Volume-4 , Issue-6 , Jun - 2016 Go Back

With the rapid growth of internet the various digital methods has been proposed to protect the multimedia information from the non authorized accesses use and change. Among all the proposed methods the watermarking technique is the most common technique for protecting the multimedia data for unauthorized access. There are various algorithms for watermarking. The DCT-SVD based technique takes more processing time and has less capacity and imperceptibility. To increase the efficiency of SVD technique the DWT and SVD methods are combined and new method is generated which has less processing time and more robust to different type of security attacks. But the proposed hybrid algorithm is more robust to security attacks due to which the PSNR values have been increased. Then we will apply kalman filter to increase the PSNR value by removing noise.

SVD, DWT-DCT, Kalman filtering, Salt and Pepper and Robustness

[1] Al-Haj, Ali. "Combined DWT-DCT digital image watermarking." Journal of computer science 3, no. 9 (2007): 740.
[2] Alexander Sverdlov, “Secure DCT-SVD Domain Image Watermarking: Embedding Data in All Frequencies”, Proceedings of IEEE Region 10 Technical Conference on Convergent Technologies for the Asia-Pacific, Bangalore, India, October 14-17, 2003.
[3] Anthony T.S.Ho et.al, “A Robust Digital Image-in-Image Watermarking Algorithm Using the Fast Hadamard Transform”, Springer, 2011.
[4] Chaturvedi, Navnidhi, and S. J. Basha. "Comparison of Digital Image watermarking Methods DWT & DWT-DCT on the Basis of PSNR." image 2 (2012).
[5] D. Gabor, “Theory of communication,” Journal of Institution of Electrical Engineers, vol. 93, pp. 429–457, 1946.
[6] E. Simoncelli, W. Freeman, E. Adelson, and D. Heeger, “Shiftable multiscale transforms,” IEEE Transactions on Information Theory, vol. 38,no. 2, pp. 587–607, 1992.
[7] Fang Li, “Salt and Pepper Noise Removal by Adaptive Median Filter and Minimal Surface Inpainting”, Image and Signal Processing, 2009. CISP '09. 2nd International Congress on, 2009
[8] J. Ilonen, J.-K. Kamarainen, P. Paalanen, M. Hamouz, J. Kittler, and H. K¨ alvi¨ ainen, “Image feature localization by multiple hypothesis testing of Gabor features,” IEEE Trans. on Image Processing, vol. 17, no. 3, pp. 311–325, 2008.
[9] J.-K. Kamarainen, V. Kyrki, and H. K ¨ alvi¨ ainen, “Invariance properties of Gabor filter based features - overview and applications,” IEEE Trans. on Image Processing, vol. 15, no. 5, pp. 1088–1099, 2006.
[10] J. Sampo, J.-K. Kamarainen, M. Heili ¨ o, and H. K¨ alvi¨ ainen, “Measuring translation shiftability of frames,” Computers & Mathematics with Applications, vol. 52, no. 6-7, pp. 1089–1098, 2006.
[11] Kashyap, Nikita, and G. R. Sinha. "Image watermarking using 3-level discrete wavelet transform (DWT)." International Journal of Modern Education and Computer Science (IJMECS) 4.3 (2012): 50.
[12] Lu, Chun-Shien, and H-YM Liao. "Multipurpose watermarking for image authentication and protection." Image Processing, IEEE Transactions on 10, no. 10 (2001): 1579-1592.
[13] Mansouri, A., A. MahmoudiAznaveh, and F. Torkamani Azar. "SVD-based digital image watermarking using complex wavelet transform." Sadhana 34, no. 3 (2009): 393-406.

Khushbu and Deepinder Kaur, "Enhancement in Watermarking Approach Using DCT-DWT-SVD Techniques by Applying Kalman Filter," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.50-53, 2016.

Electronic structure calculations are carried out under the density functional formalism for understanding the structure and energetic of gold atoms and gold clusters containing up to four atoms adsorbed on the Si(001) surface. The stable adsorption sites of gold atoms and the gold clusters on the Si(001):p(2x1) surface and the structural change of the clusters due to their interaction with the surface are presented. Also, the adsorption of Au clusters on Si((001):p(2x1) in presence of defects are studied. However, most significant finding of our calculations is that the formation of Au3 cluster lattice on the Si(001) is possible and, as a consequence, the Si(001) surface becomes metallic.

