iosr-JCE   Volume-10 ~ Issue-6

Abstract: Data mining is the knowledge discovery process by analyzing the large volumes of data from various perspectives and summarizing it into useful information; data mining has become an essential component in various fields of human life. It is used to identify hidden patterns in a large data set. Classification techniques are supervised learning techniques that classify data item into predefined class label. It is one of the most useful techniques in data mining to build classification models from an input data set; these techniques commonly build models that are used to predict future data trends. In this paper we have worked with different data mining applications and various classification algorithms, these algorithms have been applied on different dataset to find out the efficiency of the algorithm and improve the performance by applying data preprocessing techniques and feature selection and also prediction of new class labels.

Keywords: Classification, Mining Techniques, Algorithms.

[1]. SERHAT ÖZEKES and A.YILMAZ ÇAMURCU:" CLASSIFICATION AND PREDICTION IN A DATA MINING APPLICATION "Journal of Marmara for Pure and Applied Sciences, 18 (2002) 159-174 Marmara University, Printed in Turkey.
[2]. Kaushik H and Raviya Biren Gajjar ,"Performance Evaluation of Different Data Mining Classification Algorithm Using WEKA",Indian Journal of Research(PARIPEX) Volume : 2 | Issue : 1 | January 2013 ISSN - 2250-1991.
[3]. WaiHoAu,KeithC.C.Chan;XinYao.ANovelEvolutionaryDataMiningAlgorithmwithApplicationstoChurn Prediction. IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION, Vol. 7, No. 6, Dec 2003, PP: 532- 545
[4]. Reza Allahyari Soeini and Keyvan Vahidy Rodpysh: "Evaluations of Data Mining Methods in Order to Provide the Optimum Method for Customer Churn Prediction: Case Study Insurance Industry."2012 International Conference on Information and Computer Applications (ICICA 2012)IPCSI vol. 24 (2012) © (2012) IACSIT Press, Singapore.
[5]. Surjeet Kumar Yadav and Saurabh Pal:" Data Mining: A Prediction for Performance Improvement of Engineering Students using Classification"World of Computer Science and Information Technology Journal (WCSIT) ISSN: 2221-0741 Vol. 2, No. 2, 51-56, 2012.
[6]. Zhong, N.; Zhou, L.: "Methodologies for Knowledge Discovery and Data Mining", The Third Pacific-Asia Conference, Pakdd-99, Beijing, China, April 26-28, 1999; Proceedings, Springer Verlag, (1999).
[7]. Raj Kumar, Dr. Rajesh Verma:" Classification Algorithms for Data Mining: A Survey"International Journal of Innovations in Engineering and Technology (IJIET)
[8]. Fayyad, U.: "Mining Databases: Towards Algorithms for Knowledge Discovery", IEEE Bulletin of the Technical Committee on Data Engineering, 21 (1) (1998) 41-48.
[9]. Ling Liu:" From Data Privacy to Location Privacy: Models and Algorithms"September 23-28, 2007, Vienna, Austria.
[10]. Qi Li and Donald W. Tufts:,"Principal Feature Classification " IEEE TRANSACTIONS ON NEURAL NETWORKS, VOL. 8, NO. 1, JANUARY 1997.

Abstract: This work presents and investigates the discriminatory capability of contourlet coefficient co-occurrence matrix features in the analysis of mammogram images and its classification. It has been revealed that contourlet transform has a remarkable potential for analysis of images representing smooth contours and fine geometrical structures, thus suitable for textural details. Initially the ROI (Region of Interest) is cropped from the original image and its contrast is enhanced using histogram equalization. The ROI is decomposed using contourlet transform and the co-occurrence matrices are generated for four different directions (θ=0°, 45°, 90° and 135°) and distance (d= 1 pixel). For each co-occurrence matrix a variety of second order statistical texture features are extracted and the dimensionality of the features is reduced using Sequential Floating Forward Selection (SFFS) algorithm. A PNN is used for the purpose of classification. For experimental evaluation, 200 images are taken from mini MIAS (Mammographic Image Analysis Society) database. Experimental results show that the proposed methodology is more efficient and maximum classification accuracy of 92.5% is achieved. The results prove that contourlet coefficient co-occurrence matrix texture features can be successfully applied for the classification of mammogram images.

Keywords-Contourlet Transform, Mammogram, SFFS, PNN, ROI, MIAS

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[2] A. Jemal, R. Siegel, E. Ward, Y. Hao, J. Xu, T. Murray,and M. J. Thun, "Cancer Statistics, 2008," CA: A CancerJournal for Clinicians, vol. 58, pp. 71-96, 2008.

