iosr-JCE   Volume-16 ~ Issue-6 ~ Nov-Dec 2014

Abstract: Lazy learning concept is performing the k-nearest neighbor algorithm, Is used to classification and similarly to clustering of k-nearest neighbor algorithm both are based on Euclidean distance based algorithm. Lazy learning is more advantages for complex and dynamic learning on data streams. In this lazy learning process is consumes the high memory and low prediction Efficiency .this process is less support to the data stream applications. Lazy learning stores the trained data and the inductive process is different until a query is appears, In the data stream applications, the data records flow is continuously in huge volume of data and the prediction of class labels are need to be made in the timely manner.

[1] A. K. Jain and R. C. Dubes. Algorithms for Clustering Data. Prentice-Hall, 1988

[2] D. Pelleg and A. Moore. X-means: Extending k-meanswith efficient estimation of the number of clusters.ICML Conf., pages 727-734, 2000

[3] Peng Zhang,Xingquan Zhu,Byron.Enabling Fast Lazy Learning for Data Streams,Dec 2011

[4] P. Ciaccia, M. Patella, and P. Zezula. M-tree Efficient access method for similarity search in metric spaces. In Proc. of VLDB 1997.

[5] D. Aha. Lazy learning. Artif. Intell. 1997.

Abstract: IEEE has standardized 802.11 protocol for Wireless Local Area Networks.The 802.11 MAC standard specifies two access mechanisms, the Distributed Coordination Function (DCF) and Point Coordination Function (PCF). DCF is considered as the basic MAC mechanism, based on the carrier sensing multiple access with collision avoidance (CSMA-CA) protocol, which is introduced to avoid the collision.The main problem with 802.11 MAC layer is the implementation defects of DCF.The Distributed Coordination Function (DCF) in the IEEE 802.11 standard preserves fairness for all clients at the frame level.

[1]. Bo Zhang, Student Member, IEEE, and Xiaohua Jia, Senior Member IEEE "MultiHop Relay Networks with Consideration of Contention Overhead of Relay Nodes in IEEE 802.11 DCF" in IEEE Transactions on Communications, February 2013.
[2]. M. Heusse, F. Rousseau, G. Berger-Sabbatel, and A. Duda " Perfor- mance anomaly of 802.11b" in Proc. 2003 IEEE INFOCOM, vol. 2, pp. 836843.
[3]. M. Heusse, F. Rousseau, R. Guillier, and A. Duda, " Idle sense: an optimal access method for high throughput and fairness in rate diverse wireless LANs" in ACM SIGCOMM, vol. 35, no. 4, pp. 121132, 2005.
[4]. Victor Bahl, Ranveer Chandra, Patrick P. C. Lee, Vishal Misra, Jitendra Padhye, Dan Rubenstein, Yan Yu "Opportunistic Use of Client Re- peaters to Improve Performance of WLANs" in International Journal of Computer Science Engineering Technology
[5]. S. Lee, S. Banerjee, and B. Bhattacharjee, "The case for a multi-hop wireless local area network" , in Proc. 2004 IEEE INFOCOM, vol. 2, pp. 894905.

Abstract:This scheme proposes a secure and authenticated reversible data hiding in cipher images. Nowadays, we pay more attention to reversible data hiding in encrypted images, as the original cover can be reversibly recovered after embedded data is retrieved. In the first phase, the content owner encrypts the original image using an encryption key. Then, a data hider compresses the least significant bits (LSB's) of the encrypted image using a data hiding key to create space to store some additional data. Then, if a receiver has the data hiding key, he can extract the additional data from the encrypted image though he is unaware of the image content. If the receiver has the encryption key with him, then he can decrypt the data to obtain an image similar to the original image. If the receiver has both, the data hiding key as well as the encryption key, then he can extract the additional data as well as he can recover the original content.

Keywords: Encryption, Steganography, Data Hiding.

