Volume-6 ~ Issue-1
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| Paper Type | : | Research Paper |
| Title | : | Secure Model for Cloud Computing Based Storage and Retrieval |
| Country | : | India |
| Authors | : | Yaga Reddemma, Lingala Thirupath2, Sreekanth Gunti |
 |
: | 10.9790/0661-0610105  |
Abstract:Enterprises protect their internal storage and retrieval process using firewalls and also protect it
from insider attacks by formulating secure data access procedures. If the enterprises are willing to store data in
cloud, cloud computing service providers have to take care of data privacy and security. A common way to
achieve security is encryption/decryption mechanism employed by cloud service providers. However,
performing both tasks such as storage and encryption/decryption mechanism by cloud server causes security
problems as the administrators know the sensitive information and may involve in illegal practices. To
overcome this problem, this paper presents a mechanism where the storage is done by one provider while
encryption/decryption mechanisms are provided by another service provider. In the proposed system the party
that uses cloud storage services must encrypt data before sending it to cloud while the service provider who is
responsible for encryption/decryption must delete data once encryption/decryption process is completed. To
illustrate the proposed mechanism, this paper uses a CRM service example that demonstrates how the parties
involved in secure storage and retrieval when data is saved to cloud. It also provides insights into multi-party
SLAs for the proposed system.
Keywords: SLAs, cloud computing, encryption and decryption, secure storage and retrieval
Keywords: SLAs, cloud computing, encryption and decryption, secure storage and retrieval
[1] A. Weiss, "Computing in the clouds", netWorker, vol. 11, no. 4, pp. 16 -25, December 2007.
[2] C. S. Yeo, S. Venugopal, X. Chu, and R. Buyya, "Autonomic metered pricing for a utility computing service", Future Generation
Computer Systems, vol. 26, issue 8, pp. 1368-1380, October 2010.
[3] M. Baker, R. Buyya, and D. Laforenza, "Grids and grid technologies for wide-area distributed computing," International Journal
of Software: Practice and Experience, vol.32, pp. 1437-1466, 2002.
[4] B. R. Kandukuri, V, R. Paturi and A. Rakshit, "Cloud security issues," in Proceedings of the 2009 IEEE International Conference
on Services Computing, pp. 517-520, September 2009.
[5] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud computing and emerging IT platforms: vision, hype, and
reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, issue 6, pp. 599 -616, June 2008.
[6] R. Sterritt, "Autonomic computing," Innovations in Systems and Software Engineering, vol. 1, no. 1, Springer, pp. 79-88. 2005.
[7] L. M. Vaquero,L. Rodero-Merino,J. Caceres, and M. Lindner, "A break in the clouds: towards a cloud definition," ACM
SIGCOMM Computer Communication Review, vol. 39, no. 1, pp. 50-55, January 2009.
[8] N. Hawthorn, "Finding security in the cloud," Computer Fraud & Security, vol. 2009, issue 10, pp. 19-20, October 2009.
[9] A. Parakh and S. Kak, "Online data storage using implicit security", Information Sciences, vol. 179, issue 19, pp. 3323-3333,
September 2009.
[10] R. Rivest, A. Shamir, and L. Adleman, "A method for obtaining digital signatures and public key cryptosystems",
Communications of the ACM, vol. 21, no. 2, pp.120-126, 1978.
[2] C. S. Yeo, S. Venugopal, X. Chu, and R. Buyya, "Autonomic metered pricing for a utility computing service", Future Generation
Computer Systems, vol. 26, issue 8, pp. 1368-1380, October 2010.
[3] M. Baker, R. Buyya, and D. Laforenza, "Grids and grid technologies for wide-area distributed computing," International Journal
of Software: Practice and Experience, vol.32, pp. 1437-1466, 2002.
[4] B. R. Kandukuri, V, R. Paturi and A. Rakshit, "Cloud security issues," in Proceedings of the 2009 IEEE International Conference
on Services Computing, pp. 517-520, September 2009.
[5] R. Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, "Cloud computing and emerging IT platforms: vision, hype, and
reality for delivering computing as the 5th utility," Future Generation Computer Systems, vol. 25, issue 6, pp. 599 -616, June 2008.
[6] R. Sterritt, "Autonomic computing," Innovations in Systems and Software Engineering, vol. 1, no. 1, Springer, pp. 79-88. 2005.