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[8] A. O. Malagelada, Automatic mass segmentation in mammographic images, PhD Thesis,Universitat de Girona, Spain, 2004.

[9] D. Raba, A. Oliver, J. Marti, M. Peracaula and J. Espunya, Breast segmentation with pectoral muscle suppression on digital mammograms, Springer-Verlag: Medical Imaging: Pattern Recognition and Image Analysis, 3523 (2005), 471-478.

Abstract: Using AI Hans peter Wickelgren applying the usage of text-based passwords is common authentication system in any Application. This conventional authentication scheme faces some kind of limitations and drawbacks with usability and crypto-graphical security issues that bring troubles to users. For example, user tends to pick passwords that can be easily guessed. On the contrary, if a password is hard to guess, then it is often hard to remember. An alternative system is required to overcome these problems. To deal with these drawbacks, authentication scheme that use photo ,image, or set of pattern as password is proposed using knowledge Recall-Based System(KRBS).Graphical passwords consist of clicking or dragging activities on the pictures rather than typing textual characters, might be the option to overcome the problems that arise from the text-based passwords authentication system. In this paper, a comprehensive Artificial Intelligence(AI) study of the existing graphical password schemes is performed. The graphical password authentication systems are categorized into two AI approach types: An approach on recognition-based System (RBS) and second approach on Recall-based system (RCBS). We discuss adequately the strengths and limitations of each method in terms of usability and security aspects .

Keywords- Graphical Passwords using Hans peter Wickelgren, Recognition-Based Graphical User Authentication, Recall-Based Graphical User Authentication, Pure Recall-Based Authentication, Knowledge Recall-Based Authentication System, Usability, Security , Artificial Intelligence(AI) ,Knowledge-Based Development Systems(KBDS).

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Abstract: Over the recent years, a great deal of effort has been made to age estimation & gender recognition from face images. It has been reported that age can be accurately estimated under controlled environment such as frontal faces, no expression, and static lighting conditions. However, it is not straightforward to achieve the same accuracy level in real-world environment because of considerable variations in camera settings, facial poses, and illumination conditions. In this paper, we apply features based approach for gender recognition & histogram base matching for age prediction to get desired objectives. Through real-world age estimation experiments, we demonstrate the usefulness of our proposed method.

Keywords: Face Detection, Skin Color Segmentation, Face Features extraction, Features recognition, Fuzzy rules,Histogram,Image mining

[1]. Edward D. Mysak, "Pitch duration characteristics of older males," Journal of Speech and Hearing Research, vol. 2, pp.46–54, 1959.
[2]. Sue E. Linville, Vocal Aging, Singular Publishing Group, SanDiego, CA; USA, 2001.
[3]. Christian M¨uller, Frank Wittig, and J¨org Baus, "Exploiting speech for recognizing elderly users to respond to their special needs," in Proc. Euro speech 2003, Geneva; Switzerland, Sept.2003, ISCA.
[4]. Nobuaki Minematsu, Mariko Sekiguchi, and Keikichi Hirose,"Automatic estimation of one's age with his/ her speech based upon acoustic modeling techniques of speakers," in Proc.ICASSP 2002, Orlando, FL; USA, May 2002, IEEE.
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Abstract: This paper addresses an approach to build lexical and parser tool for Component Based Object Oriented Program (CBOOP). In this Paper,Lexical analysis tool use for the scanning of CBOOP program. Lexical tool reads the input characters of the source program and return the tokens. Parser will generate the syntax tree of CBOOP program and check the syntax of program. The driver program, i.e., main program will open the file containing CBOOP program according to input.

Keywords: CBOOP,Lexical Analyzer, Tokens, Parser, Driver program

[1] ArvinderKaur, Kulvinder Singh, Component Selection for Component based Software engineering, International Journal of Computer, Applications,2(1),2010,0975-8887

[2] Miroslav D. C´ iric´ and Svetozar R. Rancˇic, Parsing in Different Languages,FACTA UNIVERSITATIS,18(1),2005,299-307

[3] João Costa Seco, Ricardo Silva and Margarida Piriquito, A Component-Based Programming Language with Dynamic Reconfiguration,International Journal of Software and Information Systems Vol. 5,2008

[4] Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D.Ullman, Compilers(Pearson,2007)

[5] AndyJuAn Wang, Kai Qian,Component-Oriented Programming(John Wiley & Sons, 2005)
[6] XIAOQING WU, BARRETT R. BRYANT, JEFF GRAY, MARJAN MERNIK, ALAN SPRAGUE, MURAT TANIK, Component-Based Language Implementation with Object-Oriented Syntax and Aspect-Oriented Semantics,The University of Alabama at Birmingham

