iosr-JCE   Volume-18 ~ Issue-4 ~ July-August 2016

Abstract: The Web usage mining is a branch of web mining in which by clustering the datasets, frequently accessed patterns can be obtained for betterment of social portals, websites. The divisive analysis is one of the types of hierarchical method of data clustering that is used to separate each dataset from the clustered data depending on previous small clusters. Apriori & k-Apriori are commonly used hierarchical algorithms for web usage mining but they are less efficient for mining dynamic item sets such as twitter dataset................

Keyword: Apriori, Association rule mining, frequent item set, D-Apriori, k-Apriori, k-D-Apriori

[1] Kavita Sharma, Gulshan Shrivastava, and Vikas Kumar, "Web Mining: Today and Tomorrow", Issues and Challenges in Proc. Electronics Computer Technology, Apr. 2011.
[2] DataMining:www.anderson.ucla.edu/faculty/jason.frand/teacher/.../datamining.htm.
[3] Lokesh s., Deepti s., sheetal s., and Khushboo s., "Clustering Techniques: A Brief Survey of Different Clustering Algorithms" International Journal of Latest Trends in Engineering and Technology, Vol 1, issue 3, pp 82-87 Sep. 2012.
[4] Gupta, A. Arora, R. Sikarwar, and R. Saxena, N, "Web usage mining using improved Frequent Pattern Tree algorithms", Issues and Challenges in Proc. Intelligent Computing Techniques (ICIC), Feb. 2014.
[5] Kumar Ashok, Charlet Annie and M. C. Loraine, "Web Log Mining using K-Apriori Algorithm", International Journal of Computer Applications, Vol. 41, issue 11, pp. 16-20, Nov. 2012.

Abstract: In customary, frequent itemsets are propogated from large data sets by employing association rule mining algorithms like Apriori, Partition, Pincer-Search, Incremental and Border algorithm etc., which gains inordinately longer computer time to cast up all the frequent itemsets. On utilizing Genetic Algorithm (GA) the scheme is reformed.. The outstanding benefit of utilizing GA in determining the frequent itemsets is to discharge exhaustive survey and its time convolution subsides in collation with other algorithms, since GA is built on the greedy mode. The effective plan of this report is to detect all the frequent itemsets and to generate the association rules at various levels of minimum support and confidence defined by the user, with very less time and less memory from the furnished data sets using genetic algorithm.

Keyword: Genetic Algorithm (GA), Association Rule, Frequent itemset, Support, Confidence.

[1]. Islam A.M.B.R. and Tae-Sun Chung, "An Improved Frequent Pattern Tree Based Association Rule Mining Technique", International Conference on Information Science and Applications, pp. 1-8, 2011.
[2]. Das S and Saha B, "Data Quality Mining using Genetic Algorithm", International Journal of Computer Science and Security, Vol. 3, No. 2, pp. 105-112, 2009.
[3]. Dou W, Hu J, Hirasawa K and Wu G, "Quick Response Data Mining Model Using Genetic Algorithm", Institute for Credentialing Excellence Annual Conference, pp. 1214-1219, 2008.
[4]. Fonesca M and Fleming J, "Multi-objective Optimization and Multiple Constraint Handling with Evolutionary Algorithms," Part I: A Unified Formulation. IEEE Transactions on Systems, Man and Cybernetics - Part A: Systems and Humans, 28(1), pp. 26-37, 1998.
[5]. Freitas A, "Survey of Evolutionary Algorithms for Data Mining and Knowledge Discovery", Advances in evolutionary computing: theory nd applications, pp 819 – 845, 2003.

Abstract: Cloud Computing provide the chance to use computing resources over the internet without owning the infrastructure. The main content of Cloud Computing is to manage Software application, data storage and processing capacity which are assigned to external users on demand through the internet and pay only for what they use. Task scheduling in cloud computing is the biggest challenges because many tasks need to be executed by the available resources in order to meet user's requirements. To achieve best performance, minimize total completion time, minimize response time and maximize resources utilization there is need to address these challenges...............

