Volume-3 , Issue-10 , Oct - 2015 Go Back

Data is growing at a rate which cannot be handled by the traditional methods of computing. To store and process such data new data analysis and storage techniques have emerged over the last few years. Hadoop is one such parallel processing open source framework which provides distributed storage and processing of big data. This paper introduces Big Data, a new platform which enables accessing, manipulating, analyzing, and visualizing data residing on a Hadoop cluster. In this paper a survey is done on big data analysis using Hadoop and other utility tools like Pig and Hive. The majority of large-scale data intensive applications executed by data centers are based on Map-Reduce or its open-source implementation, Hadoop. Such applications are executed on large clusters requiring large amounts of energy, making the energy costs a large fraction of the data center’s overall costs. Therefore to minimizing the energy consumption when executing Map-Reduce jobs is a critical concern for data centers. In this survey Flight data has been analyzed in terms of the mentioned parameters such as time complexity and energy consumption information’s are retrieved using Hadoop.

Map reduce, big data, Hadoop, HDFS, Pig & Hive, Flight data

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[1] Toshimori Honjo, Kazuki Oikawa "Hardware acceleration of Hadoop Map-Reduce" in 2013 IEEE International Conference on Big Data.
[2] Ming Meng, Jing Gao, Jun-jie Chen "Blast-Parallel: The Parallelizing Implementation Of Sequence Alignment Algorithms Based On Hadoop Platform" in 2013 6th International Conference on Biomedical Engineering and Informatics (BMEI 2013).
[3] Madhury Mohandas, Dhanya P M "An Approach for Log Analysis Based Failure Monitoring in Hadoop Cluster" in 2013 IEEE.
[4] Ilja Kromonov, Pelle Jakovits, Satish Narayana Srirama "NEWT - A Resilient BSP Framework for Iterative Algorithms on Hadoop YARN" in 2014 IEEE.
[5] Lena Mashayekhy, Mahyar Movahed Nejad, Daniel Grosu, Dajun Lu, Weisong Shi "Energy-aware Scheduling of MapReduce Jobs" in 2014 IEEE International Congress on Big Data.
[6] Oscar D. Lara, Weiqiang Zhuang, and Adarsh Pannu "Big R: Large-scale Analytics on Hadoop using R" in 2014 IEEE International Congress on Big Data
[7] Kiran M., Amresh Kumar "Verification and Validation of Parallel Support Vector Machine Algorithm based on Map-Reduce Program Model on Hadoop Cluster" in 2013 International Conference on Advanced Computing and Communication Systems (ICACCS -2013), Dec. 19 – 21, 2013, Coimbatore, INDIA

Ankita Kadre and S.R Yadav, "A Survey on Map Reduce Algorithm for Big data analysis using Hadoop, Pig and Hive utility tools," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.52-57, 2015.

Thinning is a challenging and important part while involvement with any image extraction process many works have been presented in this area, many thinning algorithms though have produced good results but still there needs a lot of improvement. Most of the algorithms based on thinning an image cannot justify the connectivity of various parts of the image. This paper presents algorithm for automated thinning of linear features by maintaining the integrity of pixels in order to maintain the connectedness. The connectivity of various pixels is achieved by traversing through the pixels and considering those pixels which have maximum number of neighbors. The iterative algorithm for thinning as presented in this paper takes the image into sections and represents each section as matrix to perform number of traversals. Thinning is an essential step of data compression useful in recognition and extraction of various morphological features from topographic sheets.

