Volume-5 , Issue-2 , Feb - 2017 Go Back

The thermal effects of the suction velocity, in conjunction with other flow parameters, on an unsteady free convective viscous incompressible flow past an infinite vertical flat plate submersed in a saturated porous medium is investigated. The effect of various flow parameters like chemical reaction parameter Kr, Schimdt number Sc, radiation absorption coefficient Q1, heat absorption coefficient φ radiation parameter N, Magnetic parameter M, Permeability parameter K, and time t on the velocity, temperature and concentration as well as the skin friction, rates of heat and mass transfer are obtained numerically and discussed. The results obtained are presented with the help of graphs.

Magnetohydrodynamic; Porous media; Radiation; Suction velocity;Schimidt number;Nusselt number

[1] Soundalgekar, V.M. and P.D. Wavre, "Unsteady free convection flow past an infinite vertical plate with constant suction and mass transfer", Int. J. Heat Mass Transfer, 20(1977a), pp. 1363-1373.
[2] A.V.Dubewar and V.M.Soundalgekar, Mass transfer effects on transient free convection flow past an infinite plate with periodic heat flux, J. Chin. Inst. Chem. Engrs, 2005, 36(2), pp.285-293.
[3] A.V.Dubewar and V.M.Soundalgekar ,Mass transfer effects on free convection flow past an infinite vertical porous plate ,10(4) 2005,pp.605-615.
[4] V. Ambethkar Numerical solutions of heat and mass transfer effects of an unsteady MHD free convective flow past an infinite vertical plate with constant suction, Journal of Naval Architecture and Marine Engineering (5) 2008, pp. 28-36.
[5] Singh, A. K. and Singh, N. P., Heat and mass transfer in MHD flow of a viscous fluid past a vertical plate under oscillatory suction velocity, Indian. J. Pure Appl. Math., 34(3) 2003 pp. 429-442.
[6] Sahoo, P.K., Datta, N. and Biswal, S. Magnetohydrodynamic unsteady free convection flow past an infinite vertical plate with constant suction and heat sink, Indian J. Pure Appl. Math., 34(1) 2003, pp .145-155.
[7] Muthucumaraswamy, R. and Kumar, G. S. Heat and mass transfer effects on moving vertical plate in the presence of thermal radiation, Theoret. Appl. Mech., 31(1) 2004, pp. 35-46.
[8] Acharya, M., Dash, G.C. and Singh, L. P., Magnetic field effects on the free convection and mass transfer flow through porous medium with constant suction and constant heat flux, Indian J. Pure Appl. Math. 31(1) 2000, pp.1-18.

S.P. Dahake, A.V. Dubewar , "Effects of Radiation on Magnetohydrodynamic Convection Flow past an Impulsively Started Vertical Plate Submersed in a Porous Medium with Suction," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.1-7, 2017.

A One-Time Password (OTP) is an auto - generated, string of characters (password) that validates the user to carry out a single transaction or session on digital devices like a Computer, Smartphone, Tablet etc. Various unique techniques underwent implementation time and again for producing an optimal and efficient OTP. In most of the techniques, the OTP generated is of a shorter length and comprised of only letters of English alphabet (a – z, A - Z), digits (0 – 9) and characters like @ etc. In this paper a novel approach for generating One-Time Password (OTP) has been proposed using Rubik’s cube principle based on a 4 × 4 cube in which each box of the cube is labeled with characters present on the keyboard such that when the layers of the cube are scrambled in various ways, it creates a 16 character OTP. This technique using Rubik’s cube have never been applied before to generate an OTP.

One TimePassword(OTP); Hash Function; Random Function; Replay Attacks

[1] Saini T., “One Time Password Generator System”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4(3), pp.781–785, March 2014.
[2] Huang Y., Huang Z., Zhao H., Lai X., “A new One-time Password Method”, In the Proceedings of 2013 International Conference on Electronic Engineering and Computer Science , pp.32–37, 2013.
[3] Parmar H., Nainan N. and Thaseen S., “GENERATION OF SECURE ONE-TIME PASSWORD BASED ON IMAGE AUTHENTICATION”, In the Proceedings of Academy & Industrial Research Collaboration Center - Computer Science Conference Proceedings, pp.195- 206, 2012.
[4] Fatangare S., Prof. Lomte A., “Robust OTP Generation Using Secure Authentication Protocol”, International Journal of COMPUTER TECHNOLOGY AND APPLICATIONS, Vol.5(2), pp.546-552, March – April 2014.
[5] Vishwakarma N., Gangrade K., “Secure Image Based One Time Password”, International Journal of Science and Research, Vol.5(11), pp.680–683, November 2016.