DFS, Au, SET, Atom, Cluster

1. J. L. Li, J. F. Jia, X. J. Liang, X. Liu, J.Z. Wang, Q. K. Xue, Z. Q. Li, J.S. Tse, Z. Zhang, and S. B. Zhang, Phys. Rev. Lett. 88, 066101 (2002).
2. J.F. Jia, X. Liu, J.Z. Wang, J. L .Li, X. S. Wang, Q. K. Xue, Z. Q. Li, Z. Zhang, and S. B. Zhang, Phys. Rev. B 66, 165412 (2002).
3. Kehui Wu, Y. Fujikawa, T. Nagao, et al, Phys. Rev. Lett. 91, 126101 (2003).
4. Shao Chun Li, Jin Feng Jia, Rui Fen Dou, et al, Phys. Rev. Lett., 93, 116103 (2004).
5. J. Jia, Jun Zhong Wang, et al Appl. Phys. Lett. 80, 3186 (2002).
6. M. Y. Lai and Y.L. Wang, Phys. Rev. B, 64, 241404(R)2001).
7. H. H. Chang, M. Y. Lai, et al, Phys. Rev. Lett., 92, 066103 (2003).
8. J. R. Ahn, J. H. Byun, W. H. Choi, H. W. Yeom, Phys. Rev. B, 70, 113304 (2004).
9. J.M. Zuo and B. Q. Li, Phys. Rev. Lett. 88, 255502 (2002).
10. V. G. Kotlyar, A. V. Zotov, A. A. Saranin, et al, Phys. Rev. B 66, 165401 (2002).
11. Saroj. K. Nayak and P. Jena, et al Phys. Rev. B 56, 6952 (1997).
12. B. Gates, Chem. Rev. (Washington D. C.) 95, 511 (1995).
13. M. Haruta, Catal. Today 36, 153 (1997).
14. G. Bond and D. Thompson, Catal. Rev. Sci. and Eng. 41, 319 (1999).
15. R. Meyer, C. Lemire, S. K. Shaikhutdinov, and H. Freud, Gold Bull. 37, 72 (2004).
16. C. R. Henry, Prog. Surf. Sci., 80, 92 (2005).
17. Y. Xiao, F. Patolsky, E. Katz, et al, Science 299, 1877 (2003).
18. W. L. Barnes, A. Dereux, and T. W. Ebbesen, Nature (London). 424, 824 (2003).
19. Dinesh Kumar Vankatachalam, Dinesh Kumar Sood and Suresh Kumar Bhargava, Nanotechnology 19, 015605 (2008)
20. Lixin Zhang, S. B. Zhang, Qi-Kun Xue, Jin-Feng Jia and E. G. Wang, Phys. Rev. B. 72, 033315 (2005).
21. Ivan Ost’adal, Pavel Kocan, Pavel Sobotik and Jan Pudl, Phys. Rev. Lett. 95, 146101 (2005).
22. G. Kresse and J. Furthmuller, Phys. Rev. B 54, 11169 (1996).
23. G. Kresse and J. Hafner, Phys. Rev. B 47, 558 (1993).
24. H. J. Monkhorst and J. D. Pack, Phys. Rev. B, 13, 5118 (1976).
25. P. Sen, S. Ciraci, I. P. Batra and C. H. Grein, Phys. Rev. B 64, 193310 (2001).
26. A. J. Ciani, P. Sen, and I. P. Batra, Phys. Rev. B 69, 245308 (2004).
27. Jin Zhao, Jinglong Yang, and J. G. Hou, Phys. Rev. B 67, 085404 (2003).
28. P. Sen, B. C. Gupta and I. P. Batra, Phys. Rev. B 73, 085319 (2006) .
29. B. C. Gupta and I. P. Batra, Phys. Rev. B, 69, 165322, (2004).
30. W. T. Wallace, B. K. Min and D. W. Goodman, Jl Mol. Cat. A: Chem 228, 3 (2005).
31. E. Kim, C. Chen, T. Pang, and Y. H. Lee, Phys. Rev. B 60, 8680 (1999).
32. M. H. Tasi, Y. Tasi, C. S. Chang, Y. Wei, and I. Tsang, Phys Rev. B 56, 7435 (1997).
33. Michael Walter, Pentti Frondelius, Karolina Honkala, Hannu Hakkinen, Phys. Rev. Lett. 99, 096102 (2007).
34. J. Tersoff and D. R Hamann, Phys. Rev. Lett. 50, 1998 (1983).

Rudra Prasad Bose, Kisalaya Chakrabarti, "A Density Functional Study of Au Clusters Adsorbed on Si(001): Formation of Cluster Lattice and a Transition From Non-Metallicity to Metallicity," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.54-62, 2016.

