Volume-07 , Special Issue-01 , Jan - 2019 Go Back

Missing data imputation has a significant impact in data mining task. Data mining algorithms cannot be executed effectively due to missing attribute values. Improper handle of missing values affects the data mining and classification accuracy. Imputation based preprocessing approach is very effective technique for handling missing value. In this paper most similar object used to impute missing value. For searching similar object core attributes have to give highest priority after that reduct attributes. In the proposed method to fill missing value concept of core and reduct attributes has been used. Rough set is most suitable to handle discrete data. Fuzzy set can handle continuous data in a better way. Hybridization methodology like fuzzy-rough set are more powerful to deal with imprecision and uncertainty for discrete as well as continuous data. Detail study has been given to impute missing value. Fuzzy rough set based fuzz- rough core reduct based (FRCRB) algorithm has been proposed for missing value imputation.

Missing value, Imputation, Rough set, Fuzzy set and fuzzy-rough set, data analysis.

[1] P. K. Dey and S. Mukhopadhyay, “Core reduct based preprocessing approach to incomplete data”, International Journal of Intelligent Engineering and Systems, Vol. 10, No. 5, pp. 19-28, 2017.
[2] A. C. Acock, “Working with Missing Values”, Journal of Marriage and Family, Vol.67, No.4, pp. 1012-1028, 2005.
[3] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993.
[4] J. W. Grzymala-Busse and A. Y. Wang, “Modified algorithms LEM1 and LEM2 for rule induction from data with missing attribute values”, In: Proc. of Fifth international Workshop on Rough Sets and Soft Computing (RSSC’97), Third Joint Conference on Information Sciences (JCIS’97), pp.69-72, 1997.
[5] J. W. Grzymala-Busse, “Data with missing attribute values: Generalization of idiscernibility relation and rule induction”, Transactions on Rough Sets, Lecture Notes in Computer Science Journal Subline, Springer-Verlag, vol. 1 , pp. 78–95, 2004.
[6] D. Aha, D. Kibler and M. Albert, “Instance-based learning algorithms”, Machine Learning, Vol.6, pp. 37-66, 1991.
[7] J. Han and M. Kamber, Data Mining: Concepts and Techniques, Morgan Kaufmann, 2001.
[8] P. K. Dey and S. Mukhopadhyay, “Modified Deviation Approach to Deal with Missing Attribute values in Data Mining with Different percentage of Missing Values”, International Journal of Computer Applications, Vol.73, No. 5, pp. 1-6, 2013.
[9] J. V. Hulse and T. M. Khoshgoftaar, “Incomplete-case nearest neighbor imputation in software measurement data”, Information Sciences, Vol. 259, No. 2, pp. 596-610, 2014.
[10] R. R. Andridge and R. J. A. Little, “A review of hot deck imputation for survey non- response”, International Statistical Review, Vol.78, No.1, pp. 40-64, 2010.
[11] A. Dempster, N. Laired and D. Rubin, “Maximum likelihood from incomplete data via the EM algorithm”, Journal of the Royal Statistical Society, Vol.39, No.1, pp. 1-38, 1977.
[12] D. B. Rubin, Multiple Imputation for Nonresponse in Surveys, Wiley, New York, 1987.
[13] J. L. Schafer, “Multiple imputation: a primer”, Statistical Methods in Medical Research, Vol. 8, pp. 3–15, 1999.
[14] W. Zhou, W. Zhang and Y. Fu , “An incomplete data analysis approach using rough set theory”, Intelligent Mechatronics and Automation, IEEE, pp.332-338, 2004.
[15] M. Kryszkiewicz, “Rough set approach to incomplete information systems”, Information Sciences, Elsevier, Vol. 112, pp.39-49, 1998.
[16] J. W. Grzymala-Busse, “Three Approaches to Missing Attribute Values— A Rough Set Perspective”, In: Proc. of the Workshop on Foundations of Data Mining, associated with the fourth IEEE International Conference on Data Mining, Brighton, UK, November 1–4, 2004.
[17] J. W. Grzymala-Busse and S. Siddhaye, “Rough Set Approaches to Rule Induction from Incomplete Data”, In: Proceedings of the IPMU`2004, the 10th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Perugia, Italy, July 4–9, vol. 2, 923–930, 2004.
[18] M. Amiri and R. Jensen, “Missing data imputation using fuzzy-rough methods”, Neurocomputing, Elsevier, Vol.205, pp. 152-164, 2016.
[19] D. Dubois, H. Prade, “Rough fuzzy sets and fuzzy rough sets”, International Journal of General Systems, Vol.17, No.1, pp. 191–209, 1990
[20] D.S. Yeung, D.G. Chen, E.C.C. Tsang, J.W.T. Lee and X.Z. Wang, “On the generalization of fuzzy rough sets”, IEEE Trans. Fuzzy Systems, Vol.13, No.3, pp. 343–361, 2005.
[21] W. Wu and W. Zhang, “Constructive and axiomatic approaches of fuzzy approximation operators”, Information Sciences, Vol.159, pp. 233–254, 2004.
[22] R. Jensen, Q. Shen, “Fuzzy-rough attribute reduction with application to web categorization”, Fuzzy Sets and Systems, Elsevier, Vol.141, pp. 469–485, 2004.
[23] Q. Shen, R. Jensen, “On robust fuzzy rough set models”, Pattern Recognition, Vol. 37(7), pp. 1351–1363, 2004.
[24] R. Jensen, Q. Shen, “Semantics-preserving dimensionality reduction: Rough and fuzzy-rough-based approaches”, IEEE Transactions on Knowledge and Data Engineering, Vol.16, pp. 1457-1471, 2004.
[25] R. B. Bhatt, M. Gopal, “On fuzzy-rough sets approach to feature selection”, Pattern Recognition Letter, Vol.26, No.7, pp. 965–975, 2005.
[26] R.B. Bhatt, M. Gopal, “On the compact computational domain of fuzzy rough sets”, Pattern Recognition Letter, Vol.26, No.11, pp. 1632–1640, 2005.
[27] R. Jensen, Q. Shen, “New approaches to fuzzy-rough feature selection”, IEEE Transactions on Fuzzy Systems, Vol.17, No.4, pp. 824-838, 2009.
[28] A. Mieszkowicz-Rolka and L. Rolka, “Variable precision fuzzy rough sets”, Transactions on Rough sets, Springer, Vol. LNCS-3100, pp. 144-160,2004.
[29] S. Zhao, E. C. C. Tsang, and D. Chen, “The model of fuzzy variable precision rough sets”, IEEE Transactions on Fuzzy Systems, Vol.17, No2, pp. 451–467, 2009.
[30] Q. H. Hu, D. R. Yu, Z. X. Xie, and J. F. Liu, “Fuzzy probabilistic approximation spaces and their information measures”, IEEE Transactions on Fuzzy Systems, Vol.14, No.2, pp. 191–201, 2006.
[31] Q. H. Hu, D. R. Yu and Z. X. Xie , “Information-preserving hybrid data reduction based on fuzzy-rough techniques”, Pattern Recognition Letters, Vol.27, No.5, pp. 414–423, 2006.
[32] Q. Hu., Z. Xie and Daren Yu, “Hybrid attribute reduction based on a novel fuzzy-rough model and information granulation”, Pattern Recognition, Vol.40, pp. 3509 – 3521, 2007.
[33] P. Maji and P. Garai, “Fuzzy Rough simultaneous Attribute selection and Feature Extraction Algorithm”, IEEE Transactions on Cybernetics, Vol.43, No.4, pp. 1166–1177, 2013.
[34] E. C. C. Tsang, D. G. Chen, D. S. Yeung and X. Z. Wang, “Fuzzy probabilistic approximation spaces and their information measures”, IEEE Transactions on Fuzzy Systems, Vol.16, No.5, pp. 1130–1141, 2008.
[35] D. Chen, L. Zhang, S. Zhao, Q. Hu and P. Zhu, “A noval algorithm for finding reducts with fuzzy rough sets”, IEEE Transactions on Fuzzy Systems, Vol.20, No.2, pp. 385–389, 2012.
[36] C. C. Nghia and N. L. Giang, “Fuzzy Rough set based Attribute Reduction in Fuzzy Decision Tables”, International Journal of Computer Applicationss, Vol.132, No.4, pp. 32–37, 2015.
[37] E. C. C. Tsang, D. Chen, D. S. Young, X. Wang and J. Lee, “Attribute Reduction Using Fuzzy Rough Sets”, IEEE Transactions on Fuzzy Systems, Vol.16, No.5, pp. 1130-1140, 2008.

