ISSN ONLINE(2320-9801) PRINT (2320-9798)

Yakışıklı erkek tatil için bir beldeye gidiyor burada kendisine türk Porno güzel bir seksi kadın ayarlıyor Onunla beraber otel odasına gidiyorlar Otel odasına rokettube giren kadın ilk önce erkekle sohbet ederek işi yavaş halletmeye çalışıyor sex hikayeleri Kocası fabrikatör olan sarışın Rus hatun şehirden biraz uzak olan bir türk porno kasabaya son derece lüks bir villa yaptırıp yerleşiyor Kocasını işe gönderip mobil porno istediği erkeği eve atan Rus hatun son olarak fotoğraf çekimi yapmak üzere türk porno evine gelen genç adamı bahçede azdırıyor Güzel hatun zengin bir iş adamının porno indir dostu olmayı kabul ediyor Adamın kendisine aldığı yazlık evde sikiş kalmaya başlayan hatun bir süre sonra kendi erkek arkadaşlarını bir bir çağırarak onlarla porno izle yapıyor Son olarak çağırdığı arkadaşını kapıda üzerinde beyaz gömleğin açık sikiş düğmelerinden fışkıran dik memeleri ile karşılayıp içeri girer girmez sikiş dudaklarına yapışarak sevişiyor Evin her köşesine yayılan inleme seslerinin eşliğinde yorgun düşerek orgazm oluyor

An Advanced Moving Object Detection Algorithm for Automatic Traffic Monitoring In Real-World Limited Bandwidth Networks

K.G.S. Venkatesan1, Dr. V. Khanaa2, M.Sriram3, Lekha Sri4
  1. Associate Professor, Dept. of C.S.E., Bharath University, Chennai. Tamil Nadu, India.
  2. Dean of Information Technology, Bharath University, Chennai, Tamil Nadu, India.
  3. Assistant Professor, Dept. of C.S.E., Bharath University, Chennai, Tamil Nadu, India.
  4. Dept. of C.S.E., Bharath University, Chennai, Tamil Nadu, India.
Related article at Pubmed, Scholar Google

Visit for more related articles at International Journal of Innovative Research in Computer and Communication Engineering

Abstract

Machine-controlled motion detection technology is Associate in nursing integral element of intelligent transportation systems, and is especially essential for management of traffic and maintenance of traffic police investigation systems. Traffic police investigation systems mistreatment video communication over real-world networks with restricted information measure typically encounter difficulties attributable to network congestion and/or unstable information measure. This is often particularly problematic in wireless video communication. This has necessitated the event of a rate management theme that alters the bit-rate to match the procurable network information measure, thereby manufacturing variable bit-rate video streams. However, complete and correct detection of moving objects beneath variable bit-rate video streams could be a terribly tough task. During this paper, we tend to propose Associate in nursing approach for motion detection that utilizes Associate in nursing analysis - primarily based radial basis perform network as its principal element. This approach is applicable not solely in high bit-rate video streams, however in low bit- rate video streams, as well. The planned approach consists of a varied background generation stage and a moving object detection stage. Throughout the assorted background generation stage, the lower-dimensional Eigen-patterns and also the adjustive background model are established in variable bit -rate video streams by mistreatment the planned approach so as to accommodate the properties of variable bit-rate video streams. Throughout the moving object detection stage, moving objects are extracted via the planned approach in each low bit -rate and high bit- rate video streams; detection results are then generated through the output worth of the planned approach. The detection results created through our approach indicate it to be extremely effective in variable bit-rate video streams over real-world restricted information measure networks. Additionally, the planned methodology will be simply achieved for period of time application. Quantitative and qualitative evaluations demonstrate that it offers blessings over different state- of-the -art ways. For example, and accuracy rates created via the planned approach were up to eighty six.38% and 89.88% beyond those created via different compared ways.

Keywords

Intelligent transportation systems, moving object detection, neural network, principal element analysis, variable bit-rate.

