iosr-JCE   Volume-9 ~ Issue-5

Abstract: Private Cloud computing provides attractive & cost efficient Server Based Computing (SBC). The implementation of Thin client computing for private cloud computing will reduce the IT Cost and consumes less power. Most cloud services run in browser based environment so we don't need a fat client to use in the private Cloud environment. Implementing Thin Client Technology along with Private Cloud Computing will help to reduce the IT Operational Cost by 90% by saving power, space and maintenance. It requires only minimal power for cooling the Infrastructure. Thin Client with private Cloud Computing can be referred as purest form of green computing & carbon free computing.

Keywords: Thin Clients with Cloud computing; Green computing; Private Cloud Terminal Computing; Carbon Free Computing; Low Powered Computing.

[1] Segal, I.. ""When Is Zero Client Not Zero Client?", SysGen, Inc"
[2] Mell, P. and Grance, T. (2011-09). "The NIST Definition of Cloud Computing. NIST Special Publication 800-145 (September 2011). National Institute of Standards and Technology, U.S. Department of Commerce"
[3] Sherbak, T., Sweere, N., and Belapurkar, V.. "Virtualized Enterprise Storage for Flexible, Scalable Private Clouds. Reprinted from Dell Power Solutions, 2012 Issue 1"
[4] Chou, Timothy. Introduction to Cloud Computing: Business &Technology. http://www.scribd.com/doc/64699897/Introduction-to-Cloud-Computing-Business-and-Technology
[5] Wang, R."Tuesday's Tip: Understanding The Many Flavors ofCloudComputingandSaaS".
[6] Nieh, Jason; Novik, Naomi &., Yang, S. Jae (December, 2005). "A Comparison of Thin-Client Computing Architectures". Technical Report CUCS-022-00 (New York: Network Computing Laboratory, Columbia University
[7] http://www.nomachine.com/documentation/pdf/cucs-022-00.pdf
[8] Madden, B. (May 19, 2012) (2010-05-19). "Wyse hopes to shake up the thin client industry with a new zero client platform. Will it work?"
[9] Greaves, J. (of Carpathia Hosting) and Potti, S. (of Citrix). Uploaded by CarpathiaHosting on Feb 22, 2010. "Flex-Tenancy: Secure Multi-Tenancy Network Environments"
[10] I.S. Jacobs and C.P. Bean, "Fine particles, thin films and exchange anisotropy," in Magnetism, vol. III, G.T. Rado and H. Suhl, Eds. New York: Academic, 1963, pp. 271-350.

Abstract: This paper represents comparative evaluation of different type of algorithms for association rule mining that works on frequent item sets. Association rule mining between different items in large-scale database is an important data mining problem. Now a day there is lots of algorithms available for association rule mining. To perform comparative study of different algorithms various factor considered in this paper like number of transaction, minimum support and execution time. Comparisons of algorithms are generated based on experimental data which gives final conclusion.

Keywords – Apriori, Association Rules, Data Mining, Frequent Pattern

[1] R. Agrawal, R. Srikant. "Fast algorithms for mining association rules in large databases" Proc. of 20th Int'l conf. on VLDB: 487-499, 1994.
[2] J. Han, J. Pei, Y. Yin. "Mining Frequent Patterns without Candidate Generation" Proc. of ACM-SIGMOD, 2000.
[3] D. Cristofor, L. Cristofor, and D. Simovici. Galois connection and data mining. Journal of Universal Computer Science, 6(1):60–73, 2000.
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[5] M. H. Dunham. "Data Mining. Introductory and Advanced Topics". Prentice Hall, 2003, ISBN 0-13-088892-3.
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[8] R. J. Bayardo. "Efficiently mining long patterns from databases". In SIGMOD'98, pp. 85-93.
[9] G. Dong and J. Li. "Efficient mining of emerging patterns: Discovering trends and differences". In KDD'99, pp. 43-52.
[10] Zaki, M.J. and Hsiao, C.J. "CHARM: An efficient algorithm for closed itemset mining". In Proc. SIAM Int. Conf. Data Mining, Arlington, VA, pp. 457–473, 2002.

Abstract: Anonymizing networks such as Tor allow users to access Internet services privately by using a series of routers to hide the client's IP address from the server. The success of such networks, however, has been limited by users employing this anonymity for abusive purposes such as defacing popular Web sites. Web site administrators routinely rely on IP-address blocking for disabling access to misbehaving users, but blocking IP addresses is not practical if the abuser routes through an anonymizing network. As a result, administrators block all known exit nodes of anonymizing networks, denying anonymous access to misbehaving and behaving users alike. To address this problem, we present Nymble, a system in which servers can "blacklist" misbehaving users, thereby blocking users without compromising their anonymity. Our system is thus agnostic to different servers' definitions of misbehavior—servers can blacklist users for whatever reason, and the privacy of blacklisted users is maintained.