Pallab kumar Dey, "Imputation of Missing Data using Fuzzy-Rough Hybridization", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.226-231, 2019.

In today’s world Campus placements at any Technical college in India and in the life of any student/graduate are very defining moments that students look up to and prepare themselves thoroughly to score high and well but as well as to impress their potential future employers. It is the one time that students get a precise chance to make that practical application of their technical and employable soundness to the representatives of the corporate management of the particular industry they opted for or chose in the first place. Campus Placement is a process of performance evaluation of each selected candidates with respect to some pre-assigned specific criteria by the experts. For these, an attempt has been made here to assessment of some criteria by (TOPSIS) “Technique for Order Preference by Similarity to Ideal Solution” and (AHP) “Analytical Hierarchy Process” under Interval Type-1 fuzzy environment (IT1F) and Interval Type-2 fuzzy environment (IT2F). These factors for Placement system are identified through the different company’s placement procedure. Here several experts gave their opinion on the basis of students’ performance. The result showed that the proposed model yields more realistic way to evaluate the performance for each student according to pre assigned criteria.

IT1F set, IT2F set, AHP, TOPSIS, Group Decision Making and Spearman Rank Correlation Method

[1] C.L. Hwang, K. Yoon, “Multiple Attribute Decision Making Methods and Applications”, Springer, Berlin Heidelberg, (1981).
[2] Dipendra Nath Ghosh,”Analytic Hierarchy Process & TOPSIS Method to Evaluate Faculty Performance in Engineering Education”, UNIASCIT, Vol. 1(2), 2011, 63-70 (2011).
[3] D.Y. Chang, Applications of the Extent Analysis Method on Fuzzy AHP, European Journal of Operational Research, 95, pp. 649-655 (1996).
[4] F. Zahedi, “The analytic Hierarchy process-A survey of the method and its applications,” Interfaces, Vol. 16, pp.343-350, 1977.
[5] G.R. Jahanshahloo, F. HosseinzadehLotfi, M. Izadikhah, “An algorithmic method to extend TOPSIS for decision-making problems with interval data”, Applied Mathematics and Computation, 2005.
[6] G. Bojadziev and M. Bojadziev, 1998.Fuzzy sets and fuzzy logic applications, World Scientific, Singapore (1998).
[7] H. J. Zimmermann, Fuzzy set theory and its applications, Kluwer, Boston (1992).
[8] Karnik, N.N., Mendel, J.M.: Centroid of a type-2 fuzzy set. Inf. Sci. 132, 195– 220 (2001) 8. Karnik, N.N., Mendel, J.M., Liang, Q.: Type-2 fuzzy logic systems. IEEE Trans. Fuzzy Syst. 7, 643–658 (1999).
[9] M.A. Abo-Sinna, & A.H. Amer, “Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems”, Applied Mathematics and Computation 162, 243–256, 2005.
[10] Mendel, J.M., John, R., Liu, F., 2006. Interval type-2 fuzzy logic systems made simple. IEEE Trans. Fuzzy Syst. 14 (6), 808–821.
[11] Mendel, J.M., 2007. Type-2 fuzzy sets and systems: an overview. Comput.Intell. Mag., IEEE 2 (1), 20–29.
[12] Pabitra Kumar Dey, Saindhab Chattaraj and Dipendra Nath Ghosh,”Faculty Selection in Engineering Organization using AHP & TOPSIS”, International Journal of Information and Computing Science (IJICS) 06/2012; 15(1),(2012).
[13] Pin-Chang Chen,”A Fuzzy Multiple Criteria Decision Making Model in Employment Recruitment”, IJCSNS International Journal of Computer Science and Network Security, VOL.9 No.7, pp: 113-117, July (2009).
[14] P.H. Liu and C.C. Wei A Group Decision-Making Method for Appraising the Performance of Organizations, International Journal of the Computer, the Internet and Management, 8 (2), pp. 39-49 (2000). F. Zahedi, “The analytic Hierarchy process-A survey of the method and its applications,” Interfaces, Vol. 16, pp.343-350, 1977.
[15] Sonu Rana, Pabitra Kumar Dey & Dipendra Nath Ghosh “Best Engineering College Selection Through Fuzzy Multi- Criteria Decision Making Approach: A Case Study”, UNIASCIT, Vol 2(2), 2012, 246-256.
[16] Sukarna Dey and Dipendra Nath Ghosh “Non-Teaching Staff Performance Analysis Using Multi-Criteria Group Decision Making Approach “International Journal of Education and Learning Vol.4, No.2 (2015), pp.35- 50.
[17] T.L.Saaty,The Analytic Hierarchy Process, McGraw-Hill, New York, 1980.
[18] T.L.Saaty,"Priority Setting in Complex Problems," IEEE Transactions on Engineering Management, Vol.30, No.3, pp.140- 155, 1983.
[19] Zadeh, L.A., 1975. The concept of a linguistic variable and its application to approximate reasoning—II. Inf. Sci. 8 (4), 301–357.

