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Most Viewed Article in the last 30days in Academia - International Journal of Distributed and Parallel Systems (IJDPS)

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The growth of Internet and other web technologies requires the development of new algorithms and architectures for parallel and distributed computing. International journal of Distributed and parallel systems is a bi monthly open access peer-reviewed journal aims to publish high quality scientific papers arising from original research and development from the international community in the areas of parallel and distributed systems. IJDPS serves as a platform for engineers and researchers to present new ideas and system technology, with an interactive and friendly, but strongly professional atmosphere.

The growth of Internet and other web technologies requires the development of new algorithms and architectures for parallel and distributed computing. International journal of Distributed and parallel systems is a bi monthly open access peer-reviewed journal aims to publish high quality scientific papers arising from original research and development from the international community in the areas of parallel and distributed systems. IJDPS serves as a platform for engineers and researchers to present new ideas and system technology, with an interactive and friendly, but strongly professional atmosphere.

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Most Viewed Article in the last 30days in Academia - International Journal of Distributed and Parallel Systems (IJDPS)

  1. 1. Most Viewed Article in the last 30days in Academia International Journal of Distributed and Parallel Systems (IJDPS) ISSN : 0976 - 9757 [Online] ; 2229 - 3957 [Print] http://airccse.org/journal/ijdps/ijdps.html
  2. 2. BREAST CANCER DIAGNOSIS USING MACHINE LEARNING ALGORITHMS –A SURVEY B.M.Gayathri1 ,C.P.Sumathi2 and T.Santhanam3 1 Department of Computer Science, SDNB Vaishnav College for Women, Chennai, India 2 Department of Computer Science, SDNB Vaishnav College for Women, Chennai, India 3 Department of Computer Application, D.G.Vaishnav College for Men, Arumbakkam, Chennai, India ABSTRACT Breast cancer has become a common factor now-a-days. Despite the fact, not all general hospitals have the facilities to diagnose breast cancer through mammograms. Waiting for diagnosing a breast cancer for a long time may increase the possibility of the cancer spreading. Therefore a computerized breast cancer diagnosis has been developed to reduce the time taken to diagnose the breast cancer and reduce the death rate. This paper summarizes the survey on breast cancer diagnosis using various machine learning algorithms and methods, which are used to improve the accuracy of predicting cancer. This survey can also help us to know about number of papers that are implemented to diagnose the breast cancer. KEYWORDS Neural networks, SVM, RVM, ELM For More Details : http://airccse.org/journal/ijdps/papers/4313ijdps09.pdf Volume Link : http://airccse.org/journal/ijdps/current2013.html
  3. 3. REFERENCES [1] M.A.H Akhand & M.Murase “Neural network ensemble construction fusing multiple popular methods.”. International journal of computer science 2004. [2] Tuba kiyan, Tulay Yildirim “Breast cancer diagnosis using statistical neural networks”, Journal of Electrical and Electronic Engineering. [3] P.K.Chenniappan & N.Anusheela “Early detection of cancer cells to lung cancer survival data by neural network testing” International journal of neural network and application. [4] Chih-Lin Chi, W. Nick Street, and William H. Wolberg ” Application of Artificial Neural NetworkBased Survival Analysis on Two Breast Cancer Datasets”- AMIA Annu Symp Proc. 2007; 2007: 130–134. [5] Val´erie Bourd`es, St´ephane Bonnevay, Paolo Lisboa, R´emy Defrance, David P´erol, Sylvie Chabaud, Thomas Bachelot, Th´er`ese Gargi,6 and Sylvie N´egrier “Comparison of Artificial Neural 2Network