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• Open Access   Article

  Gaming Prediction Analysis Using Hybrid Chi Square - Support Vector Machine Model and Historical Data

  Shruti Chopra, Vijay Kumar Joshi

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.1-5, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.15

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  Abstract
  Data Science is a powerful domain that helps in extracting information from a given dataset for giving useful information. Prediction Analysis is also a part of Data Science which uses scientific techniques for determining the prediction results with a given set of input data. Gaming Prediction is an exciting field and lot of researches are being performed on this to determine factors of improvement in terms of player performance, management of matches and imparting a good understanding to Guides, Sponsors and the Players. They also aim the high accuracy of results and have proved fruitful but still includes lot of more advancements in the techniques utilized. This paper has utilized the benefit of advanced algorithm which does a primary feature selection in Basketball Matches which is followed by developing fuzzy rules to check the impact of these features of final result. Finally, Support Vector Machine Technique is employed to determine the Accuracy of Prediction Logic obtained.

  Key-Words / Index Term
  Fuzzy Logic, Prediction Analysis, Support Vector Machine, National Basket Ball Association, Chi-Square

  References
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  [10] P. Kvan , J.S. Sokol, "A logistic regression/Markov chain model for NCAA basketball", Naval Research Logistics, Vol. 53, pp. 786-803, 2006
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  Citation
  Shruti Chopra, Vijay Kumar Joshi, "Gaming Prediction Analysis Using Hybrid Chi Square - Support Vector Machine Model and Historical Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.1-5, 2017

• Open Access   Article

  A New Approach on Solving Intuitionistic Fuzzy Nonlinear Programming Problem

  C. Loganathan, M. Lalitha

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.5-9, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.59

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  In this paper we propose an algorithm for solving Intuitionistic fuzzy nonlinear programming problems (IFNLPP) involving triangular Intuitionistic fuzzy numbers (TIFN). Here, the multi-objective nonlinear problem is converted into a single objective nonlinear programming problem and the problem is defuzzified by using triangular Intuitionistic fuzzy numbers. Then the problem is converted into crisp nonlinear programming problem. Numerical examples are provided to show the efficiency of the proposed algorithm.

  Key-Words / Index Term
  Intuitionistic fuzzy set, Triangular Intuitionistic fuzzy numbers, membership and non-membership value, Ambiguity and score value

  References
  [1] sanjaya Kumar Behera., Jyoti Ranjan Nayak., Solutions of Multi-Obective Programmig Problems in Fuzzy approach, International Journal of Computer Science and Engineering, Vol.3, Issue 12, pp. 3790-3799, 2011.
  [2] K.T Atanassov, Intuitionistic fuzzy sets, Fuzzy Sets and Systems, Vol.20, pp. 87-96, 1986.
  [3] Dipti Dubey., Aparna Mehra., Linear Programming with Triangular Intuitionistic Fuzzy Number, Advances in Intelligent Systems research, Vol.1, pp. 563-569, 2011.
  [4] D.F Li, J.X Nan, and M.J Zhang , A ranking method of triangular intuitionistic fuzzy numbers and application to decision making, International Journal of Computational Intelligence Systems, Vol.3, Issue 5, pp.522-530, 2010.
  [5] Deng-Feng-Li, A ratio ranking method of Triangular Intuitionic fuzzy number and its Application to MADM problems, computers Mathematics with applications, Vol.60, pp.1557-1570, 2010.
  [6] S.Salahshour, G.A Shekari, A. Hakimzadeh, A Novel approach Ranking triangular Intuitionistic fuzzy numbers, AWER Proedia Information Technology, Vol.1, pp.442-446, 2012.
  [7] L.A Zadeh, Fuzzy sets Information and Control, Vol.8, pp. 338 – 353, 1969.
  [8] P. Burillo, H. Bustince, V. Mohedano, Some definitions of intuitionistic fuzzy number. First Properties; the Proceedings of the 1st Workshop on Fuzzy Based Expert Systems, 1994.
  [9] J.Q Wang, Z. Zhang, Multi-criteria decision-making method with incomplete certain Information based on intuitionistic fuzzy number, Control and Decision, Vol.24, Issue 2, pp.226-230, 2009.
  [10] V.L.G Nayagam, G.Venkateshwari and G.Sivaraman, Ranking of intuitionistic fuzzy numbers, IEEE International Conference, pp.1971-1974, 2008.
  [11] Palash Dutta, Hrishikesh Boruah, Tazid Ali, Fuzzy Arithmetic with and without using α–cut method: A comparative study, International Journal of Latest trends in Computing, Vol.2, Issue.1, pp. 99-107, 2011.
  