Sukarna Dey Mondal, Dipendra Nath Ghosh, "Comparative Evaluation of Students’ Performance in Campus Recruitment of a Technical Institution through Fuzzy-Multi Criteria Decision Making Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.232-236, 2019.

The quantum cryptography has changed the landscape of the conventional cryptography theory and the field of security itself. The Quantum cryptography differs from the classical cryptography in the sense that data and the information are kept secret by the properties of quantum mechanics without importing any extra formulation. In case of classical cryptography the security is based on the conjecture difficulty of factoring and computing a special mathematical function. The first Quantum Key Distribution (QKD) protocol was proposed by C H Bennet and Brassard in 1984[1](BB84). In course of time many variants of QKD protocols have been proposed, all are basically based more or less in the same principle. In this paper role and the beauty of the Quantum Mechanics behind the QKD protocol have been unfolded and explained. The pros-and cons of the protocol have been analyzed in details. The quality of the QKD protocol is measured through a factor called QBER (Quantum Bit Error Rate). The bit error rate is an essential phenomena during the transmission of quantum bit along the quantum channel. Both quantum mechanical and mathematical analysis of QBER have been discussed in the paper. An empirical formula for QBER has been proposed too.

Quantum mechanics, quantum cryptography, light(photon), eavesdropping, quantum bit error rate(QBER)

[1]. C. H. Bennett and G. Brassard, “Quantum cryptography: public key distribution and coin tossing,” Proc. of the IEEE International Conference on Computers, Systems and Signal Processing, Bangalore, India, pp. 175-179, 1984.
[2]. [Ekert, A.k. “Quantum cryptography based on Bell‟s Theorem”, Physical Review Letters vol.67.no6 5th august 1991, pp.661-663.
[3]. D.Mayers, Journal. of ACM 48, 351 (2001), preliminary version in Mayers, D. Advances in Cryptology-Proc. Crypto 96, vol 1109 of Lecture Notes in Computer Science, Kobiltz, N.Ed.(Springer-Verlag, New York, 1996) pp. 343-357.
[4]. E.Biham, M.Boyer, P.O.Boykin, T.Mor and V.Roychowdhury, in Proc of the thirty second annual ACM symposium on theory of computing (Portland, Oregon, United States, 2000), pp. 715-724
[5]. H.K.Lo and H.F.Chau Science 283, 2050 (1999)
[6]. P.W.Shor and J.Preskill, Phys.Rev. Lett 85,441(2000), arXiv: quant-ph/0003004.
[7]. D.Gottesman, H.K.Lo, N.Liikenhaus and J.Preksill, „Quantum Information and Computation 5, 325 (2004), arXiv: quant-ph/0212066.
[8]. H.Inamori, N.Liikenhaus, and D.Mayers (2001), arXiv.quant-ph/0107017.
[9]. W.Y.Hwang, Phys.Rev. Lett. 91, 057901 (2003).
[10]. H.K.Lo, X.Ma and K.Chen, Phys.Rev. Lett 94, 230504 (2005)
[11]. H.k.Lo, in Proc of IEEE International Symposium on Information Theory (ISIT) (2004), p.137, arXiv.quant-ph/0509076.
[12]. Pradeep Kumar and A. Prabhakar,Bit error rates in a frequency coded quantum key distribution system,Optics Communications 282 (2009)3827-3833.
[13]. C.H. Bennett Quantum cryptography using any two non orthogonal states, Physical Review Letters 68 (21) (1992) 3121-3124
[14]. Mart Haitjema, “A Survey of the Prominent Quantum Key Distribution Protocols “http://www.cs.wustl.edu/~jain/cse571-07/ftp/quantum/index.html#b92
[15]. Scarani, A.Acin, Ribordy, G.Gisin.N.”Quantum Cryptography protocols robust against Photon number Splitting attack.” Physical Review Letters, vol.92.2004 http://www.qci.jst.go.jp/eqsi03/program/papers/O26-Scarani.pdf
[16]. N.Gisin. talk presented at the workshop on Quantum Computation, Torino. July 1997; D.bruss. Physical review letter. Vol 81.no3018 (1998)
[17]. Bechmann-Pasquinucci, H and Gisin.N “Incoherent and coherent eavesdropping in the six-state protocol of quantum cryptography.” Physical Review Letter A59, 4238-4248; (1999).
[18]. N.Gisin, G.Ribordy, W.Tittel, H.Zbinden, “Quantum Cryptography”, Review of Modern Physics, Vol 74 No 1, pp145-194, 2002
[19]. D.Stucki et al., Appl. Phys. Lett.87, 194108 (2005)
[20]. [Gisin N, Ribordy G, Zbinden H, Stucki D, Brunner N and Scarani V 2004, “ Towards practical and fast quantum cryptography”, arXiv:quant-ph/0411022
[21]. K.Inoue, E.Waks and Y.Yamanoto.” Differential-phase-shift quantum key distribution using coherent light.” Phys.Rev. A 68.022317 (2003).
[22]. E.Waks, H.Takesue and Y. Yamamoto, “Security of differential-Phase-Shift quantum key distribution against individual attacks.” Phys.Rev.A 73,012344 (2006).
[23]. Muhammad Mubashir Khan et al. “High error-rate quantum key distribution for long distance communication” New J.Phys. 11 063043 http://iopscience.iop.org/1367-2630/11/6/063043/
[24]. Eduin Esteban, Hernandez Serna, “Quantum Key Distribution protocol with private- public key” arXiv: 0908.2146v4 quant-ph 12th may 2012.
[25] Hitesh Singh,D.L. Gupta,A. K. Singh,Quantum Key Distribution Protocols:AReview,IOCR Journal of Computer Engineering(IOSR-JCE),e-ISSN:2278-0661,p-ISSN:2279-8727 Volume 16,Isuue 2,Ver.XI(Mar-Apr.2014),PP 01-09
[26] M. Lopes and N. Sarwade, Cryptography from Quantum Mechanical View Point, Int. Jour. Of Cryptography and Information Security, Vol. 4. No.2 Page 13(2014).
[27] L. Lydersen, Practical security of quantumcryptography, Ph.D. thesis, Norwegian University ofScience and Technology, 2011.
[28] M. E. Rifai, Quantum secure communication usingpolarization hopping multi-stage protocols, Ph.Dthesis, University of Oklahoma, 2016.