with Logistic regression as Classification Models for Variable Selection for Prediction of Breast Cancer Patient outcomes” [6] I Guyon, J Weston, S Barnhill “Gene selection for cancer classification using support vector machines” … - Machine learning, 2002 – Springer [7] MF Akay “Support vector machines combined with feature selection for breast cancer diagnosis”- Expert systems with applications, 2009 – Elsevier. [8] CL Huang, HC Liao “Prediction model building and feature selection with support vector machines in breast cancer diagnosis “Expert Systems with Applications, 2008 – Elsevier. [9] Ilias Maglogiannis, E Zafiropoulos “An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers” Applied Intelligence, 2009 – Springer. [10] AM Bagirov, B Ferguson, S Ivkovic “New algorithms for multi-class cancer diagnosis using tumor gene expression signatures”, 2003 - Oxford Univ Press. [11] RF Chang, WJ Wu, WK Moon, YH Chou “Support vector machines for diagnosis of breast tumors on US images” - Academic radiology, 2003 – Elsevier [12] Y Rejani- “Early detection of breast cancer using SVM”. 2009 -arxiv [13] L Rong ,”Diagnosis of Breast Tumor Using SVM-KNN Classifier” - 2010 - Intelligent Systems (GCIS). [14] T Mu “Detection of breast cancer using v-SVM and RBF network.” - 2005 – C Medical Applications of Signal. [15] Dr.C.P.Sumathi, Dr.T.Santhanam, A.Punitha “Combination of genetic algorithm and ART neural network for breast cancer diagnosis”, 2007 -- Asian Journal of information technology, Medwell journals. [16] F.Paulin, A.Santhakumaran “Classification of Breast cancer by comparing Back propagation training algorithms”, 2011-International Journal on Computer Science and Engineering (IJCSE) [17] Dr. K. Usha Rani” Parallel Approach for Diagnosis of Breast Cancer using Neural Network Technique” International Journal of Computer Applications, Volume 10– No.3, November 2010. [18] G. Sophia Reena P. Rajeswari -2011 “A Survey of Human Cancer Classification using Micro Array Data”- International Journal of Computer Tech. Appl., [19] Balaji Krishnapuram, Alexander Hartemink, Lawrence Carin “Applying logistic regression and RVM to achieve accurate probabilistic cancer diagnosis from gene expression profiles” - citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.12.5909 [20] A. Bharathi and A.M. Natarajan “Cancer Classification using Support Vector Machines and
  4. 4. Relevance Vector Machine based on Analysis of Variance Features”- Journal of Computer Science 7(9): 1393-1399, 2011. [21] Fatima Eddaoudi , Fakhita Regragui , Abdelhak Mahmoudi , Najib Lamouri “Masses Detection Using SVM Classifier Based on Textures Analysis”- Applied Mathematical Sciences, Vol. 5, 2011, no. 8, 367 – 379. [22] Guoqiang Peter Zhang “Neural Networks for Classification: A Survey” IEEE TRANSACTIONS ON SYSTEMS, MAN, AND CYBERNETICS—PART C: APPLICATIONS AND REVIEWS, VOL. 30, NO. 4, NOVEMBER 2000. [23] S. Aruna, Dr .S.P. Rajagopalan “ A Novel SVM based CSSFFS Feature Selection Algorithm for Detecting Breast Cancer” International Journal of Computer Applications (0975 – 8887) Volume 31–No.8, October 2011. [24] Xin Yao, Yong Liu “Neural Networks for Breast Cancer Diagnosis” 01999 IEEE [25] Dimitrios Siganos ”Neural Networks in medicine”, www.doc.ic.ac.uk/~nd/surprise_96/journal/vol2/ds12/article2.html. [26] David B.Fogel, Eugene C, Wasson, Edward M.Boughton “Evolving neural networks for detecting breast cancer”. 1995 Elsevier Science Ireland Ltd. [27] Dr.Santhosh baboo, S.Sasikala “A Survey on data mining techniques in gene selection and cancer classification”-April 2010 International journal of Computer science and information technology. [28] Afzan Adam1 Khairuddin Omar2 “Computerized Breast Cancer Diagnosis with Genetic Algorithms and Neural Network”- fitt.mmu.edu.my/caiic/papers/afzaniCAIET.pdf. [29] Zhang Qinli; Wang Shitong; Guo Qi; “A Novel SVM and Its Application to Breast Cancer Diagnosis” http://ieeexplore.ieee.org/xpl/freeabs_all.jsp?arnumber=4272649.
