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Data Science has emerged as a super science in almost all the sectors of analytics. Data Mining is the key runner and the pillar stone of data analytics. The analysis and study of any form of data has become so relevant in todays’ scenario and the output from these studies give great societal contributions and hence are of great value. Data analytics involves many steps and one of the primary and the most important one is data pre-processing stage. Raw data has to be cleaned, stabilized and processed to a new form to make the analysis easier and correct. Many pre-processing tools are available but this paper specifically deals with the comparative study of two tools such as Rapid Miner and R programming language which are predominantly used by data analysts. The output of the paper gives an insight into the weightage of the particular tool which can be recommended for better data pre-processing.

Data analytics, data pre-processing, noise removal, clean data, Rapid Miner, R programming

[1] D. Thornton, G. Van Capelleveen, M. Poel, J. Van Hillegersberg, and R. M. Mueller, “Outlier-based Health Insurance Fraud Detection for U.S. Medicaid Data,” Proc. 16th Int. Conf. Enterp. Inf. Syst., pp. 684–694, 2014.
[2] S. B. Kotsiantis, D. Kanellopoulos, and P. E. Pintelas, “Data preprocessing for supervised leaning,” Int. J. Comput. Sci., vol. 1, no. 1, pp. 111–117, 2006.
[3] D. P. Methods, “Data_Preprocessing.”
[4] T. H. Zolt´an Prekopcs´ak, G´abor Makrai and C. G´asp´ar-Papanek∗, “Radoop: Analyzing Big Data with RapidMiner and Hadoop.” http://www.iasri.res.in/ebook/win_school_aa/notes/Data_Preprocessing.pdf, “No Title.” .
[6] A. FAMILI, W. SHEN, R. WEBER, and E. SIMOUDIS, “Data preprocessing and intelligent data analysis,” Intell. Data Anal., vol. 1, no. 1–4, pp. 3–23, 1997.
[7] C. M. Teng, “Correcting noisy data,” Proc 16th Int. Conf Mach. Learn., pp. 239–248, 1999.
[8] K. Rangra and K. L. Bansal, “Comparative Study of Data Mining Tools,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 4, no. 6, pp. 2277–128, 2014.
[9] https://en.wikipedia.org/wiki/RapidMiner, “No Title.” .
[10] Y. Ramamohan, K. Vasantharao, C. K. Chakravarti, and A. S. K. Ratnam, “A Study of Data Mining Tools in Knowledge Discovery Process,” Int. J. Soft Comput. Eng., 2012.
[11] Https://www.r-project.org/about.html, “No Title.” .
[12] A. Jović, K. Brkić, and N. Bogunović, “An overview of free software tools for general data mining,” 2014 37th Int. Conv. Inf. Commun. Technol. Electron. Microelectron. MIPRO 2014 - Proc., no. May, pp. 1112–1117, 2014.

Surya Susan Thomas, Ananthi Sheshasaayee, "An Analysis on the Performance of Rapid Miner and R Programming Language as Data Pre-processing Tools for Unsupervised Form of Insurance Claim Dataset", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.1-4, 2019.

In this paper, we are analyzing the finite source queueing system with catastrophe. This model is completely solved by using continued fraction method. We have calculated the compact solutions of steady state probabilities of number of occupants in the system and various system performance measures. Analytical and pictorial studies are also carried out.

Finite source queue - Catastrophe - Steady state probabilities - Continued fraction method - performance measures

[1] Artalejo. J.R, “A versatile approach for work removal in queueing networks, Journal of Operational Research”; Vol. 126, No. 2, pp 233-249, 2000.
[2] Donald Gross, JohnF.shortle,JamesM.Thompson, Carl M.Harris,” Fundamentals of queueing theory” John Wiley & sons publishers 4th edition, 2011.
[3] Hanson.F.Band H.C. Tuckwell, “Logistic growth with random density independent disasters”, Theoret. Popn. Biol., Vol. 19, pp 1-18, 1981.
[4] Hisao Kameda, “A finite source queue with different customers”, ACM, pp-478-479, vol.29, No.2 April, 2014.
[5] Jani.M, “Finite Capacity M/M/r Queuing system with Queue-dependent servers”,an International Journal Computer and mathematics with application 50 ,187-199,2005.
[6] Kalyanaraman.R, “A single server retrial queueing system with two types of arrivals and finite number of repeated customers”, open access article, March, 2015.
[7] Krishna Kumar.B, A. Krishnamoorthy, S. PavaiMadheswari and S. SadiqBasha, “Transient analysis of single server Queue with catastrophes, failures and repairs, Queueing systems”, Vol. 56, PP 133-141,2007.
[8] Muthuganapathi Subramanian.A, Gayathri .S,” A single server queuing system with catastrophe”, International journal of applied engineering research,ISSN 0973-4562, vol.10.No.72, 2015.