Keywords: Anonymous Blacklisting, Anonymizing Networks, Backward Unlinkability, Privacy, Revocation, Realibility and Security.

[1] P.C. Johnson, A. Kapadia, P.P. Tsang, and S.W. Smith, ―Nymble: Anonymous IP-Address Blocking‖ in Proc, Conf. Privacy Enhancing Technologies, Springer, pp. 113-133, 2007.
[2] B.N. Levine, C. Shields, and N.B. Margolin, ―A Survey of Solutions to the Sybil Attack‖, Technical Report 2006-052, Univ. of Massachusetts, Oct. 2006.
[3] D. Boneh and H. Shacham, ―Group Signatures with Verifier-Local Revocation‖, Proc. ACM Conf. Computer and Comm. Security, pp. 168-177, 2004.
[4] T. Nakanishi and N. Funabiki, ―Verifier-Local Revocation Group Signature Schemes with Backward Unlinkability from Bilinear Maps‖, Proc. Int'l Conf. Theory and Application of Cryptology and Information Security (ASIACRYPT), Springer, pp. 533-548, 2005.
[5] G. Ateniese, J. Camenisch, M. Joye, and G. Tsudik,‖ A Practical and Provably Secure Coalition-Resistant Group Signature, Scheme‖, Proc. Ann. Int'l Cryptology Conf. (CRYPTO), Springer, pp. 255-270, 2000.
[6] R. Dingledine, N. Mathewson, and P. Syverson, ―Tor: The Second- Generation Onion Router,‖ Proc. Usenix Security Symp., pp. 303-320, Aug. 2004.
[7] C. Cornelius, A. Kapadia, P.P. Tsang, and S.W. Smith, ―Nymble: Blocking Misbehaving Users in Anonymizing Networks,‖ Tech- nical Report TR2008-637, Dartmouth College, Computer Science, Dec. 2008.
[8] J.E. Holt and K.E. Seamons, ―Nym: Practical Pseudonymity for Anonymous Networks,‖ Internet Security Research Lab Technical Report 2006-4, Brigham Young Univ., June 2006.
[9] A. Lysyanskaya, R.L. Rivest, A. Sahai, and S. Wolf, ―Pseudonym Systems,‖ Proc. Conf. Selected Areas in Cryptography, Springer, pp. 184-199, 1999.
[10] J.R. Douceur, ―The Sybil Attack,‖ Proc. Int'l Workshop on Peer-to- Peer Systems (IPTPS), Springer, pp. 251-260, 2002.

Abstract: In Wireless Sensor network, nodes are interconnected and information is shared among them. In a situation, many attacks are involved to misuse the wireless sensor network. One of the attacks is replica node replication attack, in which the adversary can detain and conciliation of sensor nodes by hacking IP address of node to make replicas of them and then mount a variety of attacks with these replicas. Opponent controls the entire network and misuse the whole network. Our goal is to detect and block the nodes which are captured by attacker. Sequential Probability Ratio Test is a technique which is used to find the capture node by using two approaches. However, this technique is used to detect the capture node only which affects the neighbor node and have a chance to collapse the network. Blocking Technique is needed to block the particular node captured by attacker. In this work, we propose a fast and effective method for improving the detection of node using the Black Roll technique to effectively block the node by creating a blacklist table which contains block node IP address. Protowall is a tool which is used to block the IP address that is on a blacklist table.

Keywords-Wireless Sensor Network, Sequential Test, Black roll, Protowall

[1] Jun-Won Ho, Mathew Wright and Sajal K.Das (2011), "Fast Detection of Mobile Replica Node Attacks in Wireless Sensor Networks using Sequential Hypothesis Testing‟, IEEE Transactions on Mobile computing.

[2] J.-Y.L. Boudec and M. Vojnovi_c, "Perfect Simulation and Stationary of a Class of Mobility Models," Proc. IEEE INFOCOM,pp. 2743-2754, Mar. 2005.pp. 2743-2754, Mar. 2005.

[3] S. Capkun and J.P. Hubaux, "Secure Positioning in Wireless Networks," IEEE J. Selected Areas in Comm., vol. 24, no. 2, pp. 221-232, Feb. 2006.