[7] L. M. Vaquero,L. Rodero-Merino,J. Caceres, and M. Lindner, "A break in the clouds: towards a cloud definition," ACM
SIGCOMM Computer Communication Review, vol. 39, no. 1, pp. 50-55, January 2009.
[8] N. Hawthorn, "Finding security in the cloud," Computer Fraud & Security, vol. 2009, issue 10, pp. 19-20, October 2009.
[9] A. Parakh and S. Kak, "Online data storage using implicit security", Information Sciences, vol. 179, issue 19, pp. 3323-3333,
September 2009.
[10] R. Rivest, A. Shamir, and L. Adleman, "A method for obtaining digital signatures and public key cryptosystems",
Communications of the ACM, vol. 21, no. 2, pp.120-126, 1978.
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| Paper Type | : | Research Paper |
| Title | : | Enabling Public Audit Ability and Data Dynamics for Storage Security in Cloud Computing Data Integrity Proofs In Cloud Storage |
| Country | : | India |
| Authors | : | K.C Ravi Kumar, E.Krishnaveni Reddy , Priyanka Rakam |
|
: | 10.9790/0661-0610611 |
Abstract:IT has moved into next generation with cloud computing being realized. The way application
software and databases are stored has been changed. Now they are stored in cloud data centers in which
security is a concern from client point of view. The new phenomenon which is used to store and manage data
without capital investment has brought many security challenges which are not thoroughly understood. This
paper focuses on the security and integrity of data stored in cloud data servers. The data integrity verification is
done by using a third party auditor who is authorized to check integrity of data periodically on behalf of client.
The client of the data gets notifications from third party auditor when data integrity is lost. Not only verification
of data integrity, the proposed system also supports data dynamics. The work that has been done in this line
lacks data dynamics and true public auditability. The auditing task monitors data modifications, insertions and
deletions. The proposed system is capable of supporting both public auditability and data dynamics. The review
of literature has revealed the problems with existing systems and that is the motivation behind taking up this
work. Merkle Hash Tree is used to improve block level authentication. In order to handle auditing tasks
simultaneously, bilinear aggregate signature is used. This enables TPA to perform auditing concurrently for
multiple clients. The experiments reveal that the proposed system is very efficient and also secure.
Keywords - Cloud computing, public audit ability, cloud storage, cloud service provider
Keywords - Cloud computing, public audit ability, cloud storage, cloud service provider
[1] G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z.Peterson, and D. Song, "Provable Data Possession at
UntrustedStores," Proc. 14th ACM Conf. Computer and Comm. Security (CCS'07), pp. 598-609, 2007.
[2] A. Juels and B.S. Kaliski Jr., "Pors: Proofs of Retrievability forLarge Files," Proc. 14th ACM Conf. Computer and Comm.
Security(CCS '07), pp. 584-597, 2007.
[3] H. Shacham and B. Waters, "Compact Proofs of Retrievability,"Proc. 14th Int'l Conf. Theory and Application of Cryptology
andInformation Security: Advances in Cryptology (ASIACRYPT '08),pp. 90-107, 2008.
[4] K.D. Bowers, A. Juels, and A. Oprea, "Proofs of Retrievability:Theory and Implementation," Report 2008/175, Cryptology
ePrintArchive, 2008.
[5] M. Naor and G.N. Rothblum, "The Complexity of Online MemoryChecking," Proc. 46th Ann. IEEE Symp. Foundations of
ComputerScience (FOCS '05), pp. 573-584, 2005.
[6] E.-C. Chang and J. Xu, "Remote Integrity Check with DishonestStorage Server," Proc. 13th European Symp. Research in
ComputerSecurity (ESORICS '08), pp. 223-237, 2008.
[7] M.A. Shah, R. Swaminathan, and M. Baker, "Privacy-PreservingAudit and Extraction of Digital Contents," Report
2008/186,Cryptology ePrint Archive, 2008.
[8] A. Oprea, M.K. Reiter, and K. Yang, "Space-Efficient Block Storage Integrity," Proc. 12th Ann. Network and Distributed System
Security Symp. (NDSS '05), 2005.
[9] T. Schwarz and E.L. Miller, "Store, Forget, and Check: UsingAlgebraic Signatures to Check Remotely Administered
Storage,"Proc. 26th IEEE Int'l Conf. Distributed Computing Systems (ICDCS'06), p. 12, 2006.