Thinning, Morphological features

[1] G.S G.S.Ng, R.W.Zhou, C.Quek, A Novel Single Pass Thinning Algorithm, Nayang Technological University, School Of Applied Science, Research Laboratory II,Nayang Avenue,Singapore,2263.
[2] L. Liu1, E.W. Chambers2, D. Letscher2, and T. Ju1, A simple and robust thinning algorithm on cell complexes, 1Washington University in St. Louis, USA 2St. Louis University, USA Pacific Graphics 2010P. Alliez, K. Bala, and K. Zhou(Guest Editors)Volume 29 (2010), Number 7, Journal compilation c 2010 The Euro graphics Association and Blackwell Publishing Ltd.Published by Blackwell Publishing, 9600 Garsington Road, Oxford OX4 2DQ, UK and350 Main Street, Malden, MA 02148, USA.
[3] Lei Huang, Genxun Wan, Changping Liu, An Improved Parallel Thinning Algorithm, Institute of Automation, Chinese Academy of Sciences, P. R. China, Proceedings of the Seventh International Conference on Document Analysis and Recognition (ICDAR 2003).
[4] Jingliang Peng, An Efficient Algorithm of ThinninScanned pencil Drawings Computer Science Department, Peking University.
[5] Dongjun Xin, Xianzhong Zhou, Huali Zheng, Contour Line Extraction from Paper-based Topographic Maps College of Automation, Nanjing University of Science & Technology, Nanjing 210094 School of Management and Engineering, Nanjing university, Nanjing 210093 Artillery and Air DefencArmy Research Institute of Equipment & Technology,Beijing 100012
[6] R. Pradhan, S. Kumar, R. Agarwal, Mohan P. Pradhan & M. K. Ghose , Contour Line Tracing Algorithm for Digital Topographic Maps , International Journal of Image Processing (IJIP), Volume (4): Issue (2) , 2010.

Kapila Sharma, "An Effective Approach of Thinning for Morphological Features An Effective Approach of Thinning for Morphological Features," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.58-60, 2015.

Access control mechanisms shield sensitive data from unauthorized users. On the other hand, when sensitive information is released and a Privacy Protection Mechanism (PPM) is not in set up, an authorized user can still compromise the privacy of a person leading to identity exposure. A PPM can use concealment and speculation of social information to anonymize and fulfill protection prerequisites here some algorithm i.e. k-anonymity and l-diversity used against identity as well as attribute disclosure. However, security is accomplished at the expense of exactness of authorized data or information. Paper describes an accuracy-constrained privacy-preserving access control model. Role based access control policies define selection predicates available to roles and it should be satisfy the k-anonymity or l-diversity. An extra limitation that should be fulfilled by the PPM is the imprecision headed for every choice predicate. However, the problem of satisfying the accuracy constraints used for multiple roles has not been studied before. In our formulation ,technique used heuristics for anonymity algorithms and also done experiments to show proposed approach satisfies imprecision bounds for more permissions and find has lower total imprecision than the earlier methods.

