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These days email system is one of the major sources of communication and users’ depend heavily on it. Even after the evolution of new mobile applications, social networks etc. emails are extensively used on both personal and professional platforms. Pertaining to this extensive use, inboxes these days usually become a chunk with unnecessary messages from social media, advertisements, subscriptions etc. which might not be of that much importance. Thus there’s a need of classification so that the user does not have to surf through the chunk for one particularly important mail. In this paper, we propose a solution for email classification using back propagation technique which has user defined categories where word search is made on the content of the email. The output of this solution will give the user a selected number of emails according to the category he/she chooses.

Email, Classification, User-Defined, Back Propagation, Categories

[1] Schuff, D., O. Turetke, D. Croson, F 2007, ‘Managing Email Overload: Solutions and Future Challenges’, IEEE Computer Society, vol. 40, No. 2, pp. 31-36.
[2] Kushmerick, N., Lau, T. 2005, ‘Automated Email Activity Management: An Unsupervised learning Approach’, Proceedings of 10th International Conference on Intelligent User Interfaces, ACM Press, pp. 67-74.
[3] Helfman, J., Isbell, C. 1995, ‘Ishmail: Immediate Identification of Important Information’, AT&T Labs.
[4] Boone, G. 1998, ‘Concept Features in Re: Agent, An Intelligent Email Agent’, Proceedings of 2nd International Conference on autonomous agents, ACM Press, pp.141-148.
[5] Balter, O., Sidner, C. Bifrost Inbox Organizer: Giving Users Control over the Inbox. In In Proceedings of the Second Nordic Conference on Human-Computer interaction. 2002. Aarhus, Denmark: ACM Press.
[6] Ramos, J. (2002). Using TF-IDF to Determine Word Relevance in Document Queries, Department of Computer Science, Rutgers University, Piscataway, NJ, 08855.
[7] Yukun, C., Xiaofeng, L., Yunfeng, L. (2007). An Email Filtering Approach Using Neural Network, Springer Berlin, pp. 688-694.
[8] United States. The Board of Trustees of the University of Illinois. (2003). D2K™ Data to Knowledge™ Text Mining: Email Classification.

Arichi Arzare, Suneha Chaudhari, Sayalee Desai and Sonali Jadhav, "User-Defined Classification for Email System using Back Propagation Algorithm," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.1-5, 2015.

Wireless technology has become the most exciting area during the past decades and also brings with it a whole host of complex design issues, concerning network design, signal detection, interference cancellation, and resource allocation. In the field of communication systems Orthogonal Frequency Division Multiplexing (OFDM) is being widely used for bulk data in single frequency band i.e., encoding digital data on multiple carrier frequency and for its ability to enhance the data rate and reduce the bandwidth. In this paper, the comparison of the performance of OFDM system using Quadrature Amplitude Modulation (QAM) under the influence of AWGN, Rayleigh, and Rician fading channels are analysed. Simulation of OFDM signals are carried out with different faded signals by increasing the number of OFDM frames in AWGN, Rayleigh and Rician fading channels to understand the effect of channel fading and to obtain optimum value of Bit Error Rate (BER) for different number of antennas.