  5. 5. BLUETOOTH SECURITY THREATS AND SOLUTIONS: A SURVEY Nateq Be-Nazir Ibn Minar1 and Mohammed Tarique2 1 Department of Electrical and Electronic Engineering, American International University, Bangladesh 2 Department of Electronic and Communication, Ajman University of Science and Technology, United Arab Emirates ABSTRACT Bluetooth technology has become an integral part of this modern society. The availability of mobile phones, game controllers, Personal Digital Assistant (PDA) and personal computers has made Bluetooth a popular technology for short range wireless communication. However, as the Bluetooth technology becomes widespread, vulnerabilities in its security protocols are increasing which can be potentially dangerous to the privacy of a user’s personal information. The security issues of Bluetooth have been an active area of research for the last few years. This paper presents the vulnerabilities in the security protocols of this technology along with some past security threats and possible countermeasures as reported in the literatures which have been surveyed and summarized in this paper. It also presents some tips that end-users can implement immediately to become more cautious about their private information. Finally, the paper concludes with some recommendations for future security enhancements that can be implemented in the Bluetooth standard. KEYWORDS Bluetooth, encryption, security protocols, security threats, countermeasures, Bluetooth enhancements For More Details : http://airccse.org/journal/ijdps/papers/0112ijdps10.pdf Volume Link : http://airccse.org/journal/ijdps/current2012.html
  6. 6. REFERENCES [1] “The Bluetooth Blues”, available at http://www.information- age.com/article/2001/may/the_bluetooth_blues [2] Bluetooth SIG, Specification of the Bluetooth System: Volume 2, Profile, Version 1.1, Feb. 22, 2001. available at: https://www.bluetooth.org/About/bluetooth_sig.htm [3] “The History of Bluetooth”, available at: http://www.bluetomorrow.com/about- bluetoothtechnology/history-of-bluetooth/bluetoothhistory.html [4] Monson, Heidi - "Bluetooth Technology and Implications" available at: http://www.sysopt.com/features/ network/article.php/3532506 (1999-12- 14). [5] “How Bluetooth Works", available at: http://en.kioskea.net/contents/bluetooth/bluetooth- fonctionnement.php3. [6] Mohammed Mana, Mohammed Feham, and Boucif Amar Bensaber, “A light weight protocol to provide location privacy in wireless body area networks”, International Journal of Network Security and its Applications (IJNSA), Vol.3, No.2, March 2011 [7] Yasir Arfat Malkani and Lachhman Das Dhomeja, “PSim: A tool for analysis of device pairing methods”, International Journal of Network Security & Its Applications (IJNSA), Vol.1, No.3, October 2009 [8] Kumar, A., et al. Caveat eptor, “A comparative study of secure device pairing methods”, IEEE International Conference on Pervasive Computing and Communications (PerCom-09). 2009. [9] Jochen Schiller, “Mobile Communications”, Second Edition, Addison Wesley Publications, 2003, pp. 290-292 [10] Bluetooth Profiles” Bluetooth Resource Center, Palowireless Pty Ltd. available at: http://www.palowireless.com/ infotooth/tutorial/profiles.asp [11] SaileshRathi, “Bluetooth Protocol Architecture”, Microware Systems Corporation available at: http://www.dedicated-systems.com/Magazine/00q4/2000q4_p028.pdf [12] “Bluetooth Versions”, summary of all the Bluetooth versions released to date, available at: http://www.bluetomorrow.com/about-bluetooth-technology/general- bluetoothinformation/bluetooth-versions.html [13] Bluetooth Version 4.0 Released. Bluetooth SIG, available at: http://www.bluetooth.com/Pages/High-Speed.aspx
