Volume-1 , Issue-1 , Feb 2013, ISSN 2320-7639 (Online) Go Back

• Open Access   Article

  Comparison of Intel Single-Core and Intel Dual-Core Processor Performance

  A. Fasiku, Ayodeji Ireti, B. Olawale, Jimoh Babatunde, C. Abiola Oluwatoyin B.

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.1-9, Jan-2013

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  Computer System Performance and Evaluation deal with investigation of computer components (both hardware and software) with a view of establishing the level of their performances. This research work, carry out performance evaluation studies on Intel single-core and dual-core process to know which of the processor have better execution time and throughput. The architecture of Intel single-core and Intel duo-core processor were studied. Dual-core processors deliver better performance-to-cost ratios relative to their single-core predecessors through on-chip multi-threading. However, they present challenges in developing high performance multi-threaded code. SPEC CPU2006 benchmarks suite was used to measure the performance of the processors. The overall execution time and throughput measurement of Intel single and dual core processors was reported and compared to each other. The research results showed that the execution time of CQ56 Intel Pentium Dual-Core Processor is about 3.42% faster than Intel Celeron Single Core Processor while the throughput of Intel Pentium Dual-Core Processor was found to be 1.03 times higher than Intel Celeron Single Core Processor.

  Key-Words / Index Term
  Intel Dual-Core Processor, Intel Single-Core Processor, architecture and SPEC CPU2006

  References
  [1]. O.S. Adeoye, (2005); Performance Evaluation of Pentium IV Processor, M.Tech Thesis, Federal University of Technology, Akure, Nigeria.
  [2]. J. Emer, (2005); “Microprocessor Evolution:4004 to Pentium-4” Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, Massachusetts.
  [3]. S.O. Falaki and O.S. Adewale, (2000); Trends in Computer Systems Architecture, BJACS Volume 1, No 1, University of Benin Journal of Science.
  [4]. A. I. Fasiku, (2012); Performance Evaluation of Multicore Processors, M.Tech Thesis, Federal University of Technology, Akure, Nigeria.
  [5]. C. Hughes and T. Hughes, (2008); “Professional Multicore Programming, (Design and Implementation for C++ Developers)”, Published by Wiley Publishing, Inc. 10475 Crosspoint Boulevard, Indianapolis, IN 46256.
  [6]. D. M. Pase and M. A. Eckl, (2005); A Comparison of Single-Core and Dual-Core Opteron Processor Performance for HPC, IBM Systems and Technology Group, IBM xSeries Performance Development and Analysis, 3039 Cornwallis Road, Research Triangle Park, NC 27709-2195,USA.
  [7]. D.A. Patterson and J.L. Hennessy, (1996); ‘Computer Architecture: A Quantitative Approach, 2nd Edition, Published by Morgan Kaufmann Publishers, inc. San Francisco California, Printed in the United States of America.
  [8]. D.A. Patterson and J.L. Hennessy, (2007); Computer Architecture A Quantitative Approach, 4th Edition, Published by Morgan Kaufmann publications, inc. San Francisco California, Printed in the United States of America.
  [9]. M. K. Prabhu and K. Olukotun, (2006); Exposing Speculative Thread Parallelism in SPEC2000, Stanford University, Computer Systems Laboratory, Stanford, California 94305.
  [10]. SPEC, (2006); Standard Performance Evaluation Corporation, www.spec.org, retrieved on 20/01/12.
  [11]. G. Hinton, D. Sager, M. Upton, D. Boggs, D. Carmean, A. Kyker, and P. Roussel, (2001); “The Microarchitecture of the Pentium® 4 Processor”, Desktop Platforms Group, Intel Corp., Intel Technology Journal Q.
  

  Citation
  A. Fasiku, Ayodeji Ireti, B. Olawale, Jimoh Babatunde, C. Abiola Oluwatoyin B., "Comparison of Intel Single-Core and Intel Dual-Core Processor Performance," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.1-9, 2013

• Open Access   Article

  Word level detection of Galo and Adi language using acoustical cues

  M. Sora, J. Talukdhar, S. Majumder, P.H Talukdhar, U.Sharmah

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.10-13, Jan-2013

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  Word level Speech recognition is the process of automatic extracting and determining linguistic information Conveyed by a speech wave using computers.A successful speech detection system can helpful in many applications. Study of acoustical cues of Galo and Adi language is main theme. The Galo and Adi tribe belongs to the Sino-Tibetan family of languages. The state of Arunachal Pradesh has evolved a conspicuous dialect that distinguishes them from the rest of the tribes. A further classification can be based on the "dialect" of the language. The variations of Galo and Adi spoken in these tribe differ from each other in intonation, sentence formation and word usage.