[7] Luis Quesada, Fernando Berzal, and Francisco J.Cortijo,A Lexical Analysis Tool with Ambiguity Support,CITIC, University of Granada

[8] TIM A. WAGNER and SUSAN L. GRAHAM, General Incremental Lexical Analysis, University of California, Berkeley

[9] Oh-Cheon Kwon, Seok-Jin Yoon and Gyu-Sang Shin, Computer & Software Technology Laboratory, ETRI(Electronics and Telecommunications Research Institute)Taejon, Korea

[10] K. L. P. Mishra, N. CHANDRASEKARAN,Theory of Computer Science: Automata, Languages and Computation(Prentice-Hall,2007)

Abstract: This paper presents a shape-based approach to curve evolution for the segmentation of medical images. Automatic interpretation of medical images is a very difficult problem in computer vision. Several methods have been developed in last decade to improve the segmentation performance in computer vision. A promising mathematical framework based on variational models and partial differential equations has been investigated to solve the image segmentation problem. This approach benefits from well-established mathematical theories that allow people to analyze, understand and extend segmentation methods. In this paper, a variational formulation is considered to the segmentation using active contours models.

Keywords - Active Contour, Image Segmentation, Level Set Method, Morphological Erosion, Thresholding, Variational Level Set Method, Contour Evaluation.

[1] Rafael C. Gonzalez and Richard E. Woods, Digital Image Processing, 3rd edition, Published by Pearson Prentice Hall, 2009.
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Material Science.
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Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05)

Abstract: An ad-hoc network is a temporary network without any form of centralized administration. Multiple hops might be necessary to reach other nodes in the network. For this reason, each node acts both as a router and a host, meaning that every node must be willing to forward packets for other nodes. For this reason a routing protocol is needed.

Keywords: Ad-hoc, Routing, Wireless.

[1] Dimitri Bertsekas and Robert Gallager, "Data Networks-2nd ed". Prentice Hall, New Jersey, ISBN 013-200916-1

[2] Bommaiah, McAulley and Talpade. AMRoute, "Adhoc Multicast Routing Protocols", Internat draft, drafttalpade-manet-amroute-00.txt, august 1998.

[3] Josh Broch, David B. Johnson, David A. Maltz," The Dynmic Source Routing Protocol for mobile adhoc networks". Internet draft, draft-ietf-manet-dsr-00.txt.

[4] Kevin Fall and Kannan Varadhan, "ns ntes and documentation". The VINT project, UC Berkley, LBL, USC/ISI, and Xerox PARC. [5] IEEE Computer society LAN MAN Standards Committee ," Wireless LAN Medium Access Control(MAC) and Physical Layer (PHY) Specifications", IEEE std 802.11-1997. The Institute of Electrical and Engineers, New York.

[6] Mingling Jiang, Jingang Li and Yong Chiang Tay," Cluster Based Routing Protocol(CBRP) Functional SPECIFICATION". Internet draft, draft-ietf-manet-cbrp-spec-00.txt.

[7] David B Johnson and David A. Maltz," Dynaamic source routing in ad hoc wireless networks". In Mobile computing, edited by Tomasz Imielinski and Hank Korth, chapter 5, pages 153-181. Kluner Academic Publication.

[8] David B Johnson and David A. Maltz,"Security architecture for the internet protocol", Internet draft,draft-ietf-ipsec-arch-sec-07.txt.

Abstract: The paper shows performance comparison of three proposed methods with orthogonal wavelet alias Hartley,Slant &Kekre‟s wavelet using Normalization for "Color to Gray and Back‟. The color information of the image is embedded into its intermediate gray scale version with wavelet using normalization method. Instead of using the original color image for storage and transmission, intermediate gray image (Gray scale version with embedded color information) can be used, resulting into better bandwidth or storage utilization. Among three algorithms considered the second algorithm give better performance as compared to first and third algorithm. In our experimental results second algorithm for Kekre‟s wavelet using Normalization gives better performance in "Color to gray and Back‟ w.r.t all other wavelet transforms in method 1, method 2 and method 3. The intent is to achieve compression of 1/3 and to print color images with black and white printers and to be able to recover the color information.

Keywords- Color Embedding, Color-to-Gray Conversion, Transforms, Wavelets,Normalization ,Compression.