S. Bose, D.R. Chowdhury , "OTP Generation Algorithm: A Rubik’s Cube Principle Implementation," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.8-14, 2017.

The Privacy is paramount when communicating subtle information, and humans have devised some unusual ways to encode their conversations. The Quantum Key Distribution agrees for the secure transmission of unbreakable encryption keys and it provides a flawless secure coding to solve the problem of key distribution. At present this is more mature application in the field of quantum computing. The fundamental concept of this protocol involves two parties, wishing to exchange a key both with access to a classical public communication channel and a quantum communication channel. The entanglement distillation approach of BB84 is widely used because the act of reading a quantum bit (QuBits) changes the bit, it is difficult for hackers to interfere without being detected sufficient number of bits. But this technique uses only four directions of electron movements so it is possible to guess the key. To overcome these drawbacks here two level QRNA technique is proposed for security. In first level DNA is applied on plain text after BB84 protocol is applied. This method provides better security than both BB84 protocol and DNA alone.

BB84 protocol, Quantum Cryptography (QC), DNA, QDNA and QuBits

[1] B.Jyoshna”Mechanisms for secure data transmission A Survey”, Published in International of Computer Science and Engineering(IJCSE), Vol.-2(8), PP(82-83) August 2014.
[2] R.Shah and Y. S. Chouhan, "Encoding of Hindi Text Using Steganography Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.2(1),pp. 22-28, Feb 2014
[3] Alia, M.A., Yahya,A., “Public–Key Steganography Based on Matching Method”, European Journal of Scientific Research, Vol(2), PP223-231 Aug (2010).
[4] G. Cui, L. Qin, Y. Wang and X. Zhang, “An encryption scheme using DNA technology”, Bio Inspired Computing: Theories and Applications, pp. 37-42, 2008.
[5] Z. Chen and J. Xu, "One-time-pads encryption in the tile assembly model," Bio-Inspired Computing: Theories and Applications,Vol-46 pp.23- 30,may 2008.
[6] E Suresh Babu, C Nagaraju, MHM Krishna Prasad “Analysis of Secure Routing Protocol for Wireless Adhoc Networks Using Efficient DNA Based Cryptographic Mechanism” published in Procedia Computer Sciencedec-. Vol-70PP:341-347 , Oct 2015.
[7] Ashok Sharma, R S Thakur and Shailesh Jaloree, "Investigation of Efficient Cryptic Algorithm for image files Encryption in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol. 4(5), pp.5-11, Oct 2016
[8] R Pradeep Kumar Reddy, C Nagaraju, N Subramanyam ”Text encryption through level based privacy using dna steganography” published in International Journal of Emerging Trends & Technology in Computer Science (IJETTCS) ,vol-3 PP168-172,May-jun 2014.
[9] Mamta Rani1 and Sandeep Jain,”DNA Computing and Recent Developments”. International journal of computer science and Engineering, Vol.2, PP.16-19,April 2014.
[10] Kritika Gupta, “ DNA Based Cryptographic Techniques: A Review”, International Journal of Advanced Research in Computer Science and Software Engineering,vol(3), pp. 607-610, March 2013.
[11] Komal Kumbharkar, “An improved Symmetric key cryptography with DNA based strong cipher”, international journal of advanced and innovative research, (IJCSE) ,Vol(2),PP2278-7844,May-jun2013.
[12] Sharmeen kaur, Raveena Singh and Shivya Gagneja “Network Security and Methods of Encoding and Decoding”. International journal of computer science and Engineering, Vol.-2(2), pp (11-15) Feb 2014.
[13] Pallab Banerjee1 and Anita Kumari2,Puja Jha3 “Comparative Performance Analysis of Optimized Performance Round Robin Scheduling Algorithm(OPRR) with AN Based Round Robin Scheduling Algorithm using Dynamic Time Quantum in Real Time System with Arrival Time”. International journal of computer sciences and Engineering, Vol.-3(5), pp. 309-316, May 2015.
[14] Azarderakhsh, mehran mozaffar kermani, David jao,”post quantum cryptography on FPGA based on isolegines on elliptic curves” IEEE journal,VOL(64),pp86-99,2017