Due to the wireless nature and infrastructure-less environment of WSN, they are more vulnerable to many types of security attacks. This paper proposes a technique to detect the black-hole attack using multiple base-stations and a check agent based technology. This technique is Energy efficient, Fast, Lightweight and Reduces message complexity. An effective solution is proposed that uses multiple base stations to improve the delivery of the packets from the sensor nodes reaching at least one base station in the network, thus ensuring high packet delivery success. The proposed technique is more efficient than the previous techniques and gives better results. Check agent is a software program which is self-controlling and monitoring, smart homes, structures, target it moves from node to node and checks wireless nodes envelop embedded electronic sensors along with battery and RF devices. The purpose of these sensors is to sense and recognize diverse biological parameters, for instance, temperature, pressure, air pollution etc., to communicate with the neighboring nodes and compute the gathered data. Their application space is huge as they can be deployed in various fields like agriculture the presence tracking, health care, and military surveillance and of black-hole nodes in the network. Routing through multiple base stations algorithm is only activated when there is a chance of black-hole attack on the network. This method prevents the black hole attack imposed by both single and multiple black hole nodes. The tool used to implement the proposed algorithm is NS2, which is an object oriented event drive software package. The result of the simulation study expected to get good network performance by minimizing the packet losses as well as effectively prevent the black hole attack against wireless sensor networks. A solution to avoid the black hole attack has been proposed. The solution will be implemented in NS-2.

WSN, Black-hole attacks, multiple base stations & Check agent.

[1] Sanjeev Gangwar “Mobile Ad Hoc Network Routing Protocols: a Detailed Performance Examination of AODV, DSR and DSDV” , International Journal of Computer Applications , Volume 49– No.9, July 2012.
[2] Simanta Sarma, Binita Devi ,” Security Attacks on Routing Protocols in Ad Hoc Wireless Networks” , International Journal of Modern Engineering Research (IJMER), Vol.2, Issue.6, Nov-Dec. 2012 pp-4502-4509 ISSN: 2249-6645.
[3] Sahabul Alam and Debashis De ,” Analysis of Security threats in wireless sensor network ”, International Journal of Wireless & Mobile Networks(IJWMN) Vol. 6, No. 2, April 2014.
[4] Raju M, Selvan M “An Approach in Detection of Replication Node in Wireless Sensor Networks: A Survey ”, International Journal of Computer Science and Information Technologies, Vol. 5 (1) , 2014, 192-196.
[5] H.Weerasinge and H.Fu ―Preventing Black Hole journal of engineering science and technology, vol. 4,Attack in Mobile Ad hoc Networks: simulation, no. 2 pp. implementation and evaluation international journal of software engg. and its applications, vol2,no3 in MANET‖, Journal Of Networks, Vol. 3, NO. 5.
[6] Jian Yin, Sanjay Madria, ―A Hierarchical Secure Routing Protocol against Black Hole, IEEE SUTC 2012 Taiwan, 5-7 June 2012.
[7] Dokurer, S.;Ert, Y.M.;and Acar, C.E.(2011). Performance analysis of ad hoc networks under black hole attacks. SoutheastCon, 2011, Proceedings IEEE,148–153.
[8] Dr. Karim Konate and Abdourahime Gaye(2011) a proposal mechanism against the attacks: cooperative black hole, blackmail, overflow and selfish in routing protocol of mobile ad hoc network”, international journal of future generation communication and networking Vol.4, No. 2.
[9] S. Roy, S. Singh, S. Choudhary, and N. Debnath. ―Countering sinkhole and black hole attacks on networks using dynamic trust management ‖; In IEEE Symposium on Computers and Communications, 2008; pp. 537–542.
[10] Niharika Singh Matharu and 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, Volume-03, 2015, Issue-08
[11] Tao Shu, Marwan Krunz and Sisi Liu, ―Secure Data Collection in Wireless Sensor Networks Using Randomized Dispersive Routes‖ In IEEE INFOCOM, 2009. pp. 2846–2850.
[12] G. Sladic , M. Vidakovic and Z. Konjovic ―Agent based system for network availability and vulnerability monitoring 2011 IEEE 9th International Symposium on Intelligent Systems and Informatics, September 8-10, 2011, Subotica, Serbia.
[13] Satyajayant Misra, Kabi Bhattarai, and Guoliang Xue ―BAMBi: Black hole Attacks Mitigation with Multiple Base Stations in Wireless Sensor Networks IEEE Communications Society subject matter experts for publication in the IEEE ICC 2011 proceedings.
[14] Atul Yadav et al., ―Study of Network Layer Attacks and Countermeasures in Wireless Sensor Network International Journal of Computer Science and Network (IJCSN) Volume 1, Issue 4, August 2012.
[15] Gulshan Kumar, Mritunjay Rai and Gang-soo Lee ― Implementation of Cipher Block Chaining in Wireless Sensor Networks for Security Enhancement- International Journal of Security and Its Applications Vol. 6, No. 1, January, 2012
[16] M. Ketel, N.Dogan, A.Homaifar―Distributed Sensor Networks Based on Mobile Agents Paradigm‖ International Conference on Artificial Intelligence and Embedded Systems (ICAIES'2012) Singapore, 2012;
[17] S. Sharma and R. Gupta, (2012) ―Simulation study of black hole attack in the mobile ad-hoc networks.

