iosr-JCE   Volume-6 ~ Issue-1

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

[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

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

[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.

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).

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.

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.

[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/.