[10] Q. Wang, K. Ren, W. Lou, and Y. Zhang, "Dependable and Secure Sensor Data Storage with Dynamic Integrity Assurance,"
Proc.IEEE INFOCOM, pp. 954-962, Apr. 2009.
UntrustedStores," Proc. 14th ACM Conf. Computer and Comm. Security (CCS'07), pp. 598-609, 2007.
[2] A. Juels and B.S. Kaliski Jr., "Pors: Proofs of Retrievability forLarge Files," Proc. 14th ACM Conf. Computer and Comm.
Security(CCS '07), pp. 584-597, 2007.
[3] H. Shacham and B. Waters, "Compact Proofs of Retrievability,"Proc. 14th Int'l Conf. Theory and Application of Cryptology
andInformation Security: Advances in Cryptology (ASIACRYPT '08),pp. 90-107, 2008.
[4] K.D. Bowers, A. Juels, and A. Oprea, "Proofs of Retrievability:Theory and Implementation," Report 2008/175, Cryptology
ePrintArchive, 2008.
[5] M. Naor and G.N. Rothblum, "The Complexity of Online MemoryChecking," Proc. 46th Ann. IEEE Symp. Foundations of
ComputerScience (FOCS '05), pp. 573-584, 2005.
[6] E.-C. Chang and J. Xu, "Remote Integrity Check with DishonestStorage Server," Proc. 13th European Symp. Research in
ComputerSecurity (ESORICS '08), pp. 223-237, 2008.
[7] M.A. Shah, R. Swaminathan, and M. Baker, "Privacy-PreservingAudit and Extraction of Digital Contents," Report
2008/186,Cryptology ePrint Archive, 2008.
[8] A. Oprea, M.K. Reiter, and K. Yang, "Space-Efficient Block Storage Integrity," Proc. 12th Ann. Network and Distributed System
Security Symp. (NDSS '05), 2005.
[9] T. Schwarz and E.L. Miller, "Store, Forget, and Check: UsingAlgebraic Signatures to Check Remotely Administered
Storage,"Proc. 26th IEEE Int'l Conf. Distributed Computing Systems (ICDCS'06), p. 12, 2006.
[10] Q. Wang, K. Ren, W. Lou, and Y. Zhang, "Dependable and Secure Sensor Data Storage with Dynamic Integrity Assurance,"
Proc.IEEE INFOCOM, pp. 954-962, Apr. 2009.
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| Paper Type | : | Research Paper |
| Title | : | Efficiency of Prediction Algorithms for Mining Biological Databases |
| Country | : | India |
| Authors | : | A. Lekha, Dr. C V Srikrishna , Dr. Viji Vinod |
|
: | 10.9790/0661-0611221 |
Abstract: The paper deals with the analysis of the efficiency of prediction algorithms for mining biological
databases and also suggests possible ways of improving the efficiency for a given dataset. The study reveals that
the efficiency of a mining algorithm is a function of many variables of the dataset. The study proposes a
predictive model through a case study
Keywords: Biological databases, Breast Cancer, Efficiency Predictive algorithms, Statistical Analysis
Keywords: Biological databases, Breast Cancer, Efficiency Predictive algorithms, Statistical Analysis
[1] Avery, John. Information Theory and Evolution. USA: World Scientific Publishing Co. 2003.
[2] David Weatherall, Brian Greenwood, Heng Leng Chee and Prawase Wasi, Science and Technology for Disease Control: Past,
Present, and Future, Disease Control Priorities in Developing Countries
[3] Leser, Ulf, et al. Data integration in the Life Science. USA: Springer. 2006.
[4] Paradis, Emmanuel. Analysis of Phylogenetics and Evolution with R.USA: Springer. 2011.
[5] Selzer, Paul. Applied Bioinformatics: An Introduction. USA: Springer. 2008.
[6] Rob, Peter, et al. Database systems: design, implementation and management. USA: Cengage Learning. 2009.
[7] SDART. Software Design and Research Technology Ltd., n.d. Web. 29 April 2012.
[8] Ramaswamy, Sridhar, et al. Efficient Algorithms for Mining Outliers from Large Data Sets. The Pennsylvania State University,
2010. Web. 29 April 2012.
[9] Cornell University Library. "A Comparison Between Data Mining Prediction Algorithms for Fault Detection (Case study:
Ahanpishegan co.)" 2012. Web. 29 April 2012.
[10] Pandey, Hari. Design Analysis and Algorithm. New Delhi: University Science Press. 2008.