AWGN, BER, ODFM, Rayleigh fading channel model, Ricin fading channel model

[1]. Anurag Pandey and Sandeep Sharma, “BER performance of OFDM System in AWGN and Rayleigh Fading Channel” International Journal of Engineering Trends and Technology, ISSN: 2231-5381, Volume 13, Number 3, July 2014, pp. 126-128.
[2]. A. Sudhir Babu, et al, “ Evaluation of BER for AWGN, Rayleigh and Rician Fading Channels under Various Modulation Schemes”, International Journal of Computer Applications, ISSN: 0975-8887, Volume 26, Number 9, July 2011, pp. 23-28.
[3]. Mohammed Slim Alouini and Andrea J. Goldsmith “Capacity of Rayleigh fading channels under different Adaptive Transmission and Diversity combing Techniques”, IEEE Transaction on Vehicular Technology, ISSN:0018-9545, Volume 48, Number 4, July 1999, pp. 1165-1181.
[4]. Sai Krishna Borra, et al, “Performance Evaluation of OFDM System with Rayleigh, Rician and AWGN Channels, ISSN: 2250-2459, Volume 3, Issue 3, March 2013, pp. 678-682.
[5]. S. Weinstein and P.Ebert , “Data transmission by frequency-division multiplexing using the discrete Fourier transform”, Volume 19, Number 5, October 1971,pp. 628-634.
[6]. A. Sadat and W.B. Mikhael, “Fast Fourier Transform for High speed OFDM Wireless Multimedia System,” in Proc. of the 44th IEEE 2001 Midwest Symp. On Circuits and Systems. MWSCAS 2001, Dayton., Ohio, 2001, pp. 149-150.
[7]. Yahong Rosa Zheng and Chengshan Xiao, “Simulation models with correct statistical properties for Rayleigh fading channels”, IEEE Transactions on communications, Volume 51, Number 6, June 2003, pp. 920-928.
[8]. Fumiyaki Adachi, “error Rate Analysis of Differentially Encoded and detected 16-APSK under Rician fading”, IEEE Transaction on vehicular Technology, ISSN: 0018-9545,Volume 45, Number 1, February 1996, pp. 1-11.
[9]. "Nextel Flash-OFDM: The Best Network You May Never Use". PC Magazine. March 2, 2005. Retrieved July 23, 2011
[10]. Raza Akbar, Syed Aqeel Raza, Usman Shafique, “PERFORMANCE EVALUATION OF WIMAX”, Blekinge Institute of Technology, March 2009.
[11]. Dennis Roddy, “Satellite Communcations,” Third edition, McGraw-Hill Telecom Engineering.
[12]. Theodore S. Rappaport, “Wireless Communications Principles and Practice,” Prentice-Hall of India Private Limited, 2004.
[13]. J. G. Proakis, Digital Communications, McGraw-Hill Inc., New York, NY, 1995 (Third Edition).
[14]. Jingxin Chen, “CARRIER RECOVERY IN BURST-MODE 16- QAM”, June 2004.

B. Benarji, Prof G. Sasibhusana Rao and S.Pallam Setty, "BER Performance of OFDM System with various OFDM frames in AWGN, Rayleigh and Rician Fading Channel," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.6-11, 2015.

Most of the existing search engines retrieve web pages by means of finding exact keywords. Traditional keyword-based search engines suffer several problems. First, synonyms and terms similar to keywords are not taken into consideration to search web pages. Users may need to input several similar keywords individually to complete a search [1]. Second, traditional search engines treat all the keywords as the same importance and cannot differentiate the importance of one keyword from that of another. Third, traditional search engines lack an applicable classification mechanism to reduce the search space and improve the search results. In this system, we develop a Semantic Search Engine. First, a fuzzy ontology is constructed by using fuzzy logic to capture the similarities of terms in the ontology, which offering appropriate semantic distances between terms to accomplish the semantic search of keywords. Second, users can check or uncheck the pages results based on their needs to show or hide it next time they search it. The totally satisfactory degree of keyword scam be aggregated based on their degrees of importance and degrees of satisfaction [2] [3]. Third, the domain classification of web pages offers users to select the appropriate domain for searching web pages, which excludes web pages in the inappropriate domains to reduce the search space and to improve the search results.