INTRODUCTION

IN RECENT years, intelligent transportation systems (ITS) became a crucial element of traffic management for the alleviation of traffic jam, advancement of transportation safety, and improvement of traffic flow. These systems integrate the utilization of advanced technologies such as intelligent computing, network communications, visual-based analysis, economical device physical science, and so on. One crucial issue of ITS that supports these tasks is that the ability to extract data concerning moving objects in traffic police investigation systems. Hence, automatic motion detection is a crucial component of traffic management [1]. It facilitates the gathering of elaborate data concerning traffic conditions, and it's additionally the primary essential method within the development of traffic police investigation systems which give object classification and pursuit, behavior recognition, activity analysis, and so on. Moreover, the applications of motion detection in traffic police investigation systems are several, and span from description and analysis of traffic things to pedestrian collision prediction and driver help [2].
According to previous analysis, existing approaches for motion detection in traffic police investigation systems will be divided loosely into the 3 categories: temporal distinction, optical flow, and background subtraction. Though temporal distinction approaches could also be adjustive to environmental changes, their use typically leads to incomplete detection of the shapes of moving objects. This is often very true once objects that are unmoving or feature restricted quality are gift. Optical flow approaches are utilized to sight moving objects by mistreatment the papered motion within the image plane with correct approximation [3]. Sadly, these ways inevitably end in the generation of noise and excess procedure burden. With the exception of the above-named classes, background subtraction approaches are wide used for the detection of moving objects attributable to their ability to accomplish correct detection of moving objects whereas exhibiting solely moderate procedure complexness. This is often achieved by scrutiny the variations between element options of this image and people of the reference background model of the previous image [4].
In this paper, we tend to propose a brand new theme that uses the Principal element Analysis-based Radial Basis perform Network, so as to sight moving objects in variable bit-rate video streams over real-world net-works with restricted information measure. The planned methodology is capable of providing the foremost complete and correct detection results of moving objects in each low bit-rate and high bit-rate video streams as compared with previous state- of- the- art back-ground subtraction ways. The key options of our planned methodology are organized into 2 planned stages as follows:
1) The initial planned stage involves the generation of assorted backgrounds. To accomplish this, the lower dimensional Eigen-patterns and a background model capable of adapting to completely different bit-rate video streams are made through the utilization of the PCA-based RBF network [5].
2) Moving objects are detected within the second stage. To do so, every incoming element is mapped to the adjustive background model generated throughout the previous stage. By doing therefore, motion detection is accomplished fully and accurately in variable bit-rate video streams.

BACKGROUND

In the traffic recording platform, the video supply captured by traffic cameras is transmitted by wireless communication to a traffic analysis station when the video supply is compressed as bit- rate video streams within the video encoder. So as to attain the important -time video communication and avoid the network congestion within the restricted information measure of real-world networks, variable-bit-rate encryption for video streaming is typically created by using the video rate management technique [10].
Because of this, the video decoder within the traffic analysis station typically produces varied video qualities supported the cur-rent bit- rate of video streams. However, effective detection of moving objects in such environments will be problematic for many previous progressive background subtraction ways [9]. These fluctuations vary from low bitrate to high bitrate over a brief span of your time. The background models generated by previous background subtraction ways regard the stable background signals of the tree as background once in operation in low bitrate video streams. How-ever, once spare network information measure exists, the speed control theme will increase the bit rate of the streams to match the obtainable network information measure. Thus, high bitrate video streams are the result. It demonstrates that the previous back-ground subtraction ways simply mistake the highquality background signals as moving objects once they operate in high bitrate video streams.
image
The rate management theme compensates by manufacturing low bitrate video streams. This will have a damaging impact on motion detection, as is illustrated in that shows that the signal of a moving vehicle within the low bitrate video stream may well be misinterpreted as background by previous background subtraction strategies.

