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• Open Access   Article

  REVIN: Reduced Energy Virtuous Immune Network for WSN

  G. Sharma, A. Kumar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.1-8, Apr-2017

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  Sink node’s location privacy is one of the critical issues in wireless sensor network (WSN), as sink node is the central point of sensed data collection and act as gateway between wireless network and wired infrastructure. An attacker can attack on sink node to capture whole network due to shared nature of wireless channel. In existing work, large energy consumption is there in providing security to sink node. So there is a trade-off between energy consumption and security in WSN. To address these issues, we have proposed an algorithm in this paper, named REVIN (Reduced Energy Virtuous Immune Network). In proposed algorithm, a small fraction of advanced nodes are used to prolong the network lifetime. Sleep scheduling mechanism is implemented in static fixed clusters. Region based clustering is done by sink node. To provide privacy of sink node lo cation, at least 15 other nodes in the WSN have similar traffic statistics as the sink node. Simulation results show that our proposed algorithm perform better in terms of energy consuption and provide better sink node privacy.

  Key-Words / Index Term
  Sink Node, Security, Localization, Clustering, Energy Constrained, WSN

  References
  [1] C. M. George, T. Jacob, “Privacy Towards Base Station In Wireless Sensor Networks Against a Global Eavesdropper – A Survey”, International Journal of Computer Science and Management Research, Vol.2, Issue.4, pp. 1493-1497, 2013.
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  [3] M. Younis, Z. Ren, “Effect of Mobility and Count of Base-stations on the Anonymity of Wireless Sensor Networks”, 7th International Wireless Communications and Mobile Computing Conference, USA, pp. 436-441, 2011.
  [4] M. Conti, B. Crispo, and J. Willemsen, “Providing Source Location Privacy in Wireless Sensor Networks: A Survey”, IEEE Communications Surveys & Tutorials, Vol.15, Issue.3, pp.410-419 2013.
  [5] Niharika Singh Matharu, Avtar Singh Buttar, “An Efficient Approach for Localization using Trilateration Algorithm based on Received Signal Strength in Wireless Sensor Network”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.11-16, 2015.
  [6] S.S. Kumar, A.G. Selvarani, “Improving Energy Efficiency by Using Tree-Based Routing Protocol for Wireless Sensor Network”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.201-206, 2015.
  [7] E. Ngai, “On providing sink anonymity for sensor networks”, in Proceedings of 2009 International Conference on Wireless Communications and Mobile Computing: Connecting the World Wirelessly, US, pp. 269–273, 2009.
  [8] S. Tyagi, J. Kaur, “A Literature Review in Wireless Sensor Hole Detection Along with Node Scheduling Algorithm”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.8, pp.28-32, 2016.
  [9] Y. Jian, L. Zhang , S. Chen, Z. Zhang, “A novel scheme for protecting receiver’s location privacy in wireless sensor networks,” Wireless Communications IEEE Transactions, Vol.7, Issue.10, pp. 3769–3779, 2008.
  [10] K. Mehta, M. Wright, D. Liu, “Location privacy in sensor networks against a global eavesdropper,” IEEE International Conference on. IEEE, pp. 314–323, 2007.
  [11] A. F. Callanan, P. Thulasiraman,” Achieving Sink Node Anonymity Under Energy Constraints in Tactical Wireless Sensor Networks”, IEEE International Multi-Disciplinary Conference on Cognitive Methods in Situation Awareness and Decision, Orlando, pp-186-191, 2015
  [12] Y. Ebrahimi and M. Younis, “Using deceptive packets to increase base station anonymity in wireless sensor network”, 7th International Wireless Communications and Mobile Computing Conference, Istanbul, pp.842–847, 2016
  [13] G. Anastasi, M. Conti, M. Di Francesco, A. Passarella, “Energy conservation in wireless sensor networks: A survey”, Ad Hoc Networks, Vol.7, Issue. 3, pp. 537–568, May 2009.
  [14] Aditya Singh Mandloi, Vinita Choudhary, “An Efficient Clustering Technique for Deterministically Deployed Wireless Sensor Networks”, International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.1, pp.6-10, 2013
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  [16] J. Kulik, W. Heinzelman, H. Balakrishnan, “Negotiation-based protocols for disseminating information in wireless sensor networks”, Wireless Networks, Vol. 8, Issue.2, pp. 169–185, 2002.
  [17] R. Kachal, S. Suri, “Comparative Study and Analysis of DSR, DSDVAND ZRP in Mobile Ad-Hoc Networks”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.5, pp.148-152, 2014.
  [18] Uma Korupolu, S Kartik, G Kalyan Chakravarthi, “An Efficient Approach for Secure Data Aggregation Method in Wireless Sensor Networks with the impact of Collusion Attacks”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.3, pp.25-28, 2016.
  [19] R. Nathiya, S.G. Santhi, “Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
  