J. Indhumathi, A. Muthu Ganapathi Subramanian, Gopal Sekar, "Analysis of Finite Source Queueing System with Catastrophe", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.5-9, 2019.

In this paper the Fibonacci array based on the dimension are defined and analysed. The bordered width of the Fibonacci array is a Fibonacci number is shown. The concept of secondary transformation, linear tandem, diagonal tandem of an array are introduced. The combinatorial properties of the sub arrays are investigated. The Fibonacci array based on tree is represented and also Parikh vector concepts are discussed.

Fibonacci array; Parikh Vectors; Secondary Transpose

[1] Alberto Apostolico, Valentin E, Brimkov, “Fibonacci arrays and their two dimensional repetitions”, Theoretical Computer Science, 237, 263-273, 2000.
[2] Aldo de LUCA, “A Combinatorial property of the Fibonacci words”, Ins. Di mat. Dell Uni. Di Napoli. Vol12, 4, 1981.
[3] Anna lee., “Secondary symmetric secondary skew symmetric secondary orthogonal matrices”, period. math, Hungary, vol.7, 63-70, 1976.
[4] N. Jansirani , V. Rajkumar Dare and K.G. Subramanian, “Sturmian Arrays”, Advances in Image Analysis and Applications, (Chapter 9) Research Publishing, Printed in Singapore ISBN-13:978-08-7923-5, ISBN-10:381-08-7923-7, May 2011.
[5] N. Jansirani and V. Rajkumar Dare, “Special Properties of Fibonacci Array”, Mathematical Sciences International Research Journal , 560–569, 2012.
[6] Lothaire M, “Combinatorics on words”, Addison Wesley Publishing Company, 1983.
[7] Mahalingam K, Sivasankar M, Krithivasan K, “Palindromic properties of two- dimensional Fibonacci words”, Romanian Journal of information science and technology.
[8] Wen Chean Teh, “On Core Words and the Parikh Matrix Mapping”, International Journal of Foundations of Computer Science,vol-26,No-1,123-142,2015.

N. Jansirani, V. Subharani, V. R. Dare, "Special properties of Fibonacci Array Based on Dimension", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.10-15, 2019.

In physics, mechanics and engineering, Duffing equations are used in describing the oscillatory systems with non linearities and is famous in study of nonlinear dynamics. Here, we study the asymptotic stability of the fractional order unforced damped Duffing equation with quadratic nonlinearity. Local asymptotic stability conditions for commensurate order fractional derivative system with order lying in (0,2) is discussed without considering integer order. The stability of the system is investigated with fractional orders in two ranges (0,1) and (1,2). For different values of the parameters, examples with simulations are performed. Sensitivity of the system for the small variation in fractional order is analyzed with 2-Dimensional time plots. Lyapunov exponents for the system is investigated with plots and values of Lyapunov exponents are tabulated.

Duffing equation, Fractional order system, Stability Nonlinear system

[1] Zhiliang Wang, Dong sheng Yang, Huaguang Zhang, “Stability analysis on a class of nonlinear fractional-order systems”, Non-linear Dyn. 86(2), 1023–1033 (2016)
[2] Zhao, L.D., Hu, J.B., Fang, J.A., Zhang, W.B.: “Studying on the stability of fractional-order nonlinear system”. Nonlinear Dyn. 70, 475–479 (2012)
[3] Ryabov, Y., Puzenko, A.: “Damped oscillation in view of the fractional oscillator equation”. Phys. Rev. B 66, 184–201 (2002)
[4] Chen, L.P., Chai, Y., Wu, R.C., Yang, J.: “Stability and stabiliza-tion of a class of nonlinear fractional-order systems with Caputo derivative”. IEEE Trans. Circuits Syst. II, Express Briefs 59, 602–606 (2012)
[5] Podlubny I. “Fractional differential equations”.San Diego: Aca-demic Press;1999.
[6] Chen, L.P., He, Y., Chai,Y.,Wu, R.C.: “New results on stability and stabilization of a class of nonlinear fractional-order systems”. Nonlinear Dyn. 75(4), 633–641 (2014)
[7] Li Y,Chen YQ, Podlubny I:” Mittag–Leffler stability of fractional order nonlinear dynamic systems”. Automatica 2009;45:1965–9.
[8] A.George Maria Selvam and D.Vignesh, “Stabilization of Memristor based Commensurate Order Fractional Derivative System”, American International Journal of Research in Science, Technology Engineering & Mathematics, 3rd January, 2019, pp. 18-23.
[9] A. George Maria Selvam, D. Vignesh and R.Dhineshbabu, “Sta-bilization of Fractional Order Nonlinear Duffing Equation Sys-tem with Cubic and Quintic Terms” , Cikitusi Journal For Multi-disciplinary Research, Volume 6, Issue 1, January 2019.

