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Inductive learning is the learning that is based on induction. In inductive learning Decision tree algorithms are very famous. For the appropriate classification of the objects with the given attributes inductive methods use these algorithms basically. Decision tree is an important method for both induction research and data mining, which is mainly used for model classification and prediction. ID3 algorithm is the most widely used algorithm in the decision tree so far. Through illustrating on the basic ideas of decision tree in data mining, in this paper, the shortcoming of ID3�s inclining to choose attributes with many values is discussed, and then a new decision tree algorithm combining ID3 and Association Function (AF) is presented. The experiment results show that the proposed algorithm can overcome ID3�s shortcoming effectively and get more reasonable and effective rules. The algorithm is implemented in the java language.

Data Mining, Decision tree, ID3Algorithm, Association Function (AF), Classification

[1]. I. H. Witten, E. Frank, �Data Mining Practical Machine Learning Tools and Techniques�, San Francisco: Morgan Kaufmann Publishers. China Machine Press, second edition ISBN 0-12-088407-0,560 pp, 2005.
[2]. D. Jiang, Information Theory and Coding [M]: Science and Technology of China University Press, 2001.
[3]. S. F. Chen, Z. Q. Chen, �An Artificial intelligence in knowledge engineering [M]�. Nanjing: Nanjing University Press, 1997.
[4]. M. Zhu, �Data Mining [M]�. Hefei: China University of Science and Technology Press Page No (67-72), 2002.
[5]. A. P. Engelbrecht., �A new pruning heuristic based on variance analysis of sensitivity information [J]�. IEEE Trans on Neural Networks, Volume-12 Issue-06, Page No (1386-1399), November 2001.
[6]. N. Kwad, C. H. Choi, �Input feature selection for classification problem [J]�, IEEE Trans on Neural Networks, Volume-13 Issue-01, Page No (143- 159), 2002.
[7]. X. J. Li, P. Wang, �Rule extraction based on data dimensionality reduction using RBF neural networks�. ICON IP2001 Proceedings, 8th International Conference on Neural Information Processing [C]. Shanghai, China, Page No (149- 153), 2001.
[8]. S. L. Han, H. Zhang, H. P. Zhou, �correlation function based on decision tree classification algorithm for computer application�, November 2000.
[9]. S. Y. Zhang, Z. Y. Zhu, �Study on decision tree algorithm based on autocorrelation function�. Systems Engineering and Electronic Volume-27 Issue-07 Jul. 2005.
[10]. Bharati.M, Ramageri,�Data Mining Techniques and Applications�, Indian journal of Computer Science and Engineering, Volume-01, Issue-04, Page NO (301-305), 2010.
[11]. Kalpesh Adhatrao, Aditya Gaykar, Amiraj Dhawan, Rohit Jha and Vipul Honrao,�Predicting,�Students Performance Using ID3 and C4.5 classification Algorithms�, International journal Data mining and knowledge management process,Volume-03,Issue-05,September 2013.

M. Jayakameswaraiah, S. Ramakrishna, "Implementation of an Improved ID3 Decision Tree Algorithm in Data Mining System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.51-54, 2014.

IRIS recognition is a method of biometric authentication that uses pattern recognition techniques based on high-resolution images of the irises of an individual�s eyes. Not to be confused with another less prevalent ocular-based technology, retina scanning, IRIS recognition uses camera technology, and subtle IR illumination to reduce specular-reflection from the convex cornea to create images of the detail-rich, intricate structures of the iris. These unique structures converted into digital templates, provide mathematical representations of the iris that yield unambiguous positive identification of an individual. Iris recognition efficacy is rarely impeded by glasses or contact lenses. Iris technology has the smallest outlier (those who cannot use/enroll) group of all biometric technologies. The only biometric authentication technology designed for use in a one-to many search environment, a key advantage of iris recognition is its stability, or template longevity as, barring trauma, a single enrollment can last a lifetime.

