iosr-JCE   Volume-11 ~ Issue-6

Abstract: This paper presents a fault prediction model using reliability relevant software metrics and fuzzy inference system. For this a new approach is discussed to develop fuzzy profile of software metrics which are more relevant for software fault prediction. The proposed model predicts the fault density at the end of each phase of software development using relevant software metrics. On the basis of fault density at the end of testing phase, total number of faults in the software is predicted. The model seems to useful for both software engineer as well as project manager to optimally allocate resources and achieve more reliable software within the time and cost constraints. To validate the prediction accuracy, the model results are validated using PROMISE Software Engineering Repository Data set.

Keywords: Reliability Relevant Software Metrics, Software Fault Prediction, Fault Density, Fuzzy profile, Fuzzy Inference System (FIS)

[1] ANSI/IEEE Standard Glossary of Software Engineering Terminology, IEEE STD-729, 1991.

[2] J. D. Musa, A. Iannino, K. Okumoto, Software Reliability: Measurement, Prediction, Application, McGraw-Hill Publishers, New York, 1987.

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Abstract: Data Compression is the technique through which, we can reduce the quantity of data, used to represent content without excessively reducing the quality of the content. This paper examines the performance of a set of lossless data compression algorithm, on different form of text data. A set of selected algorithms are implemented to evaluate the performance in compressing text data. A set of defined text file are used as test bed. The performance of different algorithms are measured on the basis of different parameter and tabulated in this article. The article is concluded by a comparison of these algorithms from different aspects.

Keywords - Encryption, Entropy Encoding, Dictionary Encoding, Compression Ratio, Compression time, Test Bed.

[1] Pu, I.M., 2006, Fundamental Data Compression, Elsevier, Britain.

[2] Kaufman, K. and T. Shmuel, 2005. Semi-lossless text compression, Intl. J. Foundations of Computer Sci., 16: 1167-1178.

[3] Kesheng, W., J. Otoo and S. Arie, 2006. Optimizing bitmap indices with efficient compression, ACM Trans. Database Systems, 31: 1-38.

[4] Introduction to Data Compression, Khalid Sayood, Ed Fox(Editor), March 2000

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[9] Improved wordaligned binary compression for text indexing, Vo Ngoc and M. Alistair, 2006. IEEE Trans. Knowledge & Data Engineering, 18: 857-861.

[10] Data compression using dynamic Markov modeling, Cormak, V. and S. Horspool, 1987. Comput. J., 30: 541–550.

Abstract: Due to the huge development of computer science which was escorted by another vast development in the hiding techniques, which became of great possibilitiesin which it is difficult to break those techniques.Those techniques were classified depending on the method of embedding like inserting, replacing or exchange positions.

[1]. Refrence: book: ―DATA HIDING FUNDAMENTALS AND APPLICATRIONS‖ AUG-2004,HusrwSencar,
[2]. chin-Chen Chang, ― International Journal of Pattern Recognition and Artifitial Intelligence‖, Volume 16, Issue 04, June 2002.
[3]. ab Grady Booch,‖ Object-Oriented Analysis and Design with Applications‖, . Addison-Wesley, 2007, ISBN 0-201-89551-X, p. 51-52.
[4]. http://en,Wikipedia.org/wiki/Information_hiding.
[5]. Pahati, OJ (2001-11-29). "Confounding Carnivore: How to Protect Your Online Privacy". AlterNet.Archived from the original on 2007-07-16.http://web.archive.org/web/20070716093719/http://www.alternet.org/story/11986/. Retrieved 2008-09-02.
[6]. Chvarkova, Iryna; Tsikhanenka, Siarhei; Sadau, Vasili (15 February 2008). "Steganographic Data Embedding Security Schemes Classification".Steganography: Digital Data Embedding Techniques. Intelligent Systems Scientific Community, Belarus.http://scientist.by/index.php?option=com_content&view=article&id=37%3Asteganography-digital-data-embedding-techniques&catid=9&Itemid=27&limitstart=5. Retrieved 25 March 2011.
[7]. Joshua R. Smith and Barrett O. Comisky, ―Modulation and Information Hiding in Images‖, Cambridge, USA May 2009.