Information retrieval, Clustering, Semantic Web, Fuzzy ontology

[1] Lien-Fu Lai, Chao-Chin Wu, Pei-Ying Lin, “Developing a Fuzzy Search Engine Based on Fuzzy Ontology and Semantic Search”. Dept. of Computer Science and Information Engineering National Changhua University of Education Changhua, R.O.C.
[2] en.wikipedia.org/wiki/Web_Ontology_Language
[3] en.wikipedia.org/wiki/Semantic_search
[4]gaia.isti.cnr.it/straccia./software/FuzzyOWL/index.html
[5] J. C. Bezdek, Pattern Recognition with Fuzzy Objective Function Algorithms. New York: Plenum, 1981.
[6] P.T. Chang, K.C. Hung, K.P. Lin, and C.H. Chang, a Comparison of Discrete Algorithms for Fuzzy Weighted Average, IEEE Transactions on Fuzzy Systems, pp.:663-675, Oct. 2006.
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[9] L.F. Lai, C.C. Wu, M.Y. Shih, L.T. Huang, and W. Chiou. Parallel Processing for Fuzzy Queries in Human Resources Websites. Journal of Internet Technology, 7(11):943-953, Dec. 2010.
[10] Y.C. Lin, L.F. Lai, C.C. Wu, and L.T. Huang. A Self-Adaptation Approach to Fuzzy-Go Search Engine. The 2010 InternationalComputer Symposium (ICS 2010), pp. 1020-1025, Dec. 2010.

Daksh Agrawal, Hirali Sanghani, Sonali Jadhav and Supriya Shinde, "Ontology based Domain Specific Web Search Engine," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.12-15, 2015.

The trip planning is very difficult task for peoples, for the places which are not known. So creating an efficient and economic trip plan is the most essential. Although some travel agency can provide some predefined planning of days, which is not suitable for a specific or individual customer. A route search is an enhancement of an ordinary geographic search. Instead of merely returning a set of entities, the result is a route that goes via entities that are relevant to the search [2].For Constructing Travel Itineraries from Tagged Geo Temporal Breadcrumbs develops an end-to-end approach for constructing intra-city travel itineraries automatically by tapping a latent source reacting geo-temporal breadcrumbs left by millions of tourists[3]. A nature-pushed figuring ,imitating the without any preparation creation system for music players, has been beginning late made and named Harmony Search (HS)[4].The extraction of Event and Place Semantics from Flickr Tags that contains, Tags usually manifest in the form of a freely-chosen, short list of keyword associated by a user with a resource such as a photo, web page, or blog entry[5].

Harmony Search, Generlized Orienting Problem, Tag Semantics

[1] Automatic Itinerary Planning for Traveling Services Gang Chen, Sai Wu, Jingbo Zhou, and Anthony K.H. Tung.
[2] R. Levin, Y. Kanza, E. Safra, and Y. Sagiv, “Interactive Route Search in the Presence of Order Constraints,” Proc. VLDB.
[3]C.-H. Tai, D.-N. Yang, L.-T. Lin, and M.-S. Chen, “Recommending Personalized Scenic Itinerary with Geo-Tagged Photos,” Proc.IEEE Int’l Conf. Multimedia and Expo (ICME), pp. 1209-1212, 2008.
[4] Zong Woo Geem, Chung-Li Tseng, and Yongjin Park: Harmony Search for Generalized Orienteering Problem: Best Touring in China.
[5] T. Rattenbury, N. Good, and M. Naaman, “Toward Automatic Extraction of Event and Place Semantics from Flickr Tags,” Proc.30th Ann. Int’l ACM SIGIR Conf. Research and Development in Information Retrieval (SIGIR ’07), pp. 103-110, 2007.
[6]. Wang, Q., Sun, C., and Golden, B. L.: Using Artificial Neural Networks to Solve Generalized Orienteering Problems. Proceedings of Artificial Neural Networks in Engineering Conference (ANNIE ’96). (1996).
[7]. Geem, Z. W., Kim, J. H., and Loganathan, G. V.: A New Heuristic Optimization Algorithm: Harmony Search. Simulation. 76(2) (2001) 60-68.
[8]. Kim, J. H., Geem, Z. W., and Kim, E. S.: Parameter Estimation of the Nonlinear Muskingum Model using Harmony Search. Journal of the American Water Resources Association. 37(5) (2001) 1131-1138.
[9]. Geem, Z. W., Kim, J. H., and Loganathan. G. V.: Harmony Search Optimization: Application to Pipe Network Design. International Journal of Modelling and Simulation. 22(2) (2002) 125-133.
[10]. Kang, S. L., and Geem, Z. W.: A New Structural Optimization Method Based on the Harmony Search Algorithm. Computers and Structures. 82(9-10) (2004) 781-798.
[11] M. Vlachos, C. Meek, Z. Vagena, and D. Gunopulos. Identifying similarities, periodicities and bursts for online search queries. In Proc. SIGMOD, p 131–142. ACM, 2004.
[12] M. Kulldorff. Spatial scan statistics: models, calculations, and applications. In Scan Statistics and Applications, p 303–322,1999.
[13] A. Witkin. Scale space filtering. In Proc. Int’l Joint Conf. Artificial Intelligence, p 1019–1022, 1983.