Keyword: Cloud Computing, Task scheduling, Makespan, Min-Min Algorithm, Max-Min Algorithm.

[1]. Singh, Raja Manish, Paul, Sanchita and Kumar, Abhishek. Task Scheduling in Cloud computing: Review. 6, 2014, International Journal of Computer Science and Information Technologies (IJCSIT), Vol. 5, pp. 40-44. 0975-9646.
[2]. Lakshmi, R Durga and Asu, N Srinivasu. A Dynamic Approach to Task Scheduling in Cloud Computing Using Genentic Algorithm. 2, 2016, Journal of Theoritical and Applied Information Technology, Vol. 85. 1992-8645.
[3]. Saxena, Deepika, Chauhan, R K and Kait, Ramesh. Dynamic Fair Priority Optiumization Task Scheduling Algorithm in Cloud Computing: Concepts and Implementations. 2016, I. J Computer Network and Information Security, pp. 41-48.
[4]. Chawda, Prerit and Chakraborty, Partha Sarathi. An Improved Min-Min Task Scheduling Algorithm for Load Balancing in Cloud Computing. 4, 2016, International Journal on Recent and Innovation Trends in Computing and Communication, Vol. 4, pp. 60-64. 2321-8169.
[5]. Kaur, Rajwinder and Patra, Prasenjit Kumar. Resource Allocation with improved Min-Min Algorithm. 15, 2013, International Journal of Computer Applications, Vol. 76, pp. 61-65. 0975-8887.

Abstract: Video has to be segmented into objects for content-based processing. A number of video object segmentation algorithms have been proposed such as semiautomatic and automatic. Semiautomatic methods adds burden to users and also not suitable for some applications. Automatic segmentation systems are still a challenge, although they are required by many applications. The proposed work aims at contributing to identify the gaps that are present in the current segmentation system and also to give the possible solutions to overcome those gaps so that the accurate and efficient video segmentation system can be developed..............

Keyword: Moving Object, Region based Segmentation, Block Matching, Global Motion Estimation and Compensation.

[1]. Shao-Yi Chien, Yu-Wen Huang, Bing-Yu Hsieh, Shyh-Yih Ma, and Liang-Gee Chen,"Fast Video Segmentation algorithm with Shadow Cancellation, Global Motion compensation, and Adaptive Threshold Techniques," IEEE Trans. on Circuits and System for Video Technology., vol. 6, pp. 732- 748, no. 5, Oct. 2004.
[2]. Dong Zhang1, Omar Javed2, Mubarak Shah1," Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions," 2013 IEEE Conference on Computer Vision and Pattern Recognition
[3]. Camille Couprie,"Causal Graph based video segmentation"(2012)
[4]. McFralane, N. J. B. and Schofield, C.P. "Segmentation and tracking of piglets in images". Machine Vision and Applications, Vol. 8, No. 3, 187-193. 2005.
[5]. Ricardo Augusto Castellanos Jimenez "Event Detection In Surveillance Video" Florida Atlantic UniversityBoca Raton, Florida May 2010.

Abstract: Genomic studies provide massive amount of data including thousands of Single Nucleotide Polymorphisms (SNPs). The analysis of SNPs helps to identify genetic variants related to complex traits. Therefore, it is essential to provide an efficient method to find a small subset of candidate SNPs as good representatives of the rest of SNPs.In this study, a new feature/SNP selection method based on the relationship between filter and wrapper criteria (i.e. correlation and model accuracy) is propoed. The method is based on the prediction of Mean Square Error (MSE) in terms of the number of features, Mean Feature-Feature Correlation (MFFC) and Mean Feature-Target Correlation (MFTC). It trains a Neural Network (NN) to predict the accuracy in terms of the number of features, MFFC and MFTC...............

Keyword:Feature Selection, Filter FS method, Wrapper FS method, Single Nucleotide Polymorphism, candidate SNPs, SNPs selection.