Abstract: Intrusion detection is the process of detecting unauthorized traffic on a network or a device. Intrusion Detection Systems (IDS) are designed to detect the real-time intrusions and to stop the attack. An IDS is a software or a physical device that monitors traffic on the network and detect unauthorized entry that violates security policy. We present in this paper the various Neural Network approaches adopted by the different Intrusion Detection Systems. Artificial Intelligence plays significantly role in intrusion detection. Machine learning can also be applied to intrusion detection systems. Artificial Neural Networks are modelled inline with the learning processes that take place in biological systems. The Neural Networks are basically consists of a set of inputs, some intermediate layers and one output. They are capable of identifying the patterns and its variations. They can be "trained" to produce an accurate output for a given input. Neural Networks are capable of predicting new observations from other observations after executing a process of so called learning from existing data.

Keywords: Intrusion detection, Neural networks, Prevention system, Security, Technique, Traffic.

[1] Zhang Wei, Wang Hao-yu, 2010, Intrusive Detection Systems Design based on BP Neural Network, IEEE.

[2] Paulo M. Mafra, Vinicius Moll, Joni da Silva Fraga, 2010, Octopus-IIDS: An Anomaly Based Intelligent Intrusion Detection System, IEEE.

[3] Milan Tuba, Dusan Bulatovic, 2010, Design of an Intrusion Detection System Based on Bayesian Networks, ACM.

[4] Naeeam Seilya, Taghi M. Khoshgoftaar, ―Active Learning with Neural Networks for Intrusion Detection‖ Knowledge Discovery and Data Mining, 2010.WKDD '10. 3rd International Conference on, Jan. 2010, pp. 601–604.

[5] Iftikhar Ahmad, Azween B Abdullah, Abdullah S Alghamdi ―Comparative Analysis of Intrusion Detection Approaches‖, 2010 12th International Conference on Computer.

[6] G. Liu, Z. Yi, and S. Yang, ―A hierarchical intrusion detection model based on the pca neural networks,‖ Journal of Information Science and Technology, pp. 1561–1568, 2006.

[7] G. Giacinto, F. Roli, and L. Didaci, Fusion of multiple classifiers for intrusion detection in computer networks, 2003.

[8] R. Mukkamala, J. Gagnon, and S. Jajodia, ―Integrating data mining techniques with intrusion detection methods,‖ in Advances in Database and Information Systems Security, 2000.

[9] T. Lunt, ―Detecting intruders in computer systems,‖ in Conference on Auditing and Computer Technology, 1993.

Abstract: Human face recognition systems have gained a considerable attention during last decade due to its vast applications in the field of computer and advantages over previous biometric methods. There are many applications with respect to security, sensitivity and secrecy. Face detection is the most important and first step of recognition system. This paper introduces a new approach to face detection systems using the skin color of a subject. This system can detect a face regardless of the background of the picture, which is an important phase for face identification. The images used in this system are color images which give additional information about the images than the gray images provide. In face detection, the two respective classes are the "face area" and the "non-face area". This new approach to face detection is based on color tone values specially defined for skin area detection within the image frame. This system first resizes the image, and then separates it into its component R, G, and B bands. These bands are transformed into another color space which is YCbCr space and then into YC'bC'r space (the skin color tone). The morphological process is implemented on the presented image to make it more accurate. At last, the projection face area is taken by this system to determine the face area. Experimental results show that the proposed algorithm is good enough to localize a human face in an image with an accuracy of 92.69%. Keywords – Color Space, Face detection, Skin Color.