Bhosale Pallavi, Holkar Sarika, Takale Shailaja, Divekar Shweta and Ambole Rajaram , "Review paper on Automatic Itinerary Planning for Traveling Services," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.16-20, 2015.

Image compression is the natural technology for handling the increased spatial resolutions of today's imaging sensors and evolving broadcast television standards. Image compression plays an important role in many important and diverse applications including conferencing, remote sensing, document and medical imaging, and the control of remotely piloted vehicles in military, space, and dangerous waste management applications. In this paper focus is given on the main Lossy Compression of SAR Image data. And at the end of this stage the different Quality Evaluation mechanisms are highlighted to measure the quality of resultant image and also to measure the efficiency of the algorithm. These quality measures are useful to check the quality of decompressed image and verify the competitiveness of the algorithm. This work covers the Discrete Cosine Transform (DCT), Block Truncation Coding (BTC) and Gaussian Pyramidal (GP) based compression techniques.

Lossy Compression, DCT, BTC, GP, SAR image

[1] Fatma A. Sakaya and Dong Wei and Serkan Emek, “An Evaluation Of Sar Image Compression Techniques”, Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97), Pages 2833-2836.
[2] U. Benz, K. Strodl, and A. Moreira, “A comparison of several algorithms for SAR raw data compression”, IEEE Transactions on Geosciences and Remote Sensing, vol. 33, pp. 1266-1276, Sept. 1995.
[3] Wang Zhenhua,Xu Hongbho, Tian Jinwen, Liu jian, “Integer Haar Wavelet for Romote Sensing Image Compression”, ICSP’02 Proceedings, Pages715-718.
[4] I. Cumming, J. Wang, “Polarmetric SAR Data Compression Using Wavelet Packets in a Block Coding Scheme”, 2002 IEEE International Symposium, IGARSS '02 on Geoscience and Remote Sensing, Pages 1126 – 1128.
[5] Ian Cumming and Jing Wang, “Compression of RADARSAT Data with Block Adaptive Wavelets”, Proceedings of the Data Compression Conference (DCC’03)
[6] D. Wei J. E. Odegard H. Guo M. Lang C. S. Burrus, “Simultaneous Noise Reduction And Sar Image Data Compression Using Best Wavelet Packet Basis”, Proceedings of the 1995 International Conference on Image Processing (ICIP '95), Pages 200- 203.
[7] Khalid Sayood “Introduction to Data Compression”, Second Edition, Morgan Kaufman publisher. 2003.
[8] R. C. Gonzalez, R. E. Woods, “Digital Image Processing” Second Edition, Pearson Education, 2004.
[9] David Salomon, “Data Compression the Complete Reference”, 2nd Ed. Springer-2001.
[10] A.K. Jain, “Fundamentals of Digital image processing” PHI, 2004.
[11] V. Sterela , P. N Heller, P. Topiwala and C-hall, “The Applications of Mulitwavelets Filter banks to image Processing”, IEEE Trans Image Processing Vol.8.April, 1999
[12] V. Sterela. Multiwavelets: Theory and Applications”, Ph.D Thesis Massachusetts Institute of Technology, 1996
[13] Baxter, R.A., “SAR image compression with the Gabor transform”, IEEE Transactions on Geosciences and Remote Sensing, Volume 37, Issue 1, Part 2, Jan. 1999.
[14] Venkatraman, M., Kwon, H., Nasrabadi, N.M., “Object-based SAR image compression using vector quantization”, IEEE Transactions on Aerospace and Electronic Systems, Volume 36, Issue 4, Oct. 2000 Page(s):1036 - 1046
[15] Mercier, G., “Reflectivity estimation for SAR image compression”, IEEE Transactions on Geosciences and Remote Sensing, Volume 41, Issue 4, Part 2, April 2003.
[16] Zhaohui Zeng; Cumming, I.G., “SAR image data compression using a tree-structured wavelet transform”, IEEE Transactions on Geosciences and Remote Sensing, Volume 39, Issue 3, March 2001 Page(s):546 – 552.
[17] Kim, A.; Krim, H., “Hierarchical stochastic modeling of SAR imagery for segmentation/compression ”, IEEE Transactions on Signal Processing , Volume 47, Issue 2, Feb. 1999 Page(s):458 - 468
[18] Dony, R.D.; Haykin, S., “Compression of SAR images using KLT, VQ and mixture of principal component”,IEEE Transactions on Radar, Sonar and Navigation , Volume 144, Issue 3, June 1997 Page(s):113 – 120.
[19] Ahmet M. Eskicioglu, “Quality Measurement for Monochrome Compressed Images in the past 25 Years”, Thomson Consumer Electronics, USA.
[20] Karen Lees, “Image Compression Using Wavelets”, Report of MS, (2002)