Susmita Nayek, Utpal Roy, "Quantum Mechanics Inside Quantum Communication and Quantum Bit Error Rate(QBER)", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.237-244, 2019.

Defect prediction for a software system is a technique that is used extensively nowadays to predict defects from historical database. But only a good data mining model is not enough to extract defect from software bug record. Intelligent agents are helpful in this case by making the decision process easier at some point. This paper describes frame work to generate software defect from the historical database and also propose one algorithm that is used find policy to forecast software defects efficiently than the current methods.

Cost, Classification, Intelligent agents ,Data mining, Database, Defect, Testing

[1]. Ms. P.J Kaur, Ms. Pallavi, “Data Mining Techniques for Software Defect Prediction” , International Journal of Software and Ib Sciences (IJSWS)
[2]. W Sunindyo, T Moser, D Winkler , “Improving Open Source Software Process Quality based on Defect Data Mining” , Christian Doppler Laboratory for Software Engineering Integration for Flexible Automation Systems,Vienna University of Technology.
[3]. K.B.S Sastry, Dr.B.V.Subba Rao, Dr K.V.Sambasiva Rao, “Software Defect Prediction from Historical Data” ,International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 8, August 2013
[4]. N Azeem, S Usmani, “Analysis of Data Mining Based Software Defect Prediction Techniques”,Global Journal of Computer Science and Technology, Volume 11 Issue 16 Version 1.0 September 2011
[5]. T Xie, S Thummalapenta, D Lo, and C Liu, “Data mining for software engineering.” Computer, 42(8):55-62, 2009.
[6]. M Baojun, K Dejaeger, J Vanthienen, and B Baesens, “Software defect prediction based on association rule classification” SSRN 1785381, 2011.

Amitava Bondyopadhyay, "A Framework of Software Defect Prediction By Data Mining Techniques Using Historical Data Set and Intelligent Agents", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.245-248, 2019.

E-Learning is one of the most effective applications of Information and Communication Technology (ICT). In e-Learning environment participants such as Teacher, Student and Administrator perform their transaction electronically through Internet which is inherently insecure. During transaction process intruders may manipulate the message. Digital signature may be used to detect any change caused by intruders. To ensure information authenticity, the digital signature is the most suitable replacement of hand written signature. In this paper, we have applied ElGamal Digital Signature Algorithm (ElGamal DSA) combined with Secure Hash Algorithm (SHA-256) to check the authenticity of the sender and we have also used International Data Encryption Algorithm (IDEA) to impose security of digital document during transaction between student and administrator of an e-Learning system. The proposed model accommodates security of ElGamal Digital Signature Algorithm in combination with SHA-256 as hashing technique and IDEA encryption technique. The performance of the proposed model is analyzed with the help of supporting tables and charts.

Electronic Learning, Public Key Cryptography, ElGamal Digital Signature Algorithm, Secure Hash Algorithm, International Data Encryption Algorithm, Data Encryption Standard, Advanced Encryption Standard, Message-Digest Algorithm.