Image Processing, IRIS recognition, Biometrics For Data Security, Secure Biometrics

[1] IRIS Recognition System using Biometric Template Matching Technology Sudha Gupta, Asst. Professor, LMIETE, LMISTE,Viral Doshi, Abhinav Jain and Sreeram Iyer, K.J.S.C.E.Mumbai India 2010 International Journal of Computer Applications (0975 8887)Volume 1,No. 2.
[2] IRIS Recognition, Shirke Swati D.,Shirke Suvarna D.,Gupta, Emerging Trends in Computer Science and Information Technology -2012(ETCSIT2012)Proceedings published in International Journal of Computer Applications (IJCA).
[3] http://IRIS .di.ubi.pt/about.htm
[4] http://IRIS .di.ubi.pt/
[5] Stephen Johnson (2006). Stephen Johnson on Digital Photography (http://books.google. com/ books?id=0UVRXzF91gcC& pg=PA17&dq=grayscale+ black-and-white-continuous-tone& ei=XlwqSdGVOILmkwTalPiIDw). O`Reilly. ISBN 0-596-52370-X.
[6] Bryan S. Morse. Lecture 15: Segmentation (edge based, hough transform). Brigham Young University:Lecture Notes, 2000.
[7] Object Detection using Circular Hough Transform, American Journal of Applied Sciences 2 (12): 1606-1609, 2005,ISSN 1546-9239.
[8] Mahboubeh Shamsi, Abdolreza Rasouli �A Novel Approach for IRIS Segmentation and Normalization� Faculty of Computer Science & Information System Islamic Azad University.
[9] http://en.wikipedia.org/wiki/Local_binary_patterns
[10] Christel-lo�c TISSE1, Lionel MARTIN1, Lionel TORRES 2, Michel ROBERT �Person identification technique using human IRIS recognition� 21Advanced System Technology STMicroelectronics � ZI Rousset � 13106 Rousset Cedex, France.
[11] ]Rivest, R.; A. Shamir; L. Adleman (1978). "A Method for Obtaining Digital Signatures and Public-Key Cryptosystems". Communications of the ACM 21 (2): 120�126. doi:10.1145/359340.359342.

Y. Badhe, H. Balbatti, N. Kaladagi, K. Kumar, "IRIS Recognition and Authentication System for Enhancing Data Security," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.55-59, 2014.

A Mobile ad-hoc Network (MANET) is a collection of self oriented mobile nodes dynamically organize a temporary network without any fixed infrastructure and centralized administration control stations wirelessly. In this paper we discuss various readymade simulators and their disadvantages over simulation methodology. Here we are introducing a new simulator and its advantage over traditional one. The various routing protocols are widely analyzed and compared using various simulating environment. The proposed work is basically emphasized on the augmentation in the NS-2 and the Qualnet for standard simulation.

MANET, DSDV, DSR, AODV Routing protocol, NS-2, Qualnet

[1] The network simulator - ns-2. http://www.isi.edu/nsnam/ns/.
[2] User Manual for IMPORTANT Mobility Tool GeneratorinNS-2Simulator. http://nile.usc.edu/important/software.htm, Release Date February 2004.
[3] Kevin Fall and Kannan Varadhan, �ns notes and documentation�. The VINT project, UC Berkeley, LBL, USC/ISI, and Xerox PARC, May 1998. Work in progress.
[4] QualNet 4.5 Programmer�s Guide - Evaluation Version 2008,http://www.scalablenetworks.com/products/developer/new_in_45.php.
[5] QualNet 4.5 User�s Guide -Evaluation Version 2008, ww.eurecom.fr/~chenj/SimulatorManual-3.1.pdf.
[6] NAVAL Postgraduate Schoolmonterey, California Thesis Approved for public release; distribution is unlimited simulation and evaluation of routing protocols for mobile ad hoc networks (MANETs) by Georgios Kioumourtzis September 2005.
[7] Sahil Gupta Sunanda Arora, Gaurav Banga, �Simulation Based Performance Comparison of AODV and DSR Routing Protocols in MANETS�, International Journal of Applied Engineering Research, ISSN 0973-4562 Vol.7 No.11 (2012).

N.K. Pandey, A.K. Mishra, "An Augmentation in a Readymade Simulators Used for MANET Routing Protocols: Comparison and Analysis," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.60-63, 2014.