Vipul Kumar, Musheer Vaqar, "BLACK HOLE Attack: A New Detection Technique," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.63-67, 2016.

this paper provides a new theoretical approach of code review, considering its existing challenges in current software industry with upward trend in agile methodology adoption. This article captures both Process aspects and Technical aspects of Code Review. It tries to establish the importance of Ownership, Authority, and Transparency in Process. Technically this solution tries to identify most important four deciding factors in generating function vulnerability score with Red-Amber-Green criteria for all the four factors. It formulates easy steps of determining values for those four factors which are feasible to utilize in real life scenario. Also it explains process of identifying the fifth deciding factor based upon the outcome of a project’s defect prevention analysis. It explains ways of capturing review effectiveness by appropriate metric values which can be used for quantified reporting to senior management on a pre-defined interval

Code Review Effectiveness, TDCE, RE, Cyclomatic Complexity, Time Complexity

[1] Qualiteers – Defending Software Quality, 2005 Qualiteers | info@qualiteers.com
http://www.qualiteers.com/symptom_ineffective.php
[2] Dr. Aviel D. Rubin, Dr. Seth J. Nielson, Dr.Sam Small, Dr. Christopher K. Monson; “Guidelines for Source Code Review in Hi‐Tech Litigation”; Harbor Labs White Paper;
http://harborlabs.com/codereview.pdf
[3] Yanqing Wang, Bo Zheng, Hujie Huang; “Complying with Coding Standards or Retaining Programming Style: A Quality Outlook at Source Code Level”;
J. Software Engineering & Applications, 2008, 1:88-91 published Online December 2008 in SciRes
[4] “Modernizing the Peer Code Review Process”; KLOCWORK | WHITE PAPER | APRIL 2010;
[5] “Five Types of Review”;
Pros and cons of formal, over-the-shoulder, email pass-around, pair-programming, and tool-assisted reviews
www.ccs.neu.edu/home/lieber/courses/cs4500/f07/lectures/code-review-types.pdf
[6] Jason Cohen, Steven Teleki, Eric Brown; “Best Kept Secrets of Peer Code Review”;
Collaborator by SMARTBEAR;http://smartbear.com
[7] Archana Srivastava, S.K.Singh and Syed Qamar Abbas;
International Journal of Computer Sciences and Engineering; “Proposed Quality Paradigm for End User Development”;International Journal of Computer Sciences and Engineering, Review Paper, Volume-4 Issue-4, E-ISSN: 2347-2693;
[8] Suvra Nandi; “Quality Maintenance Effort Optimization in Software Industry”; International Journal of Computer Sciences and Engineering, Case Study, Volume-4 Issue-5, E-ISSN: 2347-2693;
[9] THOMAS J. McCABE; “A Complexity Measure”;
IEEE Transactions On Software Engineering, Vol. Se-2, No.4, December 1976;
[10] “LIST OF SUCCESS INDICATORS AND METRICS”;
http://www.bth.se/com/mun.nsf/attachments/Metric%20examples_pdf/$file/Metric%20examples.pdf

Suvra Nandi, Suvankar Dhar, "A new Proposition for Software Code Review Process," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.68-74, 2016.

With today’s technology enhancement, it is possible to store your whole life on your hard drive. But at the same time it increases the risk of information theft. Human generally create passwords that are easy to remember. There are many techniques used to crack passwords like Eves dropping, shoulder surfing, social engineering, etc. To make hacking difficult, we should find out techniques that will increase the security level. So this paper proposes a combination of textual session password and graphical passwords. Session passwords enhance security as they are valid only for certain period of time. Graphical passwords on the other hand are easy to memorize and are better resistant to hacking. Algorithms like Pair based algorithm and Cued Click Point algorithm are used to facilitate the combination.

SSA, GSP, Security, Authentication

[1] J. Kanagarag and K. Noel Binny, “A safe and powerful technique for visual based (secret word) password confirmation”, International journal of advance research in computer science and management studies, Vol. 2, Issue 11, November 2014.
[2] M Shreelatha,M Shashi,M Anirudh,MD Sultan Ahamer,V Manoj kumar, “Authentication schemes for session password using color and images”, International journal of network security and applications, Vol. 3, No.3, May 2011.
[3] R. Dhamija, and A. Perrig. “Déjà Vu: A User Study Using Images for Authentication”. In 9th USENIX Security Symposium, 2000. Real User Corporation: Passfaces. www.passfaces.com.
[4] R. Nithya,“Graphical password”, International journal of computer science and information technology research, Vol. 2, Issue 3, September 2014

Smruti Bhosale, Shradha Botre, Gayatri Chandnani, Nikita Kamble, "Secure Server Authentication Using Graphical and Session Password," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.75-77, 2016.