[1] T. Welsh, M. Ashikhmin and K.Mueller, Transferring color to grayscale image, Proc. ACM SIGGRAPH 2002, vol.20, no.3,
pp.277-280, 2002.
[2] A. Levin, D. Lischinski and Y. Weiss, Colorization using Optimization, ACM Trans. on Graphics, vol.23, pp.689-694, 2004.
[3] T. Horiuchi, "Colorization Algorithm Using Probabilistic Relaxation," Image and Vision Computing, vol.22, no.3, pp.197-202,
2004.
[4] L. Yatziv and G.Sapiro, "Fast image and video colorization using chrominance blending", IEEE Trans. Image Processing, vol.15,
no.5, pp.1120-1129, 2006.
[5] H.B. Kekre, Sudeep D. Thepade, "Improving `Color to Gray and Back` using Kekre‟s LUV Color Space." IEEE International
Advance Computing Conference 2009, (IACC 2009),Thapar University, Patiala,pp 1218-1223.
[6] Ricardo L. de Queiroz,Ricardo L. de Queiroz, Karen M. Braun, "Color to Gray and Back: Color Embedding into Textured Gray
Images" IEEE TRANSACTIONS ON IMAGE PROCESSING, VOL. 15, NO. 6, JUNE 2006, pp 1464- 1470.
[7] H.B. Kekre, Sudeep D. Thepade, AdibParkar, "An Extended Performance Comparison of Colour to Grey and Back using the Haar,
Walsh, and Kekre Wavelet Transforms" International Journal of Advanced Computer Science and Applications (IJACSA) , 0(3),
2011, pp 92 - 99.
[8] H.B. Kekre, Sudeep D. Thepade, RatneshChaturvedi&Saurabh Gupta, "Walsh, Sine, Haar& Cosine Transform With Various Color
Spaces for "Color to Gray and Back‟", International Journal of Image Processing (IJIP), Volume (6) : Issue (5) : 2012, pp 349-356.
[9] H. B. Kekre, Sudeep D. Thepade, Ratnesh N. Chaturvedi, " NOVEL TRANSFORMED BLOCK BASED INFORMATION
HIDING USING COSINE, SINE, HARTLEY, WALSH AND HAAR TRANSFORMS", International Journal of Advances in
Engineering & Technology, Mar. 2013. ©IJAET ISSN: 2231-1963, Vol. 6, Issue 1, pp. 274-281
[10] R. N. Bracewell, "Discrete Hartley transform," Journal of the Optical Society of America, Volume 73, Issue 12, pp 1832-1835, Dec. 1, 1983.

Abstract: Social Networking Sites (SNS) are being used for over a decade, and has exponentially grown in popularity in the recent few years. They are web based services that allow individuals to: (a) make a public or semipublic profile (b) share contents with many users (c) view and traverse other user list. SNS allow users to connect, share information and other comments, chat, play games, and even add comments. Social networking sites are very useful in sharing information, making friends and keeping in touch with old friends. It is an online service, platform, or site that focuses on facilitating the building of social networks and social elation among peoples for sharing interests, activities, backgrounds, or real-life connections. But with the increasing demand of social networking sites (SNS) privacy and security concern have also increased.

[1] Vorakulpipat, C.; Marks, A.; Rezgui, Y.; Siwamogsatham, S.; , "Security and privacy issues in Social Networking sites from user's viewpoint," Technology Management in the Energy Smart World (PICMET), 2011 Proceedings of PICMET '11: , vol., no., pp.1-4, July 31 2011-Aug. 4 2011

[2] Joshi, P.; Kuo, C.-C.J.; , "Security and privacy in online social networks: A survey," Multimedia and Expo (ICME), 2011 IEEE International Conference on , vol., no., pp.1-6, 11-15 July 2011

[3] SeyedHossein Mohtasebi and Ali Dehghantanha, "A Mitigation Approach to the and Malware Threats of Social Network Services ," Multimedia Information Networking and Security, 2009. MINES '09. International Conference, vol.1, no., pp.448-459, 2011

[4] Chi Zhang; Jinyuan Sun; Xiaoyan Zhu; Yuguang Fang; , "Privacy and security for online social networks: challenges and opportunities," Network, IEEE , vol.24, no.4, pp.13-18, July-August 2010

[5] Jason Bau, Elie Bursztein, Divij Gupta, John Mitchell, " State of the Art: Automated Black-Box Web Application Vulnerability Testing", IEEE Symposium on Security and Privacy, 2010, 1081-6011

[6] Anna C.Squicciarini, Mohamed Shehab, Joshua Wede, "Privacy policies for shared content in social network sites ", The VLDB Journal(2010) 19:777-796,DOI 10.1007/s00778-010-0193-7

[7] Debatin, B. et al., 2009. Facebook and Online Privacy: Attitudes, Behaviors, and Unintended Consequences. Journal of Computer-Mediated Communication, 15(1), 83–108.