[4] M. Conti, R.D. Pietro, L.V. Mancini, and A. Mei, "A Randomized, Efficient, and Distributed Protocol for the Detection of Node Replication Attacks in Wireless Sensor Networks," Proc. ACM MobiHoc, pp. 80-89, Sept. 2007.

[5] K. Dantu, M. Rahimi, H. Shah, S. Babel, A. Dhariwal, and G.S.Sukhatme, "Robomote: Enabling Mobility in Sensor Networks,"Proc. Fourth IEEE Int‟l Symp. Information Processing in Sensor Networks (IPSN), pp. 404-409, Apr. 2005.

[6] J. Ho, M. Wright, and S.K. Das, "Fast Detection of Replica Node Attacks in Mobile Sensor Networks Using Sequential Analysis,"Proc. IEEE INFOCOM, pp. 1773-1781, Apr. 2009.

[7] Ho, D. Liu, M. Wright, and S.K. Das, "Distributed Detection of Replicas with Deployment Knowledge in Wireless Sensor Networks, "Ad Hoc Networks, vol. 7, no. 8, pp. 1476-1488, Nov. 2009.

[8] L. Hu and D. Evans, "Localization for Mobile Sensor Networks,"Proc. ACM MobiCom, pp. 45-57, Sept. 2004.

[9] J.Jung,V. Paxon, A.W. Berger,andH.Balakrishnan,"Fast Portscan Detection Using Sequential Hypothesis Testing," Proc.IEEE Symp. Security and Privacy, pp. 211-225, May 2004.

[10] K. Xing, F. Liu, X. Cheng, and H.C. Du, "Real-Time Detection of Clone Attacks in Wireless Sensor Networks," Proc. IEEE Int‟l Conf. Distributed Computing Systems (ICDCS), pp. 3-10, June 2008.

Abstract: A cloud storage system, used to store large number of data in storage server. Cloud system is used to provide large number storage servers, which provide long-term storage service over the Internet. Third party's cloud system does not provide data confidentiality. Constructing centralized storage system for the cloud system makes hackers stole data easily. General encryption schemes protect data confidentiality. In the proposed system a secure distributed storage system is formulated by integrating a threshold proxy re-encryption scheme with a decentralized erasure code. The distributed storage system not only supports secure and robust data storage and retrieval, but also lets a user forward data from one user to another without retrieving the data back. The main technical involvement is that the proxy re-encryption scheme supports encoding operations over encrypted messages as well as forwarding operations over encoded and encrypted messages. The method fully integrates encrypting, encoding, and forwarding. The proposed system is applied for military and hospital applications, then other secret data transmission.

Keywords - Decentralized erasure code, proxy re-encryption, threshold cryptography, secure storage system

[1] Adya, W.J. Bolosky, M. Castro, G. Cermak, R. Chaiken, J.R.Douceur, J. Howell, J.R. Lorch, M. Theimer, and R. Wattenhofer, "Farsite: Federated, Available, and Reliable Storage for an Incompletely Trusted Environment," Proc. Fifth Symp. Operating System Design and Implementation (OSDI), pp. 1-14, 2002.

[2] Ateniese.G, K. Fu, M. Green, and S. Hohenberger, "ImprovedProxy Re-Encryption Schemes with Applications to SecureDistributed Storage," ACM Trans. Information and System Security,vol. 9, no. 1, pp. 1-30, 2006.

[3] Blaze.M, G. Bleumer, and M. Strauss, "Divertible Protocols and Atomic Proxy Cryptography," Proc. Int'l Conf. Theory and Application of Cryptographic Techniques (EUROCRYPT), pp. 127-144, 1998.

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[5] Dimakis. A.G, V. Prabhakaran, and K. Ramchandran, "Ubiquitous Access to Distributed Data in Large-Scale Sensor Networks through Decentralized Erasure Codes," Proc. Fourth Int'l Symp. Information Processing in Sensor Networks (IPSN), pp. 111- 117, 2005.
[6] Dimakis.A.G., V. Prabhakaran, and K. Ramchandran, "Decentralized Erasure Codes for Distributed Networked Storage," IEEE Trans. Information Theory, vol. 52, no. 6 pp. 2809-2816, June 2006.

[7] Druschel. P and A. Rowstron, "PAST: A Large-Scale, Persistent Peer-to-Peer Storage Utility," Proc. Eighth Workshop Hot Topics in Operating System (HotOS VIII), pp. 75-80, 2001.