Big data is a buzzword, used to describe a massive volume of both structured and unstructured data that is so large that it's difficult to process using traditional database and software techniques. In most enterprise scenarios the data is too big or it moves too fast or it exceeds current processing capacity. Big data has the potential to help companies improve operations and make faster, more intelligent decisions.Parallel computing is a frequently used method for large scale data processing. Many computing tasks involve heavy mathematical calculations, or analysing large amounts of data. These operations can take a long time to complete using only one computer. Map Reduce is one of the most commonly used parallel computing frameworks. The execution time of the tasks and the throughput are the two important parameters of Map Reduce. Speculative execution is a method of backing up of slowly running tasks on alternate machines. Multiple speculative execution strategies have been proposed, but they have some pitfalls: (i) Use average progress rate to identify slow tasks while in reality the progress rate can be unstable and misleading, (ii) Do not consider whether backup tasks can finish earlier when choosing backup worker nodes. This project aims to improve the effectiveness of speculation execution significantly. To accurately and promptly identify the appropriate tasks, the following methods are employed: (i) Use both the progress rate and the process bandwidth within a phase to select slow tasks, (ii) Use exponentially weighted moving average (EWMA) to predict process speed and calculate a task’s remaining time, (iii) Determine which task to backup based on the load of a cluster using a cost-benefit model.

Map reduce, Cost Performance strategy, Big Data, Stragglers, Speculation

[1] J. Dean and S. Ghemawat, “Map reduce: simplified data processing on large clusters,” Commun. ACM, vol. 51, pp. 107–113, January 2008.
[2] “Apache hadoop, http://hadoop.apache.org/.”
[3] M. Isard, M. Budiu, Y. Yu, A. Birrell, and D. Fetterly, “Dryad: distributed data-parallel programs from sequential building blocks,” in Proc. of the 2nd ACM SIGOPS/Euro Sys European Conference on Computer Systems 2007, ser. Euro Sys ’07, 2007.
[4] M. Zaharia, A. Konwinski, A. D. Joseph, R. Katz, and I. Stoica, “Improving map reduce performance in heterogeneous environments,” in Proc. of the 8th USENIX conference on Operating systems design and implementation, ser. OSDI’08, 2008.
[5] G. Ananthanarayanan, S. Kandula, A. Greenberg, I. Stoica, Y. Lu, B. Saha, and E. Harris, “Reining in the outliers in map-reduce clusters using mantri,” in Proc. of the 9th USENIX conference on Operating systems design and implementation, ser. OSDI’10, 2010.
[6] Y. Kwon, M. Balazinska, and B. Howe, “A study of skew in map reduce applications,” in The 5th Open Cirrus Summit, 2011.
[7] P.H and Ellaway, “Cumulative sum technique and its application to the analysis of peri stimulus time histograms,” Electroencephalography and Clinical Neurophysiology, vol. 45, no. 2, pp. 302–304, 1978.
[8] K. Avi, K. Yaniv, L. Dor, L. Uri, and L. Anthony, “Kvm: The linux virtual machine monitor,” Proc. of the Linux Symposium, Ottawa, Ontario, 2007, 2007.
[9] Quiane-Ruiz,Pinkel, C.,Schad, J. ,Dittrich, J.“RAFTing Map Reduce: Fast recovery on the RAFT” Data Engineering (ICDE), 2011 IEEE 27th International Conference in Hannover, Publication Year: 2011.
[10] G. Ananthanarayanan, S. Agarwal, S. Kandula, A. Greenberg, I.Stoica, D. Harlan, and E. Harris, “Scarlett: Coping with Skewed Content Popularity in Map reduce Clusters,” Proc. Sixth Conf. Computer Systems (EuroSys ’11), 2011.
[11] B. Nicolae, D. Moise, G. Antoniu, L. Bouge, and M. Dorier,“Blobseer: Bringing High Throughput under Heavy Concurrency to Hadoop Map-Reduce Applications,” Proc. IEEE Int’l Symp. Parallel Distributed Processing (IPDPS), Apr. 2010.
[12] J. Leverich and C. Kozyrakis, “On the Energy (In)Efficiency of Hadoop Clusters,” ACM SIGOPS Operating Systems Rev., vol. 44,pp. 61-65, Mar. 2010.
[13] T. Sandholm and K. Lai, “Mapreduce Optimization Using Regulated Dynamic Prioritization,” Proc. 11th Int’l Joint Conf. Measurement and Modeling of Computer Systems, (SIGMETRICS ’09),2009.
[14] M. Isard, V. Prabhakaran, J. Currey, U. Wieder, K. Talwar, and A.Goldberg, “Quincy: Fair Scheduling for Distributed Computing Clusters,” Proc. ACM SIGOPS 22nd Symp. Operating Systems Principles(SOSP ’09), 2009.
[15] M. Zaharia, D. Borthakur, J. SenSarma, K. Elmeleegy, S. Shenker,and I. Stoica, “Delay Scheduling: A Simple Technique for AchievingLocality and Fairness in Cluster Scheduling,” Proc. Fifth European Conference Computer Systems (EuroSys ’10), 2010.
Kala Karun, A ; Chitharanjan, K ; "A review on hadoop — HDFS infrastructure extensions ", IEEE Conference on Information & Communication Technologies (ICT), JeJu Island, April 2013. Page(s): 132 - 137.
[16] D.Deepika1, K.Pugazhmathi, “Efficient Indexing and Searching of Big Data in HDFs”, International Journal of Computer Sciences and Engineering (IJCSE) Vol.-4(4), Apr 2016, E-ISSN: 2347-2693.
[17] Tanuja A, Swetha Ramana D, “Processing and Analyzing Big data using Hadoop”, International Journal of Computer Sciences and Engineering (IJCSE) Vol.-4(4), PP(91-94) April 2016, E-ISSN: 2347-2693.