  Citation
  C. Loganathan, M. Lalitha, "A New Approach on Solving Intuitionistic Fuzzy Nonlinear Programming Problem," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.5-9, 2017

• Open Access   Article

  Performance and Analysis of Flow between Annular Space Surrounded by a Rotating Coaxial cylinder with Co-axial Cylindrical Porous Medium

  Santosh Patil, Ediga Lingappa, Mothe Rakesh

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.10-15, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.1015

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  Due to latencies, the hadoop map reduce are complicated to scale to multiple clouds. Because of latencies, the hadoop map reduce are difficult to scale to multiple clouds. Because of this problem, to improve the performance at variable load, it provides over-provisioning in internal cloud. Here we propose a Bstream - cloud bursting framework. It consists of two major features. They are Stream-processing in the external cloud. Hadoop in the internal cloud. These two features are used to realize inter-cloud map reduce. Stream processing in external cloud enables parallel uploading; processing and also parallel downloading of data can minimize network latencies. It guarantees service-level objective (SLO) of meeting job deadlines.

  Key-Words / Index Term
  Bstream, Map Reduce, stream processing

  References
  [1] H. Herodotou, H. Lim, G. Luo, N. Borisov, L. Dong, F. B. Cetin, and S. Babu, “Starfish: A self-tuning system for big data analytics,” in Proc. 5th Int. Conf. Innovative Data Syst. Res., 2011, pp. 261–272.
  [2] C. Vecchiola, R. N. Calheiros, et al.,“Deadline-driven provisioning of resources for scientific applications in hybrid clouds with Aneka,” Future Generation Comput. Syst., vol. 28, no.1, pp. 58–65, 2012.
  [3] T. Bicer, D. Chiu, and G. Agrawal,“Time and cost sensitive data-intensive computing on hybrid clouds,” in Proc. IEEE/ACM 12th Int. Symp. Cluster, Cloud Grid Comput, 2012, pp. 636–643.
  [4] S. Kailasam, N. Gnanasambandam, J. Dharanipragada, and N.Sharma, “Optimizing ordered throughput using autonomic cloud bursting schedulers, ”IEEE Trans. Softw. Eng., vol. 39, no. 11, pp. 1564–1581, Nov. 2013.
  [5] K. Kc and K. Anyanwu, “Scheduling hadoop jobs to meet dead-lines,” in Proc. IEEE 2nd Int. Conf. Cloud Comput. Technol. Sci., 2010, pp. 388–392.
  [6] M. Mathess and R. N. Calheiros, R. Buyya,“Scaling MapReduce applications across hybrid clouds to meet soft deadlines,” in Proc. IEEE 27th Int. Conf. Adv. Inf. Netw. Appl., 2013, pp. 629–636.
  [7] Y. Luo, Z. Guo, Y. Sun, B. Plale, J. Qiu, and W. W. Li,“A hierarchi-cal framework for cross-domain MapReduce execution,” inProc. 2nd ACM Int.Workshop Emerging Comput. Methods Life Sc., 2011,pp. 15–22.
  [8] C. Olston, G. Chiou, L. Chitnis, et al., “Nova: continuous Pig/Hadoop workflows,” in Proc. ACM Int. Conf. Manage. Data, 2011, pp. 1081–1090.
  