N. Srilatha, M. Deepthi and I.R. Reddy, "Robust Quantum Key Distribution Based on Two Level Qdna Technique to Generate Encrypted Key," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.15-19, 2017.

Selection of the proper higher educational courses is absolutely necessary for the prospective students. Selecting appropriate courses are really cumbersome job for the students who are having less information about present trend of education relating to get placements or jobs and for better development in future. In this paper, trend analysis and forecasting has proposed to predict the prospects of the selected higher educational courses in the field of computer science/technology. An online survey has done to get the dataset for analysis and there were altogether 151 data selected for the study. A Feed Forward Artificial Neural Network model has proposed and the best network architecture has been selected among the top five NN considering the parameters like fitness value, AIC (Akaike’s Information Criterion) value, training, validation, test error values. The best network architecture is further analyzed using Levenberg-Marquardt (LM) and Conjugate Gradient Descent (CGD) algorithms for finding the accuracy of the trend. The study focuses on important input parameters during training of network architecture. Correct Classification Rate (CCR) for training and validation has been prepared to find the best network after a number of iterations. A comparative study between the LM and CGD algorithm has primed with a focus on confusion matrix. This study recommends and predicts the future trends of the selected higher educational computer science/technology courses by using ANN.

Artificial Neural Network (ANN); Conjugate Gradient Descent (CGD); Confusion Matrix; Feed-Forward Artificial Neural Network (FFANN); Levenberg- Marquardt (LM); Multi- Layer Preceptron (MLP); Trend Analysis

[1] Makvandi P., Jassbi J., and Khanmohammadi S., “Application of Genetic Algorithm and Neural Network in Forecasting with Good Data”, In the Proceedings of the 6th WSEAS International Conference on NEURAL NETWORKS, Lisbon, Portugal, pp.56-61, June 2005.
[2] Uma Devi B., Sundar D. and Alli P., “An Effective Time Series Analysis for Stock Trend Prediction Using ARIMA Model for Nifty Midcap-50”, International Journal of Data Mining & Knowledge Management Process (IJDKP), Vol.3(1), pp.65-78, January 2013.
[3] Sen Deepanjan, Chowdhury Dilip Roy, "Green Computing: Efficient Practices and Applications", International Journal of Computer Sciences and Engineering, Vol.4(1),pp.38-47, Feb 2016, E-ISSN: 2347-2693.
[4] Chowdhury D.R., Bhattacharjee D., “Particle Swarm Optimization and Artificial Neural Network Techniques for Predicting Neonatal Disease”, In the Proceedings of National Conference on Research Trends in Computer Science and Application (NCRTCSA-2014), Technically co-sponsored by IEEE Kolkata and CSI Siliguri, pp.45-49, Feb 2014, ISBN No: 978-93-82338-95-6.
[5] Yadav A and Harit V K., "Fault Identification in Sub-Station by Using Neuro-Fuzzy Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.4(6), pp.1-7 Dec 2016.
[6] Chowdhury D.R., Chatterjee M., Samanta R. K., “An Artificial Neural Network Model for Neonatal Disease Diagnosis”, International Journal of Artificial Intelligence and Expert Systems (IJAE), Vol.2(3), pp.96-106, August 2011.
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[10] Battiti R., "First-and second-order methods for learning: between steepest descent and Newton`s method”, Neural Computation, Vol.4(2), pp.141–166, 1992.
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D.R. Chowdhury , D. Sen , "Artificial Neural Network Based Trend Analysis and Forecasting Model for Course Selection," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.20-26, 2017.