[1] Hong, S. and al, e, "Facial feature detection using Geometrical face model: An efficient approach," journal of pattern recognition, Vols. 31, No. 3, pp. 273-282, 1998.
[2] Leung, C, "Real Time Face Recognition," B. Sc. Project, School of Information Technology and Electrical Engineering. University of Queesland, 2001.
[3] Sanjay Kr, Singh, D. S. Chauhan, Mayank Vatsa and Richa Singh, "A Robust Skin Color Based Face Detection Algorithm," Tamkang Journal of Science and Engineering, vol. 6 (4), pp. 227-234, 2003.
[4] Muhammad Tariq Mahmood, "Face Detection by Image Discriminating," 2006. [Online]. Available: http://www.bth.se/fou/cuppsats.nsf/all/6c509ae86a297ca4c12571d300512cac/$file/DVD009-MasterThesisReport.pdf..
[5] Michael Padilla and Zihong Fan, "Automatic Face Detection Using Color Based Segmentation and Template / Energy Thresholding," 2003. [Online]. Available: http://www.stanford.edu/class/ee368/Project_03/Project/reports/ee368group16.pdf.
[6] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing. 2nded, New Jersey: Prentice-Hall, 2001, pp. 75-103.
[7] C. A. Brewer, Color Use Guidelines for Data Representation, Alexandria: American Statistical Association, 1999, pp. P55-56.
[8] Sangwine, S. J. and Horne, R. E. N, The Color Image Processing Handbook, Chapman & Hall, 1st Edition, 1998.
[9] Raghuvanshi, D. S. and Agrawal, D., "Human Face Detection by using Skin Color Segmentation, Face Features and Regions Properties," International Journal of Computer Applications, vol. 38– No.9, 2012.
[10] H. H. Khaung Tin, "Robust Algorithm for Face Detection in Color Images," I.J.Modern Education and Computer Science, pp. 31-37, 2012.

Abstract: With the increase in the development of cloud computing environment, the security has become the major concern that has been raised more consistently in order to move data and applications to the cloud as individuals do not trust the third party cloud computing providers with their private and most sensitive data and information. In this paper, I proposed an encryption technique in cloud computing environment using randomization method to increase security and optimize the encrypted data in migration process. Keywords – Attribute based encryption, cloud computing, data migration, prediction based encryption, randomization

[1] 15th International Conference on Management of Data COMAD 2009, Mysore, India, December 9–12, 2009 ©Computer Society of India, 2009, A Unified and Scalable Data
Migration Service for the Cloud Environments
[2] Gartner (2008). Gartner Says Cloud Computing Will Be as Influential As E-business. Gartner press release, 26 June 2008. http://www.gartner.com/it/page.jsp?id=707508. Retrieved 3rd May 2010.
[3] Secure Migration of Various Database over A Cross Platform Environment, an International Journal Of Engineering And Computer Science ISSN:2319-7242 Volume 2 Issue 4 April, 2013
[4] Data Migration: Connecting Databases in the Cloud, a research paper published by authors: Farah Habib Chanchary and Samiul Islam in ICCIT 2012.
[5] Using the cloud for data migration: practical issues and legal implications - 16 Feb 2011 - Computing Feature
[6] A Security approach for Data Migration in Cloud Computing , an International Journal of Scientific and Research Publications, Volume 3, Issue 5, May 2013 1 ISSN 2250-3153
[7] QuickStudy: Identity-based encryption by Russell Kay
[8] Research Report RR-06-164 Enabling Secure Service Discovery with Attribute Based Encryption, by Slim Trabelsi, Yves Roudier.
Encryption Random key KeyGen Decryption Setup operation

Abstract: Volunteer Cloud Computing is based on the concept where highly distributed non-dedicated resources are harnessed to build a cloud so as to offer cloud services. As volunteer clouds are allowed to communicate with each other and with other commercial clouds also, it's necessary to implement an enhanced interoperable environment. In this paper we propose an XMPP based messaging middleware architecture that can help in implementing such an environment.

Keywords: cloud computing, commercial cloud, interoperability, middleware architecture, volunteer cloud.