Premal B. Nirpal, "Image Compression using Discrete Cosine Transform, Block Truncation Coding and Gaussian Pyramidal Approach," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.21-25, 2015.

Due to rapid growth in the e-Learning area evolution is seen in the education sector e-Learning is the most effective mode of education by providing learner convince to access the study material anytime, anywhere according to their convenient . LMS and LCMS is the backbone of any e-learning system. Learning Management System and Learning Content Management System is software tool essential to the deployment and management of e-learning courses. In e-Learning area there are various proprietary as well as Open Source LMS exists in the market. In the past few years there are numbers of open source LMS emerged as alternative to the proprietary LMS. In this paper we concentrated on the open source LMS Moodle, Sakai, eFront and Forma LMS. This will help the administrators and educators and organisation in choosing of learning management system that is most suitable for their needs.

LMS, Open source ,Moodle, Sakai, eFront and Forma Lms

[1] http://www.formalms.org/
[2] “sakai Project” https://sakaiproject.org/
[3] “Moodle” http:// moodle.org
[4] https://www.wikipedia.org/
[5] www.efrontlearning.net/
[6] Apache Tomcat, Web page at http://tomcat.apache.org,
[7] http://opensource.org/
[8] Saheli D.Bobade and Poonam A. Manjare, "A Model of Adaptive E-Learning System Based On Thinking and Learning Style", International Journal of Computer Sciences and Engineering, Volume-03, Issue-01, Page No (101-104), Jan -2015, E-ISSN: 2347-2693
[9] C. Caminero, R. Hernandez, S. Ros,A. Robles-G omez, Ll. Tobarra “Choosing the right LMS: A performance evaluation of three open-source LMS” , 2013 IEEE Global Engineering Education Conference (EDUCON) , Berlin, Germany, 2013
[10] Kumar, S.; Gankotiya, A.K.; Dutta, K., "A comparative study of moodle with other e-learning systems," Electronics Computer Technology (ICECT), 2011 3rd International Conference on , vol.5, no., pp.414,418, 8-10 April 2011

Prabha Kumari and Sanjeev Thakur , "An Evaluation of Open-Source LMS for e-Learning courses," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.26-29, 2015.

Digital world requires digital data, so it is important to optimize the data accordingly. Therefore it is important to preserve the digital space for saving digital data. This paper presents an Alternative way to optimize the digital data by utilising the technique of QR code. This QR code is obtained by encrypting the normal digital data. Here in this paper, we are utilising this technique to design Digital Marksheet system. Here the digital data would be the students record which would be saved using QR code because this optimize technique is secure and quite useful. We would be using .NET platform to design the system. This paper provides a new dimension for storing, accessing and authenticating the digital data.