  7. 7. [14] Keijo Haataja, “Security Threats and Countermeasures in Bluetooth Enabled Systems”, Kuopio University Library, 2009, pp. 55-62 [15] “The BlueBug”, a Bluetooth virus, available at: http://trifinite.org/trifinite_stuff_bluebug.html [16] John Oates, “Virus attacks mobiles via Bluetooth”, available at: http://www.theregister.co.uk/2004/06/15/symbian_virus/ [17] F-Secure Article on Lasco.A Worm, available at: http://www.f-secure.com/v- descs/lasco_a.shtml [18] Ford-Long Wong, Frank Stajano, Jolyon Clulow, “Repairing the Bluetooth pairing protocol”. University of Cambridge Computer Laboratory, available at: http://www.cl.cam.ac.uk/research/dtg/~fw242/publications/2005-WongStaClu-bluetooth.pdf [19] Phone pirates in seek and steal mission", Cambridge Evening News, available at: http://www.cambridge-news.co.uk/news/region_wide/2005 /08/17/ [20] "Going Around with Bluetooth in Full Safety”, available at: http://www.securenetwork.it/ricerca/whitepaper/download/ bluebag_brochure.pdf [21] Yaniv Shaked, Avishai Wool, “Cracking the Bluetooth PIN” School of Electrical Engineering Systems, Tel Aviv University, available at: http://www.eng.tau.ac.il/~yash/shaked- wool-mobisys05/ [22] Keijo Haataja, “Security Threats and Countermeasures in Bluetooth-Enabled Systems”, Kuopio University Library, 2009, pp. 68-80 [23] Colleen Rhodes, “Bluetooth Security”, East Carolina University, pp.6-9 [24] Karen Scarfone and John Padgette, (Bluetooth Threats) “Guide to Bluetooth Security”, Computer Security Division - National Institute of Standards and Technology, US Department of Commerce, 2008, pp. 25-26 [25] Raquel Hill and Billy Falotico, “Bluetooth Wireless Technology Security Threats and Vulnerabilities”, Indiana University Bloomington, 2008, pp. 7-8 [26] Karen Scarfone and John Padgette, (Bluetooth Vulnerabilities) “Guide to Bluetooth Security”, Computer Security Division - National Institute of Standards and Technology, US Department of Commerce, 2008, pp.24-25
  8. 8. AUTHORS Nateq Be-Nazir Ibn Minar is a Research Scientist currently rendering his expertise independently to institutions and manufacturing industries. He obtained his Bachelor of Science degree in Electrical and Electronics Engineering, with a special interest in Robotics and Industrial Automation, from American International University - Bangladesh in 2008 and later obtained his Master of Science degree, in Telecommunication Engineering, focusing on Wireless Networks. His research interests include satellite communications, network security, low-powered wireless devices, industrial automation, robotics and green technology. He is the CEO of EcoWave – a company dedicated to produce green electronic products for the benefit of the environment. He is an active member of IEEE and does volunteer work for several societies. Mohammed Tarique is an Assistant Professor in the Department of Electronic and Communication, Ajman University of Science and Technology, in United Arab Emirates. H is research interests include Adhoc Networks and wireless network security, network testing and simulations. His research primarily focuses on the design of Wireless Adhoc Networks.
  9. 9. A CATEGORICAL REVIEW OF RECOMMENDER SYSTEMS RVVSV Prasad1 and V Valli Kumari2 1 Department of Computer Science & Engineering, Bhimavaram Institute of Engineering & Technology, Bhimavaram – 534243, India 2 Department. Of Computer Science & Systems Engineering, AU College of Engineering, Andhra University, Vishakapatnam – 533003, India ABSTRACT As more and more information became available electronically, the need for effective information retrieval and implementation of filtering tools have became essential for easy access of relevant information. Recommender Systems (RS) are software tools and techniques providing suggestions for items and/or services to be of use to a user. These systems are achieving widespread success in ecommerce applications now a days, with the advent of internet. This paper presents a categorical review of the field of recommender systems and describes the state-of-the-art of the recommendation methods that are usually classified into four categories: Content based Collaborative, Demographic and Hybrid systems. This paper discusses the pro’s and con’s of the current categories as well as the trustworthiness of the recommender system in a new dimension as evaluating the evaluator for more appropriate recommendations. In the domain of recommender system, this work also put forward the use of agents as an enabling technology. KEYWORDS Collaboration, Demography, Information Filtering, Recommender System, Agent, Trust For More Details : http://airccse.org/journal/ijdps/papers/3512ijdps07.pdf Volume Link : http://airccse.org/journal/ijdps/current2012.html
