Volume-03 , Special Issue-01 , Feb - 2015 Go Back

The paper describes a test aimed at measuring the diffusion characteristic of block ciphers. Cryptographic strength of a cipher is directly proportional to the extent to which diffusion is achieved by the underlying cipher, which is measured using the test described in the paper. The paper also enlists the results obtained from the test on various block ciphers. The test algorithm described in the paper will be subsequently added as part of the already existing varied test suite to act as a distinguisher based on the diffusion characteristic of the underlying cipher.

Diffusion, Bit-by-bit Successful Matches, Block Cipher, Randomness

[1] Bhowmik, D., Datta, A., & Sinha, S.,2014," A Bit-level Block Cipher Diffusion Analysis Test – BLDAT", Proc. of the 3rd International Conference on Frontiers in Intelligent Computing Theory & Application (FICTA), AISC 327 Vol. 1 Springer ,667-674.
[2] Castro, J.C.H., Sieria, J.M., Seznec, A., Izquierdo, A., Ribagorda, A., 2005. "The Strict Avalanche Criterion Randomness Test", Mathematics and Computers in Simulation 02/2005, Elsevier Publication, 68(2005), 1-7.
[3] Deniz Toz, Ali Doğanaksoy, Meltem Sönmez Turun, "Statistical Analysis of Block Ciphers", Ulusal Kriptologi Sempozyumu, Ankara, Turkey (2005), 56-66.
[4] Katos,V, 2005. "A Randomness Test for Block Ciphers", Applied Mathematics and Computation, Elsevier Publication, 162(2005), 29-35.
[5] Paar, C. and Pelzl, J., 2010. "Understanding Cryptography", Berlin: Springer-Verleg.
[6] Soto J., L. Bassham, "Randomness Testing of the Advanced Encryption Standard Finalist Candidates", Computer Security Division, National Institute of Standards and Technology, 2000.

Dipanjan Bhowmik, Avijit Datta, Sharad Sinha, "Measuring the Diffusion Characteristic of Block Ciphers: The Bit Relationship Test (BRT)", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.76-80, 2015.

In digital image processing, understanding the scene is one of the most important and challenging tasks. Along with many features of an image like texture, color, line, point etc. edges are most vital feature that carries the information about the structure and overall geometry of objects the image is composed of. Edge detection is based on finding meaningful discontinuities in gray levels. This brief study is aimed towards exploring different edge detection techniques, the theories behind them and distinguishing their efficiency in finding suitable, meaningful edges in digital medical images.

roberts; prewittt; sobel; LoG;canny; MRI

[1] Werner Frei and Chung-Ching Chen. Fast boundary detection: A generalization and a new algorithm, IEEE Trans. On Computers, C-26(10), 2006
[2] Rafael C. Gonzalez, Richard E. Wood, ”Digital Image Processing”, 2nd Edition.
[3] Rafael C. Gonzalez, “Digital Image processing Using Matlab”, 2004.
[4] LS. Davis, "A survey of edge detection techniques", Computer Graphics and Image Processing, 248-260, 4(3), 1975
[5] Rafael C. Gonzalez, Richard E. Wood, ”Digital Image Processing”, 2nd edition. Transactions on, 988–998, 100(10), 1977.
[6] Anil K. Jain, “Fundamentals of Digital Image Processing”.
[7] Bernd Jahne,”Digital Image Processing”, 5th Revised and Extended Edition, Measurement Science and Technology, 13(9), 2002
[8] J. Canny, “A computational approach to edge detection”, IEEE Trans. Pattern Analysis and Machine. Intelligence., 679–698, 8(6), 1986.

Subhro Sarkar and Ardhendu Mandal, "Comparison of Some Classical Edge Detection Techniques with their Suitability Analysis for Medical Images Processing", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.81-87, 2015.

Classification rule-generation is a Data Mining activity. A supervised process uses a training data set to generate the rules. The objective is to predict a predefined class or goal attribute, which can never appear in the antecedent part of a rule. The generated rules are used to predict the class attribute of an unknown test data set. In this paper we have tried to generate classification rule for diabetic patients using Rough set. This present research work relates Data mining to Health Informatics. The proposed algorithm generates the different classification rules related to predict insulin dose depending upon blood glucose measurement and helps in diabetes monitoring.