Data transmission over the Internet for various purposes has become a necessary part of technology nowadays. But, the data sent over the Internet or the data to be retrieved can be hacked by any other party in several ways. To provide security, various cryptographic schemes have been developed. Among the different schemes the data integrity cannot be ensured in a number of aspects. Cryptography ensures that the message should be sent without any alternation and only the authorized person can be able to open and read the message. Cryptographic techniques can be broadly classified into two methods, namely symmetric and asymmetric. This paper focus mainly on the different kinds of the existing encryption techniques, and a comparative study of different algorithms.

Encryption, Cryptography, Symmetric, Asymmetric

[1] Stallings W., “Cryptography and Network Security”, 2nd Edition, Prentice Hall, 1999.
[2] Sharma A., Thakur R S and Jaloree S., "Investigation of Efficient Cryptic Algorithm for image files Encryption in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.4(5), pp. 5-11, Oct 2016
[3] Dipti K. S., Neha B., “Proposed System for Data Hiding Using Cryptography and Steganography”, International Journal of Computer Appilcations, Vol.8(9), pp.7-10, Oct 2010.
[4] Schneier B., “Description of a New Variable Length Key, 64 Bit Block Cipher (Blowfish)” International Workshop on Fast Software Encryption, Cambridge Security Workshop proceedings, pp.191-204, Dec 1993. ISBN 978-3-540-48456-1
[5] Minaam D. S. A., Abdual-Kader H. M., Hadhoud M.M, “Evaluating the Effects of Symmetric Cryptography Algorithms on Power Consumption for Different Data Types”, International Journal of Network Security, Vol.11(2), pp.78-87, Sep 2010.
[6] Nigam A, Singh V., "Securing Data Transmission in Cloud using Encryption Algorithms", International Journal of Computer Sciences and Engineering, Vol.4(6), pp.21-25, Jun -2016
[7] Rin M. C. J., Lin Y. L., “A VLSI implementation of the Blowfish Encryption/Decryption Algorithm”, ASP-DAC `00 Proceedings of the 2000 Asia and South Pacific Design Automation Conference, Jan 2000. ISBN:0-7803-5974-7
[8] Mathew M, Sumathi D., Ranjima P, Sivaprakash P., "Secure Cloud Data Sharing Using Key-Aggregate Cryptosystem", International Journal of Computer Sciences and Engineering, Vol.2(8), pp. 121-123, Aug -2014
[9] Agarwal M., Mishra P., “A comparitive survey on Symmetric Key Encryption Techniques” International Journal on Computer Sciences and Engineering, Vol.4(5), pp.877-882, May 2012.

L.R. Mathew , "A Survey on Different Cryptographic Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.27-29, 2017.

Missing data, a persistent problem in most scientific research, should be handled very carefully, as role of data are vital in every analysis. Mishandling missing values may cause distorted analysis or may generate biased results. Valid and reliable models require good data preparation. Dozens of techniques have been proposed by methodologists to address the problem. Appropriate method should be taken into consideration for a particular study in order to achieve efficient and valid analysis. In this study we discuss different methods to handle missing data and compare three imputation methods: Arithmetic Mean Imputation, Regression Imputation and Multiple Imputation using EMB algorithm, performed on three data sets from UCI repository under the assumption of MAR based on Root Mean Square Error (RMSE) as an evaluation criteria.

UCI database, Missing At Random (MAR), Missing Completely At Random (MCAR), Missing Not At Random (MNAR), Multiple Imputation, Expectation Maximization with Bootstrap approach (EMB), Root Mean Square Error (RMSE)