  10. 10. REFERENCES [1]. Manisha Hiralall, (2011) Recommender systems for e-shops, Business Mathematics and Informatics paper, Vrije Universiteit, Amsterdam. [2]. Jannach, D. (2006) Finding preferred query relaxations in content-based recommenders. In: 3rd International IEEE Conference on Intelligent Systems, pp. 355–360. [3]. Resnick, P., Varian, H.R., (1997) Recommender systems. Communications of the ACM 40(3), 56–58. [4]. Francesco Ricci, Lior Rokach and Bracha Shapira, (2011) Introduction to Recommender Systems, Handbook, DOI 10.1007/978-0-387-85820-3_1, © Springer Science+Business Media, LLC. [5]. Qing Li and Byeong Man Kim, (2004) Constructing User Profiles for Collaborative Recommender System, APWeb 2004, LNCS 3007, pp. 100-110, 2004, Springer-Verlag Berlin Heidelberg. [6]. Zied Zaier, Robert Goding and Lue Faucher, Evaluating Recommender Systems, International Conference on Automated solutions for Cross Media Content and Multichannel Distribution, IEEE, 2008. [7]. Panagiotis Symeonidis, Alexandros Nanopoulos and Yannis Manolopoulos, (2007) Feature- Weighted User Model for Recommender Systems, LNAI 4511, pp. 97-106, Springer Verlag Berlin Heidelberg. [8]. M. J. Pazzani, (1999) A framework for collaborative, content-based and demographic filtering, Artificial Intelligence Review, 13(5-6):393-408. [9]. Kay, J, (2006) Scrutable adaptation: Because we can and must. In: Adaptive Hypermedia and AdaptiveWeb-Based Systems, 4th International Conference, AH 2006, Dublin, Ireland, June 21-23, 2006, Proceedings, pp. 11–19. [10]. Alfarez Abdul Rahaman and Stephen Hailes, (1997) Using Recommendations for Managing Trust in Distributed Systems, Proceedings of the IEEE International Conference on Communications. [11]. Ido Guy, Naama Zwerdling, David Carmel, Inbal Ronen, Erel Uziel, Sivan Yogev, Shila Ofek-Kolfman, (2009) Personalized Recommendation of Social Software Items Based on Social Relations, , ACM. [12]. Konstantin Pussep, Sebastian Kaune, Jonas Flick, Ralf Steinmetz, (2009) A Peer-to-Peer Recommender System with Privacy Constraints, IEEE. [13]. Olli Niinivaara, (2004) Agent-Based Recommender Systems, Helsinki 17.5.2004, Sofware Agent Technology Course Paper, Department of Computer Science, UNIVERSITY OF HELSINKI. [14]. Yan Zheng Wei, Lue Moreau and Nicholas R.Jennings, (2004) Learning Users’ Interests in a Market-Based Recommendation Systems, IDEAL 2004, LNCS 3177, pp. 833-840, Springer Verlag Berling Heidelberg. [15]. Osmar R. Zaiane, (2002) Building Recommender Agent for e-Learning System, Proceedings in ICCE, IEEE. [16]. Robert H.Guttman, Alexandros G. Moukas and Pattie Maes, (1998) Agent Mediated Electronic Commerce : A Survey, Journal The Knowledge Engineering Review, Volume 13 Issue 2 Pages 147 – 159. [17]. RVVSV Prasad, Vegi Srinivas, V. Valli kumari and KVSVN Raju, “Credibility based reputation calculation in P2P networks”, Springer-Verlag Berlin Heidelberg 2008,
  11. 11. ICDCIT 2008, LNCS 5375, pp. 188-195,2008. AUTHORS RVVSV Prasad received his Masters degree in computer applications from Madurai Kamara j University and Master of Philosophy in Computer Science from Alagappa University. He is currently doctoral candidate in the Department of Computer Science & Engineering, Acharya Nagarjuna University, Nagarjuna Nagar, Guntur, India. He is working as Associate Professor in the Department of Computer Science & Engineering, Bhimavaram Institute of Engineering and Technology, Pennada, Bhimavaram, India. His research interests include recommender systems, security, privacy and trust in mobile agents and P2P networks. V.Valli Kumari received her B.E. in Electronics and Communication Engineering and M.Tech. and PhD in Computer Science and Engineering all from Andhra University, India and is currently working as Professor in the same department. . She was awarded a gold medal for best research in 2008 in Andhra University. Her areas of research interest include Web, Data Engineering, Network Security, e-commerce and has 70 publications in conferences /journals of international and national repute. She is a member of IEEE, ACM, CRSI and CSI.

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