[1]. Rini.J ,4th Semester M.Tech ,Study on Separable Reversible Data Hiding in Encrypted Image, International Journal of Advancements in Research & Technology, Volume 2, Issue 12, December-2013 223 ISSN 2278-7763.
[2]. Xinpeng Zhang, Separable Reversible Data Hiding in Encrypted Image, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 7, NO. 2, APRIL 2012.
[3]. Kede Ma, Weiming Zhang, Xianfeng Zhao, Member, IEEE, Nenghai Yu, and Fenghua Li, Reversible Data Hiding in Encrypted Images by Reserving Room Before Encryption, IEEE TRANSACTIONS ON INFORMATION FORENSICS AND SECURITY, VOL. 8, NO. 3, MARCH 2013.
[4]. Lalit Dhande, Priya Khune, Vinod Deore, Dnyaneshwar Gawade, Hide Inside-Separable Reversible Data Hiding in Encrypted Image, International Journal of Innovative Technology and Exploring Engineering (IJITEE) ISSN: 2278-3075, Volume-3, Issue-9, February 2014.
[5]. Jun Tian, Reversible Data Embedding Using a Difference Expansion, IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS FOR VIDEO TECHNOLOGY, VOL. 13, NO. 8, AUGUST .
[6]. M. Johnson, P. Ishwar, V. M. Prabhakaran, D. Schonberg, and K.Ramchandran,, On compressing encrypted data, IEEE Trans. Signal Process., vol. 52, no. 10, pp. 2992–3006, Oct. 2004.

Abstract:Now a day's entire world became a single village because due to the technology of computer science it brings whole globe peoples in front of each other on a single place like a village another thing computer is quite different with human being due its dynamic characteristics like speed, versatility, accuracy, storage and due to these super specialty characteristics computer has got variety of applications and challenges and booms of demands in every field of life. And hence the demands and market of Growing and emerging Multimedia computing and communication applications also increased since last few years.

1]. Fabio Faria, Jefersson Dos Santos, Anderson Rocha and Ricardo Da Torres, ―A Framework for Selection and Fusion of Pattern Classifiers in Multimedia Recognition‖, Pattern Recognition Letters, Vol. 20, No. 7, pp. 1-13, 2013.
[2]. Chih-Ming Chen and Hui-Ping Wang, "Using Emotion Recognition Technology to Assess the Effects of Different Multimedia materials on Learning Emotion and Performance", Information Science Research, Vol. 33, No. 44, pp. 244 –255, 2011.
[3]. Sanjeevkumar R. Jadhav, and Praveenkumar Kumbargoudar, ―Multimedia Data Mining in Digital Libraries: Standards and Features‖ in Proc. READIT-2007, p. 54
[4]. Manda Jaya Sindhu, Madhavi Latha, Samson Deva Kumar and Suresh Angadi, ―Multimedia Retrieval Using Web Mining‖, International Journal of Recent Technology and Engineering, Vol. 2, No. 1, pp. 106-108, 2013.
[5]. Farham Mohamed, Nordin Rahman, Yuzarimi Lazim and Saiful Bahri Mohamed, ―Managing Multimedia Data: A Temporal-Based Approach‖, International Journal of Multimedia and Ubiquitous Engineering, Vol. 7, No. 4, pp. 73-86, 2012.

Abstract: The paper describes an optimized gossip routing protocol between nodes in an ad-hoc network. The backbone of the application is based on an algorithm which is used for routing information in an ad-hoc network from one node to another with the help of optimized gossip routing protocol using Bluetooth as the medium of transmission. Our system dynamically determines the path optimally given a source and destination node. The routing protocol is developed using python and file system is being used for storing purpose.

Keywords: Ad-Hoc Networks, Bluetooth, DES, Gossip Protocol, Python Programming

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Abstract: Big data is large volume, heterogeneous, distributed data. Big data applications where data collection has grown continuously, it is expensive to manage, capture or extract and process data using existing software tools. For example Weather Forecasting, Electricity Demand Supply, social media and so on. With increasing size of data in data warehouse it is expensive to perform data analysis. Data cube commonly abstracting and summarizing databases. It is way of structuring data in different n dimensions for analysis over some measure of interest. For data processing Big data processing framework relay on cluster computers and parallel execution framework provided by Map-Reduce. Extending cube computation techniques to this paradigm. MR-Cube is framework (based on mapreduce)used for cube materialization and mining over massive datasets using holistic measure. MR-Cube efficiently computes cube with holistic measures over billion-tuple datasets.

Keywords: big data, data cube, cube materialization, Map Reduce, MR-Cube.