Digital Code, QR code, Digital Marksheet, Authentication,Encryption,Decryption

[1] Somdip Dey, Asoke Nath, Shalabh Agarwal ” Confidential Encrypted Data Hiding and Retrieval Using QR Authentication System”International Conference on Communication Systems and Network Technologies 2013
[2] QR Code, Wikipedia", http://en.wikipedia.org/wiki/QR_code [Online] [Retrieved 2012-02-09]
[3] Asoke Nath, Saima Ghosh, Meheboob Alam Mallik: “Symmetric Key Cryptography using Random Key generator:” Proceedings of International conference on security and management(SAM’10” held at Las Vegas, USA Jull 12-15, 2010), P-Vol-2, 239-244(2010).
[4] Neeraj Khanna,Joel James,Joyshree Nath, Sayantan Chakraborty, Amlan Chakrabarti and Asoke Nath : “ New Symmetric key Cryptographic algorithm using combined bit manipulation and MSA encryption algorithm: NJJSAA symmetric key algorithm” Proceedings of IEEE CSNT-2011 held at SMVDU(Jammu) 03-06 June 2011, Page 125-130(2011).
[5] Somdip Dey, Joyshree Nath, Asoke Nath, "An Integrated Symmetric Key Cryptographic Method – Amalgamation of TTJSA Algorithm,Advanced Caesar Cipher Algorithm, Bit Rotation and Reversal Method: SJA Algorithm", IJMECS, vol.4, no.5, pp.1-9, 2012.
[6] Somdip Dey, Joyshree Nath and Asoke Nath. Article: An Advanced Combined Symmetric Key Cryptographic Method using Bit Manipulation, Bit Reversal, Modified Caesar Cipher (SD-REE), DJSA method, TTJSA method: SJA-I Algorithm. International Journal of Computer Applications46(20): 46-53, May 2012. Published by Foundation of Computer Science, New York, USA.
[7] Somdip Dey, ”SD-EQR: A New Technique To Use QR CodesTM in Cryptography”, Proceedings of “1st International Conference on Emerging Trends in Computer and Information Technology (ICETCIT 2012)”, Coimbatore, India, pp. 11-21.
[8] Behrouz A. Forouzan,” Cryptography & Network Security”, TataMcGraw Hill Book Company.
[9] Reed and G. Solomon, “Polynomial codes over certain finite fields”, Journal of the Society for Industrial and Applied Mathematics, 8(2):300–304, 1960.
[10] "ZXING- QR Code Library ",http://code.google.com/p/zxing/
[11] N. Johnson and S. Jajodia, “Steganaly- sis: The investigation of hidden information”, Proc. Of the 1998 IEEE Information Technology Conference, 1998.
[12] Somdip Dey, Kalyan Mondal, Joyshree Nath, Asoke Nath,"Advanced Steganography Algorithm Using Randomized Intermediate QR Host Embedded With Any Encrypted Secret Message: ASA_QR Algorithm", IJMECS, vol.4, no.6, pp. 59-67, 2012.

Sanjeev kumar Pandey, Rahul Patel, Sanket Ubale and D.A.Lokare, "Forged Marksheet detection System Using QR code," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.30-32, 2015.

There are lots of cipher-text policies to provide control access mechanism. Attribute-based encryption reconsiders the concept of public-key encryption in which the secret key of a user and the ciphertext are dependent upon attributes. In this, the decryption of a ciphertext is possible if the set of attributes of the user key matches the attributes of the ciphertext. File distribution and sharing is the most commonly used services in the cloud computing. Attribute based encryption is the attractive way to manage and control file sharing in cloud with its special attribute computing properties. Attribute-based encryption (ABE) is a new cryptographic primitive which provides a promising tool for addressing the problem of secure and fine-grained data sharing and decentralized access control. The authors take a centralized approach where a key distribution center (KDC) distributes secret keys and attributes to all users. Cloud computing is a new concept of computing technique, by which computer resources are provided dynamically via internet. Decentralized scheme also has the added feature of access control in which only valid users are able to decrypt the stored information. The scheme supports creation, modification, and reading the data stored in cloud and also provide the decentralized authentication and robust. It can be comparable to centralized schemes for the communication of data, computation of data, and storage of data.