[2] David Weatherall, Brian Greenwood, Heng Leng Chee and Prawase Wasi, Science and Technology for Disease Control: Past,
Present, and Future, Disease Control Priorities in Developing Countries
[3] Leser, Ulf, et al. Data integration in the Life Science. USA: Springer. 2006.
[4] Paradis, Emmanuel. Analysis of Phylogenetics and Evolution with R.USA: Springer. 2011.
[5] Selzer, Paul. Applied Bioinformatics: An Introduction. USA: Springer. 2008.
[6] Rob, Peter, et al. Database systems: design, implementation and management. USA: Cengage Learning. 2009.
[7] SDART. Software Design and Research Technology Ltd., n.d. Web. 29 April 2012.
[8] Ramaswamy, Sridhar, et al. Efficient Algorithms for Mining Outliers from Large Data Sets. The Pennsylvania State University,
2010. Web. 29 April 2012.
[9] Cornell University Library. "A Comparison Between Data Mining Prediction Algorithms for Fault Detection (Case study:
Ahanpishegan co.)" 2012. Web. 29 April 2012.
[10] Pandey, Hari. Design Analysis and Algorithm. New Delhi: University Science Press. 2008.
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| Paper Type | : | Research Paper |
| Title | : | Business Intelligence: A Rapidly Growing Option through Web Mining |
| Country | : | India |
| Authors | : | Priyanka Rahi, Mr.Jawahar Thakur |
|
: | 10.9790/0661-0612229 |
Abstract:The World Wide Web is a popular and interactive medium to distribute information in this scenario.
The web is huge, diverse, ever changing, widely disseminated global information service center. We are familiar
with terms like e-commerce, e-governance, e-market, e-finance, e-learning, e-banking etc. for an organization it
is new challenge to maintain direct contact with customers because of the rapid growth in e-commerce, epublishing
and electronic service delivery. To deal with this there is need of intelligent marketing strategies and
CRM (customer relationship management) i.e. the effective way of integrating enterprise applications in real
time.
Web mining is the vast field that helps to understand various concepts of different fields. Web usage
mining techniques are attempted to reason about different materialized issues of Business Intelligence which
include marketing expertise as domain knowledge and are specifically designed for electronic commerce
purposes. To this end, the chapter provides an introduction to the field of Web mining and examines existing as
well as potential Web mining applications applicable for different business function, like marketing, human
resources, and fiscal administration. Suggestions for improving information technology infrastructure are
made, which can help businesses interested in Web mining hit the ground running.
Key words: Business Intelligence, CRM, e-Commerce, e-publishing, Web mining, Web usage mining
Key words: Business Intelligence, CRM, e-Commerce, e-publishing, Web mining, Web usage mining
[1] J. Srivastava, R. Cooley, M. Deshpande, and P.N. Tan, Web Usage Mining: Discovery and Applications of Usage Patterns from
Web Data. SIGKDD Explorations, vol. 1, no. 2, pp. 12-23-2000.
[2] Ajith Abraham,Business Intelligence from Web Usage Mining, journal of Information & Knowledge Management, Vol 2, No.4
(2003)
[3] Getoor, L., Link Mining: A New Data Mining Challenge. SIGKDD Explorations, 4(2), 2003.
[4] Mobasher, B., Cooley, R., and Srivastava, J., Automatic Personalization Based on Web Usage Mining. Communications of ACM,
August 2000.
[5] Kosala.R. & Blocked.H. (2000), Web mining research : A survey, ACM SIGKDD Explorations Newsletter, Volume 2 Issue 1,
June, 2000 Pages 1-15
[6] Kosala.R. & Blocked.H. (2000),Critical and future trende in Web Mining, SDH'98,Vancover, Canada, pp. 32 -37
[7] Mobasher,B., Web Usage Mining and Personalization. Practical Handbook of Internet Computing, ed. M.P. Singh, (CRC Press,
2005).
[8] Sonal Tiwari , A Web Usage Mining Framework for Business Intelligence International Journal of Electronics Communication
and Computer Technology (IJECCT) Volume 1 Issue 1 | September 2011
[9] Tan and Kumar (2000), Discovery of Web Robot Sessions Based on their Navigational Patterns, Data Mining and Knowledge
Discovery, 6, 9–35, 2002
[10] Agrwal and Srikant , Mining sequential patterns, Data Engineering 1995,proceedings of 11th international conference on
march.1995.http://ieeexplore.ieee.org
Web Data. SIGKDD Explorations, vol. 1, no. 2, pp. 12-23-2000.