A. Saran Kumar, V. Vanitha Devi, "Optimization of Map Reduce Using Maximum Cost Performance Strategy," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.78-87, 2016.

Ad hoc Networks (MANET), is a planned toward oneself system of mobile nodes joined by remote connections. The topology of MANETs is dynamic in nature so to keep up stability a cluster based methodology will be used here. Load balancing is used to balance the load among the nodes present in a cluster with the help of cluster head. So, in this research paper, various techniques of selection of a cluster head are discussed.

WCA, CH, MANET,

[1] Chang Li, Yafeng Wang, Fan Huang and Dacheng Yang, Member, IEEE “A Novel Enhanced Weighted Clustering Algorithm for Mobile Networks” Wireless Theories and Technologies Lab (WT&T)
[2] Abdel Rahman H. Hussein, SufianYousef, and Omar Arabiyat “WCA: A Weighted Clustering Algorithm for Mobile Ad Hoc Networks”
[3]Sheetal Mehta, Priyanka Sharma and KetanKotecha “A Survey on Various Cluster Head Election Algorithm for MANET” IEEE Dec2011
[4]S. Rohini and K. Indumathi “Consistent Cluster Maintenance Using Probability Based Adaptive Invoked Weighted Clustering Algorithm in MANETs “ IEEE Feb 2011
[5]Wonchang Choi, Miae Woo “A Distributed Weighted Clustering Algorithm for Mobile Ad Hoc Networks“ IEEE
[6]AbdelmajidHajami, Kamal Qudidi and Mohammed Elkoutbi “An enhanced algorithm for MANET clustering based on multi hops and network density” IEEE
[7]Alan D. Amis, Ravi Prakash “Load-Balancing Clusters in Wireless Ad Hoc Networks”University of Texas at DallasRichardson, Texas 75083-0688
[8]Vincent Bricard-Vieu,NidalNasser and NoufissaMikou “A Mobility Prediction-based Weighted Clustering Algorithm Using Local Cluster- heads Election for QoS in MANETs“ IEEE 2012
[9] MR. NALLAMALA SRI HARI “A Novel routing attack in mobile adhoc networks” Nallamala Sri Hari et. al. / Indian Journal of Computer Science and Engineering
Vol. 1 No. 4 382-391

Pritika Goel, "An Improved Load Balancing Technique in Weighted Clustering Algorithm," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.80-92, 2016.

For accurate network performance analysis of routing protocols and access technology for VANET (Vehicular Ad hoc Network), realistic road map scenarios are required. This paper refers to analysis of network performance for VANET.[1,3] Simulation of routing protocols for VANET using realistic road map scenarios provides accurate results and can be useful for design and deployment of VANET applications. For VANET, speed of mobile nodes affects the routing path stability. It is important to use real world mobility models; so that the results obtained from the simulation of VANET routing protocols correctly reflect the real-world performance. In this paper, analyses the performance in terms of throughput, packet delivery ratio, packet loss and overhead. Routing protocols like OSPF are used for performance analysis. it supposed to handle high traffic rate as well as frequent interrupt in connection. It should consume less power and utilize bandwidth efficiently. At the same time, the VANET nodes must be simple, cheap, smaller in size and efficient enough to handle traffic. In VANET system Vehicles need to communicate with the other node, for that node continuously sending RTS-CTS signals. As if number of signals increases, there is high probability of collision. Collision damages data packets .so it requires retransmission, in this we use different routing protocols through which overhead can be reduced. Effect of flooding can be suppressed by eliminating or dropping certain nodes. Hence, reliability of communication can be improved.