Access Control, Cloud Computing, Data Privacy, Fine-Grained Access Control, Attribute-Based Encryption, Ciphertext Policy

[1]. M. Suriyapriya, A. Joicy,” Attribute Based Encryption with Privacy Preserving In Clouds”, International Journal on Recent and Innovation Trends in Computing and Communication, Volume 2 Issue 2, February 2014,
[2]. Changji Wang, Jianfa Luo,” An Efficient Key-Policy Attribute-Based Encryption Scheme with Constant Ciphertext Length” Mathematical Problems in Engineering Volume 2013
[3]. Taeho Jung , Xiang-Yang Li , Zhiguo Wan,” Privacy Preserving Cloud Data Access With Multi-Authorities” arXiv:1206.2657v6 [cs.CR] 11 Apr 2013
[4]. Geetanjali. M, Saravanan. N,” Attribute Based Encryption with Privacy Protection in Clouds” International Journal of Innovative Research in Computer and Communication Engineering, Vol.2, Special Issue 1, March 2014
[5]. Jinguang Han, Student Member, IEEE, Willy Susilo, Senior Member, IEEE, ” Privacy-Preserving Decentralized Key-Policy Attribute-Based Encryption”. IEEE Transactions on Parallel And Distributed Systems Vol.23 No.11, 2012
[6]. Moligi Sangeetha,” Simulation of Secure Data Sharing Scheme for Dynamic Groups in Cloud” International Journal of Computer Engineering and Applications, Volume VII, Issue II, August 14
[7]. M. Vijayapriya, Dr. A. Malathi,” On Demand Security for Personal Health Record in Cloud Computing Using Encryption and Decryption Cryptography” International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 9, September 2013
[8]. A.Vijayalakshmi, R.Arunapriya,” Authentication of Data Storage Using Decentralized Access Control In Clouds” JGRCS, Volume 5, No. 9, September 2014
[9]. Geetanjali.M, Saravanan.N, “ attribute based encryption with privacy protection in clouds” IJIRCCE, Vol.2, special issue 1, march 2014
[10]. Moligi Sangeetha, “simulation of secure data sharing scheme for dynamic groups in cloud” IJCEA, Vol.7, issue 2, august 2014
[11]. E. Kamalakannan, Arvind .K.S,” Investigation on Improving the Security of Public Health Record System in Cloud Computing” International Journal of Innovative Research in Computer and Communication Engineering Vol. 1, Issue 8, October 2013
[12]. M. Suriyapriya, A. Joicy, “attribute based encryption with privacy preserving in clouds”, IJRITCC, VOL.2, Issue 2, Feb. 2014.
[13]. A.Malathi, M.vijayapriya, “on demand security for personal health record in cloud computing using encryption and decryption cryptography”, IJARCSSE, Vol. 3, Issue 9, sep.2013.
[14]. A.Vijayalakshmi, R.Arunapriya, “authentication of data storage using decentralized access control in clouds”, JGRCS,Vol.5, No.9, sep. 2014.
[15]. Minu George, Dr. C.Suresh Gnanadhas, Saranya.K,” A Survey on Attribute Based Encryption Scheme in Cloud Computing”IJARCCE, Vol.2, issue 11, nov.2013.
[16]. M. Suriyapriya, A. Joicy, “Attribute Based Encryption with Privacy Preserving In Clouds”, IJRITCC, Vol.2, issue 2, feb.2014.
[17]. Cheng-Chi Lee, Pei-Shan Chung, and Min-Shiang Hwang,” A Survey on Attribute-based Encryption Schemes of Access Control in Cloud Environments”, International Journal of Network Security, Vol.15, No.4, PP.231-240, July 2013.
[18]. Vipul goyal, omkant pandey, “attribute-based encryption for fine-grained access control of encryption data”.