A. George Maria Selvam, D. Vignesh, R. Janagaraj, "Stability of Fractional Order System of Duffing Equation with Quadratic and Cubic Nonlinearities", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.16-19, 2019.

Abstract For a connected graph G=(V,E) of order at least two, a set S of vertices of G is a Strong edge Monophonic set if every edge of G is contained in a fixed monophonic path between any pair of vertices of S . The minimum cardinality of the strong edge monophonic set is the strong edge monophonic number of G denoted my Sm_1 (G). In this paper, certain general properties of the strong edge monophonic sets are studied. Also the strong monophonic number of some families of graph are determined.

Monophonic set, Strong monophonic set, Edge monophonic set, Monophonic distance

[1] Harary, Frank, Emmanuel Loukakis, and Constantine Tsouros. "The geodetic number of a graph." Mathematical and Computer Modelling 17.11 (1993): 89-95.
[2] Santhakumaran, A. P., P. Titus, and K. Ganesamoorthy. "On the monophonic number of a graph." Journal of applied mathematics and informatics 32.1-2 (2014): 255-266.
[3] Hernando, Carmen, et al. "On monophonic sets in graphs." Submitted. Google Scholar (2005).
[4] Iršič, Vesna. "Strong geodetic number of complete bipartite graphs and of graphs with specified diameter." Graphs and Combinatorics 34.3 (2018): 443-456.
[9] Manuel, Paul, et al. "Strong geodetic problem in networks: computational complexity and solution for Apollonian networks." arXiv preprint arXiv:1708.03868 (2017).
[6] Klavžar, Sandi, and Paul Manuel. "Strong Geodetic Problem in Grid-Like Architectures." Bulletin of the Malaysian Mathematical Sciences Society (2018): 1-10.
[7] Iršič, Vesna, and Sandi Klavžar. "Strong geodetic problem on Cartesian products of graphs." RAIRO-Operations Research 52.1 (2018): 205-216.
[8] John, J., and P. Arul Paul Sudhahar. "On the edge monophonic number of a graph." Filomat 26.6 (2012): 1081-1089.
[9] Manuel, Paul, et al. "Strong edge geodetic problem in networks." Open Mathematics 15.1 (2017): 1225-1235.
[10] D.Antony Xavier and Elizabeth Thomas. “On the strong monophonic number of a graph.” (To appear)

D. Antony Xavier, Bino Infanta L.G, "On The Strong Edge Monophonic Number of Graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.20-24, 2019.

A Petri net is a specific type of Mathematical modeling which is useful in data analysis, simulations, business process modeling and other such scenarios. There are different types of Petri net models which models the behaviour of distributed systems. This paper presents some applications of array token Petri nets by generating certain pattern of picture languages using evolutionary rules on transitions. An Array-token Petri net (ATPN) was first defined by Beulah Immanuel by labeling tokens by arrays which generates picture languages. This paper contains two rules called elementary evolution rule (EER) and parallel evolution rule (PER) which is used to generate same pattern of picture languages in different sizes. Application of ATPN which generates English alphabetic letters treated as rectangular arrays was examined and also Petri net which generates kolam pattern was also studied. Motivated by these papers, we have generated certain patterns of picture languages using ATPNs by applying some evolution rules on transitions.