Vehicular ad-hoc network (VANET), Ant colony optimization, Routing Protocols, Performance Parameter, and Simulators etc.

[1]Zhang.Y, Han.Y and Wu.P “Community Detection Using Maximum ConnectionProbability in Opportunistic Network”,IEEE, JAN 2013
[2]Zhu.H,Mianxiong Dong and Shan “ZOOM: Scaling the Mobility for Fast Opportunistic Forwarding in Vehicular Networks”,2013
[3]Ganesh S. Khekare andApeksha V. Sakhare “A Smart City Framework for Intelligent Traffic System Using VANET”,2013
[4]Huan Zhou and Liusheng Huang “heuristic-based ant colony optimization to enhance security in vanets”,2015
[5] Daraghmi.Y and WeI.C “Forwarding Methods in Data Dissemination and Routing Protocols for Vehicular Ad Hoc Networks”,2013
[6] Rodrigues.J and Farahmand.F “GeoSpray: A geographic routing protocol for vehicular delay-tolerant networks” in Institute of Telecommunications,(2011)
[7] Gerla.M and Sanadidi.M “Scalable Opportunistic VANET Content Routing With Encounter Information” in University of California,2013
[8] Sehgal.J and Arora.P“Delay Optimization in VANET Using AntColony Optimization and WI-MAX” in International Journal of Advanced Research in Electrical Engineering, AUGUST(2014)
[9] Xiao.M and Wu.J, “Community-Aware Opportunistic Routing in Mobile Social Networks” IEEE TRANSACTIONS ON COMPUTERS, JULY (2014)
[10] Guo.P and Zhou.H, “QoS Evaluation of VANET Routing Protocols”Computer and Information Technology, Gorges University, January 2013
[11] Shi.X and Wang.Y, “A Survey of Social-Based Routing in Delay Tolerant Networks: Positive and Negative Social Effects”, IEEE COMMUNICATIONS SURVEYS (2013)
[12]Moreira.W and Mendes.P, “Opportunistic Routing Based on Daily Routines” IT, University of Aveiro, july(2014)
[13] Guo.B and Kuo.Y, “A Community-Based Data Dissemination Scheme in Opportunistic Networks”,International Journal of Future Computer and Communication, October(2015)
[14]Balmahoon.R and Peplow.R, “Vehicular Ad-Hoc Networks: An Introduction to Privacy”(2013)
[15] Zohdy.M and Nafie.M “Maximum Throughput Opportunistic NetworkCoding in Two-Way Relay Networks” in Wireless Intelligent Networks Center (WINC),2015

Pooja Chaudhary and Kamal Kumar, "An Optimal Technique in VANET Routing Using Metaheuristic Approach," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.92-96, 2016.

Agriculture is one of the most important origins of human sustenance in whole world. Due to increasing population peoples require more productive capacity of the agriculture to fulfill the demands at present. In the past, we used natural methods to grow the productivity, such as using the cow dung as a fertilizer in the farms. That resulted in grow in the productivity according to demand of the population. But later we thought of growing more profits by getting more result. Therefore, there came a revolution named “Green Revolution”. After this period demand of dangerous poisons like herbicides has increased beyond limit. By getting so people got result in growing the productivity but people have ignored the side effects spread to the Eco system, which will raise suspense in our sustenance on this dangerous environment. So, in this research, we have tried to implement some techniques or methods to decrease the usage of herbicides by using them only in the geographical areas where weed available and destroying the crops. In this paper, we have used image processing using MATLAB to identify the weed place in an image which has been taken from the fields.

