1 (<110)\tabcellsep <6.0\\
Impaired fasting glycaemia\tabcellsep <7.8 (<140)\tabcellsep ? 6.1(?110) \&\tabcellsep 6.0-6.4\\
\tabcellsep \tabcellsep <7.0(<126)\tabcellsep \\
Impaired glucose tolerance\tabcellsep ?7.8 (?140)\tabcellsep <7.0 (<126)\tabcellsep 6.0-6.4\\
Diabetes mellitus\tabcellsep ?11.1 (?200)\tabcellsep ?7.0 (?126)\tabcellsep ?6.5\\
Step 1\tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{4}{l}{At first select symptoms and test of selection criteria. Most symptoms \& test is selected based then kinds of}\\
\multicolumn{2}{l}{diabetes provide. Example then Showing bellows:}\tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_3}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{1} \par 
\begin{longtable}{P{0.25071942446043166\textwidth}P{0.12841726618705035\textwidth}P{0.13453237410071942\textwidth}P{0.1039568345323741\textwidth}P{0.11007194244604317\textwidth}P{0.12230215827338128\textwidth}}
Kind of diabetes\tabcellsep Criteria 1\tabcellsep Criteria 2\tabcellsep ???.\tabcellsep Criteria 11\tabcellsep Criteria 12\\
Type1/Type2/No Diabetes\tabcellsep Thirsty\tabcellsep Weakness\tabcellsep ???..\tabcellsep OGT\tabcellsep Hbalc\\
"\tabcellsep ''\tabcellsep ''\tabcellsep ???.\tabcellsep ''\tabcellsep ''\\
"\tabcellsep ''\tabcellsep ''\tabcellsep ??..\tabcellsep ''\tabcellsep ''\end{longtable} \par
 
\caption{\label{tab_4}Table 1 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{3} \par 
\begin{longtable}{P{0.30453514739229026\textwidth}P{0.5454648526077097\textwidth}}
Step 4\tabcellsep Weakness= (weak \textunderscore max-weak \textunderscore value)*1/( weak \textunderscore max-\\
Next, weight adjustment each criteria\tabcellsep weak \textunderscore min)*weight\textunderscore  weak;\\
particularly importance for client. After multiply weight\tabcellsep OGT= (ogt \textunderscore max-ogt \textunderscore value)*1/( ogt \textunderscore max-ogt\\
and criterion score. Using this equation is showing\tabcellsep \textunderscore min)*weight\textunderscore  ogt;\\
bellow:\tabcellsep \end{longtable} \par
 
\caption{\label{tab_5}Table 3 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{4} \par 
\begin{longtable}{P{0.5475\textwidth}P{0.085\textwidth}P{0.09\textwidth}P{0.01\textwidth}P{0.0925\textwidth}P{0.024999999999999998\textwidth}}
Kinds of diabetes\tabcellsep Thirsty\tabcellsep Weakness\tabcellsep ?\tabcellsep OGT\tabcellsep Hbalc\\
Type1\tabcellsep 1*0.6=0.6\tabcellsep 0*0.4=0\tabcellsep ?\tabcellsep 530*0.3=159\tabcellsep 8\\
Type2\tabcellsep 0*0.6=0\tabcellsep 0.2*0.4=0.08\tabcellsep ?\tabcellsep 450*0.3=135\tabcellsep 6.5\\
No diabetes\tabcellsep 0.5*0.6=0.3\tabcellsep 1*0.4=0.4\tabcellsep ?\tabcellsep 135*0.3=40.5\tabcellsep 5\\
Step 5\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{6}{l}{Finally, Add the all criteria value .Which totals are maximum this symptoms \& test are selected. Using this}\\
equation is showing bellow:\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
\multicolumn{2}{l}{Totals=Thirsty+ Weakness+??..+OGT+Hbalc;}\tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_6}Table 4 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{5} \par 
\begin{longtable}{P{0.11333333333333333\textwidth}P{0.46277777777777773\textwidth}P{0.043680555555555556\textwidth}P{0.07083333333333333\textwidth}P{0.020069444444444442\textwidth}P{0.040138888888888884\textwidth}P{0.04604166666666667\textwidth}P{0.053125\textwidth}}
\tabcellsep \multicolumn{7}{l}{Computation Model for Identifying Types of Diabetics using Multi-Selection Criteria Evaluation and}\\
\tabcellsep \tabcellsep \tabcellsep \multicolumn{3}{l}{K-Nearest Neighbor}\tabcellsep \\
\tabcellsep Kind of diabetes\tabcellsep Thirst\tabcellsep Weakness\tabcellsep \tabcellsep ???\tabcellsep OGT\tabcellsep Hbalc\\
\tabcellsep Type1\tabcellsep 5\tabcellsep 3\tabcellsep \tabcellsep ???.\tabcellsep 130\tabcellsep 6\\
\tabcellsep Type2\tabcellsep 1\tabcellsep 0\tabcellsep \tabcellsep ??..\tabcellsep 150\tabcellsep 10.5\\
\tabcellsep No diabetes\tabcellsep 2\tabcellsep 1\tabcellsep \tabcellsep ??...\tabcellsep 135\tabcellsep 00\\
Year 2018\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
14\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \\
Volume XVIII Issue IV Version I\tabcellsep Kind of diabetes Type1 Type2\tabcellsep Thirsty 1 0\tabcellsep Weakness 0 0.2\tabcellsep \multicolumn{2}{l}{??? ???. ??..}\tabcellsep OGT 130 150\tabcellsep Hbalc 0 0.5\\
C ( )\tabcellsep No diabetes\tabcellsep 0.5\tabcellsep 1\tabcellsep \tabcellsep ??...\tabcellsep 135\tabcellsep 1\\
Global Journal of Computer Science and Technology\tabcellsep \multicolumn{3}{l}{Thirsty= (thirst \textunderscore max-thirst \textunderscore value)*1/( thirst \textunderscore max-thirst \textunderscore min)*weight\textunderscore  thirst;}\tabcellsep \tabcellsep \tabcellsep \\
\tabcellsep Kinds of diabetes\tabcellsep Thirsty\tabcellsep Weakness\tabcellsep ?\tabcellsep OGT\tabcellsep Hbalc\tabcellsep Totals\\
\tabcellsep Type1\tabcellsep 0.6\tabcellsep 0\tabcellsep ?\tabcellsep 159\tabcellsep 0\tabcellsep 159.6\\
\tabcellsep Type2\tabcellsep 0\tabcellsep 0.08\tabcellsep ?\tabcellsep 135\tabcellsep 0.05\tabcellsep 135.13\\
\tabcellsep No diabetes\tabcellsep 0.3\tabcellsep 0.4\tabcellsep ?\tabcellsep 40.5\tabcellsep 0.1\tabcellsep 41.3\\
\tabcellsep © 2018 Global Journals 1\tabcellsep \tabcellsep \tabcellsep \tabcellsep \tabcellsep \end{longtable} \par
 
\caption{\label{tab_7}Table 5 :}\end{figure}
 \begin{figure}[htbp]
\noindent\textbf{6} \par 
\begin{longtable}{P{0.85\textwidth}}
Year 2018\\
15\\
Volume XVIII Issue IV Version I\\
( ) C\\
Global Journal of Computer Science and Technology\end{longtable} \par
 
\caption{\label{tab_8}Table 6 :}\end{figure}
 		 		\backmatter  			  				\begin{bibitemlist}{1}
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\end{document}