[1] Fadia Ankit, “Network Security”, Macmillan Publishers India Ltd., Second Edition, 2006.
[2] Forouzan Behrouz A, “Data Communications and Networking”, Tata McGraw-Hill Publishing Company Ltd, Fourth Edition, 2007.
[3] Hook David, “Beginning Cryptography with Java” Wiley India (P.) Ltd., First Edition, 2005.
[4] Kahate Atul, “Cryptography and Network Security”, Tata McGraw-Hill Publishing Company Ltd, Second Edition, 2003.
[5] Menezes, Oorschot, Vanstone, “Handbook of Applied Cryptography”, CRC press, Fifth Printing, 2001
[6] Schneier Bruce, “Applied Cryptography”, Wiley India (P.) Ltd., Second Edition, 2010.
[7] Stallings William, "Cryptography and Network Security: Principles and Practice", Pearson Education, Sixth Edition, 2014.
[8] Tanenbaum, Wetherall, “Computer Networks” Pearson Education, Fifth Edition, 2011.
[9] A. Aravind, K. Kumar V.G, S. Rai, Nisha, "Implementation of Two Light Weight Cryptographic Algorithms", IOSR Journal of Electronics and Communication Engineering (IOSR-JECE), Vol. 12, No. 4, Ver. II, pp. 43-47, Jul.-August 2017.
[10] A. Mousa , "Security and Performance of ElGamal Encryption Parameters" Journal of Applied Sciences, Vol. 5, pp. 883-886, 2005.
[11] A. Swaminathan, Y. Mao, Min Wu, "Robust and Secure Image Hashing" IEEE Transactions on Information Forensics and Security, Vol. 1, No. 2, pp. 215-230, June 2006.
[12] C. Chang, W. Liao, "A remote authentication scheme based upon ElGamal`s signature scheme" Elsevier Science Ltd Computers & Security, Vol. 13, pp 137-144, 1994.
[13] K. D. Sharma, H. K Verma, A. Kumar, "Study and Performance Analysis of IDEA with Variable Rounds", International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 2, No. 5, pp. 102-105, May 2012.
[14] K. Raghuvanshi, P. Khurana and P. Bindal, "Study and Comparative Analysis of Different Hash Algorithm", Journal of Engineering Computers & Applied Sciences (JECAS), Vol. 3, No. 9, Online pp. 1-3, September 2014.
[15] N. Barik, S. Karforma, "Risks and Remedies in E-Learning System", International Journal of Network Security & Its Applications (IJNSA), Vol.4, No.1, pp.51-59, January 2012.
[16] O. Almasri, H.Mat Jani2, "Introducing an Encryption Algorithm based on IDEA", International Journal of Science and Research (IJSR), Vol. 2 No. 9, pp. 334-339 September 2013.
[17] P. Ghosh, S. Karforma, "Application of International Data Encryption Algorithm in E-Learning Security: An UML (Unified Modelling Language) based approach", In the Proceedings of the 2010 International Conference on Computing and Systems (ICCS 2010), West Bengal, India, pp. 96-102, November 2010.
[18] S. Omer, A. Babiker, "Comparison of Various Encryption Algorithms and Techniques for improving secured data Communication” IOSR Journal of Computer Engineering (IOSR-JCE), Vol. 17, No 1, Ver. III, pp 62-69, Jan – Feb. 2015.
[19] W. Lee a, C. Wu a, W. Tsaur, "A novel deniable authentication protocol using generalized ElGamal signature scheme" Information Sciences Elsevier Inc, Vol. 177 pp, 1376–1381, September 2006.
[20] Commonlounge, www.commonlounge.com. [Online], https://www.commonlounge.com/discussion/35a1c2baa00b447f9275e8f71b02ef29.
[21] ResearchGate, www.researchgate.net, [Online], https://www.researchgate.net/file.PostFileLoader.html?id=534b393ad3df3e04508b45ad&assetKey=AS%3A273514844622849%401442222429260.
[22] Wikipedia, en.wikipedia.org. [Online],https://en.wikipedia.org/wiki/International_Data_Encryption_Algorithm.
[23] Wikipedia, en.wikipedia.org. [Online], https://en.wikipedia.org/wiki/SHA-2.

Anup Pasari, Kh Amirul Islam, Sunil Karforma, Sripati Mukhopadhyay, "Securing e-Learning Transactions using Digital Signature", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.249-256, 2019.

Computer science and Mathematical science go hand in hand in the current ongoing scenario. Arithmetic operations are the base of any digital circuit. The present era of digitization focuses on the increment in the speed of digital circuits as well as reduction in size and power consumption; thus increasing the efficiency of the entire digital circuit. The contribution of Ancient Indian Mathematicians in this regard is of significant importance. They provided unique techniques of speedy computation in the form of Sutras. These sutras are actually algorithms. This paper describes some of the salient features and Sutras on Fundamental Arithmetic Operations of the Ancient Indian Mathematics.

Ancient Indian Mathematics, computer Algebra System, Base, Vinculum, 10’s complement

[1] Chatterjee,S.: Crunching Numbers the Vedic way. Times of India (Bombay Times), Feb 5 (2002).
[2] Fernandes, C and Borkar, S. Application of Vedic Mathematics In Computer Architecture. IJRES. Vol. 1 (5). Pp. 40-45. 2013. ISSN (Online): 2320-9364, ISSN (Print): 2320-9356.
[3] Maharaja. J. S. S. B.T.: Vedic Mathematics. Motilal Banarsidass Publishers. Delhi (1965).
[4] Mehera, C.: Combined Application of Ancient and Modern Methods in Teaching Mathematics. Proceedings of the International Conference on Knowledge Globalization, pp.244-251.EVENTS, Bangladesh (2010).
[5] Mehera, C.: Computing Technique in Ancient India. Bio-Inspired Computing and Applications, LNBI 6840, Springer International. Vol.6840, pp. 282-289, 2012. ISSN :0302-9743. ISBN: 978-3-642-24552-7
[6] Jagtap, V.S. and Jain, Shivangi. Vedic Mathematics in Computer: A Survey, IJCSIT. Vol. 5(6). Pp. 7458-7459. ISSN: 0975-9646
[7] Pande,T.G.: Jagatguru Shankaracharya Sri Bharati Krishna Teerthaji. B.R Publishing Corporation. Delhi (1997).
[8] Rick.B : Sanskrit & Artificial Intelligence — NASA: Knowledge Representation in Sanskrit and Artificial Intelligence : Roacs, NASA Ames Research Center, Moffet Field, California.
[9] Senapati, R. and Bhoi, B.K., 2012. Urdhva Triyakbhyam Sutra: Application of Vedic Mathematics for a High Speed Multiplier. IJCMST. I(I). 59-66.
[10] Thakre, S. G., Karade, M.: Application of Nikhilam – Division by 9. Einstein Foundation International, Nagpur, Vedic Ganit, Vol 1, Bull 3 (1985).
[11] Williams, K.R. Vertically and Crosswise, Mathematics in School. Mathematical Association. Sept. (1999).
[12] William, Q. Vedic Mathematics, The History, Discoveries and The Sutras, www.vedicmathematicssecrets.com

Uttam Das, Chitralekha Mehera, "Computer Algebra System and Ancient Indian Mathematics", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.257-261, 2019.