Access control, privacy, k-anonymity, l-diversity

[1] Zahid Pervaiz, Walid G. Aref, Arif Ghafoor, Fellow, Nagabhushana Prabhu “Accuracy-Constrained Privacy-Preserving Access Control Mechanism for Relational Data” ,IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 4, APRIL 2014
[2] B. Fung, K. Wang, R. Chen, and P. Yu, “Privacy-Preserving Data Publishing: A Survey of Recent Developments,” ACM Computing Surveys, vol. 42, no. 4, article 14, 2010.
[3] Abdullah Abdulrhman AlShwaier, Dr,Ahmed Zayed Emam "A Novel Approach for DATA PRIVACY on E-HEALTH CARE SYSTEM “, International Journal of Engineering, Business and Enterprise Applications (IJEBEA)
[4] E. Bertino and R. Sandhu, “Database Security-Concepts, Approaches, and Challenges,” IEEE Trans. Dependable and Secure Computing, vol. 2, no. 1, pp. 2-19, Jan.-Mar. 2005.
[5] A Rask, D. Rubin, and B. Neumann, “Implementing Row-and Cell-Level Security in Classified Databases Using SQL Server 2005” , MS SQL Server Technical Center, 2005.
[6] P. Samarati, “Protecting Respondents’ Identities in Microdata Release” , IEEE Trans. Knowledge and Data Eng., vol. 13, no. 6, pp. 1010-1027, Nov. 2001.
[7] A. Machanavajjhala, D. Kifer, J. Gehrke, and M. Venkitasubramaniam, “L-Diversity: Privacy Beyond k-anonymity”, ACM Trans. Knowledge Discovery from Data, vol. 1, no. 1, article 3, 2007.
[8] K. Browder and M. Davidson, “The Virtual Private Database in oracle9ir2” , Oracle TechnicalWhite Paper, vol. 500, 2002.
[9] T. Iwuchukwu and J. Naughton, “K-Anonymization as Spatial Indexing: Toward Scalable and Incremental Anonymization” ,Proc. 33rd Int’l Conf. Very Large Data Bases, pp. 746-757, 2007.
[10] S. Rizvi, A. Mendelzon, S. Sudarshan, and P. Roy, “Extending Query Rewriting Techniques for Fine-Grained Access Control” , Proc. ACM SIGMOD Int’l Conf. Management of Data, pp. 551-562,2004.
[11] W. Hoeffding, “On the Distribution of the Number of Successes in Independent Trials” , The Annals of Math. Statistics, vol. 27, no. 3.
[12] S. Chaudhuri, T. Dutta, and S. Sudarshan, “Fine Grained Authorization through Predicated Grants” , Proc. IEEE 23rd Int’l Conf. Data Eng., pp. 1174-1183, 2007.
[13] N. Li, W. Qardaji, and D. Su, “Provably Private Data Anonymization:Or, k-Anonymity Meets Differential Privacy” , Arxiv preprint arXiv:1101.2604, 2011.
[14] G. Ghinita, P. Karras, P. Kalnis, and N. Mamoulis, “Fast Data
Anonymization with Low Information Loss” , Proc. 33rd Int’l Conf. Very Large Data Bases, pp. 758-769, 2007.
[15] K. LeFevre, D. DeWitt, and R. Ramakrishnan, “Workload-Aware Anonymization Techniques for Large-Scale Datasets” , ACM Trans. Database Systems, vol. 33, no. 3, pp. 1-47, 2008.
[16] Kenig B, and Tassa T, A Practical Approximation Algorithm for Optimal k-Anonymity, Division of Computer Science, The Open University, Raanana, Israel. Cited at: http://www.openu.ac.il/Personal_sites/tamirtassa/Download/Journals/optimal_k_anon.pdf
[17] D. Ferraiolo, R. Sandhu, S. Gavrila, D. Kuhn, and R. Chandramouli,“Proposed NIST Standard for Role-Based Access Control” ,ACM Trans. Information and System Security, vol. 4, no. 3, pp. 224-274, 2001.
[18] K. LeFevre, D. DeWitt, and R. Ramakrishnan, “Mondrian Multidimensional K-Anonymity”, Proc. 22nd Int’l Conf. Data Eng., pp. 25-25, 2006.
[19] J. Friedman, J. Bentley, and R. Finkel, “An Algorithm for Finding Best Matches in Logarithmic Expected Time”, ACM Trans. Mathematical Software, vol. 3, no. 3, pp. 209-226, 1977.
[20] A. Meyerson and R. Williams, “On The Complexity of Optimal k-Anonymity”, Proc. 23rd ACM SIGMOD-SIGACT-SIGART Symp.Principles of Database Systems, pp. 223-228, 2004.
[21] G. Aggarwal, T. Feder, K. Kenthapadi, R. Motwani, R. Panigrahy,D. Thomas, and A. Zhu, “Approximation Algorithms for k-Anonymity” , J. Privacy Technology, vol. 2005112001,pp. 1-18, 2005.
[22] R. Sandhu and Q. Munawer, “The Arbac99 Model for Administration of Roles” , Proc. 15th Ann. Computer Security Applications Conf.,pp. 229-238, 1999.
[23] E. Otoo, D. Rotem, and S. Seshadri, “Optimal Chunking of Large Multidimensional Arrays for Data Warehousing” , Proc. ACM 10th Int’l Workshop on Data Warehousing and OLAP, pp. 25-32, 2007.
[24] T.Monika, S.JayaPrakash, “Application specific Anonymization and Privacy – Preserving Access Control Mechanism for Relational data”, National Conference on Research Advances in Communication, Computation, Electrical Science and Structures(NCRACCESS-2015) pp. 16-22, ISSN: 2348-8387,2015 at
[25] T.M.Arun Prabu, C.Anuradha, “Privacy preserving access control mechanism for electronic mail”, International Journal of Computer Sciences and Engineering and scientific technology, March 2015.