Petri net, Array-token Petri net, Picture languages

[1] B. Immanuel, K. Rangarajan, K.G. Subramanian, “String token-Petri nets”, Proceedings of the European Conference on Artificial Intelligence, One day workshop on Symbolic Networks, at Vanlencia, Spain, 2004.
[2] B. Immanuel, K.G. Subramanian, P. Usha, “Array token petri nets and character Generation”, Proceedings of National Conference on Computational Mathematics and Soft Computing, Women’s Christian College, pp. 68-72, 2009.
[3] K. Jensen, “Coloured Petri nets”, Lecture Notes in Computer Science, 254, 1987 (248-299).
[4] S. Kannamma, K. Rangarajan, D.G. Thomas, N.G. David, “Array token Petri nets, Computing and Mathematical Modeling”, Narosa Publishing House, New Delhi, India, pp. 299-306, 2006.
[5] D. Lalitha and K. Rangarajan, “Characterisation of Pasting System using Array Token Petri Nets”, International Journal of Pure and Applied Mathematics, Vol. 70, No.3, pp. 275-284, 2011.
[6] D. Lalitha and K. Rangarajan, “Petri nets generating Kolam Patterns”, Indian Journal of Computer Science and Engineering, Vol. 3, No.1, pp. 68-74, 2012.
[7] A. Rosenfeld and R. Siromoney, “Picture languages – a survey, languages of design”, Vol. 1, pp. 229-245, 1993.
[8] G. Siromoney, R. Siromoney and Kamala Krithivasan, “Abstract families of Matrices and Picture Languages”, Computer Graphics and Image Processing 1, pp. 282-307, 1972.
[9] P. Usha, B. Immanuel, R. Sattanathan, “Application of Array token Petri nets in generating English Alphabetic letters”, The Journal of Combinatorial Mathematics and Combinatorial Computing, Vol. 79, pp. 91-98, 2011.

V. Sharon Philomena, P. Usha, R. Santhiya, "Generation of Certain Patterns Using Array-Token Petri Nets", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.25-29, 2019.

A 3-equitable prime cordial labeling is extension of prime cordial labeling .Splitting graph S^` (G) was introduced by Sampath Kumar and Walikar [6]. For a graph G the splitting graphS` of G is obtained by adding a new vertex v` corresponding to each vertex v of G such that N(v)=N(v^` ). In this paper we prove the splitting graph of cycleC_n, path P_n, bistar B_(n,n) and wheel W_n admits 3-equitable prime cordial labeling.

Cordial labeling, 3-equitable Prime cordial labeling, Splitting graph, cycle, path, bistar and wheel

[1] Cahit, Cordial graphs: A weaker version of graceful and harmonious graphs, ArsCombinatoria, Vol 23, pp. 201-207, 1987.
[2] J. A. Gallian, A dynamic survey of graph labeling, The Electronic Journal of Combinatorics, 16, 2017.
[3] F. Harary Graph Theory, Addition-Wesley, Reading Mass, 1972.
[4] S.Murugesan et al “3-equitable Prime Cordial labeling of graphs”, Internatinal journal of Applied Information Systems, volume 5July 2013.
[5] Rosa A, (1966) “On certain valuation of the vertices of a Graph theory of graphs” Int.Symposium Rome, Gordon and Breach, N Y Dound, Paris pp. 349-355.
[6] E.Sampathkumar and H.B. Walikar, On Splitting Graph of a Graph, J.Karnatak Univ. Sci., vol 25(13)(1980), pp 13-16.
[7] M.Sundaram, R.Ponraj and S.Somasundaram, Prime cordial labeling of graphs, Journal of Indian Academy of Mathematics, vol 27(2005), pp 373-390

V. Sharon Philomena, B. Kavya, "3-Equitable Prime Cordial Labeling of Standard Splitting Graphs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.30-34, 2019.

Let f:V(G)⟶N be a labeling of the vertices of a graph G by positive integers. Let S(v) denote the sum of labels of the neighbors of the vertex v in G. If v is an isolated vertex of G we put S(v)=0. A labeling f is lucky if S(u)≠S(v) for every pair of adjacent vertices u and v. The lucky number of a graph G, denoted by η(G), is the least positive integer k such that G has a lucky labeling with {1,2,…k} as the set of labels. Let l:V(G)⟶ {1,2,…k} be a labeling of the vertices of a graph G by positive integers. Define c(u)=d(u) + ∑_(v∈N(u))▒〖l(v)〗 where d(u) denotes the degree of u and N(u) denotes the neighbourhood of u. We define a labeling l as d-lucky if c(u)≠c(v), for every pair of adjacent vertices u and v in G. The d-lucky number of a graph G, denoted by η_dl (G), is the least positive integer k such that G has a d-lucky labeling with {1,2,…k} as the set of labels. In this paper, we study d-lucky labeling of Honeycomb network and Honeycomb torus network. Further we have obtained the d-lucky number for Honeycomb network and Honeycomb torus network.