PAPERED PCA – BASED RBF NETWORK APPROACH

In this section, we tend to propose fully unique moving object segmentation approach that utilizes the PCA-based RBF network so as to completely and accurately sight moving objects in variable bit-rate video streams over real- world networks with restricted information measure. the fundamental part of the theme uses a PCA technique associate degreed a three- layer RBF network with an input layer, a hidden layer, associate degreed an output layer. The schematic of the network is bestowed [11].
Our PCA-based RBF network approach is performed in 2 stages: a numerous background generation stage and a moving object detection stage. Throughout the papered numerous background generation (VBG) stage, the properties of variable bit-rate video streams square measure accommodated effectively by exploitation the PCA-based RBF network through 2 papered method: a discriminative options extraction method associate degreed an adaptational background modeling process. Every incoming frame is split into equal-sized blocks, whereat the lower-dimensional Eigen-patterns square measure generated by the discriminative options extraction method by exploitation the PCA technique inside a lower-dimensional feature house for expressing the properties of variable bit- rate video streams. The adaptational background model method produces associate degree adaptational background model by utilizing the RBF network so as to facilitate the properties of variable bit-rate video streams. Afterward this data is transmitted as impulse to the hidden layer neurons for creation of the RBF structure [16].
After the papered VBG stage has been achieved, the moving object detection (MOD) stage will begin. Complete and correct motion detection is accomplished by utilizing the PCA -based RBF network via our papered two-procedure approach, that consists of a feature choice procedure associate degreed an object extraction procedure. The papered feature choice procedure eliminates unnecessary blocks from equal- sized blocks of incoming in close that the block options square measure classified as happiness to the background and afterward eliminated through use of the PCA technique in lower-dimensional feature house. currently that we've eliminated the blocks of background through the papered feature choice procedure, we will be able to sight moving objects inside solely those blocks that are thought to be happiness to the moving objects category. This can be accomplished by exploitation the article extraction procedure via the papered RBF network [18].
The motion detection flow diagram of papered PCA-based RBF network and illustrates the assorted back-ground generation stage and therefore the moving object detection stage.

A. Numerous Background Generation :

Discriminative options extraction process, So as to support a good vary of digital video process needs, the papered PCA-based RBF network is operated in color house. Thus, element consists of light ( ), blue-difference intensity ( ), and red-difference intensity ( ), the mixture of that determines the intensity of every element for every incoming video frame.
To ensure adherence to the properties of variable bit-rate video streams throughout the discriminative options extraction method, the lower-dimensional Eigen-patterns area unit created from the statistic of every incoming frame by the optimum paperion vectors via the PCA technique. The optimum paperion vectors area unit dependent upon the most determinant of the overall scatter matrix of the papered samples [20].
B. Background Model Process :
Motion detection in variable bit-rate video streams is incredibly troublesome to accomplish. It's vital to create certain that everyone the properties of variable bit-rate video streams area unit memorized through the adjustive back-ground model within the hidden layer of the RBF network. So as to get the adjustive background model via the RBF net-work, the euclidian distance is employed to calculate the gap between the intensity values of every incoming component and people of the corresponding background candidates to the transposition of the worth. Note that the block size are often set to sixteen, through empirical observation. In accordance with the PCA technique, the optimum paperion vectors area unit obtained [22].
C. Moving Object Detection
1) Feature choice Procedure: once the structure of the PCA-based RBF network had been established within the papered VBG stage, the incoming component is transferred to the input of the PCA-based RBF network within the color area throughout the papered MOD stage. Moving objects detected by exploitation ancient ways throughout lowto- high bit-rate variation sometimes exhibit serious whole generation because of misjudgment of most background signals as happiness to moving objects in high bit-rate environments. To beat this drawback, every incoming frame is split into size blocks, where-upon we tend to compare the similarity of every block by exploitation the PCA technique for elimination of extra blocks, that area unit indicated as happiness to the background category. 2) Object Extraction Procedure: once the feature choice procedure has been completed, all blocks that are deter-mined as happiness to the background category area unit eliminated and therefore the object extraction procedure will by selection operate in barely those blocks that area unit renowned to contain moving objects [23].

EXPERIMENTAL RESULTS

This explores the qualitative and quantitative evaluations accustomed comprehensively compare our PCA-based RBF network methodology to alternative progressive background sub-traction ways, as well as the SDE methodology, the MSDE methodology, the GMM methodology, the SSD methodology, and therefore the MTD methodology. This comparison is achieved through synthesis of video sequences of variable bit- rate by the utilization of Joint Model (JM) H.264/AVC reference computer code equipped by the JVT committee (Joint Video Team of ISO/IEC MPEG and ITUT VCEG). Finally, the performance analysis of the papered methodology is rumored so as to demonstrate the procedure speed for time period applications [25].