[1] A. Andrzejak, D. Kondo, and D. P. Anderson, "Exploiting non dedicated resources for cloud computing," 2010 IEEE Network Operations and Management Symposium - NOMS 2010, pp. 341-348, 2010.
[2] A. Marosi, J. Kovács, and P. Kacsuk, "Towards a volunteer cloud system," Future Generation Computer Systems, Mar. 2012
[3] Abdulelah Alwabel, Robert Walters and Gary Wills "towards an architecture for IaaS volunteer cloud" digital research 2012.
[4] Fernando Costa, Luis Silva, Michael Dahlin, " volunteer cloud computing: mapreduce over the internet", Parallel and Distributed Processing Workshops and Phd Forum (IPDPSW), 2011 IEEE International Symposium on.
[5] D. Kondo, B. Javadi, P. Malecot, F. Cappello, and D. Anderson, "Costbenefit analysis of Cloud Computing versus desktop grids", in Proceedings of the 2009 IEEE international Symposium on Parallel & Distributed Processing, pp. 1-12, May 2009
[6] T. Dillon, C. Wu, and E. Chang, "Cloud computing: Issues and challenges," in 2010 24th IEEE International Conference on Advanced Information Networking and Applications, 2010, pp. 27–33.
[7] Vincenzo D. Cunsolo, Salvatore Distefano, Antonio Puliafito and Marco Scarpa, "Cloud@Home: Bridging the Gap between Volunteer and Cloud Computing", in ICIC'09 Proceedings of the 5th international conference on Emerging intelligent computing technology and applications, 2009.
[8] V. D. Cunsolo, S. Distefano, A. Puliafito, and M. Scarpa, "Applying Software Engineering Principles for Designing Cloud@Home," 2010 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing, pp. 618-624, 2010.

Abstract: Emotion recognition aims at automatically identifying the emotional or physical state of a human being from his or her voice. The emotional and physical states of a speaker are known as emotional aspects of speech and are included in the so called paralinguistic aspects. Although the emotional state does not alter the linguistic content, it is an important factor in human communication, because it provides feedback information in many applications as making a machine to recognize emotions from speech is not a new idea. This paper presents automatic text independent speaker emotion recognition system using the pattern classification methods such as the support vector mechanics (SVM) .Acoustic features are derived from the speech signal at the segmental level. The segmental features are the features extracted from short frames (10-30 ms) of the speech. Acoustic features are derived from the speech signal at the segmental level. Acoustic features are represented by Mel frequency cepstral coefficients .A 39 dimensional MFCC for each frame is used as acoustic feature vector. The DFT based cepstral coeffiecients are computed by computing IDFT (inverse DFT) of the log magnitude short time spectrum of speech signal. Mel wraped cepstrum is obtained by inserting an intermediate step of transforming the frequencies before computing the IDFT. The Mel scale is based on human perception of frequency of sound. SVMs are used to construct the optimal separating hyper plane for speech features .SVMs are used to build the models for each speaker and to compare with the test speaker's feature vectors.

Keywords: Inverse discrete Fourier transforms(IDFT), linear prediction coefficients(LPC), linear prediction cepstral coefficients(LPCC), Support vector mechanics(SVM), Artificial Neural Networks(ANN), Hidden Markov Model(HMM), Gaussian Mixture Model(GMM).