For the analysis of business, a lot of research attention in the field of computational statistics and data mining has been made. Due to recent technological advances in the field of data clustering, the researchers face ever-increasing challenges in extracting relevant information from enormous volumes of available data. The paper focus on large data sets obtained from online web visiting and categorizing this into clusters according some similarity it helpful tool for the top level management to take optimized and beneficial decisions of business expansion. Clustering is the assignment of a set of observations into subsets. Cluster analysis is widely used in market research when working with multivariate data from surveys. Market researcher partition the general population of consumers into market segments and understand the relationships between customers.

System Architecture, Cluster, Porter Stemmer Algorithm

[1]. Haibo Wang, Da Huo, Jun Huang,� An Approach for Improving K-Means Algorithm on Market Segmentation � IEEE Transactions on knowledge and data Engineering, Vol. 17, N0. 2, February 2012.
[2]. Chien-Liang Liu, Wen-Hsiaio, Chia-Hoang Lee, Gen-Chi Lu and Emery Jou, �Movie Review Summarization in Mobile Environment� IEEE vol 42, No 3, May 2012.
[3]. Bin Zhang Member, IEEE and Sargur N. Srihari, Fello, ,� Fast k-Nearest Neighbor Classification using Cluster Based Trees� IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 26, No 4, April 2004.
[4]. Shlomo Geva and Joaquin Site, �Adaptive Nearest Neighbor Pattern Classification� IEEE Transactions on Neural Networks, Vol 2, No 2, March 1991.
[5]. Sridhar. A, Sowndarya. S, �Efficiency of K-Means Clustering Algorithm in Mining Outliers from Large Data Sets� (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 3043-3045.
[6]. Rama.B, Jayashree.P, Salim Jiwani, �A Survey on clustering Current status and challenging issues� (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 09, 2010, 2976-2980.

S. R. Gosavi, P.R. Gohad, P.P. Sandanshi, S.T. Mahajan, N.R. Wankhade, "Market Analysis and Review Synthesis," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.64-66, 2014.

Considering the need for higher security in Secrete Message transmission we introduce Image Steganography for Message Hiding Using Genetic Algorithm. We are going to propose a system in which we can share a secrete message without letting anyone know the presence of that message behind any sample image, this term is called Steganography. To make it more powerful and secure than before we are introducing Genetic Algorithm in this technique.

Steganography; Cryptography; Secrete Message; Image Shelter

[1] Kai Wang, Xukai Zou, Yan Sui. �A Multiple Secret Sharing Scheme based on Matrix Projection�. 33rd Annual IEEE International Computer Software and Applications Conference. Department of Computer and Information Science,0730-3157/09,(400-405), 2013
[2] Guarmeet Kaur, Arati Kochhar. �A Steganography implementation based on LSB and DCT�. International Journal for Science and Emerging Technologies with latest Trends 4(I), (35-41), November 2012
[3] Dr. Mohammad Abbas Fadhil Al-Husainy. �Message Segmentation to Enhance the Security of LSB image Steganography�. International Journal for Computer Science and Application vol 3,no.3,(57-62) 2012
[4] Mantas Paulinas. �A Survey of Genetic Algorithms Applications for Image Enhancement and Segmentation�. ISSN 1392-124X Information Technology and Control.vol 36,no 3,(278-283),2012
[5] lham Ghasemi, Islamic Azad University Science and Research Branch Tehran, Iran. �A Steganographic method based on Integer Wavelet Transform and Genetic Algorithm�, IEEE, 978-1-4244-9799-7/11, (42-45), 2011.
[6] Liping ZHANG, Xiutan WANG, Yong HUANG, Yingning PENG, �A Time Domain Synthesized Binary Phase Code Side lobe Suppression Filter Based on Genetic Algorithm�, Proceedings of ICSP IEEE, 0-7803-5747-7/00, (1907-1910),2000.
[7] S.Geetha, Siva.S.Sivatha Sindhu, Dr.N.Kamaraj, �Evolving GA Classifier for breaking the Steganographic Utilities : Stools, Steganos and Jsteg�, International Conference on Computational Intelligence and Multimedia Applications, IEEE, 0-7695-3050-8/07, (230-234), 2007.
[8] Elham Ghasemi, Shanbehzadeh, �A Steganographic method based on Integer Wavelet Transform and Genetic Algorithm�, IEEE,978-1-4244-9799-7/11,42-45,2011.
[9] Liaojun Pang, Huixian Li, Ye Yao, and Yumin Wang, �A ver- ifiable (t, n) multiple secret sharing scheme and its analyses�, International Symposium on Electronic Commerce and Security, (22�26), 2013.
[10] B.Raja Rao,P.Anil Kumar, �A Novel Information Security Scheme using Cryptic Steganography�, Indian Journal of Computer Science and Engineering ,Vol. 1 No. 4, (327-332),March 2010.