Barkha Kasab, Vinayak Pottigar and Swapnaja Ubale, "Enabling Privacy Preservation Technique to Protect Sensitive Data with Access Control Mechanism Using Anonymity," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.61-65, 2015.

Clustering focuses on grouping similar objects in one cluster and dissimilar objects into another cluster. In clustering, this concept of boosting applies to the area of predictive data mining to generate multiple clusters. There is an existing cluster based boosting(CBB) system which focus on real data sets applied to it as input. It uses K-means algorithm that evolved in limited number of clusters with over fitting and it also holds two limitations: 1.Subsequent functions ignoring troublesome areas 2.Complex subsequent functions. To overcome these drawbacks hierarchical clustering is proposed and thus enhances the accuracy of desired output of CBB approach compared to popular boosting algorithm. The comparative analysis may show the improvement in performance of the system. The users may obtain refined clusters with more accuracy as desired output.

Boosting, Clustering, Hierarchical clustering, Classifier combining, Machine Learning, Supervised learning, Computer graphics, Artificial intelligence

[1]L. Dee Miller and Leen-Kiat Soh,"Cluster based Boosting", IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, Volume-27, Isuue-6,Page No (1-12),June 2015
[2]C. Zhang and Y. Ma,"Ensemble Machine Learning" New York, NY, USA: Springer,Page No (76),July 2012
[3]A. Vezhnevets and O. Barinova,"Avoiding boosting overfitting by removing confusing samples", Springer Berlin Heidelberg,Volume-4701,Page No (430–441),2007
[4]D.-S. Kim, Y.-M. Baek, and W.-Y. Kim,"Reducing overfitting of adaboost by clustering-based pruning of hard examples", The Korean Society of Broadcast Engineers, Volume-18, Issue-4, Page No (643-646),Jul 2007
[5]M. Okabe and S. Yamada,“Clustering by learning constraints priorities,” in Proc. Int. Conf. Data Mining,Page No (1050–1055),2012.
[6]A. Ganatra and Y. Kosta,"Comprehensive evolution and evaluation of boosting", Int. J. Comput. Theory Eng.,Volume-2, Page No ( 931–936),2010.
[7]D. Frossyniotis, A. Likas, and A. Stafylopatis,"A clustering method based on boosting", Pattern Recog. Lett.,Volume-25,Page No ( 641–654), 2004.
[8]L. Reyzin and R. Schapire,"How boosting the margin can also boost classifier complexity", in Proc. Int. Conf. Mach. Learn.,Page No ( 753–760),2006
[9]R. Schapire and Y. Freund,"Boosting: Foundations and Algorithms", Cambridge, MA, USA: MIT Press, 2012.
[10] J. Chou, C. Chiu, M. Farfoura, and I. Al-Taharwa, “Optimizing the prediction accuracy of concrete compressive strength based on acomparison of data-mining techniques,” J. Comp. Civil Eng.,Volume-25,Page No (242–253), 2011
[11]David Eppstein,"Fast Hierarchical Clustering and Other Applications of Dynamic Closest Pairs",ACM New York, NY, USA, Volume-5,2005
[12]Y. Freund,"An adaptive version of the boost by majority algorithm,"Mach. Learn.,Volume-43,Page No (293–318), 2001
[13]Preeti Baser and Dr. Jatinderkumar R. Saini,"A Comparative Analysis of Various Clustering Techniques used for Very Large Datasets",Volume-3,Page No (1-3),Issue-4,March 2013

Nilam Kolhe, Harshada Kulkarni, Ishita Kedia and Shivani Gaikwad, "Comparative Analysis of Cluster based Boosting," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.60-70, 2015.