  Key-Words / Index Term
  EMD, Abotani, Galo, Adii

  References
  [1]. Galo Agom published by “Galo Welfare Society” Itanagar , May 2007.
  [2]. Y.Liu,H.Yang and H.Zhou “Speaker identification based on EMD” IC-NIDC Proceeding pp.808-812,2009.
  [3]. N.E. Huang, Z. Shen, S.R. Long, M.L. Wu,H.H. Shih, Q. Zheng, N.C. Yen, C.C. Tung and H.H. Liu, “The empirical mode Decomposition and Hilbert spectrum for nonlinear and non stationary time series analysis,” Proc. Roy. Soc.London A,Vol. 454, pp. 903–995, 1998.
  [4]. Y. Zhang, Y. Gao, L. Wang, J. Chen, and X. Shi, “The removal of wall components in Doppler ultrasound signals by using the empirical mode decomposition algorithm,” IEEE Trans. Biomed. Eng., vol. 9, pp.1631–1642, Sep. 2007.
  [5]. Md. Khademul Islam Molla, Keikichi Hirose, Nobuaki Minematsu, and Md. Kamrul Hasan voiced/unvoiced detection of speech signals using empirical mode decomposition model” International Conference on Information and Communication Technology ICICT 2007, 7-9 March 2007.
  

  Citation
  M. Sora, J. Talukdhar, S. Majumder, P.H Talukdhar, U.Sharmah, "Word level detection of Galo and Adi language using acoustical cues," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.10-13, 2013

• Open Access   Article

  A Parallel Optimized Approach for Prostate Boundary Segmentation from Ultrasound Images

  V. Wasson, B. Singh, G. Wasson

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.14-19, Jan-2013

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  This paper presents an automated multi-step algorithm for segmenting the prostate boundary from ultrasound images. As the acquired ultrasound images are usually affected due to the presence of Speckle and other noise artifacts, the first step is to pre-process these images using Sticks Filtering. Further, in stage two an initial contour is determined from these pre-processed images. Finally, Ant Colony Optimization is used to segment the prostate boundary to determine the volume of the prostate. In the last section the performance of both pre-processing stage as well as segmentation stage is presented and is compared with existing algorithms.