A. Qualitative analysis:

Subsequently, we tend to gift the results of a qualitative visual scrutiny so as to assess and compare the subjective effects of the detected binary objects mask of the various ways in relevance differing bit-rate video sequences.
As incontestible through qualitative analysis, the utilization of the papered PCA-based RBF network methodology offers vital enhancements in detection results over those created by the opposite compared ways. These enhancements occurred systematically in each low and high bit-rate video streams over Real world networks with restricted information measure [28].
As are often discovered in, the “CAM” sequence options vital fluctuations in quality because of adaptation of the bitrate by the speed management theme to match the accessible net-work information measure [29]. Detection results for this case were generated through use of the SDE, MSDE, GMM, SSD, MTD, and PCA-based RBF network ways. The background models of the SDE, MSDE, GMM, SSD, and MTD ways regard these fluctuant signals as background in high bit-rate video streams once manufacturing detection results for frames 205 to 668. The SDE and MSDE ways use the filter techniques to pro-duce incomplete background models in high bit-rate environments. In relevance the GMM methodology, the utilization of mixture Gaussians techniques overly collects fluctuant signals in high bit-rate video streams [35]. Moreover, the SSD and MTD ways manufacture straightforward background models via the temporal average because the main criteria and maintain many reference frames as back-ground models, respectively. Each of those ways manufacture inadequate background models in high bit-rate video streams [36]. However, once network congestion is detected, the speed management theme decreases the bit-rate to match the accessible network information measure. Thus, low bit-rate video streams area unit created, as illustrated by frames 1383 to 1394. Once conferred with low bit-rate video streams, the SDE, MSDE, GMM, SSD, and MTD ways all mistake the signals of the moving vehicle as back-ground signals. This ends up in a failure to find the moving vehicle. In distinction, our PCAbased RBF network methodology accomplishes detection that's considerably a lot of complete and correct for all bitrates, as are often seen in frames 205 to 1394.
As are often discovered in, the “CP” sequence options a shift in video quality from low to high because of a rise of the bit-rate by the speed management theme to match the free network information measure. Detection results for this case were generated through use of the SDE, MSDE, GMM, SSD, MTD, and PCA-based RBF network ways [39]. The stable background signals area unit thought to be such by the background models of the SDE, MSDE, GMM, SSD, and MTD ways in low bit-rate video streams once detection is conducted in frames fifty eight to eighty three. The data in low bit-rate environments is inadequate to ascertain complete background models for the SDE and MSDE ways that use filter techniques, also as for the GMM methodology that uses a mix Gaussians techniques. However, since spare network information measure is gift, the speed management theme should adapt the bit-rate to utilize the accessible network information measure [41]. This ends up in the assembly of high bit-rate video streams from frame 1118 to border 1133. Once conferred with high bit-rate video streams, the SDE, MSDE, and GMM ways mistake most high - quality fluctuant background signals as moving objects, so generating excessive noise. In distinction with the SDE, MSDE, and GMM ways, the SSD and MTD ways could attain passable detection results as shown in frame 1118 to border 1133. However, the generation of either noise or ghost trails cannot be avoided in these ways, as are often seen in frame 1118 and frame 1133. Neither methodology will to deal with such mass fluctuant signals in high bit-rate video streams as a result of the SSD methodology uses the temporal average because the main criteria, and therefore the MTD methodology holds many reference frames. In distinction, the papered PCA-based RBF network methodology attains superior detection results, demonstrating its effectualness in each low bit-rate and high bit-rate video streams, as shown in frame fifty eight to border 1133 [40].
As are often discovered in, the “RD” sequence options vital and frequent variation in video quality because of shifting of the bit-rate by the speed management theme to match the gettable network information measure [45]. Detection results for this case were generated through use of the SDE, MSDE, GMM, SSD, MTD, and PCA -based RBF network ways [48]. Due to the frequent changes in video stream bit-rate, the SDE, MSDE, GMM, SSD, and MTD ways cannot manufacture background models applicable for the detection moving objects in frames ninety five to 474. The detection results mirrored in frames one hundred and one to 474 indicate that the SDE, MSDE, and GMM ways generate unsatisfactory results with serious noise. Moreover, the detection results mirrored in frames 237 to 474 indicate that the SSD and MTD ways generate incomplete results once encountering frequent bit-rate variation in video streams [44].
Ultimately, our PCA-based RBF network methodology is in a position to utterly and accurately find moving objects in each low bit- rate and high bit-rate video streams. This can be because of its ability to accommodate bit-rate variation for all take a look at frames of the “RD” sequence [52].