  Citation
  Santosh Patil, Ediga Lingappa, Mothe Rakesh, "Performance and Analysis of Flow between Annular Space Surrounded by a Rotating Coaxial cylinder with Co-axial Cylindrical Porous Medium," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.10-15, 2017

• Open Access   Article

  Challenges and Opportunities with Big Data

  K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.16-20, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.1620

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  Abstract
  Data is exploding at a rapid rate. Big data is the term for data sets so large and complicated that it becomes difficult to process using traditional data management tools or processing applications. Heterogeneity, scale, timeliness, complexity, and privacy problems with Big Data impede progress at all phases of the pipeline that can create value from data. The problems start right away during data acquisition, when the data tsunami requires us to make decisions, currently in an ad hoc manner, about what data to keep and what to discard, and how to store what we keep reliably with the right metadata. Much data today is not natively in structured format, tweets and blogs are weakly structured pieces of text, while images and video are structured for storage and display, but not for semantic content and search. Transforming such content into a structured format for later analysis is a major challenge. The value of data explodes when it can be linked with other data, thus data integration is a major creator of value. Since most data is directly generated in digital format today, we have the opportunity and the challenge both to influence the creation to facilitate later linkage and to automatically link previously created data. Data analysis, organization, retrieval, and modeling are other foundational challenges. Data analysis is a clear bottleneck in many applications, both due to lack of scalability of the underlying algorithms and due to the complexity of the data that needs to be analyzed. Finally, presentation of the results and its interpretation by non-technical domain experts is crucial to extracting actionable knowledge.

  Key-Words / Index Term
  Data analysis, Big data, Data Analysis

  References
  [1] Tim Furche, et al., “Data Wrangling for Big Data: Challenges and Opportunieis”, Intrnational Conference on existing database. Technology, March 2016.
  [2] Raju Din, Prabadevi B. , "Data Analyzing using Big Data (Hadoop) in Billing System", International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.84-88, 2017.
  [3] J.V.N. Lakshmi, Ananthi Sheshasaayee, "A Big Data Analytical Approach for Analyzing Temperature Dataset using Machine Learning Techniques", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.92-97, 2017.
  [4] V.K. Gujare, P. Malviya, "Big Data Clustering Using Data Mining Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.9-13, 2017.
  [5] G.S. Sra, R. Kaur, "A Study on Big Data and its Applications in Retail Sector", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.124-128, 2017.
  [6] Data, data everywhere. The Economist (http://www.economist.com/node/15557443), Feb 2010.
  [7] Renu Bhandari, Vaibhav Hans and Neelu Jyothi Ahuja, "Big Data Security – Challenges and Recommendations", International Journal of Computer Sciences and Engineering, Vol.4, Issue.1, pp.93-98, 2016.
  

  Citation
  K. Parimala, G. Rajkumar, A. Ruba, S. Vijayalakshmi, "Challenges and Opportunities with Big Data," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.16-20, 2017

• Open Access   Article

  A Survey on Heuristic Based Approach for Privacy Preserving in Data Mining

  Aniket Patel, Patel Shreya, Kiran Amin

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.21-25, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.2125

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  Abstract
  Data Mining has been the most researched area for researchers because of the possibilities of extension at each application of it. When the data becomes massive in volume, many problems strike for security and privacy breach. Some applications like sharing of such data to a particular user have threats of preserving the original data so that the injection of such data can be prohibited. So it is a timely need to secure the data while handling them to the known or unknown users. The requirement of not losing the essence of data and still publishing it with the actual information is a challenge. Such troubles prompted the advancement of Privacy Preserving Data Mining (PPDM) Techniques. Privacy Preserving has become an important issue in the development progress of Data Mining techniques. Methods like k-Anonymity, l-Diversity have been explored well by researchers but still, there are holes that force us to develop a more effective method and using such approach one can get better accuracy with minimum loss of data.