  Key-Words / Index Term
  Prostate, TRUS, ACO

  References
  [1]. Abolmaesumi P and Sirouspour M.R., 2004. “Segmentation of Prostate Contours from Ultrasound Images”, proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 5, pp. 517-520
  [2]. Awad Joseph, Galil Abdel and Salama M.M.A, 2003. “Prostate Boundary Detection in TRUS images using Scanning Technique”, proc. of Canadian Conference on Electrical and Computer Engineering, Montre al, pp. 1199-1203
  [3]. Cong Zhe, Schutter Bart and Babuska Robert, 2011. “A new Ant Colony Routing Approach with a trade-off between System and User Optimum”, proc. of 14th Int. IEEE Conference on Intelligent Transportation Systems, Washington DC, USA, pp.1369-1374
  [4]. Gonzalez R. and Woods R. E., 2002. “Digital Image Processing”, Prentice Hall
  [5]. Jendoubi Ahmed, ZengJianchao and Chouikha Mohamed F., 2004. “Segmentation of Prostate Ultrasound Images using an Improved Snakes Technique”, proc. of 7th International Conference on Signal Processing, USA, pp. 2568-2571
  [6]. Li Lei, RenYuemei and Gong Xiang pu, 2008. “Medical Image Segmentation Based on Modified Ant Colony Algorithm with GVF Snake Model”, proc. of IEEE Int.Seminar on Future Bio-Medical Information Engineering, Sydney, Australia, pp. 11-15
  [7]. Li Guokuan, Lu Sha, Min Zhi fang, Li Junbo and Song Enmin, 2007. “3D Prostate Boundary Reconstruction from 2D TRUS Images”, Journal of Scientific Research Foundation of Huazhong University of Science & Technology, pp. 940-943
  [8]. Lu De-Sian and Chen Chien-Chang, 2007. “Edge Detection Improvement by Ant Colony optimization”, Journal of Pattern Recognition Letters, Vol. 29, pp. 416-425
  [9]. Maini Raman and AggarwalHimanshu, 2011 . “Study and Comparison of Various Image Edge Detection Techniques”, International Journal of Image Processing, Vol. 3, Issue 1, pp. 1-12
  [10]. Mahdavi S. Sara, ChngNick, Spadinger Ingrid, Morris J. William and Salcudean E. Septimiu, 2010. “Semi-automatic Segmentation for Prostate Interventions”, Journal of Medical Image Analysis, Vol. 15, pp. 226-237
  [11]. Narayanan S.K. and Wahidabanu R.S.D., 2009. “A view on De-speckling in Ultrasound Imaging”, International Journal of Signal Processing, Image processing & Pattern Recognition, Vol. 2, No. 3, pp. 85-98
  [12]. Narsis, Morteza and AlhosseiniGhorashi,2006. “Computer-Aided Detection of Prostate Detection” proc. of International Conference on Computational Intelligence for Modelling, Control and Automation, pp. 1-5
  [13]. Pathak S.D., ChalanaVikram, Haynor David and Kim Yongmin, 2000. “Edge Guided Delineation of the Prostate in TRUS Images”, proc. of IEEE Transactions on Medical Imaging, Vol. 19, No. 12, pp. 1211-1219
  [14]. Pedemonte Martín, Nesmachnow Sergio and CancelaHéctor, 2011. “A survey on Parallel Ant Colony Optimization”, International Journal of Parallel Soft Computing, Vol. 30,pp.1-17
  [15]. Rafiee Ali, SalimiAhad and Roosta Ali Reza, 2008. “A Novel Prostate Segmentation Algorithm in TRUS Images”, World Academy of Science, Eng. and Technology 45, pp. 120-124
  [16]. SariffNohaiddaBinti and BuniyaminNorlida, 2009. “Comparative Study of GA & ACO algorithm Performances for Robot Path Planning in Global Static Environments of Different Complexities”, proc. of IEEE Int. Symposium on Computational Intelligence in Robotics and Automation, pp. 132 – 137.
  [17]. Sarode V. Milindkumar and Deshmukh R. Prashant, 2011. “Reduction of Speckle Noise & Image Enhancement of Images using Filtering Technique”, International Journal of Advancements in Technology, Vol. 2, No.1, pp. 30-38.
  [18]. Shao Fan, Ling Voon Keck, Ng Sing Wan and Wu Yun Ruo, 2003, “Prostate Boundary Detection from Ultrasonographic Images”, Journal of Ultrasound Medicine, Vol.22, pp-605-623.
  [19]. Shen Ding gang, Zhan Yiqiang and Davatzikos Christos, 2003. “Segmentation of Prostate Boundaries from Ultrasound Images Using Statistical Shape Model”, proc. of IEEE Transactions on Medical Imaging, Vol. 22, No. 4, pp. 539-551
  [20]. Thangavel K. Manavalan R. and Aroquiaraj L., 2009. “ Removal of Speckle Noise from ultrasound medical image based on Special Filters: Comparative Study”, International Journal on Graphics, Vision and Image Processing, Vol. 9, No. 3, pp. 25-32
  [21]. Uthus C. David, Riddle J. Patricia and Guesgen W. Hans, 2009. “An Ant Colony Optimization Approach to the Traveling Tournament Problem”, Proc. of the International Conference on Genetic and Evolutionary Computation, Montreal Quebec, Canada, Vol. 9, No. 7, pp. 81-88
  [22]. Wu yunRuo, Ling Keck and Ng Sing Wan, 2000. “Automatic Prostate Boundary Recognition in Son graphic Images using Feature Model & Genetic Algorithm”, Journal of Ultrasound Medicine, Vol. 19, pp. 771-782.
  [23]. Wang Nian Xiao, Feng Jing Yuan and FengZu-Ren ,2005. “Ant Colony Optimization for Image Segmentation”, Proc. of the Fourth International Conference on Machine Learning and Cybernetics, Guangzhou, pp. 5355-5360.
  [24]. Wang Lei and Zhou yu wen, 2009. “Comparative Study on Bionic Optimization Algorithms for Sewer Optimal Design”, Proc. of Fifth IEEE International Conference on Natural Computation, Vol. 3, pp. 24-29
  [25]. Zhan Yi qiang, Shen Ding gang ,2006. “Deformable segmentation of ultrasound prostate images using statistical texture matching method”, proc. of IEEE Transactions on Medical Imaging, Vol. 25, No. 3, pp. 256-272
  [26]. Zhang ying, Qian Wei and Sankar Ravi, 2005. “Prostate Boundary detection in Transrectal Ultrasound Images”, proc. of IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol. 5, pp. 617-620
  [27]. ZuNing, Downey B. Donal, Fenster Aaron and Ladak M. Hanif, 2002. “Prostate Surface Segmentation from 3D Ultrasound Images”, proc. of IEEE International Symposium on Biomedical Imaging, pp. 613-616
  [28]. http://www.cancer.org/Cancer/ProstateCancer/OverviewGuide/prostate-cancer-overview-key-statistics
  [29]. SainiKalpana et al., 2010. “Ultrasound Imaging and Image Segmentation in the area of Ultrasound: A Review”, International Journal of Advanced Science and Technology, Vol. 24, pp. 41-60
  