B. Performance Results :

The process speeds of the papered PCA-based RBF network technique , that was enforced by victimization C artificial language on associate degree Intel Core i5 three.10 gigacycle per second processor with four GB of RAM, running a Windows XP software system. we'veverified process speeds that area unit over thirty two FPS for every CIF check sequence. A lot of significantly, this means that the papered PCA-based RBF network technique will satisfy the process needs of period applications [51].

CONCLUSION AND FUTURE WORK

In this paper, we tend to gift a brand new approach for the detection of moving objects by using a PCA-based RBF network for realistic traffic scenes in variable bit-rate video streams over real-world networks with restricted information measure. The pro-posed approach is split into 2 essential stages: a varied background generation stage and a moving object detection stage. throughout the assorted background generation stage, the lower- dimensional Eigenpatterns and also the reconciling background model area unit made via the PCA-based RBF network approach by victimization the papered 2 processes to satisfy the necessities of variable bit-rate video streams. Moreover, the structure of the PCA -based RBF network is formed during this stage. A two-process procedure supported the PCAbased RBF network approach is enforced throughout the papered moving object fiction stage to utterly and accurately detect moving objects in each low and high bit-rate video streams. Quantitative evaluations victimization the results of simulation experiments in variable bit-rate video streams clearly indicate that the PCA-based RBF network approach accomplishes the foremost complete and correct motion detection when put next with alternative progressive ways. These evaluations conjointly show that our approach attains a well higher degree of detection accuracy in each low bitrate and high bit- rate video streams. Moreover, our study demonstrates that the papered PCA-based RBF network approach will satisfy the process needs for period video applications in ITS. To the simplest of our information, we tend to area unit the primary analysis cluster to with success propose a motion detection approach for traffic police investigation systems with variable bit-rate video streams over real-world networks with restricted information measure.

ACKNOWLEDGEMENT

The author would like to thank the Vice Chancellor, Dean-Engineering, Director, Secretary, Correspondent, HOD of Computer Science & Engineering, Dr. K.P. Kaliyamurthie, Bharath University, Chennai for their motivation and constant encouragement. The author would like to specially thank Dr. A. Kumaravel for his guidance and for critical review of this manuscript and for his valuable input and fruitful discussions in completing the work and the Faculty Members of Department of Computer Science &Engineering. Also, he takes privilege in extending gratitude to his parents and family members who rendered their support throughout this Research work.

References

1. L. L. Presti, S. Sclaroff, and M. L. Cascia, “Path modeling and retrieval in distributed video surveillance databases,” IEEE Trans. Multimedia, Vol. 14, No. 2, PP. 346–360, Apr. 2012.

2. M. Saini, X. Wang, P. K. Atrey, and M. Kankanhalli, “Adaptive work-load equalization in multi-camera surveillance systems,” IEEE Trans.Multimedia, Vol. 14, No. 3, PP. 555–562, Jun. 2012.

3. S. C. Huang and F. C. Cheng, “Motion detection with pyramid structure of background model for intelligent surveillance systems,” ELSEVIEREng. Appl. Artif. Intell., Vol. 25, No. 7, PP. 1338–1348, Oct. 2012.

4. N. Buch, S. A. Velastin, and J. Orwell, “A review of computer vision techniques for the analysis of urban traffic,” IEEE Trans. Intell. Transp.Syst., Vol. 12, No. 3, PP. 920–939, Sep. 2011.

5. M.-L. Shyu, Z. Xie, M. Chen, and S.-C. Chen, “Video semantic event/concept detection using a subspace-based multimedia data mining framework,” IEEE Trans. Multimedia, Vol. 10, No. 2, PP. 252–259, Feb. 2008.

6. N. Li, J. J. Jain, and C. Busso, “Modeling of driver behavior in real world scenarios using multiple noninvasive sensors,” IEEE Trans. Multimedia, vol. 15, no. 5, pp. 1213–1225, Aug. 2013.