[8] Haeberlen.A, A. Mislove, and P. Druschel, "Glacier: Highly Durable, Decentralized Storage Despite Massive Correlated Failures," Proc. Second Symp. Networked Systems Design and Implementation (NSDI), pp. 143-158, 2005.

[9] Hsiao-Ying Lin, Member, IEEE, and Wen-Guey Tzeng, Member "A Secure Erasure Code-Based Cloud Storage System with Secure Data Forwarding"vol. 23, no. 6, june 2012.

Abstract: In this paper we propose an efficient algorithm to retrieve videos from the database when a video clip is given as query. A retrieval system should have high precision, recall and low search time complexity. In this paper search time complexity is reduced by using clustering and search refinement method. Experimental results show that proposed video retrieval method is efficient and effective. Spatial and temporal properties of the video are used to retrieve the videos from the database.

Keywords - video indexing, Video retrieval

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spatiotemporal queries," IEEE Trans. Circuits Syst. Video Technol.,vol. 8, no. 5, pp. 602–615, Sep. 1998.
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Multimedia
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Technol.,11: 101-111. www.icis.ntu.edu.sg/scs-ijit/117/117_11.pdf
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Conference on Multimedia Technology and Applications, Kaohsiung, Taiwai, China, Dec. 17-19, pp: 17-19.
https://dspace.lib.cuhk.edu.hk/handle/2006/5923
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Abstract: Today's globally networked society places great demand on the dissemination and sharing of information. Privacy Preservation is an important issue in the release of data for mining purposes. How to efficiently protect individual privacy in data publishing is especially critical. With releasing of microdata such as social security number disease by some organization should contain privacy in data publishing. Data holders can remove explicit identifiers to gain privacy but other attributes which are in published data can lead to reveal privacy to adversary. So several methods such as K-anonymity, L-diversity, T-closeness, (n,t) closeness, (α,k)-anonymization, p-sensitive k-anonymity and others method come into existence to maintain privacy in data publishing.

Keywords – Data anonymization, Generalization, Data suppression

[1] C. Aggarwal, "On k-Anonymity and the Curse of Dimensionality," proc. Of the int'l conf. on very large data base (VLDB), pp. 901909, 2005.
[2] R. J. Bayardo and R. Agrawal, "Data Privacy through Optimal k- Anonymization," Proc. Int'l Conf. Data Engineering (ICDE), pp. 217-228, 2005.
[3] N. Li, T. Li, and S. Venkatasubramanian, "t-closeness: Privacy beyond k-anonymity and l-diversity," Proc. Int'l Conf. Data Engineering (ICDE), pp. 106115, 2007.
[4] A. Machanavajjhala, J. Gehrke, D. Kifer, and M. Venkitasubramaniam, "`l-Diversity: Privacy Beyond k-Anonymity," Proc. Int'l Conf. Data Engineering (ICDE), pp. 24, 2006.
[5] T. M. Truta and B. Vinay, "Privacy Protection: p-Sensitive k-Anonymity Property," Proc. Int'l Workshop on Privacy Data Management (ICDE Workshops), 2006.
[6] X. xiao and Y. Tao, "Personalized Privacy Preservation," Proc. of the ACM SIGMOD Int'l Conf. on Management of Data (SIGMOD), pp. 229- 240, 2006.
[7] Ninghui Li,tiancheng li and suresh venkatasubramanian. "Closeness: a new privacy measure for data publishing".IEEE, july 2010.
[8] G. T. Duncan and D. Lambert, "Disclosure-Limited Data Dissemination," Journal of The American Statistical Association, vol. 81, pp. 10-28, 1986.
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Abstract: LEO satellite has an important role in global communication system. They have advantages like low power requirement and lower end-to-end delay, efficient frequency spectrum utilization between satellites and spotbeams over MEO and GEO satellites. So in future they can be used as a replacement of modern terrestrial wireless networks. There are a lot of handover techniques for LEO satellites like seamless handover (SeaHO-LEO), PatHO-LEO. In our previous work, we have suggested a new handover technique for SeaHO-LEO by introducing a Handover Manager (HM) during the handover process and by simulation we have also shown that it a better approach by comparing it with other existing handover techniques as it reduces the handover latency, propagation delay, call blocking probability more than any other technique. In this paper we have evaluated the exact cost of our previous work i.e. Handover Manager based handover Method (HMBHO). Simulation results show that the cost of Handover Manager based handover management method is better than other handover methods.

Keywords: Handover latency, LEO, Mobile Node (MN),Handover Manager (HM).