[1] Troyanskaya O, Cantor M, Sherlock G, Brown P, Hastie T, “Missing value estimation methods for dna microarrays”, Bioinformatics Vol.17, pp.520-525, 2001.
[2] Lewis HD, “Missing data in clinical trials”, New England Journal of Medicine, Vol. 367, pp. 2557-2558, 2012.
[3] Rubin DB, “Inference and missing data”, Biometrica Vol. 63, pp. 581-592, 1976.
[4] Little RJA, Rubin DB, Statistical Analysis with Missing Data (2nd edn.), Wiley-Interscience, 2002.
[5] N.Durga, D.Ragupathi and V. Raj Kumar, "Uses of HDFS in Metadata Management System", International Journal of Computer Sciences and Engineering, Vol.2(9), pp.145-150, Sep 2014
[6] Schafer. J. L. & Graham, J.N., “Missing Data: Our view of the state of the art”, Psychological Methods, Vol. 7, pp. 147-177, 2002.
[7] Bhambri V., "Data Mining as a Solution for Data Management in Banking Sector", International Journal of Computer Sciences and Engineering, Vol.1(1), pp.20-25, Sep -2013.
[8] King G, Tomaz M, Wittenberg J, “Making the Most of Statistical Analyses: Improving and Presentation”, American Journal of Political Science, Vol. 44(2), pp. 341-355, 2000.
[9] Dempster A. P., Laird N. M., Rubin D. B., "Maximum Likelihood from Incomplete Data via the EM Algorithm", Journal of the Royal Statistical Society, Vol. 39(1) , pp. 1–38, 1977.
[10] Honaker J., King G., “What to do About Missing Values in Time Series Cross-Section Data”, American J. of Political Science, Vol. 54(2), pp.561-581, 2010.
[11] Horton NJ, Kleinman KP, “Much ado about nothing: A comparison of missing data methods and software to fit incomplete data regression models”, The American Statistician Vol.61, pp. 79-90, 2007.

M. Mitra, R.K. Samanta , "A Study on Missing Data Management," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.30-33, 2017.

In this modern time, face recognition has gathered much attention and its research has enormously expanded among researchers, since it has many potential applications in computer vision, communication and automatic access control system etc. Especially, human detection is an important part of crowd analysis as the primary goal for automatic human face detection. However, face detection is not very simple as faces of human varies from person to person. They varies in different aspects such as pose variation (front, profile face etc), image orientation, illuminating condition, and facial expression and so on. Therefore, an approach is put forward to evaluate the number of people present in a group image of human. The results found are encouraging.

Face counting system, MatLab, Morphological operation, Face Detection, Spatial transform

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[3].Leung T.K., Burl M.C., Perona P., “Finding Faces in ed Scenes Using Random Labeled Graph Matching”, Proc. Fifth IEEE Int’l Conf. Computer Vision, pp. 637-644, 1995.
[4].Moghaddam B., Pentland A., "Probabilistic visual learning for object representation", IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 19, no. 7, pp. 696-710, 1997 (on-line).
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[7]. Khurana K., Awasthi R.,” Techniques for Object Recognition in Images and Multi-Object Detection” in International Journal of Advanced Research in Computer Engineering & Technology(IJARCET) Vol. 2(4), pp. 118-126, 2013
[8]. Agarwal S., "Edge Detection in blurred images using Ant Colony Optimization Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.1(2), PP.21-24, Apr 2013
[9].Campadeli P., Lanzarotti R., Lipory G.,” Face Detection in Color Image of Generic Scanes”, Italia, IEEE International Conference on Computational Intelegence for Homeland Security and Personal Safety,2004.
[10]. Sudi G. S., Gadgil A.A., "Improved Color Image Segmentation using Kindred Thresholding and Region Merging", International Journal of Computer Sciences and Engineering, Vol.1(3), pp.1-9, Nov -2013
[11]. Wasson V., Singh B., Wasson G, "A Parallel Optimized Approach for Prostate Boundary Segmentation from Ultrasound Images", International Journal of Scientific Research in Computer Sciences and Engineering, Vol.1(1), pp.14-19, Feb 2013
[12]. Kaur B. and Kaur P, "A Comparative Study on Image Segmentation Techniques", International Journal of Computer Sciences and Engineering, Vol.3(2), PP.50-56, Dec -2015
[13].Kokkinos I., Maragos P.,”Synergy between Object Recognition and image segmentation using Expectation and Maximization Algorithm”, IEEE Trans. on Pattern Analysis and Machine Intelligence (PAMI), Vol. 31(8), pp. 1486-1501, 2009.
[14] Arora K. , Suri K., Arora D. and Pandey V., "Gesture Recognition Using Artificial Neural Network", International Journal of Computer Sciences and Engineering, Vol.2(4), pp. 185-189, Apr -2014

A. Sur , S. Sarkar , K. Sarkar , "An Approach towards Face Counting System using Image Processing Techniques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.34-37, 2017.