7. S. C. Huang, F. C. Cheng, and Y. S. Chiu, “Efficient contrast enhance-ment using adaptive gamma correction with weighting distribution,” IEEE Trans. Image Process., vol. 22, no. 3, pp. 1032–1041, Mar. 2013.

8. K.G.S. Venkatesan. Dr. V. Khanaa, Dr. A. Chandrasekar, “Autonomous System( AS ) for mesh network by using packet transmission & failure detection”, Inter. Journal of Innovative Research in computer & comm. Engineering, Vol . 2 , I s su e 1 2 , PP. 7 2 8 9 – 72 9 6 , Dec emb er - 2014.

9. B. Sundarraj, K.G.S. Venkatesan, M. Sriram, Vimal Chand, “An Iaas cloud system with Federation Threshold”, ”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 3 , P P. 2 5 9 3 – 25 9 8 , Ma r ch - 2015.

10. K.G.S. Venkatesan and M. Elamurugaselvam, “Design based object oriented Metrics to measure coupling & cohesion”, International journal of Advanced & Innovative Research, Vol. 2, Issue 5, PP. 778 – 785, 2013.

11. S. Sathish Raja and K.G.S. Venkatesan, “Email spam zombies scrutinizer in email sending network Infrastructures”, International journal of Scientific & Engineering Research, Vol. 4, Issue 4, PP. 366 – 373, April 2013.

12. K.G.S. Venkatesan, “Comparison of CDMA & GSM Mobile Technology”, Middle-East Journal of Scientific Research, 13 (12), PP. 1590 – 1594, 2013.

13. P. Indira Priya, K.G.S.Venkatesan, “Finding the K-Edge connectivity in MANET using DLTRT, International Journal of Applied Engineering Research, Vol. 9, Issue 22, PP. 5898 – 5904, 2014.

14. K.G.S. Venkatesan and M. Elamurugaselvam, “Using the conceptual cohesion of classes for fault prediction in object-oriented system”, International journal of Advanced & Innovative Research, Vol. 2, Issue 4, PP. 75 – 80, April 2013.

15. Ms. J.Praveena, K.G.S. Venkatesan, “Advanced Auto Adaptive edge-detection algorithm for flame monitoring & fire image processing”, International Journal of Applied Engineering Research, Vol. 9, Issue 22, PP. 5797 – 5802, 2014.

16. K.G.S. Venkatesan. Dr. V. Khanna, “Inclusion of flow management for Automatic & dynamic route discovery system by ARS”, International Journal of Advanced Research in computer science & software Engg., Vol.2, Issue 12, PP. 1 – 9, December – 2012.

17. Needhu. C, K.G.S. Venkatesan, “A System for Retrieving Information directly from online social network user Link ”, International Journal of Applied Engineering Research, Vol. 9, Issue 22, PP. 6023 – 6028, 2014.

18. K.G.S. Venkatesan, R. Resmi, R. Remya, “Anonymizimg Geographic routing for preserving location privacy using unlinkability and unobservability”, International Journal of Advanced Research in computer science & software Engg., Vol. 4, Issue 3, PP. 523 – 528, March – 2014.

19. Selvakumari. P, K.G.S. Venkatesan, “Vehicular communication using Fvmr Technique”, International Journal of Applied Engineering Research, Vol. 9, Issue 22, PP. 6133 – 6139, 2014.

20. K.G.S. Venkatesan, G. Julin Leeya, G. Dayalin Leena, “Efficient colour image watermarking using factor Entrenching method”, International Journal of Advanced Research in computer science & software Engg., Vol. 4, Issue 3, PP. 529 – 538, March – 2014.

21. Dr. K.P. Kaliyamerthie, K.G.S. Venkatesan, S. Sriram, N. Vijay, Richard Solomon, “Neighborhood based framework, Active Learning”, ”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 3 , P P. 2535 – 252 , Ma r c h - 2015.

22. K.G.S. Venkatesan. Kausik Mondal, Abhishek Kumar, “Enhancement of social network security by Third party application”, International Journal of Advanced Research in computer science & software Engg., Vol. 3, Issue 3, PP. 230 – 237, March – 2013.