[1] S. L. Kota, P. A. Leppanen, and K. Pahlavan, Broadband Satellite Communications For Internet Access, Kluwer Academic Publishers, 2004.

[2] A. Jamalipour, ―Satellites in IP networks,‖ in Wiley Encyclopedia ofTelecommunications, vol. 4, Wiley, 2002, pp. 2111–2122. [3] Satellite Mobility Pattern Scheme for Centrical and Seamless Handover Management in LEO SatelliteNetworksAys¸eg¨ul T¨uys¨uz and Fatih Alag¨oz

[4] H. Uzunalioglu, I. F. Akyildiz, Y. Yesha, and W. Yen, ―Footprint handover

[5] H. Uzunalioglu, I. F. Akyildiz, Y. Yesha, and W. Yen, ―Footprint handover Rerouting protocol for low earth orbit satellite networks,‖ Wireless Networks, vol. 5, no. 5, pp. 327–337, 1999

[6] Systems By Joydeep Banerjee D Sarddar, S.K. Saha, M.K. Naskar, T.Jana, U. Biswas

[7] H. N. Nguyen, S. Lepaja, J. Schuringa, and H. R. Van As, ―Handover management in low earth orbit satellite IP networks,‖ IEEE Global Telecommunications Conference, San Antonio, TX, USA, pp. 2730–2734, 25-29 November 2001

[8] J. T. Malinen and C.Williams, ―Micromobility taxonomy,‖ Internet Draft,IETF, Nov. 2001

[9] T¨uys¨uz and F. Alag¨oz, ―Satellite mobility pattern based handover management algorithm in LEO satellites,‖ in Proc. IEEE ICC 2006, Istanbul,Turkey, June 2006.

[10] Ays¸eg¨ul T¨uys¨uz and Fatih Alag¨oz, ―Satellite Mobility Pattern Scheme for centrical and Seamless Handover Management in LEO Satellite Networks‖, JOURNAL OF COMMUNICATIONS AND NETWORKS, VOL. 8, NO. 4, DECEMBER 2006.

Abstract: There has been an increasing interest in the study of video based fire detection algorithms as video based surveillance systems become widely available for indoor and outdoor monitoring applications. Although many video based smoke-detection algorithms have been developed and applied in various experimental or real life applications, but the standard method for evaluating their quality has not yet been proposed. In this framework, it is assumed that the compound algorithm consists of several subalgorithms, each of which yields its own decision as a real number centered around zero, representing the confidence level of that particular subalgorithm. In this project, the wavelet support vector machine (WSVM)-based model is used for Wild fire detection (WFD). Decision values are linearly combined with weights that are updated online according to an active fusion method based on performing entropic projections onto convex sets describing subalgorithms. The new wavelet kernel is proposed to improve the generalization ability of the support vector machine (SVM). More-over, the proposed model utilizes the principle of wavelet analysis to facilitate nonlinear characteristic extraction of the image data. To reduce misclassification due to fog, an efficient fog removal scheme using adaptive normalization method.

Index Terms—Active fusion, wildfire detection using video, Smoke detection, Wavelets Support vector machine, Video processing.

[1] B. U. Töreyin, Y. Dedeolu, and A. E. Çetin, "Wavelet based real-time smoke detection in video," in Proc. EUSIPCO, 2005, pp. 2–5. [2] B.C. Ko, K.H. Cheong, J.Y. Nam, Fire detection based on vision sensor and support vector machines, Fire Safety Journal 44 (3) (2009) 322–329..

[3] P. Guillemant, J. Vicente, Real-time identification of smoke images by clustering motions on a fractal curve with a temporal embedding method, Optical Engineering 40 (4) (2001) 554–563.

[4] C. Thou-Ho, Y. Yen-Hui, H. Shi-Feng, Y. Yan-Ting, The smoke detection for early fire-alarming system based on video processing, in: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, 2006, IIH-MSP '06, 2006, pp. 427–430.

[5] Z. Xu, J. Xu, Automatic fire smoke detection based on image visual features, in: International Conference on Computational Intelligence and Security Workshops, 2007, CISW 2007, 2007, pp. 316–319.

[6] P. Piccinini, S. Calderara, R. Cucchiara, Reliable smoke detection in the domains of image energy and color, in: 15th IEEE International Conference on Image Processing, 2008, ICIP 2008, 2008, pp. 1376–1379.

[7] R. Vezzani, S. Calderara, P. Piccinini, R. Cucchiara, Smoke detection in video surveillance: the use of visor (video surveillance on-line repository), in: Proceedings of the 2008 International Conference on Content-based Image and Video Retrieval, Niagara Falls, Canada, ACM Press, New York, 2008.