  Citation
  G. Sharma, A. Kumar, "REVIN: Reduced Energy Virtuous Immune Network for WSN," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.1-8, 2017

• Open Access   Article

  Big Data Clustering Using Data Mining Technique

  V.K. Gujare, P. Malviya

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.9-13, Apr-2017

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  Big data is term is basically used for the collection of huge datasets, which may contain the structured or unstructured type of information. The data is growing day by day continuously. It is very difficult to manage and analyze such data and generate prominent and useful result from this type of data. Data mining is the technique used to analyze such data by applying different useful methods like preprocessing or cleaning, classification, clustering, prediction, transformation, selection, etc. So in this paper, we apply the data mining techniques on airport dataset to analyze the airport data and generate the result according to our need.

  Key-Words / Index Term
  Big Data, Data Mining, Preprocessing, Classification, Clustering, 3v’s of Big Data

  References
  [1]. P. Sharma, V. Garg, R. Kaur, S. Sonare, "Big Data in Cloud Environment", International Journal of Computer Sciences and Engineering, Vol.1, Issue.3, pp.15-17, 2013.
  [2]. X. Wu , G.Q. Wu, W. Ding, “Data Mining with Big data”, IEEE Transactions on Knoweledge and Data Engineering, Vol 26, No.1, 2014
  [3]. M. Dhivya, D. Ragupathi, V.R. Kumar, "Hadoop Mapreduce Outline in Big Figures Analytics", International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.100-104, 2014.
  [4]. M.S. Begamr, N. Vetrivelanr, “An Analysis on Data Mining Processes on Big Data Framework”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.76-79, Dec -2014
  [5]. M. Kumarasamy, G. N. K. Suresh Babu, "Applications of Big Data in various Domains", International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.81-85, 2016.
  [6]. C. Blake and C. Merz, “UCI repository of machine learning databases”, University of California, California, pp.1-16, 1998.
  

  Citation
  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

• Open Access   Article

  Cluster Formation based Comparison of Genetic Algorithm and Particle swarm Optimization Algorithm in Wireless Sensor Network

  A. Garg, N. Batra, I. Taneja, A. Bhatnagar, A. Yadav, S. Kumar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.14-20, Apr-2017

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  Abstract
  Wireless Sensor Network is a network of small sensor nodes which sense the environment for various monitoring purposes of real life applications like military or commercial, which are required to be deployed in areas with no connectivity to the outside world. But, these nodes come with a limitation of having short battery life which needs to be recovered by applying various optimization techniques. In this paper, we are comparing two optimization algorithms that are Genetic algorithm and Particle Swarm Optimization algorithm, on the basis of their cluster head selection and cluster formation techniques to solve the problem of energy consumption by sensor nodes. We are experimenting with the different number of clusters to check the efficiency of each algorithm in MATLAB (simulation tool).

  Key-Words / Index Term
  Genetic Algorithm Clustering, Energy efficiency, Particle Swarm Optimization, Wireless Sensor Networks

  References
  [1] O. Hiteshreddy, P. Singh and S. Chahuan, “A Review on Cluster Based Data Aggregation Protocols in Wireless Sensor Network”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.37-45, 2015.
  [2] R. Nathiya, S.G. Santhi, “Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
  [3] D. Xie, Q. Zhou, X. You, B. Li, X. Yuan, “A Novel Energy­ Efficient Cluster Formation Strategy: From the Perspective of Cluster Members”, IEEE Communication Letters, Vol. 17, NO. 11, pp. 2044 – 2047, 2013.
  [4] A. Kaushil, H. Kumar, “performance Evaluation between GA versus PSO”, International Research Journal of Engineering and Technology, Vol. 3, Issue. 6, pp. 102-110, 2016.
  [5] S. Joshi, F.U. Khan, N. Thakur, “Contrasting and Evaluating Different Clustering Algorithms: A Literature Review”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.87-91, 2014.
  [6] J. Singh, H. Kaur, “Performance of Particle Swarm Optimization for Sensor Networks: A Survey”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.83-87, 2015.
  [7] Dr.Karl O Jones, “Comparison of Genetic and Particle swarm Optimisation algorithm”, Applied Soft Computing, Vol.8, Issue.4, pp.1418–1427, 2008.
  [8] J. Kennedy and R. Eberhart, “Particle swarm optimization”, IEEE International Conference on Neural Networks, China, pp.1942-1948, 1995.
  