  Citation
  V. Wasson, B. Singh, G. Wasson, "A Parallel Optimized Approach for Prostate Boundary Segmentation from Ultrasound Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.14-19, 2013

• Open Access   Article

  Defect Prediction in Software Entities Classified in Terms of Level Dependencies

  N.K. Rao, R.M. Reddy, B.K. Rao

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.20-25, Jan-2013

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  Unit testing is the core fundamental to ensure code is in accordance with the design specifications. The coding and unit testing standard reflects the stability of project (not to mention the testing effort).Code stability is greatly influenced by the efforts of unit testing, which can be automated to reduce the human efforts. In spite of several tools identified for unit testing, tools need to be able to identify the level dependencies or depth of program entity usage in software fragments. This factor greatly influences unit testing complexity. Higher the level of dependency, the greater the complexity of unit testing the code. Here based on level dependencies we predict defects in any expression. A predicting defect-prone software component is an economically important activity and so has received a good deal of attention. However, making sense of the many, and sometimes seemingly inconsistent, a result is difficult. The main objectives of this paper are unbiased and comprehensive comparison between competing prediction systems. This paper mainly focuses on two learning algorithms OneR, Naive Bayes. By using those two algorithms we calculate the error rate. We can predict defects based on those error rates.

  Key-Words / Index Term
  Unit Testing, Level Dependency, Defect Prediction

  References
  [1]. Kent beck, test-driven development by example, addison wesley, 2002.
  [2]. Roy osherove, the art of unit testing with examples in .net, manning publications, 2009.
  [3]. C.nebut, “automatic test generation: a use case driven approach” , ieee transactions on software engineering, vol. 32, no. 3,pp140-156, march 2006.
  [4]. S.vegas, “maturing software engineering knowledge through classifications: a case study on unit testing techniques” , ieee transactions on software engineering, vol. 35, no 4,pp-551-556, july/aug-2009.
  [5]. Ciupa, ilinca, et al. "experimental assessment of random testing for object-oriented software." 2007.
  [6]. Oriat, catherine. "jartege: a tool for random generation of unit tests for java classes." quality of software architectures and software quality (2005): 242-256.
  [7]. Narendra kumar rao, b., a. Rama mohan reddy, and k. Ravi. "level dependencies of individual entities in random unit testing of structured code."electronics computer technology (icect), 2011 3rd international conference on. Vol. 6. Ieee, 2011.
  [8]. Qinbao song, zihan jia, martin shepperd, shi ying, and jin liu, “a general software defect-proneness prediction framework” ieee transactions on software engineering, vol. 37, no. 3, may/june 2011.
  [9]. Buddhinath, gaya, and damien derry. "a simple enhancement to one rule classification." department of computer science & software engineering. University of melbourne, australia (2006).
  [10]. Radlinski, lukasz. "a survey of bayesian net models for software development effort prediction." international journal of software Engineering and Computing2.2 (2010): 95-109.
  

  Citation
  N.K. Rao, R.M. Reddy, B.K. Rao, "Defect Prediction in Software Entities Classified in Terms of Level Dependencies," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.20-25, 2013

• Open Access   Article

  Study of Threats, Risk and Challenges in Cloud Computing

  R.Piplode, P. Sharma and U.K. Singh

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.26-30, Jan-2013

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  Abstract
  Cloud computing security is an evolving sub-domain of computer security, network security, and, more broadly, information security. It refers to a broad set of policies, technologies, and controls deployed to protect data, applications, and the associated infrastructure of cloud computing. In this paper we present security issues that fetch by cloud computing technology and also analysis the threats, risk and challenges in cloud environment.