[1]. Pongtep Angkititrakul and John H. L. Hansen, "Discrimination in–Set/out-of-set Speaker Recognition Systems" IEEE TRANSACTIONS ON AUDIO , SPEECH , AND LANGUAGE PROCESSING, Vol.15, no.2, Feb.2007.
[2]. Ran D. Zilca , Brain Kingsbury, Jiri Navrati, Ganesh N. Ramasamy "PSEUDO PITCH SYNCHRONOUS ANALYSIS OF SPEECH WITH APPLICATION TO SPEAKER RECOGNITION", IEEE TRANSACTION ON AUDIO , SPEECH, AND LANGUAGE PROCESSING Vol.14, no. 2, Mar.2006.
[3]. "yildirim, S. Bulut, M., Lee , C.M., kazemzadeh, A., Busso, C., Deng, Z., Lee, S., Narayanan, S., 2004. An Acoustic Study Of Emotions Expressed In Speech. In: Proc. Internat. Conf. On Spoken Language Processing (ICSLP '04), Korea, Vol.
[4]. K. Sri Rama Murty and B. Yengnanarayana " Combining Evidence from Residual Phase and MFCC Features for Speaker Emotion Recognition" IEEE signals processing letters, Vol.13,no. , jan.2006
[5]. Volunteers in Technical Assistance (VITA).
[6]. Guillermo Garcia, Sung-kyo Jung, "A Statistical Approach To Performance Evaluation Of Speaker Emotion Recognition Systems" TECH/SSTP Lab., France Telecom R&D 22307 Lannion, France. W.B. Mikhel and Pravinkumar Premakanthan, "An Improved Speaker Emotion Identification Technique Employing Multiple Representations of LPC", in proceedings of IEEE , march 2000
[7]. Sachin S. Kajarekar "Four Weighing And Fusion : A Cepstral –SVM System For Speaker Emotion Recognition VOL. 23, NO. 3, AUGUST 2008. .

Abstract: In order to improve the accuracy of the static programslicing which is used to locate the faults that cause an exception,we propose a new algorithm which isstored when an exception occurred. The proposed approachconsists of various steps and figure out those methods and statements thathave not been executed. Then, these methods which are notexecuted will be ignored when building the system dependencegraph. Finally, the accurate slice will be got by adopting theimproved program slicing algorithm. And the result shows that using our approachthe slice is 8 percent less than using the general static programslicing algorithm on average.One approach to improve the comprehension ofprograms is to reduce the amount of data to be observedand inspected. Programmers tend to focus andcomprehended selected functions (outputs) and those partsof a program that are directly related to that particularfunction rather than all possible program functions. Oneapproach is to utilize program slicing, a program decomposition technique that transforms a large programinto a smaller one that contains only statements relevantto the computation of a selected function.

Keywords: Program slicing, Static slicing, Static slicing algorithm

[1] Tao Wang and Abhik Roy Choudhury,‖ Dynamic Slicing on Java Bytecode Traces‖,Singapore International Conference on Software Engineering (ICSE), 2004

[2] Hiralalagarwal,richard a. demillo and eugene h. spafford,‖ Debugging with Dynamic Slicing andBacktracking‖, Software— Practice And Experience, Vol. 23, no. 6, pp. 589–616, JUNE 1993

[3] SwarnenduBiswas and Rajib Mall, ―Regression Test Selection Techniques: A Survey‖, Information and Software Technology, Vol. 52, no. 1, January 2010

[4] Jaiprakash T Lalchandani, R Mall, ‖Regression TestingBased-on Slicing of Component-based SoftwareArchitectures‖, ISEC ,vol. 79, no. 06, pp. 19-22, 2008

[5] Rajiv Gupta, Mary Jean Harrold, Mary Lou Soffa, ―AnApproach to Regression Testing using Slicing‖, ACMTransactions on Programming Languages and Systems,vol. 12, no. 1, pp. 26-60, January 1990

[6] Yogesh Singh, ArvinderKaur and BhartiSuri, ―A Hybrid Approach for Regression Testingin Interprocedural Program‖, Journal of Information Processing Systems, Vol.6, No.1, March 2010

[7] Hongchang Zhang, Shujuan Jiang, Rong Jin, ―An Improved Static Program Slicing Algorithm Using Stack Trace‖,IEEE,2011

[8] N.Sasirekha, A.Edwin Robert, Dr.M.Hemalatha,―Program slicing techniques and its applications‖,International Journal of Software Engineering &Applications (IJSEA), Vol. 2, No. 3, pp.85-92, July 2011