[8] J. Yang, F. Chen,W. Zhang, Visual-based smoke detection using support vector machine, in: Fourth International Conference on Natural Computation, 2008, ICNC '08, vol. 4, 2008, pp. 301–305.

[9] F. Yuan,A fast accumulative motion orientation model based on integral image for video smoke detection, Pattern Recognition Letters 29 (2008) 925932.

[10] R.J.Ferrari, H. Zhang, C.R. Kube, Real-time detection of steam in video images, Pattern Recognition 40 (3) (2007) 1148–1159.

Abstract: By significant improvement in technology, Health Information Technology (HIT) should be ata higher level of quality and safer care to be more responsive to patients' demands. The major benefits of HIT are cost reducing, quality improving, and better patient experience. In this article, we explain HIT system which is used by The Universiti Teknologi Malaysia's clinic and problems they have. To find out the problems of HIT, the interview is conducted with the stakeholders of the system that included clinic staff and doctors. The findings of this research have lessons for improving the clinic's system and future researches.

Keywords: Health Information Technology; stakeholders; cost; quality; patient

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[3] Devon M. Herrick, L. G., & , J. C. G. (2010). Health Information Technology: Benefits and Problems Available from http://www.ncpa.org/pdfs/st327.pdf
[4] F. van Rosse et al.(2009). The Effect of Computerized Physician Order Entry on Medication Prescription Errors and Clinical Outcome in Pediatric and Intensive Care: A Systematic Review.Pediatrics, 123 (4), 1184-90.
[5] Improving Transitions of Care with Health Information Technology. (2010). Available from http://www.ntocc.org/Portals/0/PDF/Resources/HITPaper.pdf
[6] Joan S.Ash et al.(2004). Computerized Physician Order Entry Systems in U.S. Hospitals: Results of a 2002 Survey. Journal of the American Medical Informatics Association, 11 (2), 95-9.
[7] Kern, L. M., & Kaushal, R. (2007). Health information technology and health information exchange in New York State: New initiatives in implementation and evaluation. Journal of Biomedical Informatics, 40 (6, Supplement), S17-S20.
[8] Kijsanayotin, B., Pannarunothai, S., & Speedie, S. M. (2009). Factors influencing health information technology adoption in Thailand's community health centers: Applying the UTAUT model. International Journal of Medical Informatics, 78 (6), 404-416.
[9] Lluch, M. (2011). Healthcare professionals' organizational barriers to health information technologies—A literature review. International Journal of Medical Informatics, 80 (12), 849-862.
[10] M.H. Reckmann et al.(2009). Coes Computerized Provider Order Entry Reduce Prescribing Errors fo Hospital Inpatients? A Systematic Revies.Journal of the American Medical Informatics, 16 (5), 613-23.

Abstract: The paper presents automatic clustering using Harmony Search based clustering algorithm. In this algorithm, the capability of Improved Harmony search is used to automatically evolve the appropriate number of clusters as well as the locations of cluster centers. By incorporating the concept of variable length in each harmony vector, our strategy is able to encode variable number of candidate cluster centers at each iteration. The CH cluster validity index is used as an objective function to validate the clustering result obtained from each harmony memory vector. The proposed approach has been applied onto well-known datasets and experimental results show that the approach is able to find the appropriate number of clusters and locations of cluster centers.

Keywords – Automatic Clustering, Harmony Search, Harmony Memory Vector, Cluster Centers

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Abstract: Content-based retrieval allows finding information by searching its content rather than its attributes. Content-based search and retrieval of video data becomes a challenging and important problem. Every year video content is growing in volume and there are different techniques available to capture, compress, display, store and transmit video while editing and manipulating video based on their content is still a non-trivial activity. Recent advances in multimedia technologies allow the capture and storage of video data with relatively inexpensive computers. However, without appropriate search techniques all these data are hardly usable. Today research is focused on video retrieval.Moreover, content-based video retrieval system requires first of all segment the video stream into separate shots. Video Shot Afterwards features are extracted for video shots representation. And finally, choose a similarity/distance metric and an algorithm that is efficient enough to retrieve query – related videos results. There are two main issues in this process; the first is how to determine the best way for video segmentation and key frame selection. The second is the features used for video representation. Various features can be extracted for this sake including either low or high level features. A key issue is how to bridge the gap between low and high level features. In this paper we presented approach for content based video retrieval based on Dominant color and texture of a video image. We also talk about video Representation, feature extraction from like texture, dominant color and color histogram from video frame.