[1]. Y. Kim, W. Street, and F. Menczer, "Feature selection in unsupervised learning via evolutionary search,"inproc. KDD-2000:
Proceedings of the Sixth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 365–369.
[2]. M. Dash, K. Choi, P. Scheuermann, and H. Liu, "Feature Selection for Clustering - A Filter Solution,"in proc.ICDM 2002:
Proceedings ofIEEE International Conference on Data Mining, pp. 115-122.
[3]. H. Liu, H. Motoda, and L. Yu,"Feature Selection with Selective Sampling,"inproc.ICML-2002: Proceedings of the 19th
International Conference on Machine Learning, pp. 395-402.
[4]. M. R. Sikonja and I. Kononenko,"Theoretical and empirical analysis of Relief and ReliefF,"Machine Learning, vol. 53, pp.23-69,
Oct.-Nov. 2003.
[5]. P. Mitra, C.A. Murthy, and S.K. Pal,"Unsupervised feature selection using feature similarity,"IEEE Trans. Pattern Analysis and
Machine Intelligence, vol. 24, pp.301-312, Mar. 2002.

Abstract: The objective of Optical Character Recognition (OCR) is automatic reading of optically sensed document text materials to translate human-readable characters to machine- readable codes. In Optical Character Recognition, the text lines in a document must be segmented properly before recognition. English Character Recognition (CR) has been extensively studied in the last half century and progressed to a level, sufficient to produce technology driven applications. But same is not the case for Indian languages which are complicated in terms of structure and computations..........

Keyword: Optical Character Recognition (OCR), Character Recognition (CR).

[1]. Dan Bloomberg, "Analysis of Document Skew",Leptonica2002 [2]JONATHAN J.HULL," Document Image Skew Detection: Survey And Annotated Bibliography", Document Analysis Systems
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[3]. M.K.Jindal, R.K.Sharma, G.S.Lehal," Segmentation of Horizontally Overlapping Lines in Printed Indian Scripts", International Journal of Computational Intelligence Research.
[4]. ISSN 0973-1873Vol.3,No.4(2007), pp.277–286 Nallapareddy Priyanka, Srikanta Pal, Ranju Mandal," Line and Word Segmentation Approach for Printed Documents", IJCA Special Issue on "Recent Trends in Image Processing and Pattern Recognition" RTIPPR, 2010.
[5]. K.S.SeshKumar, A.M.Namboodiri, and C.V.Jawahar," Learning Segmentation of Documents with Complex Scripts", ICVGIP 2006,LNCS 4338, pp. 749–760,2006

Abstract: The Content-Based Image Retrieval (CBIR) techniques comprise methodologies intended to retrieve self-content descriptors over the image data set being studied according to the type of the image. The main purpose of CBIR consists in classifying images avoiding the use of manual labels related to understanding of the image by the human being vision. This paper provides an overview on Recent Trends of CBIR includes the Relevance Feedback (RF), Interactive Genetic Algorithm , Neural Network etc.. Relevance Feedback enhances the ability of CBIR effectively by dropping the semantic gap between low-level features and high level features. Interactive Genetic Algorithm is a branch of evolutionary computation which makes the retrieval process more interactive so that user can get advanced results from database matching to Query Image with his evaluation.........

Keyword: CBIR, Neuro-fuzzy logic, Relevance Feedback, Interactive Genetic Algorithm, Image Retrieval (IR).

[1] Gudivada V. N., Raghavan V. V., "Content based image retrieval systems," IEEE Computer,. 28, pp. 18-22,1995
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Abstract: Cloud Computing holds the potential to eliminate the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. Cloud services are becoming an essential part of many organizations. It promises to provide a flexible IT architecture, accessible through internet from lightweight portable devices. In a cloud computing environment, the entire data resides over a set of networked resources, enabling the data to be accessed through virtual machines. Since these data-center's may be located in any part of the world beyond the reach and control of users, there are multifarious security and privacy challenges that need to be understood and addressed..........