[8] Ai Ho; Maiga, A.; Aimeur, E.; , "Privacy protection issues in social networking sites," Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on , vol., no., pp.271-278, 10-13 May 2009 [9] Aimeur, E.; Gambs, S.; Ai Ho; , "UPP: User Privacy Policy for Social Networking Sites," Internet and Web Applications and Services, 2009. ICIW '09. Fourth International Conference on , vol., no., pp.267-272, 24-28 May 2009

[10] Xi Chen; Shuo Shi; , "A Literature Review of Privacy Research on Social Network Sites," Multimedia Information Networking and Security, 2009. MINES '09. International Conference on , vol.1, no., pp.93-97, 18-20 Nov. 2009.

Abstract: In wireless communication two main limitations are capacity and range. In the areas of high population density cellular service is far superior compared to scarcely populated areas. The initial cellular systems were designed for a single application, that is only for voice, but today with the advent of third-generation (3G) cellular systems, users expect not only good quality of voice but also many other features such as uninterrupted voice calls, clear video images and faster internet facilities. Data traffic is usually bursty in nature and requires more bandwidth than traditional voice service. 3G suffers from a limitation that it provides inadequate indoor signal penetration, which leads to poor coverage in the indoor environment where users spend most of their time. These characteristics indicate that future cellular wireless systems must be designed in a different way, hence the motivation to move towards smaller cells that operate in a licensed spectrum but are privately owned. Femtocells provide a good solution to overcome indoor coverage problems and also to deal with the traffic within Macro cells. Femtocells provide reliable and high quality of service to all customers. In this paper author has proposed the interference aware & SINR estimation of femtocell for different distance.

Keywords - Femtocell, HeNB, LC-RRM Techniquem Microcell

[1]. MikkoJärvinen, "Femtocell Deployment in 3rd Generation Networks", Master's Thesis, HELSINKI UNIVERSITY OF TECHNOLOGY, 2009
[2]. Vikram Chandrasekhar and Jeffrey G. Andrews, The University of Texas at Austin, Alan Gatherer, Texas Instrument, "Femtocell Networks: A Survey", IEEE Communications Magazine, 2008
[3]. KhaledElleithy and VarunRao , "Femto Cells: Current Status and Future Directions", International Journal of Next-Generation Networks (IJNGN) Vol.3, No.1, March 2011
[4]. Nazmus Saquib, Ekram Hossain, Long Bao Le, and Dong In Kim, "Interference Management in OFDMA Femtocell Networks: Issues and Approaches", 2011
[5]. Guillaume de la Roche, Alvaro Valcarce, David López-Pérez, and Jie Zhang, "Access Control Mechanisms for Femtocells", IEEE Communications Magazine, January 2010
[6]. Heui-Chang Lee, Dong-Chan Oh, and Yong-Hwan Lee "Mitigation of Inter-Femtocell Interference with Adaptive Fractional Frequency Reuse Orthogonal Area and Ratio of Orthogonal Area", IEEE, 2010
[7]. Ji-Hoon Yun, Member, IEEE,and Kang G. Shin, Fellow, IEEE "Adaptive Interference Management of OFDMA Femtocells for Co-Channel Deployment", IEEE JOURNAL ON SELECTED AREAS IN COMM UNICATIONS, VOL. 29, NO. 6, JUNE 2011
[8]. Francesco Pantisano, Mehdi Bennis, WalidSaad, M´erouaneDebbah, "Cooperative Interference Alignment in Femtocell Networks", IEEE, 2011
[9]. Ju Yong Lee, Member, IEEE , Sueng Jae Bae, Student Member, IEEE, Young Min Kwon, and Min Young Chung," Interference Analysis for Femtocell Deployment in OFDMA Systems Based on Fractional Frequency Reuse Member", IEEE COMMUNICATIONS LETTERS, VOL. 15, NO. 4, APRIL 2011
[10]. Yu-Shan Liang, Wei-Ho Chung, Member, IEEE , Guo-Kai Ni, Ing-Yi Chen,Hongke Zhang, and Sy-Yen Kuo,Fellow, "Resource Allocation with Interference Avoidance in OFDMA Femtocell Networks", IEEE TRANSACTIONS ON VEHICULAR TECHNOLOGY, VOL. 61, NO. 5, JUNE 2012