Keywords- Video retrieval, dominant color, Gray level co occurrence matrix. Feature extraction, Key frame extraction, Video representation, and Video segmentation. Image Retrieval, color Histogram

[1] Shweta Ghodeswar, B.B.Meshram Technicians ―Content Based Video Retrieval‖
[2] R. Kanagavalli, Dr. K. Duraiswamy ―Object Based Video Retrievals" International Journal of Communications and Engineering Volume 06– No.6, Issue: 01 March2012.
[3] Dr. S. D. Sawarkar , V. S. Kubde ―Content Based Video Retrieval using trajectory andVelocity features‖ International Journal of Electronics and Computer Science Engineering ISSN- 2277-1956
[4] Ali Amiri, Mahmood Fathy, and Atusa Naseri ―A Novel Video Retrieval System Using GED-based Similarity Measure‖ International Journal of Signal Processing, Image Processing and Pattern Recognition Vol. 2, No.3, September 2009
[5] S.Thanga Ramya 1 P.Rangarajan ―Knowledge Based Methods for Video Data Retrieval‖ International Journal of Computer Science & Information Technology (IJCSIT) Vol 3, No 5, Oct 2011
[6] B. V. Patel A. V. Deorankar, B. B. Meshram ―Content Based Video Retrieval using Entropy, Edge Detection, Black and White Color Features‖ Computer Engineering and Technology (ICCET), 2010 2nd International Conference on
[7] Gao, X. and X. Tang, 2002. Unsupervised video shot segmentation and model-free anchorperson detection for news video story parsing. IEEE Trans. Circuits Syst. Video Technol., 12: 765-776.
[8] Shan Li, Moon-Chuen Lee, 2005. An improved sliding window method for shot change detection. Proceeding of the 7th IASTED International Conference Signal and Image Processing, Aug. 15-17, IIonolulu, IIawaii, USA, pp: 464-468.
[9] Hamdy K. Elminir, Mohamed Abu ElSoud ―Multi feature content based video retrieval using high level semantic concept‖ IJCSI International Journal of Computer Science Issues, Vol. 9, Issue 4, No 2, July 2012
[10] John, S., Boreczky and D. Lynn, 1998. ―A hidden Markova model framework for video segmentation using audio an image features‖. In: Proceedings of IEEE International conference on Acoustics, Speech and Signal Processing, May 12-15, 6:

Abstract: Different types of data structure and algorithm have been proposed to extract frequent pattern from a given databases. Several tree based structure have been devised to represent the data for efficient frequent pattern discovery. One of the fastest and efficient frequent pattern mining algorithm is CATS algorithm which represent the data and allow mining with a single scan of database. CATS tree can be used with incremental update of the database. Transaction can be added or removed without rebuilding of the whole data structure.

Keywords – Frequent Pattern Mining, Transactional Databases, Minimum Support, Itemsets.

[1] Muthaimenul Adnan and Reda Alhajj, "A Bounded and Adaptive Memory-Based Approach to Mine Frequent Patterns From Very Large Databases" IEEE Transactions on Systems Management and Cybernetics- Vol.41,No. 1,February 2011.
[2] W. Cheung and O. R. Zaiane, "Incremental mining of frequent patterns without candidate generation or support constraint," in Proc. IEEE Int.Conf. Database Eng. Appl., Los Alamitos, CA, 2003, pp. 111–116.
[3] C. K.-S. Leung, Q. I. Khan, and T. Hoque, "Cantree: A tree structure for efficient incremental mining of frequent patterns," in Proc. IEEE Int.Conf. Data Minig, Los Alamitos, CA, 2005,pp. 274-281
[4] M.El-Hajj and O.R. Zaiane, "COFI approach for mining frequent itemsets revisited," In Proc. ACM SIGMOD Worksjop Res. Issues Data Mining knowl. Discovery, New York, 2004, pp. 70-75

[5] B. Rácz, "nonordfp: An FP-growth variation without rebuilding the FP-tree," in Proc. FIMI, 2004

[6] J. Pei, J. Han, and L.V.S. Lakshmanan."Mining frequent itemsets with convertible constraints", In Proc. ICDE 2001, pp. 433–442 [7] K. Sasireka, G.Kiruthinga, K.Raja, "A survey about Various Data Structures For Mining Frequent Patterns from Large Database," International Journal of Research and Reviews in Information Sciences(IJRRIS) Vol. 1, No. 3, September 2011, ISSN: 2046-6439.