C.R. Gaidhani, V.M. Deshpande, V.N. Bora, "Image Steganography for Message Hiding Using Genetic Algorithm," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.67-70, 2014.

Ad hoc networks are temporary networks connection established for a small session, self-configured and organized networks without any fixed infrastructure. In Ad hoc networks, topology is dynamic as nodes communicate the network �on the fly� for a special purpose (such as transferring data between one computer to another). A Mobile Ad hoc network (MANET) is a dynamically arrangement of wireless Mobile nodes, communicate directly or using intermediate nodes without any predefined infrastructures. In the absence of any predefined infrastructures in networks become vulnerable to number of attacks and high level security becomes a major issue. In this survey paper, we first discuss the introduction to Mobile Ad hoc network. The second section discusses weaknesses or vulnerabilities in Mobile Ad hoc network. The third section discusses the types of attack in Mobile Ad hoc network. The fourth section discusses the security goals of Mobile Ad hoc network. Finally the last section discusses the security solutions to prevent the attacks and provide a high level security to Mobile Ad hoc networks.

Mobile Ad Hoc Networks, attacks, Security, Solutions

[1]. Marco Conti, Body, Personal and Local Ad Hoc Wireless Networks, in Book The Handbook of Ad Hoc Wireless Networks (Chapter 1), CRC Press LLC, 2003.
[2]. M. Weiser, The Computer for the Twenty-First Century, Scientific American, September1991.
[3]. [3] M.S. Corson, J.P. Maker, and J.H. Cernicione, Internet-based Mobile Ad Hoc Networking, IEEE Internet Computing, pages 63�70, July-August 1999.
[4]. Amitabh Mishra and Ketan M. Nadkarni, Security in Wireless Ad Hoc Networks, in Book The Handbook of Ad Hoc Wireless Networks (Chapter 30), CRC Press LLC, 2003.
[5]. Lidong Zhou and Zygmunt J. Hass, Securing Ad Hoc Networks, IEEE Networks Special Issue on Network Security, November/December 1999.
[6]. Yongguang Zhang and Wenke Lee, Security in Mobile Ad-Hoc Networks, in Book Ad Hoc Networks Technologies and Protocols (Chapter 9), Springer, 2005.
[7]. Panagiotis Papadimitraos and Zygmunt J. Hass, Securing Mobile Ad Hoc Networks, in Book The Handbook of Ad Hoc Wireless Networks (Chapter 31), CRC Press LLC, 2003.
[8]. Yi-an Huang and Wenke Lee, A Cooperative Intrusion Detection System for Ad Hoc Networks, in Proceedings of the 1st ACM Workshop on Security of Ad hoc and Sensor Networks, Fairfax, Virginia, 2003, pp. 135 � 147.
[9]. Data Integrity, from Wikipedia, the free encyclopedia, http://en.wikipedia.org/wiki/Data_integrity.
[10]. P. Papadimitratos and Z. J. Hass, Secure Routing for Mobile Ad Hoc Networks, in Proceedings of SCS Communication Networks and Distributed Systems Modeling and Simulation Conference (CNDS), San Antonio, TX, January 2002.
[11]. Y. Hu, A. Perrig and D. Johnson, Ariadne: A Secure On-demand Routing Protocol for Ad Hoc Networks, in Proc. of ACM MOBICOM�02, 2002.
[12]. K. Sanzgiri, B. Dahill, B. N. Levine, C. Shields, and E. M. Belding-Royer, A Secure Routing Protocol for Ad Hoc Networks, in Proceedings of ICNP�02, 2002.
[13]. Y. Hu, D. Johnson, and A. Perrig, SEAD: Secure Efficient Distance Vector Routing for Mobile Wireless Ad Hoc Networks, Ad Hoc Networks, 1 (1): 175�192, July 2003.
[14]. Y. Hu, A. Perrig and D. Johnson, Packet Leashes: A Defense against Wormhole Attacks in Wireless Ad Hoc Networks, in Proceedings of IEEE INFOCOM�03, 2003.
[15]. Y. Hu, A. Perrig and D. Johnson, Rushing Attacks and Defense in Wireless Ad Hoc Network Routing Protocols, in Proceedings of ACM MobiCom Workshop - WiSe�03, 2003.
[16]. J. R. Douceur, The Sybil Attack, in Proceedings of the 1st International Workshop on Peer-to-Peer Systems (IPTPS�02), pages 251�260, March 2002, LNCS 2429.
[17]. Intrusion-detection system, from Wikipedia, the free encyclopedia, http://en.wikipedia.org/wiki/Intrusion-detection_system.
[18]. Y. Zhang and W. Lee, Intrusion Detection in Wireless Ad-hoc Networks, in Proceedings of the 6th International Conference on Mobile Computing and Networking (MobiCom 2000), pages 275�283, Boston, Massachusetts, August 2000.
[19]. Jim Parker, Anand Patwardhan, and Anupam Joshi, Detecting Wireless Misbehavior through Cross-layer Analysis, in Proceedings of the IEEE Consumer Communications and Networking Conference Special Sessions (CCNC�2006), Las Vegas, Nevada, 2006.