  Citation
  A. Garg, N. Batra, I. Taneja, A. Bhatnagar, A. Yadav, S. Kumar, "Cluster Formation based Comparison of Genetic Algorithm and Particle swarm Optimization Algorithm in Wireless Sensor Network," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.14-20, 2017

• Open Access   Article

  Full Code Rate Complex Non-Orthogonal STBCS for Eleven Transmit Antennas

  S.M. Nimmagadda

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.21-25, Apr-2017

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  Abstract
  Alamouti is Pioneer of Complex Orthogonal STBCS for two transmit branch diversity with code rate one. Later Tarkoh investigated for four to nine transmit antennas with code rate 1/2. Jafarkhni proposed for non orthogonal STBCS four transmit antennas with code rate one. O. Tirkkonen and A. Hottinen Proposed space-time block codes for eight transmit antennas of code rate ½. Recently five, six, seven and eight transmit antennas generalized complex orthogonal space-time block codes of code rate with 2/3, 2/3,5/8, and 5/8 proposed. Bao.T. et. al. investigated OSTBC with maximal rates and minimal decoding for eight transmit antennas. The theory of OSTBC was further developed by Khaled A.M. et.al. who achieved full rate transmission for OSTBCs for six and eight antennas. This paper intends new matrix non-OSTBC for eleven transmit antenna with full code rate.

  Key-Words / Index Term
  Coderate, fading, MIMO, non-OSTBC, 2G, 3G, SISO, QOSTBC

  References
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  [10]. X.B. Liang, “orthogonal designs with maximal rates” Trans. Inform theory Vol.49 , Issue.1, pp.2468.2503, 2003
  [11]. M.A.I. Jewel, M. Rahman, “Full Rate General Complex Orthogonal Space-Time Block Code for 4-Transmit Antenna”, 5th International Conference on Wireless Communications, Networking and Mobile Computing, Beijing, pp.1-4, 2009.
  [12]. N.S. Murthy, S.S. Gowri, B. Prabhakara Rao, “Two New General Complex Orthogonal Space-Time Block Codes for 6 And 16 Transmit Antenna”, International Journal of Electronics and Communication Engineering & Technology, Vol.3, Issue.2, 2012.
  [13]. N. S. Murthy, S.S. Gowri, “Full Rate General Complex Orthogonal Space-Time Block Code for 8-Transmit Antenna", ELSEVIER, SCIVERSE Science Direct Procedia Engineering in IWIEE 2012, China, 2012.
  [14]. B. Tao, J.D. Xu, H.S. Zhang, "High-rate low-delay complex orthogonal space-time block codes for eight transmit antennas", International Conference on Electronic Measurement & Instruments, Beijing, pp.174-177, 2009.
  [15]. Q. Shen, A. Wang, Z. Nie, “A Novel space-time block code design with high rates”, in 12th IEEE international Conference ICCT , USA, pp.76 – 79, 2010.
  [16]. A.M. Khaled, A. M. Ghuniem, H. Y. Soliman, "A full rate, new families of general complex orthogonal space-time block code for 6 and 8 antennas", Radio Science Conference (NRSC), Cairo, pp.194-201, 2014.
  

  Citation
  S.M. Nimmagadda, "Full Code Rate Complex Non-Orthogonal STBCS for Eleven Transmit Antennas," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.21-25, 2017