  Key-Words / Index Term
  Data Mining, Heuristic Based Approach, Privacy Preserving Data Mining

  References
  [1] Hina Vaghashia, Amit Ganatra, “A Survey: Privacy Preservation Techniques in Data Mining ”, IJCA International Journal of Computer Applications (0975 – 8887) Vol.119, No.4, pp.20-26, June 2015.
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  [12] Pierangela Samarati, Latanya Sweeney, “Generalizing Data to Provide Anonymity when Disclosing Information”, The work of Pierangela Samarati was supported in part by National Science Foundation and by DARPA, pp.1-13.
  [13] Nivedita Bairagi, Punit K. Johari, "A novel approach for privacy preserving using Animal Migration Optimization and RSA algorithm", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.193-197, 2017.
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  [16] Ashwin Machanavajjhala,Johannes Gehrke, Daniel Kifer,“l-Diversity: Privacy Beyond k –Anonymity”, Proceedings of the 22nd International Conference on Data Engineering (ICDE’06), IEEE 2006.
  [17] Yuichi Sei, Takao Takenouchi, Akihiko Ohsuga, “(l1, ..., lq)-diversity for Anonymizing Sensitive Quasi-Identifiers”, IEEE 2015, pp.596-603.
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  Citation
  Aniket Patel, Patel Shreya, Kiran Amin, "A Survey on Heuristic Based Approach for Privacy Preserving in Data Mining," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.21-25, 2017

• Open Access   Article

  A Security Determination-Reaction Architecture for Heterogeneous Distributed Network

  B. Bhasker, T. Jagadish kumar, M.V.Kamal

  Survey Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.26-34, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.2634

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  Abstract
  The main focus of this paper is to provide a global architectural solution built on the requirements for a reaction after alert detection mechanisms in the frame of Information Systems Security and more particularly applied to telecom infrastructures security. These infrastructures are distributed in nature, therefore the targeted architecture is developed in a distributed perspective and the architecture is elaborated using the multi-agent system. The Multi-Agent System decision-reaction architecture is developed in a distributed perspective and is composed of three basic layers: low level, intermediate level and high level. The low level aim to be the interface between the main architecture and the targeted infrastructure. The intermediate level is responsible of correlating the alerts coming from different domains of the infrastructure and to deploy smartly the reaction actions.This intermediate level is elaborated using multi-agents system that provide the advantages of autonomous and interaction facilities.The high level permits to have a supervision view of the whole infrastructure, and to manage business policy definition.The proposed approach has been successfully experimented for data access control mechanism.The proposed approach has been illustrated based on the network architecture for heterogeneous mobile computing developed by the BARWAN project .Accordingly: The Building Area constitutes the low level. The Campus –Area is the intermediate level. It takes care about the alerts coming from different domains and deploy the reaction actions smartly.The multi-agent system that has been associated to the OntoBayes model for decision support mechanism.This model helps agents to make decisions according to preference values and is built upon ontology based knowledge sharing , bayesian networks based uncertainity management and influence diagram based decision support.

  Key-Words / Index Term
  Security Policy; Multi-agents systems Architecture; decision system; reaction; Distributed networks; bayesian network

  References
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  [20] B. Gâteau. Modélisation et Supervision d`Institutions Multi-Agents. Ph.D. Thesis, Ecole Supérieure des Mines de Saint-Etienne, 2007.
  