Keyword: Cloud Computing, Security, Authentication.

[1] Justin LeJeune, Cara Tunstall, Kuo-pao Yang and Ihssan Alkadi, CSIT Department at SLU "An Algorithmic Approach to Improving Cloud Security: The MIST and Malachi Algorithms", 978-1-4673-7676 ,2016 IEEE
[2] Victor Chang, Muthu Ramachandran, Member, IEEE "Towards achieving Data Security with the Cloud Computing Adoption Framework", 2015,IEEE
[3] Amit Hendre and Karuna Pande Joshi CSEE Department, University of Maryland Baltimore County Baltimore, MD, USA "A Semantic Approach to Cloud Security and Compliance" 2015 IEEE
[4] Vahid Ashktorab , Seyed Reza Taghizadeh "Security Threats and Countermeasures in Cloud Computing Volume 1, Issue 2, October 2012
[5] Prince Jain Malwa Polytechnic College Faridkot, Punjab-151203, India "Security Issues and their Solution in Cloud Computing" International Journal of Computing & Business Research ISSN (Online): 2229-6166.

Abstract: Diabetes is a never ending disease which affects many major organs of the human body, including heart, blood vessels, nerves, eyes and kidneys. The World Health Organization (WHO) estimates that nearly 200 million people all over the world suffer from diabetes and this number is likely to be doubled by 2030. In India, there are nearly 50 million diabetics, according to the statistics of the International Diabetes Federation. To identify the diabetes mellitus the medical practitioner will diagnose the pattern consists of observable symptoms and based on the all respective test. The risk and costs may be differing according to the patient condition.

[1] C.M Velu and K.R.Kashwan(2012) "Visual Data mining techniques for classification of diabetic patients" 2013 3rd IEEE International Advance Computing conference(IACC).
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[5] I. K. Valavanis, S. G. Mougiakakou, K. A. Grimaldi, K. S. Nikita, "A multifactorial analysis of obesity as CVD risk factor: Use of neural network based methods in a nutrigenetics context," BMC Bioinformatics, vol. 11, pp. 453, Sep. 2010.

Abstract: Emerging multimedia applications demand content-based video processing. Content based video retrieval or concept detection systems require video to be segmented in to objects. A large number of video object segmentation algorithms have been developed such as semiautomatic and automatic. Semiautomatic methods requires the human intervention and also not suitable for some applications. Many applications require automatic segmentation but still there is lot of scope for the improvement..........

Keyword: Moving Object, Region based Segmentation, Block Matching, Global Motion Estimation and Compensation.

[1]. Shao-Yi Chien, Yu-Wen Huang, Bing-Yu Hsieh, Shyh-Yih Ma, and Liang-Gee Chen,"Fast Video Segmentation algorithm with Shadow Cancellation, Global Motion compensation, and Adaptive Threshold Techniques," IEEE Trans. on Circuits and System for Video Technology., vol. 6, pp. 732- 748, no. 5, Oct. 2004.
[2]. Dong Zhang1, Omar Javed2, Mubarak Shah1," Video Object Segmentation through Spatially Accurate and Temporally Dense Extraction of Primary Object Regions," 2013 IEEE Conference on Computer Vision and Pattern Recognition
[3]. Camille Couprie,"Causal Graph based video segmentation"(2012)
[4]. McFralane, N. J. B. and Schofield, C.P. "Segmentation and tracking of piglets in images". Machine Vision and Applications, Vol. 8, No. 3, 187-193. 2005.
[5]. Ricardo Augusto Castellanos Jimenez "Event Detection In Surveillance Video" Florida Atlantic University Boca Raton, Florida May 2010

Abstract: High Utility Itemset Mining is a challenging task as the Downward Closure Property present in frequent itemset mining does not hold here. In recent times many algorithms have been proposed for mining high utility itemsets ,but most of them follow a two- phase horizontal approach in which candidate itemsets are generated first and then the actual high utility itemsets are mined by performing another database scan. This approach generates a large number of candidate itemsets which are not actual high utility itemsets thus causing memory and time overhead to process them. To overcome this problem we propose a single phase algorithm which uses vertical database approach............