M. Kumar, T. Singh, "A Survey on Security Issue in Mobile AD-HOC Network and Solutions," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.71-75, 2014.

The Tsunami is a natural disaster which can occur over a rapid period of time. The timely report and the responses are very much important to reduce the losses. Certain methods are being followed to detect and inform the public. One of the reliable methods is the WSN. The wireless node sense the vibration of the earth crust, the changes noted will be given to the controller. The controller sends the information to the base station through the radio waves. The wireless sensor devices are equipped with the micro controller, a small RAM for data storage, a flash memory to hold the program, Wireless transceiver, antennae, ADC Converter, and a power source. Our objective is to study the different sensor network protocols to resolve the issue, thus to identify the energy competent wireless sensor network for the considerable improvement in the tsunami disaster management. We also analyze the WSN protocol based on metrics such as Energy competence, location awareness, and network lifetime.

Disaster Management, Sensors, Energy efficiency, WSN

[1]. Maneesha V.Ramesh,Sangeeth Kumar, and P.Venkat Rangan.�Wireless sensor Network for Landslide Detection�.
[2]. Kanan Casey, Alvin Lim , and Gerry Dozier.�A Sensor Network Architecture for Tsunami Detection and Response.International Journal of Distributed Sensor Networks, 4: 27-42,2008.ISSN:1550-1329 Print/ 1550-1477 online. DOI: 10.1080/15501320701774675.
[3]. Vijay Chandrasekhar, Winston KG Seah.� Localization in underwater Sensor Networks − Survey and Challenges�WUWNet�06, September 25, 2006, Los Angeles, California, USA.Copyright 2006 ACM 1-59593-484-7/06/0009...$5.00.
[4]. Melike Erol-Kantarci, Hussein T. Mouftah, and Sema Oktug� A Survey of Architectures and Localization Techniques for Underwater Acoustic Sensor Networks�
[5]. K. Casey, A. Lim, and G. Dozier, �Evolving general regression neural networks for tsunami detection and response,� in Proceedings of the International Congress on Evolutionary Computation(CEC). IEEE, July 2006.
[6]. D. Specht, �A general regression neural network,� IEEE Transactions on Neural Networks, 1991.
[7]. Chenyang Lu, Brian M. Blum, Tarek F. Abdelzaher, John A. Stankovic, and Tian He,� RAP: A Real-Time Communication Architecture for Large-Scale Wireless Sensor Networks,� IEEE Real-Time and Embedded Technology and Applications Symposium (RTAS 2002) San Jose, CA, September 2002.
[8]. Rui Tan, Guoliang Xing, Jinzhu Chen, Wen-Zhan Song, Renjie Huang. 2010. in inproceedings on �Quality driven Volcanic Earthquake Detection using wireless sensor network�, Real time system symposium
[9]. Makoto Suzuki, Narito Kurata, Hiroyuki Morikawa, Shunsuke Saruwatari.2007. �A high density earthquake monitoring system using wireless sensor networks�, International conference on Embedded network sensor System
[10]. Naveed Ahmad, Naveed Riaz, Mureed Hussain.2011. �Ad hoc wirelesses Sensor Network Architecture for Disaster Survivor Detection�, International journal of advance science and technology, vol 34, September 2011.