[9] MithunAcharya, Brian Robinson, ―Practical ChangeImpact Analysis Based on Static Program Slicing forIndustrial Software Systems‖, ICSE, vol. 11, pp. 21–28,may 2011

[10] BaowenXu, JuQian, Xiaofang Zhang, Zhongqiang, WuLin Chen,‖ A Brief Survey Of Program Slicing‖, ACMSIGSOFT Software Engineering, Vol. 30, no. 2, pp. 1-36, March 2005

Abstract: The traditional algorithms for mining frequent association patterns suffer from the problems of under prediction and over prediction of patterns. The main aim of the present paper is to develop a soft set approach for mining quantitative association patterns in order to address the issues of under prediction and over prediction of these patterns. The proposed approach is illustrated with the help of a suitable example and experiment on a real world data set of air pollution. The transactional dataset is represented as soft set using the concept of parameter co-occurrences in the transaction. The quantitative attributes are dealt with by fine –partitioning the value of each attributes and then creating new tables which represent each (fine) partition as a field. The results obtained by soft set approach are compared with those obtained by Apriori algorithm without soft set approach. The significant differences have been observed in support and confidence levels of patterns obtained by both the approaches.

Keywords: Association pattern mining, Quantitative data, Soft set, Attribute partition.

[1] R. Agrawal, T. Imieliński, and A. Swami, "Mining association rules between sets of items in large databases," International conference on Management of data, ACM SIGMOD pp. 207-216.

[2] R. Agrawal, and R. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases," Proceedings of 20th International Conference on Very Large Data Bases, pp. 487-499.

[3] J. Han, and Y. Fu, "Discovery of multiple-level association rules from large databases," VLDB '95 Proceedings of the 21th International Conference on Very Large Data Bases1995, pp. 420-431.

[4] A. Pandey, and K. Pardasani, "Rough Set Model for Discovery multidimensional Association rules," International Journal of computer science and network security, vol. 9, no. 6, 2009, pp. 159-164.

[5] R. Thakur, R. Jain, and K. Pardasani, "Mining level-crossing association rules from large databases," Journal of computer science, vol. 2, no. 1, 2006, pp. 76-81.

[6] R.S. Thakur, R.C. Jain, and K.R. Pardasani, "Fast Algorithm for mining multi-level association rules in large databases," Asian Journal of International Management, vol. 1, 2007, pp. 19-26.

[7] N. Khare, N. Adlakha, and K.R. Pardasani, "An Algorithm for Mining Multidimensional Fuzzy Association Rules" International Journal of Computer Science and Information Security, vol. 5, no. 1, 2009, pp. 72-76.

[8] R. Srikant, and R. Agrawal, "Mining quantitative association rules in large relational tables," In Proceedings of the ACM SIGMOD Conference on Management of Data.

[9] Y. Aumann, and Y. Lindell, "A statistical theory for quantitative association rules," KDD '99 Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 261-270.

[10] K.C.C. Chan and W.-H. Au, "An effective algorithm for mining interesting quantitative association rules," In Proceedings of the 1997 ACM symposium on applied computing, pp. 88-90.

Abstract: Neural networks are powerful techniques for non-linear data, which have been proven in many domains. As a result, the artificial neural networks have been applied in various fields including meteorology and climatology. This work is a contribution to the development of methods of weather prediction in general, and humidity rate in particular. In a first step, methods that are based on the study of artificial neural networks types MLP (Multilayer Perceptron) are applied for the prediction of moisture in the area of Chefchaouen in Morocco. In a second step, the proposed new architecture of neural networks of MLP type was compared to the model of multiple linear regression (MLR). The basis of learning neural model was collected between 2008 and 2011 during 1248 days. The latter consists of a number of climatic parameters, such as atmospheric pressure, air temperature, visibility, cloud cover, precipitation, dew point temperature, wind speed and humidity.Predictive models established by the MLP neural networks method are more powerful compared to those established by multiple linear regression, because of the fact that good correlation was obtained with the parameters from a neural approach with a mean squared error 5%.