  Citation
  B. Bhasker, T. Jagadish kumar, M.V.Kamal, "A Security Determination-Reaction Architecture for Heterogeneous Distributed Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.26-34, 2017

• Open Access   Article

  Detection and Elimination of Wormhole Attacks in a MANET

  A.Vani

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.35-40, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.3540

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  Abstract
  Wormhole attacks in Mobile ad hoc networks is impermeable to traditional security measures. The attack can be launched regardless of the MAC, routing, or security protocol used in the network. Two or more malicious nodes in conspiracy usually perform the wormhole attack. Two malicious nodes at different locations send received routing messages to each other via a secrete channel. In this way, although the two malicious nodes are located far from each other, they appear to be within one-hop communications range. Wormhole nodes can successfully execute such attacks without compromising any computer, and are inevitable even though some ad hoc wireless networks provide authenticity and confidentiality protection. Practically all widespread security extensions are proposed for popular routing protocols but they do not alleviate wormhole attacks. However, since wormhole attack such a severe thread to MANET security. In this situation wormhole attack methodology presented is motivated by WARP and the same procedure and terminology is used but slight modification In this work wormhole attack is detected and eliminated by simply modifying AODV routing protocol and its performance is measured.

  Key-Words / Index Term
  MANET, Routing, AODV, Wormhole attack, WARP

  References
  [1] Hu Y. C. Hu, A. Perrig, and D. B. Johnson, “Packet leashes: a defense against wormhole attacks in wireless networks," in In Proce. of IEEE INFOCOM, 2003.
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  [6] I. Khalil, S. Bagchi, and N. B. Shroff, “Mobiworp: Mitigation of the wormhole attack in mobile multihop wireless networks," Ad Hoc Netw., vol. 6, no. 3, pp. 344-362, 2008, 1328997.
  [7] L. Qian, N. Song, and X. Li, “Detection of wormhole attacks in multi-path routed wireless ad hoc networks: a statistical analysis approach," J. Netw. Comput. Appl.,vol. 30, no. 1, pp. 308-330, 2007.
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  [11] X. Wang and J. Wong, “An end-to-end detection of wormhole attack in wireless ad-hoc networks," in In Proc. of International Conference on Computer Software and Applications, 2007.
  [12] Ning Song, Lijun Qian, and Xiangfang Li. Wormhole attacks detection in wireless IEEE international parallel and distributed processing symposium (IPDPS’05); 2005.
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  [15] G.Kalpana and S.Archana, "Performance Analysis of Threshold Based Algorithms under Wormhole Attack in MANET", International Journal of Computer Sciences and Engineering, Vol.3, Issue.7, pp.133-138, 2015.
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  Citation
  A.Vani, "Detection and Elimination of Wormhole Attacks in a MANET," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.35-40, 2017

• Open Access   Article

  Effectiveness of SQLI Countermeasures

  Daljit Kaur, Parminder Kaur

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.41-46, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.4146

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  Abstract
  In the recent times web applications has become increasingly popular with the growth of web. At the same time, there is an increase in number of attacks in web applications. Attacks like injection vulnerabilities such as SQL Injection, Cross site Scripting, Cross site Request Forgery(CSRF) are common. This paper specially focuses on countermeasures of SQL Injection vulnerability. Here, we have implemented various attacks on a Giftshop web application and also classified SQL Injection countermeasures with respect to Software Development Life Cycle and tested them for their effectiveness with the help of vulnerability scanners. Finally, the result of vulnerability scanners are shown and analyzed before and after the implementation of known SQL Injection countermeasures.

  Key-Words / Index Term
  SQL Injection; Attacks; Vulnerability scanners; Threats; Web application; Security