[8] C.K.-S. Leung. Interactive constrained frequent-pattern mining system.In Proc.IDEAS 2004, pp. 49–58.

[9] R. Agrawal and R. Srikant, "Fast algorithms for mining association rules", Proc. of the 20th Very Large Data Bases International Conference, (1994), pp. 487-499, Santiago, Chile.

[10] W. Cheung, and O. Zaiane, "Incremental Mining of Frequent Patterns Without Candidate Generation or Support Constraint", Proc. of 7th International Database Engineering and Applications Symposium, (2003), pp. 111–116, Los Alamitos, CA.

Abstract: The vehicular communication is an important issue to the researchers who are engaged in preventing traffic accidents and traffic jams. The earlier vehicular models had discussed only communication among vehicles through the Road Side Units (RSU). Most of the researchers used IEEE 802.11 for vehicular communication in which the vehicles are moving inside the city [1]. But in this paper the author uses the latest VANET technology 802.11p in the Manhattan mobility model in which the nodes are moving inside the city [2]. Without using the RSUs, each vehicle in the Manhattan mobility network is treated as a router to communicate with the neighboring vehicles. The standard VANET routing protocols are applied to the Manhattan mobility model and their characteristics are compared with the use of NS 2.34 version simulator and their results are presented in this work [3].

Keywords: VANET, MANET, RSUs, AODV, DSDV, DSR, 802.11, 802.11p

[1] Yong Hao, Yu Chengcheng, Chi Zhou, Wei Song, 'A Distributed Key Management Framework with Cooperative Message Authentication in VANETs', IEEE Journal on March 2011, Volume: 29 , Page(s): 616-629.
Saeed, R.A. Naemat, A. Bin Aris, A. Bin Awang, M.K. Access Network Technol., Malaysia 'Design and evaluation of lightweight IEEE 802.11p-basedTDMA MAC method for road side -to-vehicle communications', The 12th International Conference Advanced Communication Technology (ICACT), Feb. 2010 Volume: 2 , Page(s): 1483 – 1488.
[2] Routing in Vehicular Ad hoc Networks, 'A survey Vehicular Technology' Magazine,IEEE Issue Date:June 2007 Volume: 2 Issue 2. Page(s): 12-22.

[3] Fan Bai Krishnan, 'Reliability Analysis of DSRC Wireless Communication for Vehicle Safety Applications', Intelligent Transportation Systems Conference, 2006. ITSC 06. IEEE Publication Year: 2006, Page(s): 355 – 362.

[4] Jerbi, M. Senouci, S.M. 'Characterizing Multi-Hop Communication in Vehicular Networks' Wireless Communications and Networking Conference, 2008. WCNC 2008. IEEE Publication Year: 2008, Page(s): 3309 – 3313.
[5] Fan Yu and Subir Biswas, 'Impacts of Radio Access Protocols on thePerformance of DSRC based ITS Applications', Proceeding of the 7th International Conference on ITS Telecommunications, Jun. 2007. Page(s): 34-45
[6] T. Taleb, E. Sakhaee, K. Hashimoto, A. Jamalipour, N. Kato, and Y. Nemoto, 'A stable routing protocol to support ITS Services in VANET Networks,' IEEE Trans. on Vehicular Technology, Vol. 56, No. 6, Nov. 2007. Page: 3337-3347.
[7] Lin Yang Jingdong Xu Gongyi Wu Jinhua Guo, Nankai Univ Tianjin, China 'Road Probing: RSU Assisted Data Collection in Vehicular Networks' Wireless Communications, Networking and Mobile Computing, Beijing, Volume: 16, Issue 6. Page (s): 2 - 3.

[8] Saxena, N., Tsudik, G., Jeong Hyun Yi, Polytech. Univ., Brooklyn, 'Efficient Node Admission and Certificateless Secure Communication in Short-Lived MANETs', Parallel and Distributed Systems, IEEE Transactions - Feb. 2009, Volume: 20 Issue: 2, Page(s): 158 – 170.

[9] Morshed, M.M., Ko, F.I.S. Dongwook Lim, Rahman, M.H., Mazumder, M.R.R., Ghosh, J. 'Performance evaluation of DSDV and AODV routing protocols in Mobile Ad-hoc Networks', New Trends in Information Science and Service Science (NISS), 2010 4th International Conference on Publication Year: 2010 , Page(s): 399 – 403.

[10] Ali, S. Bilal, S.M. 'An Intelligent Routing protocol fo