Keywords: Humidity, Artificial Neural Networks, MLP, Linear regression, Prediction.

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Abstract: In the competitive economic market the demand of secured and reliable system is increasing day by day. A successful system development is possible by consider equally both functional and nonfunctional requirement. But practically nonfunctional requirements are not identifying as like functional requirement. There are few generic requirements for a system like auditability, extensibility, maintainability, performance, portability, reliability, security, testability, usability and etc. among them security is very vital issue for system development. The security of web based application is vulnerable now a days. For this reason the importance of web based application security is growing over the time. Very often the system fails because of without incorporating the appropriate security specific-process. Our proposed model elicits the system security in a systematic way during requirement analysis phase. Using use case and questionnaires table our model elicits the security requirements of a system. We use Point of Sale System as a case study to identify its security.

Keywords- Identify Security, Web Application, Security Model, Functional requirement, Non Functional Requirement

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Abstract: Measuring customer attitude provides an indication of how successful the organization is at providing products and/or services to the marketplace. Indian mobile industry has witnessed a dramatic growth. Cheap mobile handsets, affordable airtime rates, low initial cost and affordable monthly rentals made it easy for anybody to go mobile. As per latest statistics India has around 160 million mobile subscribers. Le Roux (1994) defines attitude to be a positive or negative emotional relationship with or predisposition toward an object, institution or person. Customer satisfaction is a collective outcome of perception, evaluation, and psychological reactions to the consumption experience with a product or service. This research article investigated the attitude of customers to mobile communication. All the customers of mobile in Hyderabad city (Andhra Pradesh) constituted the population. Air Tel, Vodafone, Idea, Cell One are the four companies which are included in study. The sample of the study is 2600 customers that randomly selected. A questionnaire was developed and validated through pilot testing and administered to the sample for the collection of data. The internal consistency of the instrument was determined using Cronbach alpha method and the coefficient of internal consistency obtained was 0.82. The researcher personally visited respondents, thus 100% data were collected. The collected data were tabulated and analyzed by SPSS. Results showed that Air Tel has major share in market and among customers Vodafone has larger share than Air Tel. Reasons like Packages offered by Vodafone are attracting, which makes it a have a larger share. Services and network of Air Tel is good compare to the other players in the market but it is Vodafone which offers better packages. This study showed that most of the respondents need improvement in service. Majority of respondents gave an Good rate for "mobile service providers".

Keywords: Attitude , Communication , Customer, Mobile, service providers.

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Abstract: Compression is a technique used by many DBMSs to increase performance. Compression improves performance by reducing the size of data on disk, decreasing seek times, increasing the data transfer rate and increasing buffer pool hit rate [1]. Column-Oriented Data works more naturally with compression because compression schemes capture the correlation between values; therefore highly correlated data can be compressed more efficiently than uncorrelated data. The correlation between values of the same attribute is typically greater than the correlation between values of different attributes. Since a column is a sequence of values from a single attribute, it is usually more compressible than a row [4]. In this paper we proposed the Lossless method of Column-Oriented Data-Image Compression and Decompression using a simple coding technique called Huffman Coding. This technique is simple in implementation and utilizes less memory [2]. A software algorithm has been developed and implemented to compress and decompress the created Column-oriented database image using Huffman coding techniques in a MATLAB platform.

Keywords- Compression, Column-Oriented Data-Image Compression and Decompression, Huffman coding.

[1] Miguel C. Ferreira, 'Compression and Query Execution within Column Oriented Databases'.

[2] Jagadish H. Pujar, Lohit M. Kadlaskar, "A New Lossless Method Of Image Compression And Decompression Using Huffman Coding Techniques‟, Journal of Theoretical and Applied Information Technology, © 2005 - 2010 JATIT.

[3] Sushila Aghav, "Database compression techniques for performance optimization", 2010 IEEE, V6-714.

[4] Daniel J. Abadi, Query Execution in Column-Oriented Database Systems, MASSACHUSETTS INSTITUTE OF TECHNOLOGY, June 2005 (c) Massachusetts Institute of Technology 2005.