1]. Xindong Wu, Fellow, IEEE, Xingquan Zhu, Gong-Qing Wu, and Wei Ding" Data Mining with Big Data" IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014
[2]. Zhengkui Wang, Yan Chu, Kian-Lee Tan, Divyakant Agrawal, Amr EI Abbadi, Xiaolong Xu, "Scalable Data Cube Analysis over Big Data" appliarXiv:1311.5663v1 [cs.DB] 22 Nov 2013
[3]. Dhanshri S. Lad #, Rasika P. Saste, "Different Cube Computation Approaches: Survey Paper" (IJCSIT) International Journal of Computer Science and Information Technologies, Vol. 5 (3) , 2014, 4057-4061
[4]. The Apache Software Foundation"http://hadoop.apache.org/docs/current/hadoop-yarn/hadoop-yarn-site/YARN.html"
[5]. Arnab Nandi, Cong Yu, Philip Bohannon, and Raghu Ramakrishnan, Fellow, IEEE, "Data Cube Materialization and Mining over MapReduce" TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 6, NO. 1, JANUARY 2012
[6]. A. Machanavajjhala and J.P. Reiter, "Big Privacy: Protecting Confidentiality in Big Data," ACM Crossroads, vol. 19, no. 1, pp. 20-23, 2012.

Abstract: Heart attack is a leading cause of human deaths worldwide which occurs due to occlusion of coronary arteries and reduced blood flow. Hence, it requires early detection of disease in a non-invasive way through Cardiac Computed Tomography Angiogram images (CCTA). Local thresholding with Hessian matrix based Frangi's Vesselness filter has been applied for segmentation of coronary arterial branches. Stenosis as Region of Interest can be quantified in cardiac arteries by using Sobel Gradient Edge operator with threshold values 76 to 80. Mathematical model has been designed to measure the rate of change of blood flow in coronary arteries by adopting hemodynamic fluid mechanics using Hagen Poiseuille's law with Wall Shear Stress method. Computerized simulation results assist to detect healthy and cardiac diseased artery for better diagnosis.

Keywords - Coronary Artery Disease, Heart Attack, Blood Flow, CT Angiogram, Fluid Mechanics

[1]. Oksuz Ilkay, Unay Devrim, Kadipasaoglu Kamuran (2012) A Hybrid Method for Coronary Artery Stenoses Detection and Quantification in CTA Images, funded project of Turkish Ministry of Science, Industry, and Technology (grant number 00706.STZ.2010-2)
[2]. Yang Guanyu, Kitslaar Pieter, Frenay Michel, Broersen Alexander, Boogers J.Mark, Bax J.Jreoen, Reiber.C.H Johan, Dijkstra Jouke (2011) Int J Cardiovasc Imaging (2012) 28:921–933,DOI 10.1007/s10554-011-9894-2
[3]. Corciova.C, Turnea.M, Corciova.F Analysis of Blood Flow in Coronary Arterial Tree Using Mathematical Models (2012), Proceedings of the 1st International Conference on Health Science and Biomedical Systems (HSBS '12) ISBN: 978-1-61804-097-8
[4]. Labadin.J, Ahmadi.A (2006) Mathematical Modeling of the Arterial Blood Flow Mathematical Modeling of the Arterial Blood Flow, Proceedings of the 2nd IMT-GT Regional Conference on Mathematics, Statistics and Applications, Universiti Sains Malaysia, Penang
[5]. Fibich.G, Lanir.Y, Liron.N (1993) Mathematical model of blood flow in a coronary capillary, Am. J. Physiol. 265 (Heart Circ. Physiol. 34): H1829-H1840

Abstract: Popularization of cloud technology and day to day usage of mobile Internet has become very common in today's fast moving world where people are subjected to post their personal information like account numbers, passwords, notes, and different vital data. This information are cached, copied, and archived by Cloud Service Providers (CSPs), typically for users' authorization and management. There are high chances for the data to fall into wrong side usage where it could be accessed illegally without the users' knowledge. In such situations security for the data on cloud has to be increased. Self-destructing information principally aims at protecting the users' data confidentiality. All the data and their copies become destructed or unclear after a user-specified time, with no intervention from the user part. SeDas system meets this challenge by a new combination of cryptographic techniques and active storage framework based on T10 OSD standard. These security procedures and its functionalities make sure that SeDas meets all privacy-preserving policies and is easy for practical use. Compared to the system without SeDas the performance for uploading/downloading files has been achieved better.