N. Meenaksi, P. Rodrigues, "Tsunami Detection and forewarning system using Wireless Sensor Network - a Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.76-79, 2014.

Embedded designers can�t afford �the blue screen of death.� Desktop operating systems can get rebooted every so often, but embedded devices often have to run for years without a single reboot. Devices that are used in medical fields are one of the most appropriate examples in which reliability is more important. Also other embedded devices such as industrial-automation systems, security systems and automotive systems have to be more reliable. And the heart of the embedded system is the processor used in it. In recent days there is a big debate among the embedded device manufacturers in selecting the type of architectures which can be used in their embedded devices. The two most commonly used architectures in embedded devices are X86 and ARM. The decision to chose X86 or ARM needs to be investigated thoroughly based on their advantages and limitations. The selection is not only based on its performance of processors and costs but also on power consumption, the type of hardware and software components that it can support and the type of input and output devices supported. In this paper both the architectures are analyzed.

Embedded System, Architectures, ARM, x86

[1] ARM Cortex-A9 Technical Reference Manual
[2] Lamber A. et. al., �Keys to Silicon Realization of Gigahertz Performance and Low Power ARM Cortex-A15�, Unpublished.
[3] Intel Atom Processor N400 & N500 Series Datasheet - Volume 1
[4] Emily Blem, Jaikrishnan Menon, and Karthikeyan Sankaralingam �Power Struggles: Revisiting the RISC vs. CISC Debate on Contemporary ARM and x86 Architectures� IEEE International Symposium on High Performance Computer Architecture, ISBN: 978-1-4673-5585-8, Page No (1-12) Feb 23-27, 2013.
[5] Katie Roberts Hoffman and Pawankumar Hedge �ARM Cortex A8 vs. Intel Atom: Architectural and /Benchmark comparisons� Unpublished and available at www.embedded.com/electrical-engineer- community/general/4400497/Cortex-A8-vs--Intel-Atom--Architecture-Benchmark-comparisons
[6] The ARM vs. x86 wars have begun: In-depth power analysis of Atom, Krait & Cortex A15 available at �:http://www.anandtech.com/show/6536/arm-vs-x86-the-real-showdown/.�
[7] D. Bhandarkar and D. W. Clark. �Performance from architecture: comparing a RISC and a CISC with similar hardware organization�, International Conference on Architectural support for programming languages and operating systems ISBN: 0-89791-380-9, Page No (310-319) April 8-11, 1991.

A. Radhakrishnan, T. Muralikrishna , "A Study on Architectures for Embedded Devices," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.80-83, 2014.

Network and security of system has vital role in data communication environment. Web services and networks can be crashed on attempting many possible ways on forwarding by hackers or intruders. It causes malicious rapt in which it needs a technique called Intrusion Detection System through Spam Filtering. Thus gives the protection to networks. It can be done by using Open Source Network Intrusion Detection System called Snort. The process of arranging the e-mail with framed criteria called Spam Filtering. Proposed System, a Machine Learning Algorithm called Simple Probabilistic Navie Bayes Classifier used to detect the intrusion. Based on its content Probability of Spam messages can be calculated in Navie Bayes Classifier by learning it from spam and Good mail which results a robust, efficient anti-spam approach and adaption. Sniffing the packet and Fed it as input to Navie Bayes Classifier will give Test Dataset. Depends on spam and intrusion probability, the email is been classified as good or spam.