[2] Ajith Abraham,Business Intelligence from Web Usage Mining, journal of Information & Knowledge Management, Vol 2, No.4
(2003)
[3] Getoor, L., Link Mining: A New Data Mining Challenge. SIGKDD Explorations, 4(2), 2003.
[4] Mobasher, B., Cooley, R., and Srivastava, J., Automatic Personalization Based on Web Usage Mining. Communications of ACM,
August 2000.
[5] Kosala.R. & Blocked.H. (2000), Web mining research : A survey, ACM SIGKDD Explorations Newsletter, Volume 2 Issue 1,
June, 2000 Pages 1-15
[6] Kosala.R. & Blocked.H. (2000),Critical and future trende in Web Mining, SDH'98,Vancover, Canada, pp. 32 -37
[7] Mobasher,B., Web Usage Mining and Personalization. Practical Handbook of Internet Computing, ed. M.P. Singh, (CRC Press,
2005).
[8] Sonal Tiwari , A Web Usage Mining Framework for Business Intelligence International Journal of Electronics Communication
and Computer Technology (IJECCT) Volume 1 Issue 1 | September 2011
[9] Tan and Kumar (2000), Discovery of Web Robot Sessions Based on their Navigational Patterns, Data Mining and Knowledge
Discovery, 6, 9–35, 2002
[10] Agrwal and Srikant , Mining sequential patterns, Data Engineering 1995,proceedings of 11th international conference on
march.1995.http://ieeexplore.ieee.org
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| Paper Type | : | Research Paper |
| Title | : | Optimal Repeated Frame Compensation Using Efficient Video Coding |
| Country | : | India |
| Authors | : | Sruthy George, Manimurugan .S |
|
: | 10.9790/0661-0613035 |
Abstract:A large amount of storage and bandwidth is needed when data transmission is done using
uncompressed digital video. This results in a large amount of data that demands the need of video compression.
Many of the existing video compression standards share common or similar coding tools, and there is currently
no explicit way to exploit such commonalities at the level of implementations. Moreover, the possibility of taking
advantage of the continuous improvements in coding is only possible by replacing the existing approach with a
new one. This paper proposes an efficient video content representation by exploiting the temporal redundancies
using optimal extraction of repeated frames and scenes. A new standard for video coding called Optimal
Repeated Frame Compensation (ORFC) is used, in which the repeated frames are combined together to form a
single frame in order to reduce the total number of frames..
Key words:Compression, fidelity, key frame extraction, pixel prediction, video coding
Key words:Compression, fidelity, key frame extraction, pixel prediction, video coding
[1]. H. Kim, J. Lee, H. Liu, and D. Lee, "Video Linkage: Group based copied video detection," in Proc of CIVR'08, Niagara Falls,
Canada, July 7–9, 2008, pp. 397- 406.
[2]. X. Zeng, W. Hu, W. Li, X. Zhang, and B. Xu, "Keyframe extraction using dominant-set clustering," in Proc Int. Conf. Multimedia
and Expo (ICME'08), Hannover, Germany, June 2008, pp. 23-26.
[3]. Yannis S. Avrithis, Anastasios D. Doulamis, Nikolaos D. Doulamis, and Stefanos D. Kollias, "A Stochastic Framework of Optimal
Key Frame Extraction from MPEG Video Databases," in Proc. Int. Symp. Computer Vision and Image Understanding., Vol. 75(1),
pp. 3–24, July 1999.
[4]. M. Flierl, A. Mavlankar, and B. Girod, "Motion and disparity compensated coding for multi-view videos ," IEEE Transactions on
Circuits and Systems for Video Technol., vol. 17(11), pp. 1454-1484, Nov 2007.
[5]. Markus Flierl and Bernd Girod, "Multiview Video Compression-Exploiting Inter-Image Similarities," IEEE Signal Processing
Magazine, Special Issue on Multiview Imaging and 3DTV, Vol. 24(6), pp. 66-76, Nov 2007.
[6]. Z. Ming-Feng, H. Jia, and Z. Li-Ming, "Lossless video compression using combination of temporal and the spatial prediction," in
Proc. IEEE. Int. Conf. Neural Netwoks Signal Processing, pp. 1193–1196, Dec 2003.