Colouring, d-lucky labeling, Honeycomb network, Honeycomb torus network

[1] A. Ahai, A. Dehghan, M. Kazemi, E. Mollaahmedi, “Computation of Lucky number of planar graphs is NP-hard”, Information Processing Letters, vol.112,No.4,109-112,2012.
[2] D Ahima Emilet, Indra Rajasingh, “d-Lucky Labeling of Cycle of ladder, n-sunlet and Helm graphs,” International Journal of Pure and Applied Mathematics,vol.109, No.10, 219-227, 2016.
[3]S. Akhari, M. Ghanbari, R. Manariyat, S. Zare, “On the lucky choice number of graphs”, Graphs and Combinatorics, Vol. 29,No.2, 157-163, 2013.
[4] D Antony Xavier , R C Thivyarathi, “Proper Lucky Number of Hexagonal Mesh and Honeycomb Network,” International Journal of Mathematics Trends and Technology, vol. 48, No. 4 August 2017.
[5] D Antony Xavier , R C Thivyarathi, “Proper Lucky Number of Torus Network”, International Journal of Pure and Applied Mathematics,vol.117, No.13, 421-427, 2017.
[6] S. Czerwinski, J. Grytczuk, V. Zelazny, “Lucky labeling of graphs”, Information Processing Letters, Vol.109,No.18,1078-1081, 2009.
[7] Indra Rajasigh, S. Teresa Arockiamary, “Total Edge Irregularity Strength of Honeycomb Torus Networks”, Global Journal of Pure and Applied Mathematics,vol.13, No.4,1135-1142, 2017.
[8] Mikra Miller, Indra Rajasingh, D Ahima Emilet, D Azubha Jemilet, “d-Lucky Labeling of Graphs,” Procedia computer Science 57, 766-771, 2015.
[9] P. Sivagami, Indra Rajasingh, Sharmila Mary Arul, “On 3-Rainbow Domination in Hexagonal networks and Honeycomb Networks” , International Journal of Pure and Applied Mathematics,vol.101, No.5,839-847, 2015.

Rini Angeline Sahayamary A, Teresa Arockiamary S, "d-Lucky Labeling of Honeycomb Network", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.35-39, 2019.

Sensitivity analysis is used to find the robustness of a normalization technique. It is applied in two ways, one by applying equal weights to all the criteria (MSA_EQ) and other by exchanging of weights (MSA_EX). The result obtained for sensitivity analysis when it is conducted with equal weight is described in this paper. The impact of sensitivity analysis on assigning equal weights to criteria is described with different shaded colours for each of the normalization technique. The change in ranking order of the alternatives before and after of sensitivity analysis is described with shaded colour. In this analysis, linear max min normalization has minimum number of altered ranking order for alternatives. In both of these analysis (assigning equal weight to the criteria and exchanging weight of the criteria), selected six normalization techniques maintains different number of alterations in ranking order of alternatives.

Multi criteria decision making, sensitivity analysis, TOPSIS, simplified TOPSIS, sFTOPSIS, MCDM Evaluation Metrics, Normalization Techniques, Evaluation of normalization techniques