23. R. Karthikeyan, K.G.S. Venkatesan, M.L. Ambikha, S. Asha, “Assist Autism spectrum, Data Acquisition method using Spatio-temporal Model”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 2 , PP. 871 – 877 , Fe bru a ry – 2015

24. K.G.S. Venkatesan, Dr. Kathir. Viswalingam, N.G. Vijitha, “ Associate Adaptable Transactions Information store in the cloud using Distributed storage and meta data manager”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue3 , PP . 1548 – 1555 , Ma r ch - 2015.

25. K.G.S. Venkatesan, Dr. V. Khanaa, Jay Prakash Thakur, Banbari Kumar, “Mining User profile Exploitation cluster from computer program Logs”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 3 , PP. 1557 – 1561 , March - 2015.

26. K.G.S. Venkatesan. Kishore, Mukthar Hussain, “SAT : A Security Architecture in wireless mesh networks”, International Journal of Advanced Research in computer science & software Engineering, Vol. 3, Issue 3, PP. 325 – 331, April – 2013.

27. K.G.S. Venkatesan, “Planning in FARS by dynamic multipath reconfiguration system failure recovery in wireless mesh network”, International Journal of Innovative Research in computer & comm. Engineering, Vol . 2 , I s su e 8 , Au gu s t - 2014.

28. B. Sundarraj, K.G.S. Venkatesan, Vimal Chand, “A Stochastic Model to Investigate Data center performance & QOS in IaaS cloud computing systems”, ”, International Journal of Innovative Research in computer & communication Engineering, Vol .3 , Issue 3 , PP. 2 5 6 0 – 2565 , Ma r ch - 2015.

29. K.G.S. Venkatesan, AR. Arunachalam, S. Vijayalakshmi, V. Vinotha, “Implementation of optimized cost, Load & service monitoring for grid computing”, International Journal of Innovative Research in computer & comm. Engineering, Vol . 3 , Issue 2 , PP. 864 – 870 , Fe bru a ry - 2015.

30. K.G.S. Venkatesan, B. Sundar Raj, V. Keerthiga, M. Aishwarya, “Transmission of data between sensors by devolved Recognition”, International Journal of Innovative Research in computer & comm. Engineering, Vol . 3 , Issue 2 , PP. 8 7 8 – 886 , February - 2015.

31. Anish Kumar Anbakarasan, Ilampiria Nagarajan, K.G.S. Venkatesan, “Moral Hacking : A way to boost data security by using vulnerability scanning Tools”, ”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue 3 , P P. 2605 – 2613 , March - 2015.

32. K.G.S. Venkatesan, N.G. Vijitha, R. Karthikeyan, “Secure data transaction in Multi cloud using Two-phase validation”, International Journal of Innovative Research in computer & comm. Engineering, Vo l . 3 , I s su e 2 , P P. 8 4 5 – 8 5 3 , Feb ru a ry - 2015.

33. K. Dhanalakshmi, A. Anitha, G. Michael, K.G.S. Venkatesan, “Recommendation system based on clustering & collaborative Filtering”, International Journal of Innovative Research in computer & communication Engineering, Vo l . 3 , Issue 3 , P P. 2482 – 2488 , March - 2015.

34. K.G.S. Venkatesan, “Automatic Detection and control of Malware spread in decentralized peer to peer network”, International Journal of Innovative Research in computer & comm. Engineering, Vol . 1 , I s su e 7 , PP. 15157 – 15159 , Se pt emb er - 2013.

35. Satthish Raja, S K.G.S. Venkatesan, “Electronic Mail spam zombies purify in email connection”, International Journal of Advanced Research in Computer Science Engineering & Information Technology, Vol. 1, Issue 1, PP. 26 – 36, June – 2013.

36. K.G.S. Venkatesan. Dr. V. Khanna, S.B. Amarnath Reddy, “Providing Security for social Networks from Inference Attack”, International Journal of Computer Science Engineering & Scientific Technology, March – 2015.

37. A.R. Arunachalam, K.G.S. Venkatesan, Abdul Basith.K.V., M. Sriram, “Traffic Identification Method Engine : An open platform for Traffic classification “,”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 3 , PP. 2 4 7 5 – 24 8 1 , Ma r ch - 2015.