[7]. K. H. Yang and A. F. Faryar, "A context-based predictive coder for lossless and near-lossless compression of video," in Proc. Int.
Conf. Image Processing., vol. 1, pp. 144–147, Sep. 2000.
[8]. Sung-Eun Kim, Jong-Ki Han, and Jae-Gon Kim, "An Efficient Scheme for Motion Estimation Using Multi-reference Frames in
H.264/AVC," IEEE Transactions on Multimedia.,Vol. 8(3), pp. 457-466., June 2006.
[9]. Ruan Xiaodong, and Song Xiangqun, "Research On The Algorithm of the Binary Image Cross- Correlation For Unsteady Flow
Field Measurement," ACTA MECHANICA SINICA (English Series), Vol. 15(1), Feb 1999.
[10]. K.Dinesh and T. Arumuga Maria Devi, " Motion Detection and Object Tracking in Video frame sequence on IWT of Adaptive
Pixel Based Prediction Coding," in Proc. IRACST-Engineering Science Technology: An International Journal (ESTIJ), ISSN:
2250-3498,Vol.2, No. 4, August 2012.
Canada, July 7–9, 2008, pp. 397- 406.
[2]. X. Zeng, W. Hu, W. Li, X. Zhang, and B. Xu, "Keyframe extraction using dominant-set clustering," in Proc Int. Conf. Multimedia
and Expo (ICME'08), Hannover, Germany, June 2008, pp. 23-26.
[3]. Yannis S. Avrithis, Anastasios D. Doulamis, Nikolaos D. Doulamis, and Stefanos D. Kollias, "A Stochastic Framework of Optimal
Key Frame Extraction from MPEG Video Databases," in Proc. Int. Symp. Computer Vision and Image Understanding., Vol. 75(1),
pp. 3–24, July 1999.
[4]. M. Flierl, A. Mavlankar, and B. Girod, "Motion and disparity compensated coding for multi-view videos ," IEEE Transactions on
Circuits and Systems for Video Technol., vol. 17(11), pp. 1454-1484, Nov 2007.
[5]. Markus Flierl and Bernd Girod, "Multiview Video Compression-Exploiting Inter-Image Similarities," IEEE Signal Processing
Magazine, Special Issue on Multiview Imaging and 3DTV, Vol. 24(6), pp. 66-76, Nov 2007.
[6]. Z. Ming-Feng, H. Jia, and Z. Li-Ming, "Lossless video compression using combination of temporal and the spatial prediction," in
Proc. IEEE. Int. Conf. Neural Netwoks Signal Processing, pp. 1193–1196, Dec 2003.
[7]. K. H. Yang and A. F. Faryar, "A context-based predictive coder for lossless and near-lossless compression of video," in Proc. Int.
Conf. Image Processing., vol. 1, pp. 144–147, Sep. 2000.
[8]. Sung-Eun Kim, Jong-Ki Han, and Jae-Gon Kim, "An Efficient Scheme for Motion Estimation Using Multi-reference Frames in
H.264/AVC," IEEE Transactions on Multimedia.,Vol. 8(3), pp. 457-466., June 2006.
[9]. Ruan Xiaodong, and Song Xiangqun, "Research On The Algorithm of the Binary Image Cross- Correlation For Unsteady Flow
Field Measurement," ACTA MECHANICA SINICA (English Series), Vol. 15(1), Feb 1999.
[10]. K.Dinesh and T. Arumuga Maria Devi, " Motion Detection and Object Tracking in Video frame sequence on IWT of Adaptive
Pixel Based Prediction Coding," in Proc. IRACST-Engineering Science Technology: An International Journal (ESTIJ), ISSN:
2250-3498,Vol.2, No. 4, August 2012.
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| Paper Type | : | Research Paper |
| Title | : | Lsb Based Digital Image Watermarking For Gray Scale Image |
| Country | : | India |
| Authors | : | Deepshikha Chopra, Preeti Gupta, Gaur Sanjay B.C., Anil Gupta |
|
: | 10.9790/0661-0613641 |
Abstract:In recent years, internet revolution resulted in an explosive growth in multimedia applications.
The rapid advancement of internet has made it easier to send the data/image accurate and faster to the
destination. Besides this, it is easier to modify and misuse the valuable information through hacking at the same
time. Digital watermarking is one of the proposed solutions for copyright protection of multimedia data. A
watermark is a form, image or text that is impressed onto paper, which provides evidence of its authenticity.