• Open Access   Article

  Cryptosporidiosis: Challenges for Chemotherapy to AIDS Subpopulation

  Kalpana, R.K. Srivastava, R. Nath, N. Singh

  Review Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.26-32, Apr-2017

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  Cryptosporidium parasites, especially C.parvum and C.hominis are obligate intracellular apicomplexans protozoan parasite that infect epithelial cells of the small intestine posing life threatening Cryptosporidiosis disease to immunocompromised person and young malnourished children. It is also well known to be a troublesome waterborne pathogen. Pathogen transmission is naturally via contaminated drinking or recreational water supplies by the environmentally resistant and chlorine resistant oocysts. Cryptosporidium is classified in category B of bioweapons agents. However nitazoxanide, a nitrothiazole benzamide was approved by FDA Department for treatment of cryptosporidiosis in immunocompetent individuals and Paramomycin is used to treat C.parvum infections in animals. Cryptosporidium infections starts by ingestion of oocysts with contaminated water or food. Oocysts undergo excystation in gastrointestinal tract where four naked sporozoites are formed which then infect epithelial cell and initiate asexual development. IMPDH an NAD+ enzyme remains a promising drug target for anti-parasitic drugs. Cryptosporidium obtains guanine nucleotides via a streamlined pathway that requires IMPDH. Curiously, the gene encoding CpIMPDH seems to have been obtained from a bacteria through lateral gene transfer so that the parasite enzyme is very different from host ortholog. IMPDH is a homotetramer showing square planner symmetry. Inhibition of IMPDH is already used to treat viral infection and could be used to treat parasitic infection as well. IMPDH has been isolated from different sources and 26 crystal structure are deposited in PDB including mammals to apicomplexans and bacteria. There are several FDA approved inhibitors that target IMPDH.

  Key-Words / Index Term
  Cryptosporidium Parvum, Cryptosporidiosis, GMP, IMP, Immunocompromised, Immunocompetent, IMPDH, PVM , XMP

  References
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  [4] D.P. Casemore, C.A. Garder, C. OMahony, “Cryptosporidial infection, with special reference to nosocomial transmission of Cryptosporidium parvum: a review”, Folia Parasitol, Vol.41, Issue.1, pp.17-21, 1994.
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  Citation
  Kalpana, R.K. Srivastava, R. Nath, N. Singh, "Cryptosporidiosis: Challenges for Chemotherapy to AIDS Subpopulation," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.26-32, 2017

• Open Access   Article

  Energy Constrained Routing Algorithm for Wireless Networks

  K. Kaushik, B. Bhushan, R. Sharma

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.33-35, Apr-2017

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  Abstract
  The aim of following paper is to introduce an Energy Constrained Routing algorithm for wireless network to enhance the performance of network. The following schemes PECE there are two stages cluster formation and stable data transfer. In stage one we used an energy saving clustering routing algorithm based on degree of the nodes, left energy and relative distance between nodes. Whereas in stage two where we used BCO (Bee Colony Optimization). On these basis of considering a precise definition of the route from source node to sink node. This algorithm helps in reducing energy consumption and increases the survival time.

  Key-Words / Index Term
  Wireless Network, SI, Algorithm, Agents

  References
  [1] DG Zhang, “Design and implementation of embedded un-interruptible power supply system (EUPSS) for web based mobile application”, Enterprise Information System, Vol.6, Issue.4, pp.473–489, 2012.
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  [4] A. Rana, M. Bala, Varsha , “Review Paper on MSEEC: Energy Efficient Clustering Protocol in HWSN”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.5, pp.71-75, 2016.
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  [10] DG Zhang, “Novel Quick Start (QS) Method for Optimization of TCP”, Wireless Networks, Vol.22, Issue.1, pp 211–222, 2016.
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  [12] S Lindsey, “PEGASIS: power efficient gathering in sensor information systems”, IEEE Aerospace Conference, China, pp.1152–1130, 2002.
  

  Citation
  K. Kaushik, B. Bhushan, R. Sharma, "Energy Constrained Routing Algorithm for Wireless Networks," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.33-35, 2017

• Open Access   Article

  Privacy Preserving using Encryption Proxy in Data Security

  V.K. Saxena, S. Pushkar

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.36-41, Apr-2017

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  Abstract
  Cloud computing is altering the approach that organizations handle their information, suitable to its strength, low down expenditure and omnipresent nature. Privacy concerns occur each time susceptible information is redistributed to the cloud. This paper presents architecture for storage to the cloud database that averts the partial proprietor along with the cloud supervisor to find out the redistributed database substance. Furthermore, machine legible rights terms are used in a bid to bound end users of the database. These restrictions are not unstable by supervisors after the database associated request is launched, because a novel task of privilege editors is defined once an application is launched. Additionally, dependable computing is functional to bind cryptographic key notwithstanding trusted states. By preventive the obligatory belief in both commercial as well as outside supervisors and service providers, we work against the often scarified privacy and understanding risks of commercial cloud computing.