  Key-Words / Index Term
  Cloud Computing, Security Risk, Service Models, Deployment Models

  References
  [1] National Institute Of Standard and technology (2009). www.csrc.nist.gov/groups/SNS/cloud-computing/cloud-def-v15.doc,
  [2] Open Security Architecture http://www.opensecurityarchitecture.org/
  [3] Tim Mather, Subra Kumaraswamy, Shahed Latif Cloud Security and Privacy : An Enterprise perspective of Risks
  [4] K.Mukherjee , G.Sahoo, “A Secure Cloud Computing”, Proc. 2010 International Conference on Recent Trends in Information, Telecommunication and Computing
  [5] Krešimir Popović, Željko Hocenski, “Cloud computing security issues and challenges” Proc. MIPRO 2010, May 24-28, 2010, Opatija, Croatia
  [6] Cloud computing security retrived from
  http://en.wikipedia.org/wiki/Cloud_computing _security
  [7] Meiko Jensen, J¨org Schwenk, Nils Gruschka, Luigi Lo Iacono “ On Technical Security Issues in Cloud Computing” 2009 IEEE International Conference on Cloud Computing.
  [8] Rituik Dubey, Muhammad Asim Jamshed, Xiaohui Wang, Rama Krishna Batalla “Addressing Security Issues in Cloud Computing”.
  [9] Shilpashree Srinivasamurthy, David Q. Liu, “ Survey on Cloud Computing Security”.
  [10] Salesforce, Salesforce.com, 2010
  [11] Windows Azure, www.microsoft.com/azure, 2010
  [12] Security Guidance for Critical Areas of Focus in Cloud Computing, April 2009. www.cloudsecurityalliance.org/topthreats/csathreats.v1.0.pdf
  [13] Richard Chow, Philippe Golle, Markus Jakobsson, Elaine Shi, Jessica Staddon, Ryusuke Masuoka, Jesus Molina. Controlling Data in the Cloud Outsourcing Computation without Outsourcing Control. CCSW `09: Proceedings of the 2009 ACM workshop on Cloud computing security, pages 85-90. November 2009.
  [14] Steve Hanna. A security analysis of Cloud Computing. Cloud Computing Journal.
  www.cloudcomputing.sys-con.com/node/1203943 .
  [15] Matthew Glotzbach, Product Management Director, Google Enterprise, “What We Learned from 1 Million Businesses in the Cloud,” www.googleblog.blogspot.com/2008/10/whatwe-learned-from-1-illion.html, 30 Oct 2008.
  [16] Krügel, C., Toth, T., & Kirda, E. (2002). Service specific anomaly detection for network intrusion detection. In Proceedings of the 2002 ACM symposium on Applied computing (pp. 201-208). Madrid, Spain: ACM. Retrieved from www.portal.acm.org.library.capella.edu/citation.cfm?id=508835&dl=GUIDE&coll=GUIDE&CFID=80867670&CFTOKEN=24312614
  [17] Shivlal Mewada, Umesh Kumar Singh, Pradeep Sharma, “Security Based Model for Cloud Computing”, IRACST–International Journal of Computer Networks and Wireless Communications (IJCNWC), Vol. 1, No. 1, pp(13-19), December 2011. http://www.iracst.org/ijcnwc/vol1no1.htm)
  

  Citation
  R.Piplode, P. Sharma and U.K. Singh, "Study of Threats, Risk and Challenges in Cloud Computing," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.26-30, 2013

• Open Access   Article

  Security Enhancement in Cloud Computing (CC)

  S.L.Mewada, U.K. Singh, P. Sharma

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.31-37, Jan-2013

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  Abstract
  Cloud computing (CC) is set of resources and services offered through the Internet. Cloud services are delivered from data centers located throughout the world. Cloud computing facilitates its consumers by providing virtual resources via internet. CC is becoming one of the most important areas in the Information Technology world. Several issues and challenges are being raised from the adoption of this computational paradigm including security, privacy and authentication and association. In this paper aims to introduce new security ideas in cloud computing. In this paper, we describe the specifics of cloud computing, we address the principal security issues for cloud computing and we discuss primary cloud operations that need to be secured and we discuss the data security based model for cloud computing. We identify the new challenges and opportunities posed by this new cloud computing environment and explore approaches to secure its communication.

