iosr-JCE   Volume-17 ~ Issue-6 ~ Nov-Dec 2015

Abstract: Cloud computing is an important transition that makes change in service oriented computing technology. Cloud service provider follows pay-as-you-go pricing approach which means consumer uses as many resources as he need and billed by the provider based on the resource consumed. CSP give a quality of service in the form of a service level agreement. For transparent billing, each billing transaction should be protected against forgery and false modifications. Although CSPs provide service billing records, they cannot provide trustworthiness. It is due to user or CSP can modify the billing records. In this case even a third party cannot confirm that the user's record is correct or CSPs record is correct. To overcome these limitations we introduced a secure billing system called THEMIS. For secure billing system THEMIS introduces a concept of cloud notary authority (CNA).

[1]. Ki-Woong Park, Member, IEEE, Jaesun Han, Member, IEEE, JaeWoong Chung, Member, IEEE, and Kyu Ho Park, Member, IEEE Transactions on information forensics and security, Vol. 6, No. 2, JUNE 2012
[2]. Microsoft, "Microsoft, windows azure platform," 2010. [Online].Available: http://www.microsoft.com/windowsazure
[3]. M. Armbrust and A. E. Fox, "Above the clouds: A Berkeley view of cloud computing," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2009-28, Feb 2009.
[4]. N. Santos, K. P. Gummadi, and R. Rodrigues, "Towards trusted cloud computing," in Proc. of USENIX HotCloud 2009.
[5]. R. T. Snodgrass, S. S. Yao, and C. Collberg, "Tamper detection in audit logs," in Proc. of 30th intl. conf. on Very large data bases, ser. VLDB '04. VLDB Endowment, 2004, pp. 504–515.
[6]. L. C. M. C. Rob Byrom, Roney Cordenonsib, "Apel: An implementation of grid accounting using r-gma," UK e-Science All Hands Conference, Nottingham, September 2005.

Abstract: This paper represents or analyzes the security of system based on Blowfish. Blowfish mainly focuses on the encrypt and decrypt techniques and algorithms apply for cryptanalysis. It describe the algorithms for encryption as well as decryption algorithms and also give the sufficient description of key generation, key expansion, function and working principle of Blowfish cipher with proper explanations. Taking the current era, Most of the famous systems which offer security for a network or web or to a data are vulnerability to attacks and they are broken at some point of time by effective cryptanalysis methods, irrespective of its complex algorithmic design. In the general, today's cryptography world is bounded to an interpretive of following any one or multi encryption scheme and that too for a single iteration on a single file only. This is evident in the maximum of the encryption-decryption cases..

[1]. M. Anand Kumar and Dr. S. Karthikeyan Published Online March 2012 in MECS (http://www.mecs-press.org/) DOI: 10.5815/ijcnis.2012.02.04.
[2]. Cryptography And Network Security-William Stallings.
[3]. Gurjeevan Singh, Ashwani Kumar, K. S. Sandha /International Journal of Engineering Research and Applications (IJERA) ISSN: 2248-9622
[4]. M. Anand Kumar and Dr.S.Karthikeyan I. J. Computer Network and Information Security, 2012, 2, 22-
[5]. Jasdeep Singh Bhalla, Preeti Nagrath International Journal of Scientific and Research Publications, Volume 3, Issue 4, April 2013 ISSN 2250-3153

Abstract: The issue of deploying IPv6 Technology has been a topic of debate for more than a decade now. Professionals have been discussing on the transition from Internet Protocol version 4 (IPVv4) to Internet Protocol version 6 (IPv6) due to the fact that the IPv4 address space would soon be exhausted. In this paper, we analyse the IPv4 and IPv6 technologies and look at the benefits of migrating to IPv6, its social implications, risks & challenges and the opportunities the IPv6 migration offers.

[1]. AfriNIC (2014). Introduction to IPv6. Retrieved from: http://www.afrinic.net/en/services/ipv6-programme/704
[2]. APNIC (2013), IPv6 for Decision Makers, Retrieved from: http://www.apnic.net/community/ipv6-program/ipv6-for-decision-makers/ipv6_for_decision_makers.pdf
[3]. Chandler N. (2012), Business Case For Delivering IPv6 Service now. Retrieved from: http://www.cisco.com/c/dam/en/us/solutions/collateral/ios-nx-os-software/enterprise-ipv6-solution/idc_ipv6_economics.pdf
[4]. Eslambochi (2013), IPv6 Migration & Its Contingencies, Complexities & Implication, Part 6, Retrieved from: https://www.linkedin.com/today/post/article/20130729172819-5213223-ipv6-migration-its-contingencies-complexities-implications-part-6#
[5]. Google (2014), Per Country IPv6 Adoption. Retrieved from: https://www.google.com/intl/en/ipv6/statistics.html#tab=per-country-ipv6-adoption
[6]. Google (2015), IPv6 Adoption. Retrieved from: https://www.google.com/intl/en/ipv6/statistics.html.