Image re-ranking, is an emphatic way to advance the results of web-based image search and has been accept by current economic search engines such as Bing and Google. When a query keyword is given, a list of images are first redeem based on textual dossier given by the user. By asking the user to select a query image from the pool of images, the remaining images are re-ranked based on their index with the query image. A major contempt is that sometimes semantic meanings may clarify user’s search intention. Many people recently expected to match images in a semantic space which used attributes or mention classes closely associated to the semantic meanings of images as basis. In this paper, we introduce a novel image re -ranking framework, in which axiomatically offline learns different linguistic spaces for different query keywords and displays with the image particulars in the form of augmented images. The images are envisaged into their associated semantic spaces to get semantic signatures with the help of one click feedback from the user. At the online stage, images are re-ranked by analyze their semantic signatures access from the semantic space described by the query keyword given by the user. The expected query-specific semantic signatures significantly advance both the efficiency and capability of image re-ranking. Experimental results show that 25-40 percent related improvement has been accomplished on re-ranking precisions correlated with the state-of-the-art methods.

Image search, Image re-ranking, Semantic space, Semantic signature, Keyword extension, One click feedback

[1] Xiaogang Wang, Member, IEEE , Shi Qiu, Ke Liu, and Xiaoou Tang, Fellow, IEEE, “Web Image Re-Ranking Using Query-Specific Semantic Signatures,” IEEE transactions on pattern analysis and machine intelligence, vol. 36, no. 4, april 2014
[2] R. Datta, D. Joshi, and J.Z. Wang, “Image Retrieval: Ideas, Influences, and Trends of the New Age,” ACM Computing Surveys, vol. 40, article 5, 2007.
[3] A.W.M. Smeulders, M. Worring, S. Santini, A. Gupta, and R. Jain, “Content-Based Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 22, no. 12, pp. 1349-1380, Dec. 2000.
[4] Y. Rui, T.S. Huang, M. Ortega, and S. Mehrotra, “Relevance Feedback: A Power Tool for Interactive Content-Based Image Retrieval,” IEEE Trans. Circuits and Systems for Video Technology, vol. 8, no. 5, pp. 644-655, Sept. 1998.
[5] X.S. Zhou and T.S. Huang, “Relevance Feedback in Image Retrieval: A Comprehensive Review,” Multimedia Systems, vol. 8, pp. 536-544, 2003.
[6] D. Tao, X. Tang, X. Li, and X. Wu, “Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1088-1099, July 2006.
[7] J. Cui, F. Wen, and X. Tang, “Real Time Google and Live Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.
[8] J. Cui, F. Wen, and X. Tang, “Intent Search: Interactive on-Line Image Search Re-Ranking,” Proc. 16th ACM Int’l Conf. Multimedia, 2008.
[9] X. Tang, K. Liu, J. Cui, F. Wen, and X. Wang, “Intent Search: Capturing User Intention for One-Click Internet Image Search,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 34, no. 7, pp. 1342-1353, July 2012.
[10] W. Hsu, L. Kennedy, and S.F. Chang, “Video Search Re-ranking via Information Bottleneck Principle,” Proc. 14th Ann. ACM Int’l Conf. Multimedia, 2006.
[11] D. Tao, X. Tang, X. Li, and X. Wu, “Asymmetric Bagging and Random Subspace for Support Vector Machines-Based Relevance Feedback in Image Retrieval,” IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 28, no. 7, pp. 1088-1099, July 2006.
[12] Y. Rui, T.S. Huang, and S. Mehrotra, ”Content-Based Image Retrieval with Relevance Feedback in MARS,” Proc. IEEE Int’l Conf. Image Processing, vol. 2, pp. 815-818, 1997.
[13] G. Cauwenberghs and T. Poggio, “Incremental and Decremental Support Vector Machine Learning,” Proc. Advances in Neural Information Processing Systems (NIPS), 2001.
[14] J.C. Platt, “Fast Training of Support Vector Machines Using Sequential Minimum Optimization,” in Sch¨olkopf, Burges and Smola, Eds., Advances in Kernel Methods– Support VectorLearning, Cambridge MA: MIT Press, 1998, pp 185-208.
[15] T.-T. Frieß, N. Cristianini and C. Campbell, “The Kernel Adatron Algorithm: A Fast and Simple Learning Procedure for Support Vector Machines,” in 15th Int. Conf. Machine Learning,Morgan Kaufman, 1998.
[16] L. Torresani, M. Szummer, and A. Fitzgibbon, “Efficient Object Category Recognition using Classemes,” Proc. European Conf. Computer Vision (ECCV), 2010.
[17] J. Lui, B. Kuipers, and S. Savarese, “Recognizing Human Actions by Attributes,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2011.
[18] M. Sahami and T.D. Heilman, “A Web-Based Kernel Function for Measuring the Similarity of Short Text Snippets,” Proc. 15th Int’l Conf. World Wide Web (WWW), 2006.
[19] W.J. Scheirer, N. Kumar, P.N. Belhumeur, and T.E. Boult, “Multi-Attribute Spaces: Calibration for Attribute Fusion and Similarity Search,” Proc. IEEE Conf. Computer Vision and Pattern Recognition (CVPR), 2012.