[5] Infobright,"Analytic Applications With PHP and a Columnar Database", 403-47 Colborne St Toronto, Ontario M5E 1P8 Canada.

[6] C. Saravanan, M. Surender, 'Enhancing Efficiency of Huffman Coding using Lempel Ziv Coding for Image Compression', International Journal of Soft Computing and Engineering (IJSCE) ISSN: 2231-2307, Volume-2, Issue-6, January 2013

[7] Daniel J. Abadi, Peter A. Boncz, Stavros Harizopoulos,'Column-oriented Database Systems', VLDB '09, August 24-28, 2009, Lyon, France.

[8] C. Saravanan & R. Ponalagusamy "Lossless Grey-scale Image Compression using Source Symbols Reduction and Huffman Coding", International Journal of Image Processing (IJIP), Volume (3): Issue (5).
[9] SyBase, David Loshin (President, Knowledge Integrity Inc) , ' Gaining the Performance Edge Using a Column-Oriented Database Management System'. en.wikipedia.org/wiki/Peak_Signal-to-noise_ratio.

Abstract: Biometric person recognition for secure access to restricted data/services using PC with internet connection. To study, an application PC to be used as a biometric capturing device that captures the video and recognition can be performed during a standard web session. The main contribution of this novel proposal is, making comparison of portrait. Centroid context algorithm is used for selecting an random movements from an video and stored it in a database and make them to compare with video. To better characterize a portrait in a sequence, triangulate it into triangular meshes, which we extract the features: skeleton feature and centroid feature. Skeleton feature and centroid context feature working together makes human movement analysis a very efficient and accurate process. Depth first search(DFS) scheme is used to extract the skeletal feature of a portrait from triangulation result, from skeletal feature result, centroid context feature is extracted, which is a finer representation that can characterize the shape of a whole movements. For efficient and accurate process, generate a set of key portrait from a movement sequence. The ordered key portrait sequence is represented by string. For arbitrary matching action, string matching algorithm is used for implementing the concept.

Key Words: Human Movement Analysis, String Matching, Triangulation.

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[2] R.Cucchiara, C.Grana, A. Prati, and R.Vezzani, "Probabilities posture classification for human-behavior analysis," IEEE Trans. Syst., Man, Cybern. A, vol. 35, no. 1, pp. 42–54, Jan. 2005.

[3] S.Belongie, J. Malik, and J. Puzicha, "Shape matching and object recognition using shape contexts," IEEE Trans. Pattern Recognit. Machine Intell., vol. 24, no. 4, pp. 509–522, Apr. 2002.

[4] Jun-Wei Hsieh, Member, IEEE, Yung-Tai Hsu, Hong-Yuan Mark Liao, "Video Based Human Movement Analysis and Its Application to Surveillance Systems", IEEE Transactions on multimedia, vol. 10, no. 3, april 2008.

[5] N.Werghi, "A discriminative 3D wavelet-based descriptors: Application to the recognition of human body postures,"Pattern Recognit Lett., vol. 26, no. 5, pp. 663–677, 2005.

[6] M.S.Ryooand and J.K.Aggarwal,"Spatio-Temporal Relationship Match: Video Structure Comparison for Recognition of Complex Human Activities" Proceedings of the IEEE International Conference on Computer Vision (ICCV), Kyoto, Japan, Oct 2009.

[7] Shuo Wang and Jing Liu P. R. China, "Biometrics on Mobile Phone", vol. 24, no. 4, pp. 509–522, Apr. 2005.

[8] JulioCezar SilveiraJacques and RositoJungSoraia "Background Subtraction and Shadow Detection in Grayscale Video Sequences" IEEE Transactions on multimedia, vol. 10, no. 3, april 2009.

[9] Anil K. Jain, Ajay Kumar," Biometrics of Next Generation: An Overview","second generation biometrics‟ springer, 2010.