  Key-Words / Index Term
  Cloud Computing; Security Model; Privacy; Authentication

  References
  REFERENCE
  [1] Ricardo vilaca, Rui oliveira, “Clouder : A Flexible Large Scale Decentralized Object Store. Architecture Overview”, Proceeding of WDDDM, 2009.
  [2] Michael Miller, “Cloud Computing-Web Based Application that change the way you collaborate online”, Publishing of QUE, 2nd print 2009.
  [3] Shivlal Mewada, Umesh Kumar Singh and Pradeep Kumar Sharma, “Security Based Model for Cloud Computing”, IRACST-International Journal of Computer Networks and Wireless Communications (IJCNWC), Vol. 1, No. 1, 2011.
  [4] Wayne Jansen, Timothy Grance, Guidelines on Security and Privacy in Public Cloud Computing, NIST, Draft Special Publication 800-144, January 2011 http://csrc.nist.gov/publications/drafts/800-144/Draft-SP-800 144_cloudcomputing.pdf
  [5] ENISA, Cloud Computing: Benefits, Risks and Recommendations for information Security, November 2009 http://www.enisa.europa.eu/act/rm/files/deliverables/cloud-computingrisk-assessment/at_download/fullReport
  [6] The NIST Definition of Cloud Computing, version 15, by Peter Mell and Tim Grance, October 7, 2009, National Institute of Standards and Technology (NIST), Information Technology Laboratory (www.csrc.nist.gov )
  [7] S. Subashini, and V. Kavitha. (2010) “Asurvey on security issues in service delivery models of cloud computing.” J Network Computer Application doi:10.1016/j.jnca.2010.07.006. Jul., 2010.
  [8] M. Klems, A. Lenk, J. Nimis, T. Sandholm and S. Tai. “What’s Inside the Cloud? An Architectural Map of the Cloud Landscape.” IEEE Xplore, pp 23-31, Jun. 2009.
  [9] R. Woolley and D. Fletcher “The Hybrid Cloud: Bringing Cloud-Based IT Services to State Government October 4, 2009” Department of Technology Services.
  [10] T. kraska “Building Database Applications in the Cloud” Swiss federal institute of technology Zurich 2010
  [11] Global Netoptex Incorporated , 2009, Demystifying the cloud. Important opportunities, crucial choices, http://www.gni.com , pp 4-14, viewed 13 December 2009.
  [12] Security Guidance for Critical Areas of Focus in Cloud Computing, April 2009. DOI = http://www.cloudsecurityalliance.org/topthreats/csat reats.v1.0.pdf
  [13] ISO. ISO 7498-2:1989. Information processing systems- Open Systems Interconnection. ISO 7498-2
  [14] Dlamini M T, Eloff M M and Eloff J H P, ‘Internet of People, Things and Services – The Convergence of Security, Trust and Privacy’, 2009.
  [15] Siani Pearson. Taking Account of Privacy when Designing Cloud Computing Services. CLOUD 09: Proceedings of the 2009 ICSE Workshop on Software Engineering Challenges of Cloud Computing, pages 44-52. May 2009.
  [16] Discovering Identity: Cloud Computing: Identity and Access Management DOI = http://blogs.sun.com/identity/entry/cloud_computing_identity_and_access
  [17] Worldwide Infrastructure Security Report, Arbor networks, 2010 http://www.arbornetworks.com/dmdocuments/ISR2010_EN.pdf
  [18] Dai Yuefa, Wu Bo, Gu Yaqiang, Zhang Quan, Tang Chaojing, “Data Security Model for Cloud Computing” Proceedings of the 2009 International Workshop on Information Security and Application (IWISA 2009) Qingdao, China, November 21-22, 2009 (ACADEMY PUBLISHER AP-PROC-CS-09CN004)
  

  Citation
  S.L.Mewada, U.K. Singh, P. Sharma, "Security Enhancement in Cloud Computing (CC)," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.31-37, 2013

• Open Access   Article

  Test Case Generation Using Discrete Particle Swarm Optimization Algorithm

  C.P. Patidar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.38-42, Jan-2013

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  Abstract
  Today`s era is of software. To deliver the best software to the customer it is very important to test the software before delivery. So the software testing becomes an independent field of software engineering. The crucial section of software testing is to generate the test cases. In this paper I proposed a model which is based on sequence diagram. That generate more dynamic test cases because of sequence diagram. Whereas a testing which is based on use case diagram is generates only static test cases. In this paper information will be extracted from sequence diagram. For this first of all sequecne diagram is created on the given problem. After this dependency table will be generated. Dependency graph will be generated from dependency table. At last test cases will be generated from dependency graph. These test cases will be optimized by Discrete Particle Swarm Intelligence Algorithm.