B. UshaRani and M.V.S.N.Maheswar, "Re-Ranking of Images Using Semantic Signatures with Queries," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.71-75, 2015.

The health care industry is generally rich in information which is not feasible to handle manually. These large amounts of data are very important in the field of Data Mining to extract useful information and generate relationship amongst the attributes. In the health care industry, for predicting the diseases from the datasets data mining is used. Heart disease prediction is treated as most complicated task in the field of medical sciences. This paper investigates a number of techniques in the detection of heart disease. This paper includes a blueprint of application of data mining in heart disease prediction.

Naive Baiye, Decision Tree, Data mining, Classification, Clustering

[1] Jesmin Nahar, Tasadduq Imam, and Kevin S.Tickle, “Computational intelligence for heart disease diagnosis: A medical knowledge driven approach”, Elsevier Ltd, 2012.
[2] Sivagowry .S, Dr. Durairaj. M,Persia.A, “An Empirical Study on applying Data Mining Techniques for the Analysis and Prediction of Heart Disease”, IEEE, 2013.
[3] M.Akhil jabbar,B.L Deekshatulua ,Priti Chandra, “Classification of Heart Disease Using K-Neighbor and Genetic Algorithm, CIMTA”, Elsevier Ltd,2013
[4] Ankita Dewan,Meghna Sharma,”Prediction of Heart Disease using Hybrid Technique in Data Mining Classification”,IEEE,2015.
[5] Jyothi Soni, Uzma ansari and Dipesh Ansariss “Intelligent and Effective Heart Disease Prediction System using Weighted Associate Classifer”, IJCSE, Vol 3(6), pp 2385-2392, June 2011.

Sairabi Mujawar and Prakash Devale, "Heart Disease Prediction Using Modified K Means and Using Naive Baiyes," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.76-78, 2015.

The idea of this paper is to study the symmetric key algorithms like DES, RC5, AES, Triple DES, BLOWFISH etc. so that the better algorithm can be picked to import its functionality in the Android based mobile devices. Here, we analyse their performance mainly this beats, end memory requirements and speed are studied to pick suitable algorithms to be implemented on the mobile and handheld devices. The above algorithms will be first implemented in C, C++ or JAVA and their performance will be analysed. The proposal extends as when an encrypted message is sent from one mobile phone to the other, a standard and essential key is provided which is symmetric in nature. If any of the two or more parties want to open the message that is sent to them, they have to utilize the secret code in the provided key without leaving a single digit. The message will be decrypted only after the full key version is matched with the given symmetry of the key .This makes the data or any information more secure even in the mobile devices for the common users by non-repudiation. This ensures the use of cryptography and its advantages by the end users of mobile phones for confidentiality.