  Key-Words / Index Term
  Data security, Access control, Outsourcing, Privacy, Software Architecture

  References
  [1] A. Basu, J. Vaidya, H. Kikuchi, T. Dimitrakos, S.K. Nair, “Privacy preserving collaborative filtering for SaaS enabling PaaS clouds”, Journal of Cloud Computing: Advances, Systems and Applications, Vol.1, Issue.1, pp.1-8, 2012.
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  Citation
  V.K. Saxena, S. Pushkar, "Privacy Preserving using Encryption Proxy in Data Security," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.36-41, 2017

• Open Access   Article

  Agent- Based Traffic Simulator for Autonomous Vehicles

  K. Saha, S. Rathee

  Technical Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.42-45, Apr-2017

  Abstract

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  Abstract
  In near future, there is a great possibility of autonomous vehicle navigation. These days stop signs, traffic lights, and intersections assist the human-driven cars to cross the traffic. Now the computers will take over the human drivers i.e., the computers will “behind the wheel”. With the help of computerized drivers, automobile travel will be much more safe and efficient because of more accurate control, highly defined sensors and quick reaction times.

  Key-Words / Index Term
  AORTA, AIM, Intersection control policy, multi-agent approach, Project Object Model, Open Street Map, reservation idea, traffic simulation

  References
  VII. REFERENCES
  [1] M. Buehler, K. Iagnemma, S. Singh, “The DARPA Urban Challenge: Autonomous Vehicles in City Traffic”, Springer, Berlin, 2009.
  [2] K. Dresner, P. Stone, “A multiagent approach to autonomous intersection management”, Journal of Artificial Intelligence Research, Vol.31, pp. 591-656, 2008.
  [3] D. Fajardo, T.-C. Au, S. T. Waller, P. Stone, D. Yang, “Automated intersection control: Performance of a future innovation versus current traffic signal control”, Transportation Research Record: Journal of the Transportation Research Board, Vol.2259, Issue.3, pp. 223–232, 2012.
  [4] Nhtsa, “Preliminary statement of policy concerning automated vehicles”, National Highway Traffic Safety Administration, Washington, pp.1-14, 2013.
  [5] S. Shladover, C. Desoer, J. Hedrick, M. Tomizuka, J. Walrand, W.-B. Zhang, D. McMahon, H. Peng, S. Sheikholeslam, N. McKeown, “Automated vehicle control developments in the path program”, In IEEE Transactions on Vehicular Technology, vol. 40, no. 1, pp. 114–130, 1991.
  [6] C. Wuthishuwongm, A. Traechtler, “Coordination of multiple autonomous intersections by using local neighborhood information”, In Proceedings of IEEE International Conference on Connected Vehicles and Expo (ICCVE), ISA, pp. 48-53, 2013.
  [7] M. VanMiddlesworth, K. Dresner, and P. Stone, “Replacing the stop sign: Unmanaged intersection control for autonomous vehicles”, In AAMAS Workshop on Agents in Traffic and Transportation, Portugal, pp. 94–101, 2008.
  [8] C. Wuthishuwong and A. Traechtler, “Vehicle to infrastructure based safe trajectory planning for Autonomous Intersection Management”, In Proceedings of IEEE International Conference on ITS Telecommunications (ITST), pp. 175-180, 2013.
  

  Citation
  K. Saha, S. Rathee, "Agent- Based Traffic Simulator for Autonomous Vehicles," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.42-45, 2017

• Open Access   Article

  Information Sharing Portal for Indus Sub-systems

  R.K. Verma, R. Mishra, S. Prajapat

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.46-55, Apr-2017

  Abstract

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  Abstract
  Indus complex is a synchrotron particle accelerator facility as particle accelerator in Raja Ramanna Centre for Advanced Technology (RRCAT), Indore: Indus-1 and Indus-2. Indus-1 and Indus-2 comprises of several subsystem like Magnet Power Supply (MPS) system, Vacuum system etc. There exists an automated Indus data acquisition system which stores the periodic data from these subsystems to SQL database. This raw data stored in the database is usually served to end user from web in tabular form. Extracting meaningful information from raw data is difficult and limits user to perform significant analysis on raw data. This work aims to provide solution to this real problem by providing the common portal where data analysis reports can be deployed to serve end users and to reduce the data retrieving load of the centralized database server. The reports are scheduled to be delivered in excel format at non-peak hours which improves the performance of database system. To develop initial prototype, a common task of tracing and displaying the power supplies data of Magnet Power Supply (MPS) sub-system during “ramping” and “cycling” events was selected. Daily generated graphical and tabular reports are deployed successfully on web portal.