Intrusion Detection, Navie Bayes Algorithm, Spam Filtering, Dynamic Tuning Mechanism

Androutsopoulos, J. Koutsias, V. Chandrinos, and D. Dpyropoulos, �An experimental comparison of Naive Naive Bayes and keyword-based anti-spam filtering with personal e-mail messages� In 23rd Annual Int. ACM SIGIR Conference on Research and Development in Information Retrieval, ISBN:1-58113-226-3, page no.160-167 , 2000.
[2]. Blum, T. Mitchell, �Combining labeled and unlabeled data with co-training�, in Proc. Workshop on Computational Learning Theory,ISBN:1-58113-057-0, page no.92-100, 1998.
[3]. Daniel Grossman, Pedro Domingos University of Washington, Seattle, WA �Learning Naive Bayes network classifiers by maximizing conditional likelihood� 21st international conference on Machine learning table of contents Banff, Alberta, Canada, Learning (IDEAL04), UK
ISBN: 1-58113-838-5, page no.361-368, 2004.
[4]. H. Drucker, D. Wu, and V.N. Vapnik, �Support vector machines for spam categorization �IEEE Transactions on Neural Networks, vol. 10, no. 5, page no. 1048-1054 , 1999.
[5]. Jonathan Palmer, �Naive Bayes Classification for Intrusion Detection using Live Packet Capture�, data mining in bioinformatics, 2011.
[6]. J. Provost �Naive-Bayes vs. rule-learning in classification of e-mail� The University of Texas at Austin, Department of Computer Sciences Rep, AI-TR .99-284, 1999.
[7]. M Rogati, Y Yang, �High-Performing Feature Selection for Text Classification`, CSD, Carnegie Mellon University, CIKM�02, ISBN: 1-58113-492-4, page no.659-661, 2002.
[8]. G. Sakkis, I. Androutsopoulos, G. Paliouras, �A memory-based approach to anti-spam filtering,� Information Retrieval, vol. 6, no.1, page no. 49-73, 2003.
[9]. F. Sebastiani, �Machine learning in automated text categorization� ACM Computing Surveys I vol. 34, no.1, page no.1-47, 2002.
[10]. X.-L. Wang, I. Cloete, �Learning to classify e-mail: A survey� In Proc. of the 4th Int. Conference. On Machine Learning and Cybernetics, Guangzhou.vol. 9, ISBN: 0-7803-9091-1, page no.5716-5719, 2005.

R.S. Priya, V. Anusha, N. Kumar, "Intrusion Detection and Violation of Compliance by Monitoring the Network," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.84-91, 2014.

OpenACC it is a standard programming language for programming heterogeneous computers built from CPUs, GPUs and DSP. It includes a framework of OpenAcc to define the platform in terms of a host (e.g. a CPU) and one or more graphical compute devices (e.g. a GPU) plus a C-based programming language for writing programs & for executing program for the computer devices. Using OpenAcc a programmer can write task-based programming and data-parallel programming that are use all the resources of the heterogeneous computer system. This will be a �future introduction to programming language� where we cover the ideas behind OpenAcc & other platforms. Thereby providing a pedagogically useful example that experienced heterogeneous computing programmers will need to quickly become productive & efficient OpenAcc programmer�s model. We can also show how these ideas are translated into source code & how they are executed in the given system. We will do this through a series of progressively more challenging examples for heterogeneous computing system.

OpenAcc, Heterogeneous computing, HAS, OpenCL, CUDA

[1]. www.openacc.org/sites/default/files/OpenACC%202%200.pdf
[2]. en.wikipedia.org/wiki/Heterogeneous_computing
[3]. http://www.drdobbs.com/parallel/the-openacc-execution-model/240006334?pgno=1
[4]. http://www.diva-portal.org/smash/get/diva2:655634/FULLTEXT01.pdf
[5]. http://developer.amd.com/resources/heterogeneous-computing/what-is-heterogeneous-system-architecture-hsa/
[6]. www.youtube.com/watch?v=r6r2NJxj3kI‎
[7]. http://www.ece.cmu.edu/~ece447/s13/lib/exe/fetch.php?media=onur-447-spring13-lecture33-heterogeneousmulticore-afterlecture.pdf
[8]. www.nvidia.com/gpudirectives
[9]. ww.linksceem.eu/ls2/images/stories/OpenACC.pdf‎
[10]. www.training.praceri.eu/uploads/tx.../HeterogeneousComputingJU.pdf‎
[11]. www.pgroup.com/lit/whitepapers/pgi_accel_prog_model_1.2.pdf

N.R. Chauhan, M.S. Burange, "Multicore Heterogeneous Computing with OpenACC," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.92-97, 2014.