38. K.G.S. Venkatesan, Dr. Kathir. Viswalingam, N.G. Vijitha, “ Associate Adaptable Transactions Information store in the cloud using Distributed storage and meta data manager”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , I s su e 3 , PP . 1 5 4 8 – 1 55 5 , Ma r ch - 2015.

39. K.G.S. Venkatesan, Dr. V. Khanna, Jay Prakash Thakur, Banbari Kumar, “Mining User profile Exploitation cluster from computer program Logs”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue3 , PP. 1 5 5 7 – 15 6 1 , Ma r ch - 2015.

40. Ms.J.Praveena, K.G.S.Venkatesan, “Advanced Auto Adaptive edge-detection algorithm for flame monitoring & fire image processing”, International Journal of Applied Engineering Research, Vol. 9, Issue 22, PP. 5797 – 5802, 2014.

41. K.G.S.Venkatesan, “Planning in FARS by dynamic multipath reconfiguration system failure recovery in wireless mesh network”, International Journal of Innovative Research in computer & comm. Engineering, Vol . 2 , Issue 8 , Au gu st -2014.

42. K.G.S. Venkatesan, Dr. V. Khanna, S.B. Amarnath Reddy, “Network Monitoring using Test Packet Generation”, IJSCONLINE, PP. 1-12, March – 2015.

43. K.G.S. Venkatesan, Dr. V. Khanaa, Dr. A. Chandrasekar, “Reduced path, Sink failures in Autonomous Network Reconfiguration System ( ANRS ) Techniques”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue 3 , PP. 2 5 6 6 – 25 7 1 , March - 2015.

44. S. Srigowthem, K.G.S. Venkatesan, Sourav Kumar Nag, Suraj Raj, “Human Effects to enhance clustering Techniques that Assists user in grouping the Friends”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue 3 , PP. 1 6 2 8 – 1635 , March - 2015.

45. K.G.S. Venkatesan, U. Muthu Selvam, J. Samualprabhu, “Multi-Hop overlay Transport for high throughput transfers in the Internet”, International Journal of Innovative Research in computer & communication Engineering, Vo l . 3 , I s su e 3 , P P. 2 5 6 6 – 25 7 1 , Ma r c h - 2015.

46. Akyildiz, I.,Wang,X., and Wang,W.,2005, “Wireless mesh networks: A survey,” Computer Networks., Vol. 47, No.4, PP. 445–487.

47. Kyu-Han Kim, Member, IEEE, and Kang G. Shin,Fellow, IEEE, ACM, 2011, “Self-Reconfigurable Wireless Mesh Networks” IEEE/ACMTRANSACTIONS ON NETWORKING, Vol. 19, No.2.

48. Kodialam,M., and Nandagopal,T.,2005 “Characterizing the capacity region in multi-radio multi-channel wireless mesh networks,” in Proc. ACM MobiCom, Cologne,Germany, PP. 73–87 .

49. K.G.S. Venkatesan and M. Elamurugaselvam, “Design based object oriented Metrics to measure coupling & cohesion”, International journal of Advanced & Innovative Research, Vol. 2, Issue 5, PP. 778 – 785, 2013.

50. K.G.S. Venkatesan, Dr. V. Khanaa, Dr. A. Chandrasekar, “Reduced path, Sink failures in Autonomous Network Reconfiguration System ( ANRS ) Techniques”, International Journal of Innovative Research in computer & communication Engineering, Vol . 3 , Issue 3 , PP. 2566 – 25 7 1 , Ma r ch - 2015.

51. K.G.S. Venkatesan. Dr. V. Khanaa, Dr. A. Chandrasekar, “Autonomous System( AS ) for mesh network by using packet transmission & failure detection”, Inter. Journal of Innovative Research in computer & comm. Engineering, Vol . 2 , Issue 12 , PP. 7289 – 7296 , Dec emb er - 2014.

52. Brzezinski,M., Zussman,G., and Modiano,E., 2006“Enabling distributed throughput maximization in wireless mesh networks: A partitioning approach,” in Proc. ACM MobiCom, Los Angeles, CA, PP.26–37.