[1]. R. Agrawal and R. Srikant. Fast algorithms for mining association rules. In Proc. of the 20th Int'l Conf. on Very Large Data Bases, pp. 487-499, 1994.
[2]. C. F. Ahmed, S. K. Tanbeer, B.-S. Jeong, and Y.-K. Lee. Efficient tree structures for high utility pattern mining in incremental databases. In IEEE Transactions on Knowledge and Data Engineering, Vol. 21, Issue 12, pp. 1708-1721, 2009.
[3]. B.-E. Shie, V. S. Tseng, and P. S. Yu. Online mining of temporal maximal utility itemsets from data streams. In Proc. of the 25th Annual ACM Symposium on Applied Computing, Switzerland, Mar., 2010..
[4]. Vincent S. Tseng1 , Cheng-Wei Wu1 , Bai-En Shie1 , and Philip S. Yu2. UP-Growth: An Efficient Algorithm for High Utility Itemset Mining, In IEEE Transactions on Knowledge and Data Engineering , Vol. 25, No. 8, August 2013.
[5]. A. Erwin, R.P. Gopalan, and N.R. Achuthan, "Efficient Mining of High Utility Itemsets from Large Data Sets," Proc. 12th Pacific-Asia Conf. Advances in Knowledge Discovery and Data Mining (PAKDD), pp. 554-561, 2008.

Abstract: Financial services organizations around the world are experiencing drastic change. Financial services firms are turning to big data technologies and Hadoop to reduce risk, analyze fraud patterns. Ability of Big Data platforms to process and analyze unstructured data efficiently unlike current platforms that are limited to handling structured data. This flexibility makes it possible to gain insights from different variety of data. Big data can be analyzed for insights that lead to better decisions and strategic business moves. For most organizations, big data is the reality of doing business..........

Keyword: Big data analytics, Fraud Detection, Hadoop Clusters, Tools

[1]. Magoulas, Roger; Lorica, Ben (February 2009). "Introduction to Big Data". Release 2.0. Sebastopol CA: O'Reilly Media (11).
[2]. Hu, Han; Wen, Yonggang; Chua, Tat-Seng; Li, Xuelong (2014). "Towards scalable systems for big data analytics: a technology tutorial". IEEE Access. 2: 652–687. doi:10.1109/ACCESS.2014.2332453.
[3]. Wu, D., Liu. X., Hebert, S., Gentzsch, W., Terpenny, J. (2015). Performance Evaluation of Cloud-Based High Performance Computing for Finite Element Analysis. Proceedings of the ASME 2015 International Design Engineering Technical Conference & Computers and Information in Engineering Conference (IDETC/CIE2015), Boston, Massachusetts, U.S.
[4]. Wu, D.; Rosen, D.W.; Wang, L.; Schaefer, D. (2015). "Cloud-Based Design and Manufacturing: A New Paradigm in Digital Manufacturing and Design Innovation". Computer-Aided Design. 59 (1): 1–14. doi:10.1016/j.cad.2014.07.006.
[5]. Lee, Jay; Bagheri, Behrad; Kao, Hung-An (2014). "Recent Advances and Trends of Cyber-Physical Systems and Big Data Analytics in Industrial Informatics". IEEE Int. Conference on Industrial Informatics (INDIN) 2014.

Abstract: As the current industrial robots teaching systems have some disadvantages including high maintenance cost, poor portability and operational complexity, an industrial robot teaching system based on Android platform has been developed. The teaching pendant uses Android system equipment for industrial robot control terminal that the user is transmitted to the robot controller by operating the robot teaching will be achieved task command, the controller receives a corresponding command to control the movement of each axis of the robot to achieve each goal pose..........