Genealogical research is the tracing of an individual’s ancestral history using historical records, both official and unofficial. Challenges about genealogy problem like spelling names, legacy of a researcher can be measured not only in terms of his/her publications and scientific discoveries, in terms of the formation of other researchers. Now, research work is improving than oldest research. So population of researcher and scientist is increasing rapidly and it was more important now a days that to finding out who is better among all researcher. Author ranking can be solved this problem. Author ranking will not be perfect due to some causes, like naming disambiguation problem and uses of multiple name in paper. In Academic genealogy, is the relationship between advisor and advisee. Research area of advisor is more popular than his advisee research area may be good. From there we can do future prediction of an author. Another problem of author name disambiguity can be solved using genealogy tree hierarchy, as there are less chances of conflict in identifying an author based on his unique academic records. Another important challenge is that how much level (generation) we can visit from the genealogy tree. From the big dataset, we extract different metrics for an author. In this paper, we extract data of a particular author and from there we have analyze effects of an author rank.

Genealogy tree, Author name disambiguation, Citation

[1] Mehmet Ali Abdulhayoglu and Bart Thijs. 2017. Use of ResearchGate and Google CSE for author name disambiguation. Scientometrics 111, 3 (2017), 1965–1985.
[2] Wellington Dores, Fabrício Benevenuto, and Alberto HF Laender. 2016. Extracting academic genealogy trees from the networked digital library of theses and dissertations. In Proceedings of the 16th ACM/IEEE-CS on Joint Conference on Digital Libraries. ACM, 163–166.
[3] Janaína Gomide, Hugo Kling, and Daniel Figueiredo. 2017. Name
usage pattern in the synonym ambiguity problem in bibliographic
data. Scientometrics 112, 2 (2017), 747–766.
[4] Rasmus AX Persson. 2017. Bibliometric author evaluation
through linear regression on the coauthor network. Journal of
Informetrics 11, 1 (2017), 299–306.
[5] Luciano Rossi, Rafael JP Damaceno, Igor L Freire, Etelvino JH Bechara, and Jesús P Mena-Chalco. 2018. Topological metrics in academic genealogy graphs. Journal of Informetrics 12, 4 (2018), 1042–1058.
[6] Luciano Rossi, Igor L Freire, and Jesús P Mena-Chalco. 2017. Genealogical index: A metric to analyze advisor–advisee relationships. Journal of Informetrics 11, 2 (2017), 564–582.
[7] Min Song, Erin Hea-Jin Kim, and Ha Jin Kim. 2015. Exploring author name disambiguation on PubMed-scale. Journal of informetrics 9, 4 (2015), 924–941.
[8] Besiki Stvilia, Charles C Hinnant, Shuheng Wu, Adam Worrall, Dong Joon Lee, Kathleen Burnett, Gary Burnett, Michelle M Kazmer, and Paul F Marty. 2017. Toward collaborator selection and determination of data ownership and publication authorship in research collaborations. Library & Information Science Research 39, 2 (2017), 85–97.

Sovan Bhattacharya, "Discoveries of Research Genealogy from Large-Scale Academic Dataset: Issues, Challenges and Application", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.262-267, 2019.

Multi-document text summarization (MDS) is a task to generate a single summary from a set of articles related to the same topic or event. Since each input article is related to the same topic or event, the generated summary contains redundant sentences or the sentences that contain almost similar information. This paper presents a sentence similarity measure for reducing redundancy in multi-document summary. Our proposed similarity measure combines the WordNet based semantic sentence similarity measure with the traditional cosine similarity measure. We have conducted our experiments using DUC 2004 benchmark multi-document summarization dataset to judge whether the proposed similarity measure is useful for redundancy removal and improving multi-document text summarization performance or not. Our experiments reveal that our proposed similarity measure is effective for reducing redundancy and improving multi-document text summarization performance.

Text Summarization; WordNet; Semantic Relatedness measure; Hybrid Sentence Similarity Measure; Redundancy Removal