  Key-Words / Index Term
  Sequence diagram, Test Case Discrete Particle Swarm Optimization, Visual Paradigm 12.0

  References
  [1]. R. Binder, “Testing Object Oriented Systems: Models, patterns and tools”; Addison-Wesley, 2000.
  [2]. S. G. Ponnambalam, N. Jawahar and S. Chandrasekaran, “Discrete Particle Swarm Optimization Algorithm for Flowshop Scheduling”.
  [3].Emanuela G. Cartaxo, Francisco G. O. Neto and Patrίcia D. L. Machado; “Test Case Generation by means of UML Sequence Diagrams and Labeled Transition Systems”, SMC 2007: 1292-1297.
  [4]. S. Shanmuga Priya and P. D. Sheba Kezia Malarchelvi; “Test Path Generation Using Uml Sequence Diagram”, IJARCSSE, 2013.
  [5]. Bandyopadhyay, A. and Ghosh, S.; “Using UML Sequence Diagrams and State Machines for Test Input Generation”, 19th International Symposium on Software Reliability Engineering, ISSRE, 2008.
  [6]. Vivek Kumar, Aarti Gautam; “Improving Testing Architecture for MVC Based Application”, IJARCSSE, February 2014.
  [7]. Manpreet Kaur, Rupinder Singh; “Generation of Test Cases from Sliced Sequence Diagram”, IJCAR, July 2014.
  [8]. Ashalatha Nayak and Debasis Samanta; “Synthesis of Test Scenarios Using UML Sequence Diagrams”, ISRN, February 2012.
  [9]. Santosh Kumar Swain, Durga Prasad Mohapatra, and Rajib Mall; “Test Case Generation Based on Use case and Sequence Diagram”, IJSE, July 2010.
  [10]. Sven Sieverding, Christian Ellen and Peter Battram; “Sequence Diagram Test Case Specification and Virtual Integration Analysis using Timed-Arc Petri Nets”, FESCA, 2013.
  [11]. A. V. K. Shanthi and G. Mohan Kumar; “Automated Test Cases Generation from UML Sequence Diagram”, ICSCA 2012.
  [12]. Azam Andalib and Seyed Morteza, “A New Approach for Test Case Generation by Discrete Particle Swarm Optimization Algorithm”; ICEE, 2014.
  [13]. Clémentine Nebut, Frank Fleurey, Yves Le Traon, and Jean-Marc Jézéquel; “Automatic Test Generation: A Use Case Driven Approach”, IEEE Transactions on Software Engineering, Vol. 32, No. 3, March 2006.
  [14]. Watson, A. H., T. H. McCabe, and D. R. Wallance, “Structured testing: A testing methodology using the cyclomatic complexity metric”, NIST special Publicaton, 1996.
  

  Citation
  C.P. Patidar, "Test Case Generation Using Discrete Particle Swarm Optimization Algorithm," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.38-42, 2013

• Open Access   Article

  Evaluating and Summarizing Student’s Feedback Using Opinion Mining

  V. Jain

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.43-46, Jan-2013

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  Citation

  Abstract
  Collecting Students feedbacks for the subjects taught is a regular activity of an academic institution. Automating the process of collecting these feedbacks becomes an important requirement. This provides an opportunity to analyze these feedbacks efficiently and summarize the performance of a teacher in the subjects he taught. Opinion mining (or Sentiment Analysis) which is generally used for classifying customer reviews in terms of positive or negative sentiments can be used effectively in evaluating and summarizing the student’s feedback.

  Key-Words / Index Term
  Opining Mining, Information Retrieval, Text Summarization, Text Mining

  References
  [1] K. Dave, S. Lawrence, and D. M. Pennock, “Mining the peanut gallery: Opinion extraction and semantic classification of product reviews,” in Proceedings of WWW, pp. 519–528, 2003.
  [2] B. Pang and L. Lee, “A Sentimental Education: Sentiment Analysis Using Subjectivity”, Proc. of ACL, pp. 271-278, 2004.
  [3] M. Hu and B. Liu, Mining and summarizing customer reviews. In Proc. of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining (KDD `04). ACM, USA, pp. 168-177, 2004.
  [4] K. P. Shein and T. S. Nyunt, "Sentiment Classification Based on Ontology and SVM Classifier," Communication Software and Networks, 2010. ICCSN `10. Second International Conference on, Singapore, pp. 169-172, 2010.
  [5] T. Mullen and N. Collier, “Sentiment analysis using support vector machines with diverse information Sources,” in Proceedings of the Conference on Empirical Methods in Natural Language Processing (EMNLP), pp. 412–418, July, 2004.
  [6] K. Lerman, S. Goldensohn, and R. McDonald. Sentiment summarization: evaluating and learning user preferences. In Proc. of the 12th Conference of the European Chapter of the Association for Computational Linguistics (EACL `09). Association for Computational Linguistics, USA, pp. 514-522, 2009.
  [7] T. Joachims, Learning to Classify Text Using Support Vector Machines, Kluwer Academic Publishers, 2001.
  