In this paper the results obtained from Linear Cryptanalysis attack developed by Matsui has been analyzed in a different perspective and inferences were drawn accordingly. Here, a simple toy cipher has been put to Linear Cryptanalysis attack in order to understand the attack from a different perspective. The results thus obtained may become a guide towards designing block ciphers that can withstand Linear Cryptanalysis attacks.

Linear Cryptanalysis, Linear Approximation, S-Box, Toy cipher, Parity

[1] Heys H M,”A Tutorial on Linear And Differential Cryptanalysis”, Cryptologia, Vol. 25(3),pp189-221, 2002.
[2] Matsui M,”Linear Cr4yptanalysis Method For DES Cipher”, Advance in Cryptlogy-EUROCRYPT’93, Springer, Verlag, 386-397, 1994.
[3] Jakobson B T, Abyar M., Nordholt P S, “Linear And Differential Cryptanalysis”, pp. 234-242, 2006.
[4] Paar, C, Pelzl J, “Understanding Cryptography”, Berlin:Springer, Nerla, pp. 120-129, 2010.
[5] Bhowmik D, Datta A,Sinha S,”Measuring the Diffusion Characteristic of Block Ciphers: The Bit Relationship Test (BRT)”,International Journal of Computer Science and Engineering,Vol.3(1), pp.76-80,Feb 2015.
[6] Sharma A., Thakur RS and Jaloree S., "Investigation of Efficient Cryptic Algorithm for Storing Video Files in Cloud", International Journal of Scientific Research in Computer Science and Engineering, Vol.4(6), pp.8-14, Dec 2016.
[7] Shah R. and Chouhan Y. S., "Encoding of Hindi Text Using Steganography Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.2(1), pp.22-28, Feb 2014.
[8] Bhowmick A., Kapur V. and Paladi S.T., "Concealing Cipher Data using an Amalgam of Image Steganography and two-level Image Cryptography", International Journal of Computer Sciences and Engineering, Vol.3(3), pp.7-12, Mar -2015.

D. Bhowmik, A. Datta, A. Sinha , "A New Perspective of Inferring from the output of Linear Cryptanalysis Attack," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.38-42, 2017.

Holography projection is the new kind of technology that will change the traditional system of communication in near future. With this technology virtual images of real objects can be projected anywhere with accurate details and depth impression. This paper puts focus on holography projection technology. It also focuses on the importance of this technology, the process of construction of holograms and application features that will affect on several areas such as marketing, telecommunication, education and healthcare. It also puts light on future that will help to redesign many fields of life including businesses and technologies.

Holography, Holographic Projections, Hologram

[1]. Kumar D., Kaushik D., “A Review Paper on Holographic Projection”, International Journal of Innovative Research in Technology, Vol.1(6), 2014
[2]. Elmorshidy A., “Holographic Projection Technology: The World is Changing”, Journal of Telecommunications, Vol.2(2), pp. 234-238, 2010.
[3]. Yaras F., Kang H., Onural L., “State of the Art in Holographic Displays: A Survey”, Journal of Display Technology, Vol.6(10), pp.23-31 2010.
[4]. Gohane T. S., Longadge N. R., “3D Holograph Projection – Future of Visual Communication”, International Journal of Computer Science and Network, Vol.3(1), pp. 121-128, 2014.
[5]. Rana G., Patil K., Dewangan K. V., Rather P. V., Dewangan M, “Holographic Projection Technology”, International Journal of Electrical and Electronics Research, Vol.3(2), PP.551-552 , 2015.
[6]. Chaudhari A., Lakhani K., Deulkar K., “Transforming the World using Holograms”, International Journal of Computer Applications, Vol. 130(1), pp.321-329, 2015.
[7]. Amrutha C., Manikandan C. L., Akhila V. A., “ A Study and Analysis of Speckle Reduction Method in Digital Holography”, International Journal of Computer Sciences and Engineering, Vol. 4(11), 34-37, 2016.
[8]. Giridhar S S., Gadgil A. A., "Improved Color Image Segmentation using Kindred Thresholding and Region Merging", International Journal of Computer Sciences and Engineering, Vol. 1(3), pp.1-9, 2013
[9]. Limi V. L., Vekataraman D., “3D Modelling from UN Calibrated Images – A Comparative Study”, International Journal of Computer Sciences and Engineering, Vol. 5(1), pp.12-24, 2014.