Abstract: Cloud cоmрutіոg іs tyріcаlly defіոed as а type of cоmрutіոg that relies оո shаrіոg cоmрutіոg resources. The Іոfrаstructure as а Service іո cloud offers the dаtа-ceոter servіces to stоre аոd mаոаge іոfоrmаtіоո, the рrіvаte іոfоrmаtіоո cаո be shared аmоոg the busіոess-cоmраոy employees or the member's of а cоmmuոіty. Рreservіոg data рrіvаcy requires it to be encrypted before uрlоаdіոg іոtо the cloud server. The аuthоrіzed users' are оոly іոteոded to dоwոlоаd аոd decrypt usіոg а secret-key. The рreseոtly exіstіոg cryрtоgrарhіc models use key mаոаgemeոt рrоtоcоls tо address key revоcаtіоո рrоblems аոd some other uses relіаble security cоոtrоller for іssuіոg the sіgոаtures аոd аttаch secret-keys tо the users.

[1]. Certificateless Public Key Cryptography Sattam S . Al-Riyami and Kenneth G. Paterson Information Security Group, Royal Holloway, University of London, Egham, Surrey, TW20

[2]. Relations Among Notions of Security for Public- Key Encryption Scheme Bellare A. Desai D. Pointchevaly P. Rogawayz Februa 1999.

[3]. Attribute-Based Encryption for Fine- Grained Access Control of Encrypted Data Vipul Goyal Omkant Pandeyy Amit Sahaiz Brent.

[4]. R. Agrawal, J. Kiernan, R. Srikant, and Y. Xu, "Order preserving encryption for numeric data," in Proceedings of ACM SIGMOD Conference, 2004.

[5]. B. Chor, E. Kushilevitz, O. Goldreich, and M. Sudan, "Private information retrieval," ACM Computer Survey, vol.45, no. 6,pp. 965–981, 1998.

Abstract: The internet is a vast collection of billions of web pages containing terabytes of information arranged in thousands of servers using HTML. The size of this collection itself is a formidable obstacle in retrieving necessary and relevant information. This made search engines an important part of our lives. Search engines strive to retrieve information as relevant as possible. One of the building blocks of search engines is the Web Crawler. We tend to propose a two - stage framework, specifically two smart Crawler, for efficient gathering deep net interfaces. Within the first stage, smart Crawler,

[1] GUPTA, P.; JOHARI, K., "IMPLEMENTATION OF WEB CRAWLER,"EMERGING TRENDS IN ENGINEERING AND TECHNOLOGY (ICETET), 2009 2ND INTERNATIONAL CONFERENCE ON, VOL., NO., PP.838,843, 16 -18 DEC. 2009 DOI: 10.1109/ICETET.2009.124 HTTP://IEEEXPLORE.IEEE.ORG/STAMP/STAMP.JSP?TP=&ARNUMBER=6164440&ISNUMBER=6164338
[2] Manning, C., Raghavan, P., & Schutze, H. (2008). Introduction to Information Retrieval. Cambridge University Press.42 [3] Olston, C., & Najork, M. (2010). Foundations and Trends in Information Retrieval [4] Martin Hilbert. How much information is there in the ―information society‖? Significance, 9(4):8 –12, 2012. [5] ID worldwide predictions 2014: Battles for dominance – and survival –on the 3rd platform. http://www.idc.com/ research/Predictions14/index, 2014.

Abstract: Computational Intelligence is the study of the design of intelligent agents. An agent is something that react according to an environment—it does something. Agents includes worms, dogs, thermostats, airplanes, humans, and society. The purpose of computational intelligence is to understand the principles that make intelligent behavior possible, in real or artificial systems. Techniques of Computational Intelligence are designed to model the aspects of biological intelligence. These paradigms include that exhibit an ability to learn or adapt to new situations,to generalize, abstract, learn and associate. This paper gives review of comparison between computational intelligence paradigms in Wireless Sensor Network and Finally,a short conclusion is provided. Keywords: Computational Intelligence, Computational Intelligence Techniques, Swarm Intelligence ,Genetic Algorithm, Artificial Neural Network.