Keyword: Android; teach pendant; robot; Programming Module

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Abstract: Social Network is a site where individual's vocation and share data identified with the present occasions everywhere throughout the world. This specific conduct of users made us concentrate on this rationale that handling these substance may lead us to the extraction the present point of enthusiasm between the users. It additionally functions admirably even the substance of the messages are non-printed data. The algorithm demonstrate that the proposed notice peculiarity based methodologies can identify new themes at any rate as right on time as content inconsistency based methodologies, and now and again much prior when the point is inadequately recognized by the printed substance in the posts..........

Keyword: Authentication, aggregation, anomaly-detection, social network, burst detection.

[1]. Toshimitsu Takahashi, Ryota Tomioka, and Kenji Yamanishi, "Discovering Emerging Topics in Social Streams via Link-Anomaly Detection," IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING, VOL. 26, NO. 1, JANUARY 2014.
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[3]. VARUN CHANDOLA, ARINDAM BANERJEE, VIPIN KUMAR, "Anomaly Detection: A Survey," A modified version of this technical report will appear in ACM Computing Surveys, September 2009.
[4]. Artur Silie, Lovro Zmak, Bojana Dalbelo, MarieFrancine Moens, "Comparing Document Classification using K-means Clustering".

Abstract: Background: The foundation of the word corporate administration is from 'gubernate' which intends
to direct. Corporate administration would intend to control an association in the ideal course. Corporate
administration is a framework by which association are coordinated and controlled, it is a procedure by which
organization targets are built up, accomplished and observed. In this way, it is worried about relationship and
obligations between the sheets, the executives and partners inside a lawful and administrative casing work. The
essential goal of this paper is to consider the corporate administration arrangements and practices and
framework in India .Goodness of corporate administration is kept an eye based on five fundamental parameters
for example straightforwardness, possession structure, board technique, financial specialist rights and
administration systems. During the investigation........

Key Word: Corporate governance, National Productivity, National growth, Cost of capital.

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Abstract: The study aims to find out how dyslexic children and non-dyslexic children differ in their foreign
language anxiety, academic stress, and the association between the two. The study utilized a convenience
samples of dyslexic (N = 84) and non-dyslexic (N = 132) children studying in different secondary schools of
Ernakulam district. The data, collected by administering the English Language Anxiety Scale and English
Language Related Academic Stress Scale, were analysed by using appropriate statistical techniques by keeping
the hypotheses in mind. The analysis revealed that dyslexic and non-dyslexic children differ significantly in their
foreign language anxiety and English language related academic stress, wherein the dyslexic children were
found to have greater anxiety and stress in........

Keyword: Learning disability, Dyslexic children, Non-dyslexic children, Foreign language anxiety, Academic stress.

[1]. Chen, T. Y., & Chang, G. B. Y. (2004). The relationship between foreign language anxiety and learning difficulties. Foreign
Language Annals, 37(2), 279 287.
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Language Teaching, 34(2), 79-98. DOI: https:// doi.org/10.1017/S0261444800015895
[3]. Huang, Q. (2012). Study on Correlation of Foreign Language Anxiety and English Reading Anxiety. Theory and Practice in
Language Studies, 2 (7), 1520-1525. doi:10.4304/tpls. 2.7.1520-1525
[4]. Jin, L., Smith, K., Yahya, A., Chan, A., Choong, M., Lee, A., Ng, V., Poh-Wong, P., & Young, D. (2011). Perceptions and
strategies of learning in English by Singapore primary school children with dyslexia: A metaphor analysis. London: British Council.
[5]. Lodej, M. (2016). Dyslexia in first and foreign language learning: A cross-linguistic approach. Newcastle: Cambridge Scholars
Publishing

Abstract: The theoretical modified Khare model et al. [1-2], has been used to calculate the total cross sections
for K-shell ionization of 3 targets (Na, K, Rb ) due to electron impact at incident electron energy from ionization
threshold to 1 GeV. The various calculated cross sections are in remarkable agreement with available
experimental data and other theoretical cross sections.

Key Word:Ionization Cross Section; Atoms; Electron Impact; K-Shell

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