  Citation
  V. Jain, "Evaluating and Summarizing Student’s Feedback Using Opinion Mining," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.43-46, 2013

• Open Access   Article

  Frequent Navigation Pattern Mining from Web usage data

  V. Jain

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.1 , Issue.1 , pp.47-51, Jan-2013

  Abstract

  Full-Text HTML

  References

  Citation

  Abstract
  Web usage mining provides the information about the user and their behavioural aspects of the web navigation. Traditional frequent sequence pattern mining algorithms are limited in analyzing information from big datasets. However, a graph based approach with the efficient version of apriori algorithm can generate frequent patterns from large datasets. In our work, we have implemented a web graph approach for generating user sessions and apriori all algorithm for generating frequent patterns.

  Key-Words / Index Term
  Frequent Pattern Mining, Apriori Algorithm, Web Usage Mining, User Session Generation

  References
  [1] R. Agrawal, R. Srikant, “Mining sequential patterns”, In ICDE, USA, pp.–14, 1995.
  [2] M. A. Bayir, D. Guney, T. Can, “Integration of topological measures for eliminating non-specific interactions in protein interaction networks”, Discrete Applied Mathematics, Vol.157, Issue10, pp.2416–2424, 2008.
  [3] J. Borges, M. Levene, “Generating dynamic higher-order markov models in web usage mining”, PKDD`05 Proceedings of the 9th European conference on Principles and Practice of Knowledge Discovery in Databases, UK, pp.34-45, 2005.
  [4] S. Brohee, J.V. Helden, “ Evaluation of clustering algorithms for protein-protein interaction networks”, BMC bioinformatics, Vol.7, Issue.1, p.488-496, 2006.
  [5] L. D. Cat ledge, J. E. Pitkow. Characterizing browsing strategies in the world-wide web. Computer Networks and ISDN Systems, Vol.27, Issue.6, pp.1065–1073, 1995.
  [6] D. Chakrabart, C. Faloutsos, “Graph mining: Laws, generators, and algorithms”, ACM Computer Survey, Vol.38, Issue.1, pp.204-212, 2006.
  [7] R. Cooley, B. Mobasher, J. Srivastava, "Web mining: information and pattern discovery on the World Wide Web”, Proceedings Ninth IEEE International Conference on Tools with Artificial Intelligence, Newport Beach, CA, pp. 558-567, 1997.
  [8] R. Cooley, B. Mobasher, J. Srivastava, “Data preparation for mining world wide web browsing patterns”, Knowl. Inf. Syst., Vol.1, Issue.1, pp.5–32, 1999.
  [9] R. Cooley, P.-N. Tan,J. Srivastava, “Discovery of interesting usage patterns from web data”, Proceeding Web-based Knowledge Discovery and Data Mining WEBKDD`99, London, pp. 163–182, 1999.
  [10] J. Dean, S. Ghemawat. “Map reduce: Simplified data processing on large clusters”, In OSDI, USA, pp.137–150, 2004.
  [11]J. Han, J. Pei, M. Kamber, "Data mining concept and techniques ", Elsevier, Netherlands, pp.1-744, 2011.
  [12]D. Donato, L. Laura, S. Leonardi, S. Millozzi, “The web as a graph: How far we are”, ACM Transaction Internet Technology, Vol.7, Issue.1, pp. 128-136, 2007.
  [13] B. Mobasher, N. Jain, E. Han, J. Srivastava, “Web Mining: Pattern discovery from World”, University of Minnesota, USA, pp.1-12, 1996.
  [14]Wide Web transactions. Technical Report TR 96-050, Department of Computer Science, University of Minnesota, Minneapolis, pp.1-25, 1996.
  

  Citation
  V. Jain, "Frequent Navigation Pattern Mining from Web usage data," International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.47-51, 2013