  References
  [1] W.K. Torgby, N.Y.Asabere.”Structured Query Language Injection (SQLI) Attacks: Detection and Prevention Techniques in Web Application Technologies”. International Journal of Computer applications Vol. 71, Issue.11 , Pp 29-40.ISSN: 0975-8887, 2013.
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  [3] .Kaushik and G. Ojha.” SQL Injection Attack Detection and Prevention Methods :A Critical Review”, International Journal of Innovative Research in Science, engineering and Technology (IJIRSET), Vol3, Issue 4 .pp 11370-11377. ISSN: 2319-8753, 2014.
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  [6] K.X.Zhang, C.J. Lin, S. Chen, Y. Hwang. “TransSQL:A translation and Validation based solution for SQL Injection attacks”, In first international conference on Robot, Vision and Signal Processing, pp248-251.November 2011.
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  [8] T. Wei,Y.J.Feng,X.Jing. “ Attack Model Based Penetration Test for SQL Injection Vulnerability”, In IEEE 36th annual Computer Software and Applications Conference Workshops,pp. 589-594. July 2012.
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  [10] Aldar C.F.Chan, “A Security Framework for Privacy Preserving data aggregation in wireless sensor networks”, ACM Transactions on sensor networks. Vol 7, Issue 4, 29-40. DOI: 10.1145/1921621.1921623
  [11] R.Piplode,P.sharma and U.K.Singh,”Study of Threats, Risks and Challenges in Cloud Computing”, International Journal of Scientific Research in Computer Science and Engineering, Volume 1, Isuue 1, 2013.
  [12] M. Shema. “Seven Deadliest Web Application Attacks”, Elsevier Inc., pp47-69. ISBN-9781597495431,2010.
  [13] D. Kaur, P. Kaur. “Empirical Analysis of Web Attacks”. In Procedia of Computer Science. Elsevier Publications. Volume 78, pp. 298-306. DOI:10.1016/j.procs.2016.02.057, 2016.
  [14] S. Junaid. “Analytical Study of Common Web Application Attacks”. International Journal of Advanced Research in computer engineering & Technology (IJARCET)”, Vol.3, Issue3, 611-617.
  [15] G. Parmar, K.Mathur. “Proposed Preventive measures and strategies Against SQL injection Attacks”. Indian Journal of Applied Research, Vol.5, Issue 5,pp 664-671. ISSN- 2249555X, 2015.
  [16] S. Madan, S. Madan. “Bulwark Against SQL Injection attack – An Unified Approach”. International Journal of Computer Science and Network Security(IJCSNS), Vol. 10 No.5.pp 305-313. 2010.
  [17] Mahapatra and S. Khan. “A Survey of SQL Injection Countermeasures”, International Journal of Computer science &engineering(IJCSES) Vol.3, No.3,pp.55-74. DOI : 10.5121/ijcses.2012.3305 55, June 2012
  [18] William, Jeremy and Alessandro. “Comparing SQL Injection Detection Tools Using Attack Injection: An Experimental study” in IEEE 21st International Symposium on Software Reliabiliry Engineering(ISSRE).pp. 289-298 , 2010.
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  Citation
  Daljit Kaur, Parminder Kaur, "Effectiveness of SQLI Countermeasures," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.41-46, 2017

• Open Access   Article

  Performance Analysis of Existing Routing Protocols

  R. Kumari, P. Nand

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.47-50, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.4750

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  Abstract
  Keeping in mind the end goal to break down and look at the execution of the four steering conventions AODV, DSR, DYMO and ZRP, reproduction tests are performed. The motivation behind the re-enactments is to test the proficiency of the steering conventions under various system conditions and system sizes. The emphasis is focused on five execution measurements: Packet conveyance proportion, normal Jitter, end-to-end deferral, throughput and vitality utilization.

  Key-Words / Index Term
  CBR;DYMO;AODV; ZRP

  References
  [1] S Gao, Tia. Greenspan, Dan Welsh, Matt Juang, Radford Alm, Alex C.D. “Vital Signs Monitoring and Patient Tracking Over Wireless Network”, in Proceedings of the 27th Annual International Conference of the IEEE EMBS 2005.
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  [7] A. Wood, G. Virone, T. Doan, Q. Cao, L. Selavo, Y. Wu, L. Fang, Z. He, S. Lin, J. Stankovic ; C.D. ALARM-NET: “Wireless Sensor Networks for Assisted-Living and Residential Monitoring”, Technical Report CS-2006-13, 2006.
  [8] Chin-Feng Lai; Yueh-Min Huang; Jong Hyuk Park; Han-Chieh Chao; C.D. “Adaptative Body Posture Analysis Using Collaborative Multi-Sensors for Elderly Falling Detection.” IEEE Intelligent Systems , 2010.
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  Citation
  R. Kumari, P. Nand, "Performance Analysis of Existing Routing Protocols," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.47-50, 2017