Weed Detection; Plant Reflectance; Visual Texture; Inter Row Weed Detection

[1] Ajinkya Paikekari Vrushali Ghule, Rani Meshram, V.B. Raskar,” Weed detection using image processing “ Volume-3, Issue-3, Page No (1-3), IRJET 2016.
[2] Janwale Asaram Pandurng, Santosh S. Lomte ” Digital Image Processing Applications in Agriculture”, Volume-5, Issue-3, Page No (1-3), IJARCSSE 2015.
[3] Amruta A. Aware, Kavita Joshi, “Crop and Weed Detection Based on Texture and Size Features and Automatic Spraying of Herbicides”, Volume-6, Issue-1, Page No (1-7), IJARCSSE 2016.
[4] Ashitosh K Shinde, Mrudang Y Shukla,” Crop Detection by Machine Vision for Weed Management”, Volume-7, Issue-3,pp (818-826) IJAET 2014.
[5] Mrs. Latha, A Poojith, B V Amarnath Reddy, G Vittal Kumar, “Image Processing in Agriculture”, Volume-2, Issue-6, Page No (1-4), IJIREEICE 2014.
[6] Su Hnin Hlaing, Aung Soe Khaing, “Weed And Crop Segmentation And Classification Using Area Thresholding”, Volume-3, Issue-3, Page No (1-8) IJRET2014.

Jatin Choudhary, Sutapa Nayak, "A Survey on Weed Detection Using Image Processing in Agriculture," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.98-100, 2016.

Now a days, the ‘Data Science Engineering’ becoming emerging trend to discover knowledge from web videos such as- YouTube videos, Yahoo Screen, Face Book videos etc. Petabytes of web video are being shared on social websites and are being used by the trillions of users all over the world. Recently, discovering outliers among large scale web videos have attracted attention of many web multimedia mining researchers. There are plenty of outliers abnormal video exists in different category of web videos. The task of classifying and prediction of web video as- normal and abnormal have gained vital research aspect in the area of Web Mining Research. Hence, we propose novel techniques to predict outliers from the web video dataset based on their metadata objects using data mining algorithms such as Random Tree (RT) and J48 Tree algorithms. The results of Decision Tree and J48 Tree classification models are analyzed and compared as a strategy in the process of knowledge discovery from web videos.

Outliers, Decision Tree, J48 Tree, Web Video Outliers, Prediction, Knowledge Discovery

[1] Siddu P. Algur, Prashant Bhat, "Abnormal Web Video Detection Using Density Based LOF Method", International Journal of Computer Sciences and Engineering, Volume-04, Issue-04, Page No (6-14), Apr -2016, E-ISSN: 2347-2693
[2] Chueh-Wei Chang, Ti-Hua Yang and Yu-Yu Tsao, “Abnormal Spatial Event Detection and Video Content Searching in a Multi-Camera Surveillance System”, MVA2009 IAPR Conference on Machine Vision Applications, May 20-22, 2009, Yokohama, JAPAN.
[3] Dataset for "Statistics and Social Network of YouTube Videos", http://netsg.cs.sfu.ca/youtubedata/.
[4] Fan Jiang, Ying Wu, Aggelos K. Katsaggelos, “Abnormal Event Detection from Surveillance Video by Dynamic Hierarchical Clustering”, Northwestern University, USA.
[5] Tushar Sandhan et al., “Unsupervised learning approach for abnormal event detection in surveillance video by revealing infrequent patterns”, IEEE 28th International Conference on Image and Vision Computing, 2013- New Zealand
[6] Thi-Lan Le and Thanh-Hai Tran, “Real-Time Abnormal Events Detection Combining Motion Templates and Object Localization”, Advances in Intelligent Systems and Computing 341, DOI 10.1007/978-3-319-14633-1_2, Springer International Publishing-2015, Switzerland.
[7] Yang Cong et al., “Abnormal Event Detection in Crowded Scenes using Sparse Representation”, Pattern Recognition, January 30, 2013
[8] Cewu Lu et al., “Abnormal Event Detection at 150 FPS in MATLAB”, The Chinese University of Hong Kong.
[9] Yang Cong et al., “Sparse Reconstruction Cost for Abnormal Event Detection”.
[10] Bin Zhao et al., “Online Detection of Unusual Events in Videos via Dynamic Sparse Coding”, 2011.
[11] Mahmoudi Sidi Ahmed et al., “Detection of Abnormal Motions in Video”, Chania ICMI-MIAUCE’08 workshop, Crete, Greece, 2008.
[12] Du Tran et al., “Video Event Detection: From Subvolume Localization to Spatiotemporal Path Search”, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 36, 2014.
[13] Siddu P. Algur, Prashant Bhat, “Metadata Based Classification and Analysis of Large Scale Web Videos”, International Journal of Emerging Trends and Technologies in Computer Science, May-June 2015.
[14] Siddu P. Algur, Prashant Bhat, Suraj Jain, “The Role of Metadata in Web Video Mining: Issues and Perspectives”, International Journal of Engineering Sciences & Research Technology, February-2015.
[15] Chirag Shah, Charles File, “InfoExtractor – A Tool for Social Media Data Mining”, JITP 2011.

Siddu P. Algur, Prashant Bhat, "Abnormal Web Video Prediction Using RT and J48 Classification Techniques," International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.101-107, 2016.