[1] A.konar, computational intelligence: principles, techniques and applications. Springer, 2005.
[2] Anastasi, G., Conti, M., Francesco, D.M, Passarella. A, "Energy Conservation in WSNs: a Survey", Elsevier Science Publishers AdHoc Networks, vol.7, No.3 pp. 537-568, May 2009.
[3] P. Stone, M. Veloso, "Team-partitioned, opaque-transition reinforcement learning," in Proc. 3rd Annual Conf. Autonomous Agents.
[4] Dressler, Self-Organization in Sensor and Actor Networks, John Wiley & Sons, 2007.
[5] P. Stone, M. Veloso, "Team-partitioned, opaque-transition reinforcement learning," in Proc. 3rd Annual Conf. Autonomous Agents (AGENTS). Newyork, USA.
[6] Basma Solaiman and Alaa Sheta. Computational Intelligence for Wireless Sensor Networks: Applications and Clustering Algorithms. " International Journal of Computer Applications (0975 8887)":Volume 73 - No. 15, July 2013.

Abstract: Public key cryptography has now become an important means for providing confidentiality by its use of key distribution, in which users can do private communication with the help of encryption keys. It also provides digital signatures which allow users to sign keys to verify their identities. But public key cryptography has its own shortcoming regarding to high cost in keys distribution and excessive computation in encoding and decoding it. Whereas private key can omit all above problems but only if we can find a way to share private key confidentially.

[1] Encryption: Strengths and Weaknesses of Public-key Cryptography Matt Blumenthal Department of Computing Sciences Villanova University, Villanova, PA 19085 CSC 3990 – Computing Research Topics matthew.blumenthal@villanova.edu
[2] http://www.cgi.com/files/white-papers/cgi_whpr_35_pki_e.pdf [3] Private and Public Key Cryptography and Ransomware December 2014 Authored by:Ted Fischer Center for Internet Security (CIS) Security Operations Center (SOC) Analyst [4] ISSN:2249-5789 Prashant Kumar Arya et al , International Journal of Computer Science & Communication Networks,Vol 5(1),17-21 Comparative Study of Asymmetric Key Cryptographic Algorithms
[5] http://searchsecurity.techtarget.com/definition/asymmetric-cryptography [6] A Comparison of a Public and a Secret Key Cryptosystem, Adam Donlin, SE4H. 29th February, 1995.

Abstract: In distributed peer-to-peer systems, huge amount of data are dispersed. Grouping of those data from multiple sources is a tedious task. By applying effective data mining techniques the clustering of distributed data is become ease and this decreases the hurdles of clustering due to processing, storage, and transmission costs. To perform a dynamic distributed clustering, a fully decentralized clustering method has been proposed. HGD Cluster can cluster a data set which is dispersed among a large number of nodes in a distributed environment using hierarchal and grid based clustering techniques. When nodes are fully asynchronous and decentralized and also adaptable to stir, then HGD cluster will apply.

[1] Xiong, Sicheng, Javad Azimi, and Xiaoli Z. Fern. "Active learning of constraints for semi-supervised clustering." Knowledge and Data Engineering, IEEE Transactions on 26.1 (2014): 43-54.

[2] Basu, Sugato, Arindam Banerjee, and Raymond J. Mooney. "Active Semi-Supervision for Pairwise Constrained Clustering." SDM. Vol. 4. 2004.

[3] Bilenko, Mikhail, Sugato Basu, and Raymond J. Mooney. "Integrating constraints and metric learning in semi-supervised clustering." Proceedings of the twenty-first international conference on Machine learning. ACM, 2004.

[4] Davidson, Ian, Kiri L. Wagstaff, and Sugato Basu. Measuring constraint-set utility for partitional clustering algorithms. Springer Berlin Heidelberg, 2006.

[5] Greene, Derek, and Pádraig Cunningham. "Constraint selection by committee: An ensemble approach to identifying informative constraints for semi-supervised clustering." Machine Learning: ECML 2007. Springer Berlin Heidelberg, 2007. 140-151.

[6] Guo, Yuhong, and Dale Schuurmans. "Discriminative batch mode active learning." Advances in neural information processing systems. 2008.