[1] J. Goldstein, V. Mittal, J. Carbonell, and M.Kantrowitz,“Multi-document summarization by sentence extraction,” In Proceedings of the 2000 NAACL-ANLP Workshop on Automatic summarization, pp. 40-48, Association for Computational Linguistics.
[2] V. K. Gupta, T. J Siddiqui,“Multi-document summarization using sentence clustering,” In Intelligent Human Computer Interaction (IHCI), 2012 4th International Conference on , pp. 1-5, IEEE, 2012.
[3] K. Sarkar,“A Keyphrase-Based Approach to Text Summarization for English and Bengali Documents,” International Journal of Technology Diffusion (IJTD), 5(2), pp. 28-38, 2014.
[4] K. Sarkar, “Sentence clustering-based summarization of multiple text documents,” Int. J. Comput. Sci. and Commun. Tech, 2(1), pp. 225-235, 2009.
[5] K. Sarkar, and S. Bandyopadhyay,“Generating headline summary from a document set,” In International Conference on Intelligent Text Processing and Computational Linguistics, pp. 649-652, Springer Berlin Heidelberg. 2005
[6] M. Banko, V. Mittal and M. Witbrock,“Headline generation based on statistical Translation,” In Proceedings of the 38th Annual Meeting of the Association for Computational Linguistics (ACL-2000), Hong Kong, pp. 318–325, 2000.
[7] D. Zajic, B. Dorr and R. Schwartz, “Automatic Headline Generation for Newspaper Stories,” Workshop on Automatic Summarization. Philadelphia, PA, pp. 78-85, 2002.
[8] A. Rush, S. Chopra, and J. Weston,“A Neural Attention Model for Abstractive Sentence Summarization,” In Procedings of EMNLP 2015
[9] R. Nallapati, B. Zhou, and C. Santos,“Abstractive Text Summarization Using Sequence-to- RNNs and Beyond,” In Computation and Language, 2016.
[10] R. Nallapati, F. Zhai, and B. Zhou,“SummaRuNNer: A Recurrent Neural Network based Sequence Model for Extractive Summarization of Documents,”, In Computation and Language, 2016.
[11] P. B. Baxendale,“Man-made index for technical literature—An experiment,” IBM Journal of Research and Development, 2(4), pp. 354–361, 1958.
[12] H. P. Edmundson,“New methods in automatic extracting. Journal of the Association for Computing Machinery,” 16(2), pp. 264–285, 1969.
[13] H. P Luhn,“The automatic creation of literature abstracts,” IBM Journal of Research Development, 2(2), pp. 159–165, 1958.
[14] K. Sarkar,“Using domain knowledge for text summarization in medical domain,” International Journal of Recent Trends in Engineering, 1(1), pp. 200-205, 2009.
[15] K. Sarkar, M. Nasipuri, and S. Ghose, “ Using machine learning for medical document summarization,” International Journal of Database Theory and Application. vol. 4, pp. 31-49, 2011.
[16] K. Sarkar, K. Saraf, A. Ghosh,“Improving graph based multidocument text summarization using an enhanced sentence similarity measure,” In Recent Trends in Information Systems (ReTIS). IEEE 2nd International Conference on, pp. 359-365, 2015. IEEE
[17] D.R. Radev, H. Jing, M. Styś and D.Tam,“Centroid-based summarization of multiple documents,” Information Processing & Management, 40(6), pp.919-938, 2004.
[18] J. G. Carbonell, J. Goldstein,“The use of MMR, diversity-based re-ranking for reordering documents and producing summaries,” In Proceedings of the 21st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Melbourne, Australia, pp. 335–336, 1998
[19] D. Marcu, L. Gerber,“An inquiry into the nature of multi-document abstracts, extracts, and their evaluation,” In Proceedings of the NAACL-2001 Workshop on Automatic Summarization, Pittsburgh, June. NAACL, pp. 1–8, 2001.
[20] E. Boros, P. B. Kantor, D. J. Neu,“A Clustering Based Approach to Creating Multi-Document Summaries,” In Proceedings of the 24th ACM SIGIR Conference, LA, 2001.
[21] H. Hardy, N. Shimizu, T. Strzalkowski, L. Ting, G. B. Wise, and X. Zhang,“Cross-document summarization by concept classification,” In Proceedings of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Finland, pp. 121–128, 2002.
[22] M. F. Moens, C. Uyttendaele, and J. Dumortier, “Abstracting of legal cases: the potential of clustering based on the selection of representative objects,” Journal of the American Society for Information Science, 50 (2), pp. 151-161, 1999.
[23] K. Sarkar,“Sentence clustering-based summarization of multiple text documents,” Int. J. Comput. Sci. and Commun. Tech, 2(1), pp. 225-235, 2009.
[24] G. C. Stein, A. Bagga, and G. B. Wise, “Multi-Document Summarization: Methodologies and Evaluations,” In Conference TALN 2000, Lausanne, 2000.
[25] V. Hatzivassiloglou, J. Klavans, and E. Eskin,“Detecting test similarity over short passages: Exploring linguistic feature combinations via machine learning,” In Proceedings of EMNLP,1999.
[26] V. Hatzivassiloglou, J. L. Klavans, M. L. Holcombe, R. Barzilay, M-Y. Kan, and K. R. McKeown,“SimFinder: A Flexible Clustering Tool for Summarization,” NAACL, Workshop on Automatic Summarization, Pittsburgh, PA, 2001.
[27] R. Barzilay, N. Elhadad, K. R. McKeown,“Sentence ordering in multidocument summarization,” In Proceedings of the first international conference on Human language technology research, Association for Computational Linguistics, pp. 1-7, 2001.
[28] G. Erkan, D. R. Radev,“LexRank: graph-based lexical centrality as salience in text summarization,” Journal of Artificial Intelligence Research, pp. 457-479, 2004.
[29] R. Mihalcea, P. Tarau,“TextRank: Bringing order into texts,” In Proceedings of EMNLP2004, 2004.
[30] R. Mihalcea, P. Tarau, “A language independent algorithm for single and multiple document summarization,” In Proceedings of IJCNLP 2005.
[31] G. Erkan, D. R. Radev,“LexPageRank: Prestige in Multi-Document Text Summarization,” In Proceedings of EMNLP, 2004.
[32] C. Fellbaum, editor, “WordNet: An Electronic Lexical Database,” The MIT Press, Cambridge, MA, . 1998.
[33] A. Budanitsky and G. Hirst, “Evaluating WordNet-based measures of lexical semantic relatedness,” Computational Linguistics, 32(1), pp.13-47, 2006.
[34] D. Lin. “An Information-Theoretic Definition of Similarity,” Proc. Int’l Conf. Machine Learning, July 1998.
[35] P. Resnik,“Using information content to evaluate semantic similarity,” In Proceedings of the 14th International Joint Conference on Artificial Intelligence, pages 448–453, Montreal, Canada, 1995.
[36] D. R. Radev, T. Allison, S. Blair-Goldensohn, J. Blitzer, A.Celebi, S. Dimitrov, E. Drabek, A. Hakim, W. Lam, D. Liu and J. Otterbacher,“MEAD-A Platform for Multidocument Multilingual Text Summarization,” In LREC, 2004.
[37] C. Y. Lin, “ROUGE: A package for automatic evaluation of summaries,” In Proc. Workshop Text Summarization Branches Out, PostConf.Workshop ACL, pp. 25–26, 2004.
[38] T.Mikolov, I.Sutskever, K.Chen, G.S. Corrado, and J.Dean,“Distributed representations of words and phrases and their compositionality,” In Advances in neural information processing systems, pp. 3111-3119, 2013
[39] Z.Wu and M.Palmer, “Verb semantics and lexical selection,” In Proceedings ofthe 32nd Annual Meeting of the Associations for ComputationalLinguistics, pages 133–138, Las Cruces, NewMexico, 1994

Santanu Dam, Kamal Sarkar, "Using WordNet-based Semantic Relatedness Measure for Reducing Redundancy and Improving Multi-document Text Summarization", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.268-273, 2019.