Keywords: active storage, cloud computing, cloud service providers, cryptographic techniques, data confidentiality, self-destruction data system.

[1] R. Geambasu, T. Kohno, A. Levy, and H. M. Levy, Vanish: Increasing data privacy with self-destructing data, in Proc. USENIX Security Symp., Montreal, Canada, Aug. 2009, pp. 299–315.
[2] A. Shamir, How to share a secret, Commun. ACM, vol. 22, no. 11, pp. 612–613, 1979.
[3] J. R. Douceur, The sybil attack, in Proc. IPTPS '01: Revised Papers From the First Int. Workshop on Peer-to-Peer Systems, 2002.
[4] L. Zeng, Z. Shi, S. Xu, D. Feng, Safevanish: An improved data self-destruction for protecting data privacy, in Proc. Second Int. Conf. Cloud Computing Technology and Science (CloudCom), Indianapolis, IN, USA, Dec. 2010, pp. 521–528.
[5] L. Qin and D. Feng, Active storage framework for object-based storage device, in Proc. IEEE 20th Int. Conf. Advanced Information Networking and Applications (AINA), 2006.

Abstract: The computerized process in any facet of human existence or any sector of any economy promotes efficiency accuracy, effectiveness, improved service delivery and many more. This is also applies to the computerization of power monitoring processes. Computer aided monitoring devices eliminated errors due to human limitations, preserve data effectively and alleviate complaints emanating from aggrieved customers. In view of this, the study proposes a prototype of a computerized power monitoring system, as explores its associated benefits and technicalities. An indepth literature review is provided to establish a proper foundation for the appreciation of subject matter. In conclusion, the study upholds the fact that the full automation of processes in the power sector is the best for our economy, as it calls for the development of computer experts in the sector.

Keywords: Computerized process, data, EEDC and Umuahia.

[1]. Ayankola, M and Ezeobi O. (2010) Why States may invest in Electricity Distribution (online) available Http:nigerianbestforum.com/generaltopics
[2]. Ayodele, A.S. (2008) Improving and Sustaining Power (electricity) supply for socio Economic Development in Nigeria (online) Available Http:nigerianbestforum.com/general topics
[3]. Bayo,A. (2000) Marketing NEPA Resources. Ibadan: Bi Monthly Magazines.
[4]. Christer, I. (2007) Power monitoring Systems (online) available http:nigerianbestforum.com/general topics.
[5]. Ihesiudo, S.O (2007): The Principal of Business financing Chedal Global Press Aba.
[6]. Issues and Options Department of Economics University of Ibadan, Ibadan Nigeria.
[7]. Jacobson R.E (2010) The RW-300 Control and Monitoring System. The Thompson Ranowooldridge products company, Beverly Halls Califfornia.

Abstract: Due to the increase nature of computer threats and attacks, the security of the operating system is paramount in the computing world today. Every modern computer system, from network servers, workstation desktops, to laptops and hand-held devices, has a core piece of software, called operating system (OS) executed on the top of a bare machine of hardware that allocates the basic resources of the system and supervises the execution of all applications within the system. This paper investigates and evaluates the security of Google Chrome Operating System. Google Chrome Operating system is an operating system developed by google, which runs on specialized hardware. The Chrome OS differ from traditional operating system such as Windows in that it is designed to work specifically with web applications. In this operating system, the user data lives essentially on the web. Thus, if the physical machine-laptop is lost or stolen, the user can still access their data online. However, the Chromebook also allows users to access downloaded data offline, which must be kept safe. To achieve this, Chrome OS ensures that all downloaded data is protected and that code running on this Chromebook is safe to use. In order to avoid security challenges of traditional operating system such as virus and worms, ChromeBook not only ensures that the code is safe, but also incorporates an autoupdate features to add new patches to the system. Keywords: Security, Google Chrome, Web, Operating System,