Non-repudiation, symmetric algorithms, RC5,DES, AES, 3DES, full key, cryptography

[1.] Atul Kahate “Cryptography and Network Security”, Tata McGraw-Hill Companies, 2008.
[2.] William Stallings “Network Security Essentials (Applications and Standards)”, Pearson Education, 2004
[3] Ronald L. Rivest, “RC5 Encryption Algorithm”, Dr Dobbs Journal, Vol. 226, PP. 146-148, Jan 1995
[4] Ronald L. Rivest, The RC5 Encryption Algorithm, MIT Laboratory for Computer Science 545 Technology Square, Cambridge, Mass.02139 (Revised March 20, 1997).
[5]Khawlah A. AI-Rayes, Aise Zulal Sevkli, Hebah F. AI-Moaiqel, Haifa M. AI-Ajlan, Khawlah M. AI-Salem, Norah I. AI-Fantoukh "A Mobile Tourist Guide for Trip Planning" IEEE MULTIDISCIPLINARY ENGINEERING EDUCATION MAGAZINE, VOL. 6, NO. 4, DECEMBER 2011
[6]MIDP_Mobile_Media_API_Developers_Guide_v2_en
[7] Daemen, J., and Rijmen, V. "Rijndael: The Advanced Encryption Standard." Dr. Dobb's Journal, March 2001.
[8.] R.L.Rivest, A.Shamir, and L.Adleman, “A Method for Obtaining Digital Signatures and Public-Key Cryptosystems,” Communication of the ACM, Volume 21 No. 2, Feb. 1978.
[9] Ronald L. Rivest, The RC5 Encryption Algorithm, MIT Laboratory for Computer Science 545 Technology Square, Cambridge, Mass.02139 (Revised March 20, 1997). Available at: httu://theory.lcs.mit.cdu/-rivest/Rivest-rc5rev.pd
[10]“Detecting passive content leaks and pollution in android applications,” in Proceedings of the Network and Distributed System Security Symposium , 2013.

Shahebaz Ahmed Khan, P. Padmanabham and K V Naganjaneyulu, "Providing RC5 Security for Messages in Android Based Mobile Devices for Achieving Confidentiality," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.79-83, 2015.

Hadoop is popular distributed system used for the analysis of large amount of data. Hadoop is based on distributed computing having HDFS (Hadoop Distributed File System) &Map Reduce programming paradigm. Hadoop is highly fault-tolerant due to its imitation of data transversely on multiple nodes and can be set out on low cost hardware. The file system –HDFS—written in JAVA and designed for heterogeneous hardware and software. Hadoop is very much appropriate for high volume of data & where data format is different like semi structured, unstructured. Hadoop also make available the high speed admittance to the data of the application which we want to use. Hadoop architecture is cluster based (cluster consists of racks), which is consist of nodes (data note, name node), physically separate to each other, in idyllic circumstances. In Hadoop a program known as map-reduce is used to collect data according to query. As Hadoop is used for massive amount of data therefore scheduling and way of containing data in Hadoop must be efficient for better presentation. With this feature of Hadoop the traditional system is replacing with Hadoop. The research objective is to study and explore various scheduling techniques, which are used to increase performance in Hadoop. This paper include the idea of working of Hadoop, its internal details and why Hadoop is better than the Traditional system.

Hadoop, HDFS, Name node, Data node. Map Reduce, Data locality, Job Tracker, Task Tracker

[1] Transl. J. Magn. Japan, [Digests 9th Annual Conf. Magnetics Japan, Vol. 2, pp. 740-741, August 1987 pp. 301, 1982].
[2] Chris Eaton and Tom Deutsch, Understanding Big Data-Analytics for Enterprise Class Hadoop and Streaming Data.
[3] Arun C. Murthy and Vinod Kumar Vavilapalli, Apache Hadoop YARN-Moving beyond MapReduce and Batch Processing with Apache Hadoop 2.
[4] http://www.bigdatauniversity.com/web/media/player.php?file=BD001V212EN/Videos/Unit_1_What_is_Hadoop_Part1.mp4&caption=files.db2university.com/BD001V212EN/Videos/EN/Unit_1_What_i s_Hadoop_Part1.srt
[5] https://www.youtube.com/watch?v=DLutRT6K2rM
[6] Figure 2. The flow of data in a simple MapReduce job pp.62 Chris Eaton and Tom Deutsch, Understanding Big Data- Analytics for Enterprise Class Hadoop and Streaming Data.