Reenu Lathwal and Vinod Kumar Saroha, "KDC Based KP-ABE for Data Encryption in Cloud," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.30-37, 2015.

A large data is generated in different organization which is in textual format. In such data structured information is get shadowed in unstructured data. Many algorithms working on extraction of information from raw data but which is costly and not efficient and also shows impure results. Data quality is also the main issue. In existing system used annotation for query search and work on attribute suggestion which make querying feasible but annotation that use attribute value pairs require users to be more principled in their annotation efforts. Also user always has good idea in using and applying the annotations. In this we proposed new techniques that combine the working of (Collaborative Adaptive Data Sharing platform) CADS and USHER for attribute suggestion and improving data quality. In our approach we first generate CADS form and after that we evaluate real-world data sets components using USHER. This technique shows superior results compared to current approach. It improves the visibility of document and also data quality with minimum cost.

Annotation, attribute value, USHER, data quality,form design,CADS

[1] Eduardo J. Ruiz, Vagelis Hristidis, and Panagiotis G. Ipeirotis, “Facilitating Document Annotation using Content and Querying Value,” IEEE Transactions on knowledge and data engineering, Vol.26, No.2, February 2014.
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[3] K. Chen, H. Chen, N. Conway, J.M.Hellerstein, and T.S. Parikh, “Usher: Improving Data Quality with Dynamic Forms,”IEEE Transactions on knowledge and data engineering, Vol.23, No.8, August 2011.
[4] M.Jayapandian and H.V. Jagadish, “Automated Creation of a Forms-Based Database Query Interface, “Proc.VLDB Endowment, Vol.1, 2008, pp.695-709.
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[6] M.Miah, G. Das, V. Hristidis, and H. Mannila, “Standing out in a Crowd: Selecting Attributes for Maximum Visibility,” Proc.Int’1 Conf. Data Eng. (ICDE), 2008.
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Anita L. Devkar and Vandana S. Inamdar, "Combine Approach of CADS and USHER Interfaces for Document Annotation," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.38-41, 2015.

Observational research on the social impact of cell phone usage in public places suggests that the mere presence of cell phones in public conflicts the private and public spheres and inhibits social interaction with proximate others,saving the energy of the mobile phones , storage of user profile data and make sharing quickly becomes difficult. In addition to mobility, another defining characteristic of mobile systems is user social interaction. To manage this entire problem two methods have been proposed, initially E-Shadow method is proposed for distribute mobile local social networking system. E-Shadow has two main components: (1) Local profiles. They enable E-Shadow users to record and share their names, interests, and other information with fine-grained privacy controls. (2) Mobile phone based local social interaction tools. E-Shadow provides mobile phone software that enables rich social interactions. In second design and prototype an adaptive location service for mobile devices, a-Loc, that helps reduce this battery drain. The proposed design is based on the observation that the required location accuracy varies with location, and hence lower energy and lower accuracy localization methods. It continuously tune continually tunes the energy expenditure to meet the changing accuracy requirements using the available sensors. A Bayesian estimation framework is used to model user location and sensor errors. Experiments on real world Windows Mobile phones and large-scale simulations show that our system disseminates information efficiently; it helps receivers find the direction of a specific location with accuracy. The experiments demonstrate that can recognize not only whether a social interaction is taking place, but also the type of social interaction, distinguishing between formal and informal user social settings.Focusing on helping behaviour in particular, Results Indicate That While On The Cell Phone, Users Are Less Likely To Offer Help.

E-Shadow, Mobile Phone, Layered Publishing, Direction-driven Matching, Energy- Aware Localization, Bayesian Estimation framework and Social Interaction Network

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Ambat Vipin, S Uma , Subin P S and Sankar K V, "Exploiting Energy- Aware Localization with Social Network Based Interaction in Mobile Phones," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.42-47, 2015.