[1]. Azad, S. (2012). Chrome OS and System Architecture. Retrieved November 3, 2014, from http://sufianalogy.blogspot.com/
[2]. Emperador, A., & Norman, A. N. (2013). Analysing ChromeOS's Boot Performance. Austin: The University of Texas .
[3]. Fang, K., Hanus, D., & Zheng, Y. (2010). Security of Google Chromebook. Massachuset Institute of Technology Cambridge.
[4]. Habib, S. M., & Zubair, S. (2009). Security Evaluation of the Windows Mobile Operating System. Goteborg, Sweden: Chalmers University of Technology.
[5]. Kettula, A. (2009). Retrieved October 2014, from Secuirty Comparison of Mobile OSes: http://www.tml.tkk.fi/Opinnot/Tik-110.501/2000/papers/kettula.pdf
[6]. Munsee, C. L., & Lee, C. (2002). Security Evaluation of the Linux Operating System. Oregon: Oregon State University, Corvallis,.
[7]. Nathan, C. (2010, June 30). How the Google Chrome OS Works. Retrieved November 2014, from <http://computer.howstuffworks.com/google-chrome-os.htm>
[8]. Yang, C.-Q. (2003). Operating System Secuirty and Secure Operating System. SANS Institute.

Abstract: Internet usage attains a peek level in these days. It is portable due to arrival of smart phones. People can enquire information about any products they are in need using the Electronic sites. Their needs are satisfied by many sites now-a-days. This paper talks about the recommender system which gives useful information to users to assist them in finding what they are in need. In this process, data mining techniques can be used to find best recommendations. Association rule mining, classification, clustering is used here which give good personalized ideas.

Key words: Recommender System, social networks, Data mining, Association rule mining, Classification, Clustering

[1]. G. Adomavicius and A. Tuzhilin, "Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions," IEEE Trans. Knowledge and Data Eng., vol. 17, no. 6, pp. 734-749, June 2005.
[2]. Sotiris Kotsiantis, Dimitris Kanellopouios "Association Rules Mining: A Recent Overview" GESTS International Transactions on Computer Science and Engineering, Vol.32 (1),2006.
[3]. Walter Alberto Aldana, " Data Mining Industry: Emerging Trends and New Opportunities", May 2000.
[4]. E.W.T. Ngai, Li Xiu andD.C.K. Chau, "Application of data mining techniques in customer relationship management: A literature review and classification" Department of Management and Marketing, The Hong Kong Polytechnic University, Hong Kong, PR China, Department of Automation, Tsinghua University, Beijing, PR China.
[5]. J. Ben Schafer,The Application of Data-Mining to Recommender Systems ( Encyclopedia of Data Warehousing and Mining, Second Edition, 2009).
[6]. P.T. Joseph, E-Commerce book (Prentice Hall of India-New Delhi, 2009).

Abstract: Efficient frequent pattern mining algorithms are decisive for mining association rule. In this paper, we examine the matter of association rule mining for items in a massive database of sales transactions. Finding large patterns from database transactions has been suggested in many algorithms like Apriori, DHP, ECLAT, FP Growth etc. But here we have introduced newer algorithm called Improved Frequent Pattern Mining Algorithm with Indexing (IFPMAI), which is efficient for mining frequent patterns. IFPMAI uses subsume indexes i.e. those itemsets that co-occurrence with representative item can be identified quickly and directly using simple and quickest method. This will become beneficial like (i) avoid redundant operations of itemsets generation and (ii) many frequent items having the same supports as representative item, so the cost of support count is reduced hence the efficiency is improved. Then an example is used to illustrate this proposed algorithm. The results of the experiment show that the new algorithm in performance is more remarkable for mining frequent patterns.

Keywords: Association rule, Frequent pattern mining, Subsume Indexes, IFPMAI

[1] Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, MORGAN KAUFMANN PUBLISHER, An Imprint of Elsevier
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[4] Han, Pei & Yin, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, Volume 8, Issue 1 , pp 53-87,2004
[5] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, " New Algorithms for Fast Discovery of Association Rules", Proc. 3rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'97, Newport Beach, CA), 283-296 AAAI Press, Menlo Park, CA, USA 1997
[6] Shruti Aggarwal, Ranveer Kaur, "Comparative Study of Various Improved Versions of Apriori Algorithm", International Journal of Engineering Trends and Technology (IJETT) - Volume4Issue4- April 2013