• Open Access   Article

  Improved Sparse matrix Denoising Techniques using affinity matrix for Geographical Images

  Mandeep Kaur, Balkrishan Jindal

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.5 , pp.51-56, Oct-2017

  CrossRef-DOI:   https://doi.org/10.26438/ijsrcse/v5i5.5156

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  Abstract
  In this paper, noise is removed from geographical images. In this method affinity matrix is used to find the similarity between pixels in an image then traverse the image. Initial position of the pixels applied affinity matrix to compare the adjacent pixels of the image. It is calculates the probability of the pixel store in a matrix. A dissimilar pixel means unwanted or noisy pixels removed from the image as well as denoised the image. The performance of the proposed method is evaluated using Image Quality Measures (IQM) like Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), and Structural Similarity Index (SSIM) etc.Experimental results shown that the proposed method is better than Sparse Matrix method, Bayes Thresholding method and Bilateral Filter in terms of MSE, PSNR and SSIM.

  Key-Words / Index Term
  Image denoising, Geographical images, Gaussian noise, Sparse Matrix method

  References
  [1] M.Bhat,”Digital Image Processing”, International Journal of Scientific and Technology, Vol. 3, Issue. 1, pp.272-276, 2014.
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  [3] R. Verma, J. Ali,”A comparative study of various types of image noise and efficient noise removal Techniques”, International Journal of Advanced Research in Computer Science and Software Engineering,Vol.3, Issue..10, pp. 617-622, 2013.
  [4] Sumanth, Suresh, “A survey on types of noise model, noise and denoising techniques in digital image processing”, International Conference on Recent Trends in ITS Innovations, Vol.5, Issue.2, pp.50-56,2017.
  [5]. Kalpana, H. Singh,” To study the image denoising Techniques”, International Journal of engineering and technology, Vol. 2, Issue 8. pp. 127-129, 2015.
  [6] S. Gupta, Meenakshi,”A comprehensive and comparison of image Denoising Techniques”,IEEE International Conference on computing for sustainable Global Development, 5-7 March, New Delhi ( India),pp. 972-976, 2014.
  [7] L. Liu, L. Chen, P. Chen, Y .Tang, C. Pun, “Weighted Joint Sparse Representation for Removing Mixed Noise in Image”, IEEE Transaction on CYBERNETICS, Vol.47, Issue. 3, pp. 600-611, 2016.
  [8] C. Chen, L .Li, L. Chen, Y. Tang, Y .Zhou,” Weighted Couple Sparse Representation with Classified Regularization for Impulse Noise Removal”, IEEE Transactions on Image Processing, Vol.24, No.11, pp.4014-4026, 2015.
  [9] K. Dabov, A. Foi, V. Katkovnik, K. Egiazarian,” Image denoising by sparse 3-D transform domain collaborative filtering”, IEEE Transactions on image processing,Vol.16,No.8,pp.2080-2095,2007.
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  [12] T. Gan, W. Lu, “Image denoising using multistage sparse representations”, IEEE 17th International Conference on image processing (ICIP), 26-29 Sept. Hong Kong, (China), pp.1165-1168,2010.
  [13] V.Wasson, B.Singh, G.Wasson,”A parallel optimized approach for Prostate Boundary Segmentation from Ultrasound Images”, International Journal of Scientific Research in Computer

  Citation
  Mandeep Kaur, Balkrishan Jindal, "Improved Sparse matrix Denoising Techniques using affinity matrix for Geographical Images," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.5, pp.51-56, 2017