Data Mining, Rough Set, Indiscernibility, Reduct, Decision tables and decision algorithms, Classification Rule

[1] Sushmita Mitra and Tinku Acharya, DATA MINING Multimedia, Soft Computing, And Bioinformatics, Willy-Interscience, ISBN 9812-53-063-0. 2003.
[2] Julio Ponce and Adem Karahoca(Editors), “Rough Set Theory –
Fundamental Concepts,Principals, Data Extraction, and Applications”, Data Mining and Knowledge Discovery in Real Life Applications, I-Tech, Vienna, Austria, ISBN 978-3-902613- 53-0, pp. 438, February 2009.
[3] I. S. Jacobs P. Prabhavathy, Dr. B. K. Tripathy, “An Efficient Rough Set Approach in Querying Covering Based Relational Databases”, International Journal of Computer Science and Business Informatics, Vol. 1, No. 1, ISSN: 1694- 2108 , MAY 2013
[4] Pawlak, Z, 1982, „Rough Sets‟, International Journal of Computer and Information science, vol.11, no.5, pp.341-356, 1982
[5] Pawlak, Z, „Rough sets - Theoretical aspects of reasoning about data‟, Dordrecht: Kluwer Academic Publishers, pp. 68-162, 1991
[6] Michael Kahn, “ DIABETES data sets”, AIM-94 data set, Washington University, St. Louis, MO, 1994.
[7] Klingensmith, GJ., American Diabetes Association, Intensive Diabetes Management, Third Edition, p. 107, 2003.
[8] National Library of Medicine, http://www.nlm.nih.gov/tsd/acquisitions/cdm/subjects58.html.
[9] https://archive.ics.uci.edu/ml/datasets/Diabetes

Kasturi Ghosh and Sripati Mukhopadhyay, "Classification Rule Generation for Diabetic Patients using Rough Set Approach", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.88-96, 2015.

In this paper we recommend a compelling approach to minimize page fault rate utilizing the hypothesis of waiting lines, i.e. Queuing Theory. The Queuing Theory has wide applications such as making business decision, hospital management, traffic regulation etc. but in this paper we are simply using it to reduce the number of page faults. Hence for the said purpose a comparative analysis is made in this paper between the typical performance from ordinary execution of page replacement algorithm and the performance after actualizing these conventional algorithms on the queuing models. Therefore the performance is measured on the basis of page fault rate and the result from this comparative dissection helps us determine conclusion on the effective way of page fault reduction.

Memory Management, Page Fault, Queueing Theory, FIFO, LRU, OPT, M/M/1, M/M/s, M/D/1

[1] G. K. Vijay Srivastava, “A new approach to minimize page fault,” International Conferrence on Information and Computer Networks, vol. IPCSIT 27, p. 99, 2012.
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[4] G. G. Abraham S., Peter B. Galvin, “Operating systems: Design and implementation,” vol. 8th Edition, pp. 365–376, 2010.
[5] R. Berry, “Queueing theory,” Senior Project Archive, p. 1, 2006. [Online]. Available: https://www.whitman.edu/mathematics/SeniorProjectArchive/2006/berryrm.pdf
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[7] J. Y. Wang, “Operation reasearch ii,” pp. 17–9, Spring 2009.
[8] R. L. Fink, “M/d/1 waiting line,” 2000, [Online; accessed 22-December-2014]. [Online]. Available: http://bradley.bradley.edu/∼rf/wait-md1.htm

S. Biswas, D.Sengupta, R. Bhattacharjee and M. Handique, "Minimizing Page Fault Using Queueing Theory", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.97-101, 2015.

Rough surface has been produced by simulation in 1+1 dimension following different competitive growth models namely random deposition with ballistic deposition and random deposition with surface relaxation with ballistic deposition and calculated the corresponding scaling exponent. It is seen that though the nature of the interface evolution follows the well-established Edwards-Wilkinson growth model or Kardar–Parisi–Zhang model but the values of corresponding scaling exponents do not match exactly with the existing literature. Further it has been seen that the system does not switch over from growth region to saturation region suddenly after a single critical time as has been suggested by the existing theories but there are two distinct crossover regions where the system shows different scaling property. This theoretical finding has been coupled with existing Cassie-Baxter equation to relate the evolved roughness with hydrophobic response of the surface. In this regard, quantitative expression of the water contact angle based on simple assumptions has been represented.

Scaling, discrete models for surface growth, Ballistic phenomena, Roughness, Hydrophobicity

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Diptonil Banerjee, S. Mitra, "Different Competitive Growth Model for Finite Size Scaling Study of Rough Surfaces: A Smart Approach for Estimating Hydrophobicity", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.102-110, 2015.