In this paper an invisible watermarking technique (least significant bit) and a visible watermarking technique is
implemented.
Key words:Watermarking, Least Significant Bit (LSB), JPEG (Joint Photographic Experts Group), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Key words:Watermarking, Least Significant Bit (LSB), JPEG (Joint Photographic Experts Group), Mean Square Error (MSE) and Peak Signal to Noise Ratio (PSNR).
Journal Papers:
[1] Preeti Gupta, "Cryptography based digital image watermarking algorithm to increase security of watermark data", International
Journal of Scientific & Engineering Research, Volume 3, Issue 9 (September 2012) ISSN 2229-5518
[2] Manpreet Kaur, Sonika Jindal, Sunny Behal, "A Study of Digital Image Watermarking", IJREAS ,Volume 2, Issue 2 (Febru ry
2012) pp-126-136
[3] B Surekha, Dr GN Swamy, "A Spatial Domain Public Image Watermarking", International Journal of Security and Its
Applications Vol. 5 No. 1, January, 2011
[4] Robert, L., and T. Shanmugapriya, "A Study on Digital Watermarking Techniques ", International Journal of Recent Trends in
Engineering, vol. 1, no. 2, pp. 223-225, 2009.
[5] H.Arafat Ali, "Qualitative Spatial Image Data Hiding for Secure Data Transmission", GVIP Journal,Volume 7,Issue 2 , pages 35-
37, 2, August 2007
[6] Cox, Miller and Bloom, "Digital watermarking", 1st edition 2001, San Fransisco: Morgan Kaufmann Publisher
[7] Brigitte Jellinek, "Invisible Watermarking of Digital Images for Copyright Protection" University Salzburg, pp. 9 – 17, Jan 2000.
[8] Lu, C-S., Liao, H-Y., M., Huang, S-K., Sze, C-J., "Cocktail Watermarking on Images", 3rd International Workshop on Information
Hiding, Dresden, Germany, Sep 29-Oct. 1, 1999
[9] Dr. Martin Kutter and Dr. Frederic Jordan, "Digital Watermarking Technology", AlpVision, Switzerland, pp 1 – 4M Ozaki, Y.
Adachi, Y. Iwahori, and N. Ishii, Application of fuzzy theory to writer recognition of Chinese characters, International Journal of
Modelling and Simulation, 18(2), 1998, 112-116.
[10] J.J.K.O. Ruanaidh, W.J.Dowling, F.M. Boland, "Watermarking Digital Images for Copyright Protection", IEEE ProcVis. Image
Signal Process. Vol. 143, No. 4, pp 250 - 254. August 1996.
[1] Preeti Gupta, "Cryptography based digital image watermarking algorithm to increase security of watermark data", International
Journal of Scientific & Engineering Research, Volume 3, Issue 9 (September 2012) ISSN 2229-5518
[2] Manpreet Kaur, Sonika Jindal, Sunny Behal, "A Study of Digital Image Watermarking", IJREAS ,Volume 2, Issue 2 (Febru ry
2012) pp-126-136
[3] B Surekha, Dr GN Swamy, "A Spatial Domain Public Image Watermarking", International Journal of Security and Its
Applications Vol. 5 No. 1, January, 2011
[4] Robert, L., and T. Shanmugapriya, "A Study on Digital Watermarking Techniques ", International Journal of Recent Trends in
Engineering, vol. 1, no. 2, pp. 223-225, 2009.
[5] H.Arafat Ali, "Qualitative Spatial Image Data Hiding for Secure Data Transmission", GVIP Journal,Volume 7,Issue 2 , pages 35-
37, 2, August 2007
[6] Cox, Miller and Bloom, "Digital watermarking", 1st edition 2001, San Fransisco: Morgan Kaufmann Publisher
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- Citation
- Abstract
- Reference
- Full PDF
| Paper Type | : | Research Paper |
| Title | : | An Efficient Biological Sequence Compression Technique Using LUT and Repeat in the Sequence |
| Country | : | India |
| Authors | : | Subhankar Roy, Sunirmal Khatua, Sudipta Roy, Prof. Samir K. Bandyopadhyay |
|
: | 10.9790/0661-0614250 |
Abstract:Data compression plays an important role to deal with high volumes of DNA sequences in the
field of Bioinformatics. Again data compression techniques directly affect the alignment of DNA sequences.