A. Ghosh , "Significance of Holographic Technology in Modern World," International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.43-45, 2017.

Mining of frequent itemsets from large databases has been an interesting area for data miners from the beginning of data mining research. Knowing frequent patterns, data miners can determine interesting relationships among the items. In the proposed work, the original database is scanned once and the encoded database transactions are stored as a matrix. All frequent patterns are then determined from this matrix of coded transactions. An efficient algorithm has been developed to mine all frequent itemsets directly from this encoded matrix with the help of a reference matrix. The proposed approach reduces the memory size required for the database and the number of database scans to one. The algorithm finds its application in distributed data mining and secure data publishing.

Mining Frequent Pattern, Matrix Approach, Reference Matrix, Compressed Database, Market Basket Analysis, Apriori Algorithm

[1]. Al-Maolegi M., Arkok B., “An improved Apriori Algorithm for Association Rules”, International Journal on Natural Language Computing, Vol. 3(1), pp. 21-29, 2014.
[2]. Yabing J., “Research of an Improved Apriori Algorithm in Data Mining Association Rules”, International Journal of Computation and Communication Engineering, Vol. 2(1), pp. 25-27, 2013.
[3]. Rehab A., Alva H., Anasuya B., Patil V., “New Matrix based approach to improve Apriori Algorithm”, International Journal of Computer Science & Network Solutions, Vol. 1(4), pp. 102-109, 2013.
[4]. Liu Z., Sun T., Sang G., “An Algorithm of Association Rules Mining in Large Databases Based on Sampling”, International Journal of Database Theory and Application, Vol. 6(6), pp. 95-104, 2013.
[5]. Al-Shoreman H., Jbara Y., “An Efficient Algorithm for Mining Association Rules for Large Itemsets in Large Databases”, International Journal of Engineering and Innovative Technology, Vol. 3(10), pp. 237-240, 2014.
[6]. Kotsiantis S., Kanellopoulos D., “Association Rule Mining: A Recent Overview, GESTS”, International Transactions on Computer Science and Engineering, Vol. 32(1) , pp. 71-82, 2006.
[7]. Tanna P., Ghodasara Y., “Using Apriori with WEKA for frequent Pattern Mining”, International Journal of Engineering Trends and Technology, Vol. 12(3), pp. 127-134, 2014.
[8]. Mishra S., Pattanaik S., Patnaik D., “Application of association rules to determine item sets from large databases”, International Journal of Computer Science Engineering, Vol. 2(6), pp. 276-278, 2013.
[9]. Han J., Pei J., Yin Y., “Mining Frequent Patterns without Candidate Generation ACM SIGMOD”, International Conference on Management of Data, pp. 1-12, 2000.
[10]. Jain S., Dave M., Agrawal A., “Cost Vector Matrix – A new approach to Association Rule Mining”, International Journal of Recent research and Review, Vol. 7(2), pp. 68-73, 2014.
[11]. Choubey A., Patel R., Rana J., “Graph based new approach for frequent pattern mining”, International Journal of Computer Science and Information Technology, Vol. 4(1), pp. 221-235, 2012.
[12]. Grun B., Hahsler M.., “arules- A Computational Environment for Mining Association Rules and Frequent Item Sets”, Journal of Statistical Software, Vol. 14(15), pp. 1-23, 2005.
[13]. Rao C., Babu D., Shankar R., Kumar V., Rajanikanth J., Shekhar C., “Mining Association Rules Based on Boolean Algorithm – a Study in Large Databases”, International Journal of Machine Learning and Computing Vol. 3(4), pp. 347-351, 2013.
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