Mobile ad-hoc network (MANET) is one of the significant approaches with highly decentralized and dynamic configured architecture. For transferring packets from one node to another node in MANET security is a big challenge. Trust between two consecutive nodes is a prime important factor for secure data transmission through a trusted network path. As we all know trust of any particular node observed by other one varies with time for different constrains. Here we try to depict the variation of trust with fuzzy mathematics where trust value for each node is represented with triangular fuzzy number. Using fuzzy credibility distribution function we convert the triangular fuzzy trust value in interval based form and represent the trust variation in much more reliable format. These interval based trust values are used to generate direct, indirect trust value in interval based matrix format and from them we prepare overall communication trust matrix to generate the secure network path to send data from particular source to destination.

fuzzy credibility distribution, inverse credibility distribution, triangular fuzzy number, membership value

[1] V.B.Reddy, S.Venkataraman, A. Negi, “Communication and Data Trust for Wireless Sensor Networks using D-S Theory”, IEEE Sensors, Vol. 12, Issue.12, pp.3921 –3929, 2017.
[2] S. Subramaniam, , R. Saravanan, P. K Prakash, “Trust Based Routing to Improve Network Lifetime of Mobile Ad Hoc Networks”, Journal of Computing and Information Technology , Vol. 21, Issue.3, pp.149–160, 2013.
[3] G. Dhananjayan, J.Subbiah, “T2AR : trust-aware ad-hoc routing protocol for MANET”, SpringerPlus, Vol. 5, Issue.1,pp.1-6,2016.
[4] R.Akbani, T.Korkmaz, G. Raju, “ Mobile ad-hoc networks security”, In Recent Advances in Computer Science and Information Engineering, Springer Publisher, Berlin, Heidelberg, pp-659–666,2012.
[5] Theodorakopoulos , J.S. Baras, “On Trust Models and Trust Evaluation Metrics for Ad-hoc Networks”, IEEE Journal on selected Areas in Communications, Vol. 24, Issue.2, pp.318-328,2016.
[6] J.Sen, “A Distributed Trust Management Framework For Detecting Malicious Packet dropping Nodes In a Mobile Ad Hoc Network”, International Journal of Network Security & Its Applications (IJNSA), Vol. 2, Issue.4, pp-92-104,2010.
[7] P. M. Nanaware, S. D. Babar, “ Fuzzy Model for Intrusion Detection using Trust System based Bias Minimization & Application Performance Maximization In MANET”, International Journal of Computer Applications,pp.6-8,2016.
[8] I.Dokmanic , R. Parhizkar , J. Ranieri , M. Vetterli , “Euclidean Distance Matrices: Essential theory, algorithms, and applications”, IEEE Signal Processing Magazine, Vol. 32, Issue.6, pp.12-30, 2015.
[9] S. Bandyopadhyay, S.Karforma,“Improving the Performance of Fuzzy Minimum Spanning Tree based Routing Process through P-Node Fuzzy Multicasting Approach in MANET”, International Journal of Computer Network and Information Security(IJCNIS), Vol.10, No.6, pp.16-26, 2018.
[10] H. Yang, , H. Luo, F. Ye, S. W. Lu, L. Zhang, “ Security in Mobile Ad Hoc Networks: Challenges and Solutions” , IEEE Wireless Communications, Vol. 11, Issue.1, pp. 38-47,2004.
[11] Y. Ren, A. and Boukerche, “A trust-based security system for ubiquitous and pervasive computing environments”, Elsevier Journal on Computer Communications, Vol. 31, Issue.18, pp.4343–4351, 2008.
[12] [12] T. Camp, J. Boleng, V. Davies , “A survey of mobility models for ad-hoc network research”, Wireless Communications and Mobile Computing (WCMC): Special Issue Mobile Ad Hoc Network, Vol. 2,pp.483–502, 2002.

S.Bandyopadhyay, S. Karforma, "Analyzing Trust Categories and Generating Trusted Network Path of MANET using Fuzzy Credibility Distribution Function", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.274-279, 2019.

The present research paper computes a step-by-step approach to retrieve the requisite data from big data set, which is heterogeneous in nature. This is done by using R language. Also the process of cleaning, updating, sorting and merging dataset is been explained in this paper. We feel that the comprehensive idea of this research work will be helpful for the researchers, working in the area of big data analysis. Our main goal is to understand the R packages and commands used to analyze big data

R language, SPSS, Big Data, R Packages, R Sql Statements, Classification Tree

[1] V. Krotov, “Research Note: Scraping Financial Data from the Web using the R Language”, Journal of emerging technologies in Accounting, 2018, Vol. 15, No. 1, pp. 169-181.
[2] R. Ihaka, R. Gentleman, “ R: A Language for Data Analysis and Graphics”, Journal of computational and graphical statistics, 1996, Vol. 5, No. 3, pp. 299-314.
[3] S. Hill, R.Scott,” Developing an Approach to Harvesting, Cleaning, and Analyzing Data from Twitter Using R”. Information Systems Education Journal(ISEDJ), 2017, 15(3), pp. 42-54.
[4] N. J. Horton, E. R. Brown, L. Qian, “Use of R as a Toolbox for Mathematical Statistical Exploration”, Taylor & Francis Ltd. On behalf of American Statistical Association, 2004, Vol. 58, No. 4, pp. 343-357.
[5] S. Ray, A. C. Mondal, “A Brief Description of Software Tools used for Big Data Analysis in Medical Education systems Evaluation”. IMS Manthan, The Journal of Innovation, Publishing India Group.
[6] I.Mergal,”Big Data in Public Affairs Education”, Journal of Public Affairs Education, 2016, Vol. 22, No. 2, pp. 231-248.

Sumanta Ray, "A New Approach to Retrieve the Structured Data from a Big Data Set using R", International Journal of Computer Sciences and Engineering, Vol.07, Issue.01, pp.280-282, 2019.