Sunny Kumar, "Big Data Platform-A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.84-87, 2015.

Majority of the music recommendation systems in use today use historical preferences of other users having similar taste to recommend songs to a particular user. Other systems use past preferences of the current user and musical attributes of songs to make recommendations. In this paper, we use a novel approach to recommend music to users based on content-based filtering. This system can be used both as a search engine and for making recommendations. Moreover, this system does not suffer from the cold start problem which most of the recommender systems suffer from. Our system has a very small learning curve. We present a simple yet fast approach to make music recommendations using echonest’s music attributes. The system is based on calculating the Euclidean distance to find out top recommended songs. This system can be used in combination with traditional recommendation systems for more effective recommendation. We think music users will find this system easy to use and experiment with and therefore helpful to discover new music. This system will result in increased enjoyment of music for users.

Music; Recommendation; Euclidean Distance; Machine Learning; Content-Based Filtering; Echonest; Danceability

[1] Spotify, music for everyone. https://www.spotify.com, Aug 2015
[2] Collaborative Filtering at Spotify, slide 4 of 63. http://www.slideshare.net/erikbern/collaborative-filtering-at-spotify-16182818, Aug 2015
[3] Pandora Internet Radio, listen to free music you’ll love. http://www.pandora.com, Aug 2015
[4] Michael Howe. “Pandora’s Music Recommender”. http://courses.cs.washington.edu/courses/csep521/07wi/prj/michael.pdf
[5] Thierry Bertin-Mahieux, Daniel P.W. Ellis, Brian Whitman, and Paul Lamere. The Million Song Dataset. In Proceedings of the 12th International Society for Music Information Retrieval Conference (ISMIR 2011), 2011.
[6] Echonest. http://developer.echonest.com/docs/v4, Sep 2015
[7] Acoustic Attributes, The Echo Nest Developer Center. http://developer.echonest.com/acoustic-attributes.html, Sep 2015
[8] Danceability and Energy: Introducing Echo Nest. http://runningwithdata.com/post/1321504427/danceability-and-energy, Sep 2015

Priyank Jain and Vamsikrishna Patchava, "Music Recommendation Based on Subjective Attributes," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.80-91, 2015.

Intrusion Detection Systems (IDS) is a key part of system defense, where it identifies abnormal activities happening in a computer system. In general, the traditional intrusion detection relies on the extensive knowledge of security experts, in particular, on their familiarity with the computer system to be protected. To reduce this dependence, various data-mining, soft-computing and machine learning techniques have been proposed in recent years for the development of better intrusion detection systems. Many researchers used Conditional Random Fields and Layered Approach for purpose of intrusion detection. They also demonstrated that high attack detection accuracy can be achieved by using Conditional Random Fields and high efficiency by implementing the Layered approach. In the paper we explained a new method called fuzzy ARTMAP classifier (FAM) and clustering technique for effectively identifying the intrusion activities within a network. Processing huge data would make the system error prone, hence clustering the data into groups and then processing will result in having a better system. From each of the cluster, representative data is selected in the selective process for further processing. For classification process, layered fuzzy ARTMAP will have the better results when compared to other normal classifier algorithms. Finally the experiments and evaluations of the proposed intrusion detection system is using the KDD Cup 99 intrusion detection data set.

Intrusion Detection System, Layered approach, Clustering, FAM

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Kumar J S, Appa Rao S S V, Subha Sree M, "An Effective Approach for Improving Anomaly Intrusion Detection," International Journal of Computer Sciences and Engineering, Vol.3, Issue.10, pp.92-98, 2015.