Components in Component Based Software Engineering (CBSE) are designed for composition and so it is an acceptable fact that, if composition of components becomes more and more error free and easy, the market acceptance and use of component based systems would increase and finally the objective of CBSE could be achieved. As components are prepared by third parties, their composition to build a system according to the requirement specification is a big challenge in the field of CBSE. Many works have been initiated and carried out in this area and many more are yet to be done to bring this area to maturity. Literature review in this area throws light into the various aspects and details that need to be considered in composition. Literature also highlights many approaches to composition and their areas of implementation. The paper attempts to present a review on different aspects of composition by considering the works proposed by various researchers in different areas of composition. Towards the end of the paper an attempt is made to narrow down and concentrate more on the mismatching aspect of composition with a basic concept of the adaptation technique.

Component, Composition, Mismatch, Adaptation

[1] “Technical Concepts of Component-Based Software Engineering”, Vol -II, CMU/SEI-2000-TR-008
[2] Jonge,M , “ To Reuse or To Be Reused, Techniques for Component Composition and Construction” Center for Mathematics and Computer Science (CWI) in Amsterdam under the auspices of the Research school IPA(Institute for Programming research and Algorithmics), January 2003
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[4] Sametinger J,” Software Engineering with Reusable Components”, Springer, 1997
[5] Soni P, Ratti N, “ Analysis of Component Composition Approaches”, International Journal of Computer Science and Communication Engineering, Volume 2 Issue 1, Feb’2013
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[7] Curbera F, Weerawarana S and Duftler M J ,”On Component Composition Languages”, IBM T. J. Watson Research Center, April’2000
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[9] Nierstrasz O and Meijler T D, “ Research directions in software composition” , ACM Computing Surveys, 27(2), June 1995.
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[15] Lumpe M, “ A π-Calculus Based Approach for Software Composition”, January’99
[16] Xiaohui Gu, Philip S. Yu and Klara Nahrstedt, “Optimal Component Composition for Scalable Stream Processing ”
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Maushumi Lahon and Uzzal Sharma, "The Intricacies of Software Component Composition", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.111-117, 2015.

There are enough evidences of software project failures. Starting from economic losses to live losses is caused by many software project failures. Software project failures have significant impact on both social and economic factors. Hence, it is important to identify the different reasons for project failures. If these reasons are pre-known, actions can be taken during project development to reduce project failure risks. In this paper we identify and categorized the project failure root causes based on their different sources. Then briefly we have highlighted the primary features of the BRIDGE [1] process model and explored the ways and means how these project failure reasons may be reduced or alleviated by following the BRIDGE process model.

Software Engineering, Project Failure, BRIDGE Process Model, SDLC Model

[1] Mandal A., BRIDGE: A Model for Modern Software Development Process to Cater the Present Software Crisis, Proc. IEEE Int’l Conf. Advance Computing Conference, 2009. [Also available at IEEEXplore, DOI: 10.1109/ IADCC.2009.4809259 ], 494-500, 2009.
[2] Keider, S.P., Why projects fail. Datamation, 53-55, 20(12), 1974.
[3] Saleh, Y., & Alshawi, M., An alternative model for measuring the success of IS projects: the GPIS model, Journal of Enterprise Information Management, 47-63, 18(1), 2005.
[4] Walid Al-Ahmad, Khalid Al-Fagih, Khalid Khanfar, Khalid Alsamara, Saleem Abuleil, Hani Abu-Salem, A Taxonomy of an IT Project Failure: Root Causes, International Management Review, 93-106, 5(1), 2009
[5] The CHAOS Manifesto, 2013: Think Big, Act Small by The Standish Group International, 2013.
[6] Rupinder Kaur, Dr. Jyotsna Sengupta, Software Process Models and Analysis on Failure of Software Development Projects, International Journal of Scientific & Engineering Research, 1-4, 2( 2), 2011
[7] Fabriek, Matthias, Elt. Al, Reasons For Success And Failure In Offshore Software Development Projects, [Available: http://is2.lse.ac.uk/asp/aspecis/20080039.pdf], [Accessed on: 05/09/2014]
[8] Alpha Software Inc, Why Software Projects Fail: A New Assessment of Risk, [Available at http://www.alphasoftware.com/documentation/200801_wpaper/low_risk_a5.pdf], [Accessed on: 05/01/2015]
[9] Lorin J. May, Major Causes of Software Project Failures. [Available at] http://www.cic.unb.br/~genaina/ES/ManMonth/SoftwareProjectFailures.pdf [Accessed on: 10.12.14]
[10] Khaled El Emam and A. Günes¸ Koru, A Replicated Survey of IT Software Project Failures, IEEE Software, 84-90, 2008.
[11] Watts S. Humphrey, Why Big Software Projects Fail: The 12 Key Questions, CROSSTALK: The Journal of Defense Software Engineering, 25-29, 18(3), March 2005
[12] Watts S. Humphrey, Five reasons why software projects fail, [Available at: http://www.computerworld.com/s/article/71209/Why_Projects_Fail?taxonomyId=11&pageNumber=2] [Accessed on: 15/12/14]
[13] By Tom Gilb. Project Failure: Some Causes and Cures, Edited MASTER paper, Version: February 29, 2004
[14] Capers Jones, Social and Technical Reasons for Software Project Failures, CROSSTALK: The Journal of Defense Software Engineering, 2-9, 19(6), June 2006
[15] Mandal A., Pal S. C., Investigating and Analysing the Desired Characteristics of Software Development Lifecycle (Sdlc) Models, International Journal Of Software Engineering Research & Practices, 9-15, 2(4), 2012.
[16] Mandal A., Pal S. C., Achieving agility through BRIDGE process model: an approach to integrate the agile and disciplined software development, Innovations in Systems and Software Engineering: A NASA Journal, 1-7, 11(1), [DOI 10.1007/s11334-014-0239-x ], 2015
[17] Mandal A., Pal S. C., Emergence of Component Based Software Engineering, International Journal of Advanced Research in Computer Science and Software Engineering, 311-315, 2(3), 2012
[18] Mandal A., Pal S. C., A Comparative Analysis of BRIDGE and Some other Well Known Software Development Life Cycle Models, International Journal of Computer Science & Engineering Technology (IJCSET), 196-202, 5(3), 2014.