So the time needed to decompress a compressed sequence has to be given equal priorities as with
compression ratio. This article contains first introduction then a brief review of different biological
sequence compression after that my proposed work then our two improved Biological sequence
compression algorithms after that result followed by conclusion and discussion, future scope and finally
references. These algorithms gain a very good compression factor with higher saving percentage and less
time for compression and decompression than the previous Biological Sequence compression algorithms.
Key words:Hash map table, Tandem repeats, compression factor, compression time, saving percentage, compression, decompression process.
Key words:Hash map table, Tandem repeats, compression factor, compression time, saving percentage, compression, decompression process.
[1] Genbank size from: ftp://ftp.ncbi.nih.gov/genbank/gbrel.txt
[2] Hyoung Do Kim and Ju-Han Kim , "DNA Data Compression Based on the Whole Genome Sequence", Journal of Convergence
Information Technology Vol. 4, No. 3, September 2009.
[3] Pothuraju Rajarajeswari, Allam Apparao, "DNABIT Compress – Genome compression algorithm" Biomedical Informatics,
volume 5, Issue 8, pp. 350-360, 2011.
[4 ] P.Raja Rajeswari and Dr. Allam AppaRao, "GENBIT COMPRESS TOOL (GBC): A java-based tool to compress DNA sequences
and compute compression ratio (bits/base) of genomes", IJCSIT, Vol. 2, No. 3, pp. 181-191, June 2010.
[5] P.Raja Rajeswari, Dr. Allam AppaRao and Dr. R. Kiran Kumar, "HUFFBIT COMPRESS – Algorithm to compress DNA
sequences using extended binary trees", JTAIT, pp. 101-106, 2010.
[6] Heba Afify, Muhammad Islam and Manal Abdel Wahed, "DNA lossless differential compression algorithm based on similarity of
genomic sequence database", IJCSIT, Vol. 3, No 4, August 2011.
[7] R.K.Bharti, "A Biological sequence compression Based on Approximate repeat Using Variable length LUT", International
Journal of Advances in Science and Technology, Vol. 3, No.3,PP:71-75, 2011.
[8] R.K. Bharti, Prof. R.K. Singh, "A Biological Sequence Compression based on Look up Table (LUT) using Complementary
Palindrome of Fixed Size", ICJA (0975–8887), Volume 35– No.11, December 2011.
[9] R.K.Bharti, "Biological sequence Compression Based on Cross chromosomal properties using variable length LUT", CSC Journal,
Vol. 4 Issue 6, PP: 217-223. , 2011.
[10] Sequences are taken from : http://www.ncbi.nlm.nih.gov/.
[2] Hyoung Do Kim and Ju-Han Kim , "DNA Data Compression Based on the Whole Genome Sequence", Journal of Convergence
Information Technology Vol. 4, No. 3, September 2009.
[3] Pothuraju Rajarajeswari, Allam Apparao, "DNABIT Compress – Genome compression algorithm" Biomedical Informatics,
volume 5, Issue 8, pp. 350-360, 2011.
[4 ] P.Raja Rajeswari and Dr. Allam AppaRao, "GENBIT COMPRESS TOOL (GBC): A java-based tool to compress DNA sequences
and compute compression ratio (bits/base) of genomes", IJCSIT, Vol. 2, No. 3, pp. 181-191, June 2010.
[5] P.Raja Rajeswari, Dr. Allam AppaRao and Dr. R. Kiran Kumar, "HUFFBIT COMPRESS – Algorithm to compress DNA
sequences using extended binary trees", JTAIT, pp. 101-106, 2010.
[6] Heba Afify, Muhammad Islam and Manal Abdel Wahed, "DNA lossless differential compression algorithm based on similarity of
genomic sequence database", IJCSIT, Vol. 3, No 4, August 2011.
[7] R.K.Bharti, "A Biological sequence compression Based on Approximate repeat Using Variable length LUT", International
Journal of Advances in Science and Technology, Vol. 3, No.3,PP:71-75, 2011.
[8] R.K. Bharti, Prof. R.K. Singh, "A Biological Sequence Compression based on Look up Table (LUT) using Complementary
Palindrome of Fixed Size", ICJA (0975–8887), Volume 35– No.11, December 2011.
[9] R.K.Bharti, "Biological sequence Compression Based on Cross chromosomal properties using variable length LUT", CSC Journal,
Vol. 4 Issue 6, PP: 217-223. , 2011.
[10] Sequences are taken from : http://www.ncbi.nlm.nih.gov/.