[1] Alireza, Alinezhad, and Abbas Amini. "Sensitivity analysis of TOPSIS technique: the results of change in the weight of one attribute on the final ranking of alternatives." Journal of Optimization in Industrial Engineering (2011): 23-28.
[2] Hongyi, Chen, and Dundar F. Kocaoglu. "A sensitivity analysis algorithm for hierarchical decision models." European Journal of Operational Research 185, no. 1 (2008): 266-288.
[3] Masuda, Tatsuya. "Hierar Chakraborty, Subrata, and Chung-Hsing Yeh. "A simulation based comparative study of normalization procedures in multiattribute decision making." In Proceedings of the 6th Conference on 6th WSEAS Int. Conf. on Artificial Intelligence, Knowledge Engineering and Data Bases, vol. 6, pp. 102-109, (2007).
[4] Chical, sensitivity analysis of priority used in analytic hierarchy process." International Journal of Systems Science 21.2 (1990): 415-427.
[5] Maysam, Eftekhary, Peyman Gholami, Saeed Safari, and Mohammad Shojaee. "Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method." Modern Applied Science 6, no. 10 (2012): 26.
[6] Aydın , Çelen. "Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market." Informatica 25, no. 2 (2014): 185-208.
[7] Liping, Yu, Yuntao Pan, and Yishan Wu. "Research on data normalization methods in multi-attribute evaluation." In Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, pp. 1-5. IEEE, (2009).
[8] Maysam, Eftekhary, Peyman Gholami, Saeed Safari, and Mohammad Shojaee. "Ranking Normalization Methods for Improving the Accuracy of SVM Algorithm by DEA Method." Modern Applied Science 6, no. 10 (2012): 26.
[9] Milani, A. S., A. Shanian, R. Madoliat, and J. A. Nemes. "The effect of normalization norms in multiple attribute decision making models: a case study in gear material selection." Structural and multidisciplinary optimization 29, no. 4 (2005): 312-318.
[10] Miranda Lakshmi, V.Prasanna Venkatesan, A Comparison of Various Normalization in Techniques for Order Performance by Similarity to Ideal Solution (TOPSIS), International Journal of Computing Algorithm, Volume: 03, May (2014), Pages: 882-888
[11] Adil, Baykasoğlu, and İlker Gölcük. "Development of a novel multiple-attribute decision making model via fuzzy cognitive maps and hierarchical fuzzy TOPSIS." Information Sciences 301 (2015): 75-98.
[12] Alecos Kelemenis, and Dimitrios Askounis. "A new TOPSIS-based multi-criteria approach to personnel selection." Expert systems with applications 37, No. 7 (2010): 4999-5008.
[13] Alireza, Alinezhad, and Abbas Amini. "Sensitivity analysis of TOPSIS technique: the results of change in the weight of one attribute on the final ranking of alternatives." Journal of Optimization in Industrial Engineering (2011): 23-28.
[14] Aydın , Çelen. "Comparative analysis of normalization procedures in TOPSIS method: with an application to Turkish deposit banking market." Informatica 25, no. 2 (2014): 185-208.
[15] Basar ,Oztaysi. "A decision model for information technology selection using AHP integrated TOPSIS-Grey: The case of content management systems." Knowledge-Based Systems 70 (2014): 44-54.
[16] Dubey, Sanjay Kumar, and Kirti Sharawat. "Metrics based evaluation of mobile applications using AHP entropy model." In Computing for Sustainable Global Development (INDIACom), 3rd International Conference on, pp. 4041-4044. IEEE, (2016).
[17] Hamid, Naderi, Hadi Shahosseini, and Amir Jafari. "Evaluation mcdm multi-disjoint paths selection algorithms using fuzzy-copeland ranking method." International Journal of Communication Networks and Information Security (IJCNIS) 5, no. 1 (2013).
[18] Kemal Vatansever and Akkoç Soner. "Fuzzy performance evaluation with AHP and Topsis methods: evidence from turkish banking sector after the global financial crisis." Eurasian Journal of Business and Economics 6, no. 11 (2013): 53-74.
[19] Kshitij, Dashore, Shashank Singh Pawar, Nagendra Sohani, and Devendra Singh Verma. "Product Evaluation Using Entropy and Multi Criteria Decision Making Methods." International Journal of Engineering Trends and Technology (IJETT)-Volume4 Issue5-May (2013): 2183-2187.
[20] Lan, Ding, and Yao Zeng, "Evaluation of Chinese higher education by TOPSIS and IEW—The case of 68 universities belonging to the Ministry of Education in China." China Economic Review 36 (2015): 341-358.
[21] Leoncie, Niyigena, Pasi Luukka, and Mikael Collan. "Supplier evaluation with fuzzy similarity based fuzzy TOPSIS with new fuzzy similarity measure." In Computational Intelligence and Informatics (CINTI), 2012 IEEE 13th International Symposium on, pp. 237-244. IEEE, (2012).
[22] Li, Bao-jun, and Li-ying Yu. "Evaluation on pre-warning capability of public crisis in universities based on group AHP and fuzzy TOPSIS." In Management Science & Engineering (ICMSE), 2014 International Conference on, pp. 1998-2004. IEEE, (2014).
[23] Liping, Yu, Yuntao Pan, and Yishan Wu. "Research on data normalization methods in multi-attribute evaluation." In Computational Intelligence and Software Engineering, 2009. CiSE 2009. International Conference on, pp. 1-5. IEEE, (2009).
[24] Mehdi Ghazanfari, Rouhani, Saeed, and Mostafa Jafari. "Evaluation model of business intelligence for enterprise systems using fuzzy TOPSIS." Expert Systems with Applications 39, no. 3 (2012): 3764-3771.
[25] Mukhtar, Elaalem, Alexis Comber, and Pete Fisher. "Land evaluation techniques comparing fuzzy AHP with TOPSIS methods." In 13th AGILE international conference on geographic information science, (2010) 1-8.
[26] Nikoomaram, H., M. Mohammadi, M. Javad Taghipourian, and Y. Taghipourian. "Training performance evaluation of administration sciences instructors by fuzzy MCDM approach." Contemporary Engineering Sciences 2, no. 12 (2009): 559-575.
[27] Faan, Chen, Jianjun Wang, and Yajuan Deng. "Road safety risk evaluation by means of improved entropy TOPSIS–RSR." Safety science 79 (2015): 39-54.
[28] Senthil, S., B. Srirangacharyulu, and A. Ramesh. "A robust hybrid multi-criteria decision making methodology for contractor evaluation and selection in third-party reverse logistics." Expert Systems with Applications 41.1 (2014): 50-58.
[29] Srdjevic, B., Y. D. P. Medeiros, and A. S. Faria. "An objective multi-criteria evaluation of water management scenarios." Water resources management 18, no. 1 (2004): 35-54.
[30] Triantaphyllou, Evangelos, and Chi-Tun Lin. "Development and evaluation of five fuzzy multiattribute decision-making methods." international Journal of Approximate reasoning 14.4 (1996): 281-310.
[31] Xiaojun ,Wang. "A comprehensive decision making model for the evaluation of green operations initiatives." Technological Forecasting and Social Change 95 (2015): 191-207.
[32] Zavadskas, Edmundas Kazimieras, Algimantas Zakarevicius, and Jurgita Antucheviciene. "Evaluation of ranking accuracy in multi-criteria decisions." Informatica 17, no. 4 (2006): 601-618.
[33] Zavadskas, Edmundas Kazimieras, and Zenonas Turskis. "A new logarithmic normalization method in games theory." Informatica 19, no. 2 (2008): 303-314.
[34] Zavadskas, Edmundas Kazimieras, Leonas Ustinovichius, and Friedel Peldschus. "Development of software for multiple criteria evaluation." Informatica 14, no. 2 (2003): 259-272.