Ardhendu Mandal and S C Pal, "Identifying The Reasons For Software Project Failure And Some Of Their Proposed Remedial Through BRIDGE Process Models", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.118-126, 2015.

User Interface (UI) is the part of the system that acts as an intermediately between the user and system facilitating the user to interact with the system in an efficient manner. The user interface is everything the end user comes into contact with while using the system physically, perceptually, and conceptually. To the end user, the user interface is the system itself. Hence, usability of a system remains one of the most important quality attribute in determining the total quality of any software system. The challenge of user-interface design is to construct a natural dialog sequence that allows the user and computer to exchange the messages required to carry out a particular task. The user interfaces do vary from system to system and user to user. In this paper we have identified the different issues in designing efficient user interface. All these issues are discussed at length with suitable example.

User Interface, SDLC, Human Computer Interaction, Software Design, Software Engineering

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[6] Reena Saini , Graphical User Interface Design Essentials & Process, International Journal of Advanced Research in Computer Science and Software Engineering (IJARCSSE), 481-483, 3(9), 2013
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Debasmita Saha, Ardhendu Mandal and S. C. Pal, "User Interface Design Issues for Easy and Efficient Human Computer Interaction: An Explanatory Approach", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.127-135, 2015.

Information system in organisation better known as Management information system (MIS) deals with improving the performance of organizations and people through the use of information technology. The four distinct components of MIS are: information strategy, information technology strategy, information management strategy, and change Management/ implementation strategy. This paper focuses on management information system competencies and strategies. The specific fields in which MIS has become invaluable like Strategy Support, Data Processing. MIS resources can be divided into three broad categories: human, technological, and relationship resources. Consistent with the importance of intangible resources, the paper focuses on the intangible dimensions of these three resources. Specifically, it includes MIS human capital, IT infrastructure flexibility, and MIS partnership quality and shows how each of these resources will have a direct positive relationship with MIS competencies. Secondly, competencies are socially complex routines that determine the efficiency with which organizations transform inputs into outputs. MIS competencies can be divided into three categories—foundation competencies, management competencies and portfolio competencies. Thirdly, MIS strategy is a plan for developing management information systems, which maximize the ability of the organization to achieve its agreed objectives. A strategy providing the framework for the organization for ensuring competencies between systems, prioritizing development, and encouraging the elimination of redundant systems is suggested in the paper.

Management information system (MIS), MIS resources, MIS competencies, MIS strategy.

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[9] J. W. Beard, M. Sumner, “Seeking strategic advantage in the post-net era: viewing ERP systems from the resource- based perspective,” Journal of Strategic Information System, vol. 22, No. 13, pp. 129-150, 2004.

Pradip Kumar Das, "Information System in Enhancing Managerial Competencies: An Organisational Perspective", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.136-140, 2015.

Research on Speech signal processing has gained a high degree of importance due to the broad areas of application. The success of any speech recognition system and speaker identification system depends on the level of accuracy and correctness. To accomplish a high level of accuracy, it is very important to use the proper tool for the purpose. The present paper tries to use two popular tools namely MFCC and Formant Frequency Measure used for the purpose of feature extraction and to give a comparison between the two. The Bodo language is considered for the present research.

Formant frequency measure; MFCC, OSV, ASR, DCT

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Uzzal Sharma, "A Comprehensive Analysis on Vowels Present in Bodo Language", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.141-144, 2015.