  Key-Words / Index Term
  Indus-1, Indus-2 , Magnet Power Supply (MPS)

  References
  [1]. B. Larson, “Microsoft SQL Server 2012 Reporting Services 4/E”, Tata McGraw-Hill, India, 2012.
  [2]. B. Larson, “Microsoft SQL Server 2016 Reporting Services”, McGraw-Hill Education, India, pp.1-848, 2016.
  [3]. Microsoft, “User permissions and permission levels in SharePoint”, Microsoft, USA, pp.1-14 2013
  [4]. H. Consulting, S. Cameron, “Microsoft SQL Server 2008 Analysis Services Step by Step”, Pearson Education, India, pp.1-448, 2009.
  [5]. B. McDonald, S. McGehee, R. Landrum, “Pro SQL Server 2012 Reporting Services”, Apress, NY, pp.1-516, 2012.
  [6]. Nigel Chapman, “Authentication and Authorization on the Web”, Macavon Media, Scotland, pp.1-246, 2012
  [7]. Reza Alirezaei, “Real World SharePoint 2010: Indispensable Experiences from 22 MVPs”, John Wiley and Sons, Singapore, pp.1-816, 2010.
  [8]. Rick David Craig, Stefan P. Jaskiel, “Systematic Software Testing”, Artech House, Massachusetts, pp.1-536, 2002
  

  Citation
  R.K. Verma, R. Mishra, S. Prajapat, "Information Sharing Portal for Indus Sub-systems," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.46-55, 2017

• Open Access   Article

  An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR

  A. Agarwal, S.S. Bhadouria

  Research Paper | Isroset-Journal (IJSRCSE)

  Vol.5 , Issue.2 , pp.56-62, Apr-2017

  Abstract

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  Abstract
  Content based image retrieval (CBIR) presents an effectual strategy to probe the pictures from the databases. The function extraction and homogeneous attribute measures are the 2 key parameters for retrieval efficiency. A uniform attribute measure performs a paramount function in picture retrieval. “content-based” designates that the quest analyzes the contents of the image alternatively than the metadata equivalent to keyword phrases, tags, or descriptions associated with the picture. In this study, present an evaluation of dominant color descriptor (DCD) and wavelet transform in YCbCr color space for CBIR. In this work, color, feature is extracted using DCD for RGB and HSV color space and auto correlogram (AC). Texture feature is extracted using discrete wavelet transform (DWT) and Gabor Filter. Shape feature is extracted with the use of Kurtosis and Skewness. We take Corel-1000 database-African, Flowers, Food, Elephant and Horse images. Also calculate the Euclidean distance (ED), relative standard deviation (RSD), Correlation, Canberra distance (CD) and Jaccard distance (JD) between giving image and database images. For classification, used support vector machine (SVM) to organize the categories without time consuming. The performance analysis is evaluated on precision and accuracy.

  Key-Words / Index Term
  DCD, Histogram Equalization, DWT, CBIR, Precsion, Gabor filter

  References
  [1] D. Tripathi, V.K. Shukla, “DWT Based PCA and K-Means Clustering Block Level Approach for SAR Image De-Noising”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.12, pp.87-91, 2016.
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  [5] S.S. Katariya, U.B. Shinde, “Review of Content Based Image Retrieval Using Low Level Features”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.91-97, 2016.
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  [10] Singh, Nidhi, K. Singh, A.K. Sinha, “A novel approach for content based image retrieval”, Procedia technology, Vol.4, pp. 245-250, 2012.
  [11] J. KAvya, H. Shashirekha, “A Novel Approach for Image Retrieval using Combination of Features”, International Journal of Computer Technology & Applications,Vol.6, Issue.2, pp.323-327, 2015.
  [12] S. Bansal, R. Kaur, “A Review on Content Based Image Retrieval using SVM”, International Journal of Advanced Research in Computer Science and Software Engineering, Vol.4, Issue.7, pp.232-235, 2014.
  [13] R.V. Patil, S.S. Sannakki, V.S. Rajpurohit, “A Survey on Classification of Liver Diseases using Image Processing and Data Mining Techniques”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.29-34, 2017.
  S. M. Singh , K. Hemachandran, “Content- Content-Based Image Retrieval using Color Moment and Gabor Texture Feature”, International Journal of Computer Science Issues, Vol. 9, Issue.5, pp.299-309, 2012.
  

  Citation
  A. Agarwal, S.S. Bhadouria, "An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR," International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, 2017