Abstract: Controlled Environment Farming is one of the emerging technologies in the farming and agriculture industries nowadays. Aeroponics is an optimized process developed for growing crops and plants in an air medium without the use of soil or an aggregate medium by spraying the plant's roots with an atomized or sprayed, nutrient-rich water solution. Various automated farming system has been developed using sensor networks and control systems to improve agricultural produc-tivity. Traditional farming techniques are complex and strictly influenced by soil conditions, climate, weather, crop types, and so on. Authors have proposed a system in which an environment monitoring, quality evaluation and crop growth, data recording, and online data submitting and multiplatform compatibility were integrated. The control system based on agricultural information measured by field monitoring sensors is a proved effective method to improve quality of agricultural product in a greenhouse.

[1]. Volume 4, Issue 1, January 2014 ISSN: 2277 128X International Journal of Advanced Research in Computer Science and Software Engineering
[2]. Aeroponic Greenhouse as an Autonomous System using Intelligent Space for Agriculture Robotics.
[3]. IEEE Paper IJET-IJENS Vol:09 No:09
[4]. Remote Monitoring in Agricultural Greenhouse Using Wireless Sensor and Short Message Service (SMS).
[5]. http://aerofarms.com.
[6]. https : ==en:wikipedia:org=wiki=MainP age
[7]. http://www.ieee.org/

Abstract: The abstract Likewise human brain machine can be signifying diverse pattern sculpt that is proficiently identify an image based object like optical character, hand character image, fingerprint and something like this. To present the model of image based pattern recognition perspective by a machine, different stages are associated like image acquiring from the digitizing image sources, preprocessing image to remove unwanted data by the normalizing and filtering, extract the feature to represent the data as lower dimension space and at last return the decision using Multi-Layer Perceptron neural network that is feed feature vector from got the feature extraction process of a given input image. Performance observation complexity is discussed rest of the description of pattern recognition model.

[1] Dong ping, Tian, "A Review on Image Feature Extraction and Representation Techniques", International Journal of Multimedia and Ubiquitous Engineering, Vol. 8, No. 4, July, 2013, pp. 385-396.
[2] Sarvda Chauhan, Shalini Dhingra, "Pattern Recognition System using MLP Neural Networks", International Journal of Engineering Research and Development, e-ISSN: 2278-067X, Volume 4, Issue 9 (November 2012), PP. 43-46.
[3] "A Versatile Machine Vision System for Complex Industrial Parts" IEEE Trans. On Computers, Sep. 1977, No. 9, Vol. C-27.
[4] L.daF. Costa, J.R.M. Cesar, "Shape Analysis and Classification: Theory and Practice", CRC Press, Florida, USA, 2001
[5] Eugene P, Angela B, "What Is Image Acquisition in Image Processing", wisegeek.com, 2003-2015 Conjecture Corporation.
[6] A D Marshall, R R Martin, "Computer Vision, Models and Inspection", World Scientific Series in Robotics and Intelligent Systems: Volume 4, ISBN: 978-981-02-0772-4 (hardcover), March 1992.

Abstract: Data mining techniques (DMT) are extensively used in educational field to find new hidden patterns from student's data. In recent years, the greatest issues that educational institutions are facing the unstable expansion of educational data and to utilize this information data to progress the quality of managerial decisions. Educational institutions are playing a prominent role in the public and also playing an essential role for enlargement and progress of nation. The idea is predicting the paths of students, thus identifying the student achievement. The data mining methods are very useful in predicting the educational database. Educational data mining is concerns with improving techniques for determining knowledge from data which comes from the educational database.

[1] Amershi, S., and Conati, C., (2009) "Combining unsupervised andsupervisedcassificationtobuild user models for exploratory learning environments" Journal of Educational Data Mining. Vol.1, No.1, pp. 18-71. [2] Baker, R. S. J. D. "Data mining for education." International encyclopedia of education 7 (2010): 112-118.
[3] Sachin,R.B., & Vijay, M. S, "A Survey and Future Vision of Data Mining in Educational Field", Paper presented at the AdvancedComputing & Communication Technologies(ACCT), Second International Conference on 7- 8 Jan. 2012.
[4] Tair, Mohammed M. Abu, and Alaa M. El-Halees. "Mining educational data to improve students' performance: a case study." International Journal of Information 2.2 (2012).
[5] Goyal, Monika, and RajanVohra. "Applications of data mining in higher education." International journal of computer science 9.2 (2012): 113.
[6] Abdul Aziz, Azwa, NurHafieza Ismail, and Fadhilah Ahmad."MINING STUDENTS'ACADEMIC PERFORMANCE." Journal of Theoretical and Applied Information Technology 53.3 (2013): 485-485.