T. Miranda Lakshmi, A. Martin, V. Prasanna Venkatesan, "A Study on Influence of Sensitivity Analysis on Normalization Techniques by Applying Equal and Exchange of Weight Metrics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.40-45, 2019.

Computer vision, when a computer and/or machine have sight, can be used in many applications like OCR, Vision Biometrics, Object Recognition, Social Media, Smart Cars etc., Different approach evolved over a period of time in computer vision problems, which can be categorized as, one after the deep learning in computer vision problem and the other before deep learning in computer vision problem. The prior one named as classical approach (HOG & SIFT., etc), could not learn from discrimination features from images and non adoptive for diverse image and doesn’t meet human level of accuracy. So there arises a requirement for learning method in computer vision Problems. Machine learning gives computers the ability to learn without being explicitly programmed. Deep learning or machine learning overcomes the drawbacks of classical approach by learning the features in the images and the diversity in the images implicitly and thus meets more accuracy than human vision. In this paper we will study difference methods like Classical & Deep learning for image classification problems , and analyze the draw backs and how the other approach overcome the drawbacks and accuracy levels meet by these approaches over the years.

Computer Vision(CV), Convolution; Convolution Neural network(CNN) , Deep Learning ;Gradient , improvement in CV after CNN, Machine Learning, possible improvement in CV

[1] Fei Fei Li Ted Talk: “How we teach computers to understand pictures starring”, establisher of ImageNet database.
[2]Erik G. & Learned-Miller "Introduction to Computer Vision" De-partment of Computer Science University of Massachusetts, Am-herst.
[3]David A. Forsyth, Jean Ponce "Computer Vision: A Modern Approach".
[4]Andrew ng Coursera Convolution Neural Net-works https://www.coursera.org/learn/convolutional-neural-networks
[5] "Three –Dimensional Computer Vision A Geometric viewpoint" @ 1993 Mas-sachusetts Institute of Technology
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Enoch Arulprakash, A. Martin, "A Study on Different Evolution in Computer Vision", International Journal of Computer Sciences and Engineering, Vol.07, Issue.05, pp.46-54, 2019.