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Data visualization is used to transforms data to image. The data are changed into image format for more understandability of data. Giving a huge dataset for the essential task of visualization is to visualize the data to tell compelling stories by selecting, filtering and transforming the data. It also used to pick the right visualization type such as bar charts or line charts. The ultimate task is to provide more visually effective data representation. A revolutionized system in the field faces the following three main challenges and they are Visualization verification, which it used to determine whether the visualization for a given dataset is interesting, from the viewpoint of human understanding. Visualization search space checks whether the resultant visualization is a “boring" dataset, then it may become interesting after an arbitrary combination of operations such as selections, joins, and aggregations, among others or not. On-time responses is does not deplete the user’s patience. The proposed system solves the above challenge by implementing multidimensional scaling or the popular t-SNE algorithm. The t-SNE algorithm is based on non-convex optimization, has become the standard for visualization in a wide range of applications. This work gives a formal framework for the problem of data visualization – finding a 2- dimensional embedding of cluster data that correctly separates individual clusters to make them visually identifiable. Ground-truth cluster is checks the conditions assumed in earlier analyses of clustering while underlying the data. To achieve the goal of data visualization existing system used LambdaMART algorithm to learn rank technique. In proposed system the t-SNE algorithm takes place the role to create more effective visualization with the help of clustering method to group or ungrouped huge data.

Machine learning, Computer Science, Artificial, Architecture and Syatems

[1]. Ms. Lavanya Patil , Dr. Jagdeesh D. Pujari , “Data Visualization: A Handy Plug-In” , International Journal of Engineering Research in Computer Science and Engineering, vol.3,issue.5, pid.351.
[2]. Takuya Kaihatsu ; Shinya Watanabe “A proposal of a low-dimensional approach based on DIRECT method and t-SNE for single optimization problems with many variables” , in the proceedings of the 2017 International Conference on Soft Computing and Intelligent Systems.
[3]. Oluigbo Ikenna V., Nwokonkwo Obi C., Ezeh Gloria N., Ndukwe Ngoziobasi G, “Revolutionizing the Healthcare Industry in Nigeria: The Role of Internet of Things and Big Data Analytics” , International Journal of Scientific Research in Computer Sciences and Engineering , Vol.5 , Issue.6 , pp.1-12, Dec-2017 .
[4]. L.J.P. van der Maaten, G.E. Hinton, “Visualizing High-Dimensional Data Using t-SNE. Journal of Machine Learning Research 9” (Nov):2579-2605, 2008.
[5]. Chao-Kuei Hung, “Making machine-learning tools accessible to language teachers and other non-techies: T-SNE-lab and rocanr as first examples”, in the proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology.
[6]. D.A. Keim, “Information visualization and visual data mining”, IEEE Transactions on Visualization and Computer Graphics, Vol. 8, Issue. 1 , Mar 2002.
[7]. Laurens van der Maaten, “Accelerating t-SNE using Tree-Based Algorithms”, Journal of Machine Learning Research, 2014 Vol.1, Issue.21.
[8]. “Semi-supervised Learning to Rank with Preference Regularization”, Martin Szummer; Emine Yilmaz.
[9]. A.G.Aruna, Dr.M.Sangeetha, C.Sathya, “Impact of Deep Learning in Big Data Analytics”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2, Issue.3.
[10].Subham Datta, Gautam, Tapas Saha, “Development of a Rule Based Classification System to Identify a Suitable Classifier for a Particular Dataset”, International Journal of Scientific Research in Computer Science, Engineering and Information Technology, vol. 2, Issue.5.
[11]. Xianjun Shen , Xianchao Zhu ,Xingpeng Jiang , Li Gao , Tingting He , Xiaohua Hu,“Visualization of non-metric relationships by adaptive learning multiple maps t-SNE regularization”.
[12]. J. Yin, Z. Zheng, and L. Cao, USpan “An efficient algorithm for mining high utility sequential patterns”.
[13].X. Wu, C. Zhang, and S. Zhang, “Efficient mining of both positive and negative association rules”.

R.Banupriya, R.S. Karthik, "An Automatic Big Data Visualization Framework to Plot Chart Using T-SNE Algorithm," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.591-596, 2018.

Wireless sensor network is a network which consists of number of sensor nodes which sense the data and send to sink node. As detecting sensor data with defective readings is an important issue for secure communication. So, necessity of privacy and integrity of information is mandatory. Thus, cryptography is an effective technique that provides privacy and integrity to sensed data. This paper, deals with the improvement of watermarking LEACH with node level data integrity and confidentiality.

WSN, integrity, confidentiality, watermarking

[1] S. chowiak, P., “Cryptographic Key Distribution In Wireless Sensor Networks Using Bilinear Pairings,” PhD diss., Dublin City University, 2010.
[2] S., A. Reisslein, M.Towards, “Efficient wireless video sensor network: A survey of existing node architecture and proposal for a flexi-WVSNP design”, IEEE commun.surv. Tutor, pp. 462-486, 2011.
[3] B. Tavli, K. Bicakci, R. Zilan and B. Ordinas, J.M., “A survey of visual sensor network platforms multitimed Tools”, pp. 689-726, 2012 .
[4] N. kumar, J. Kaur “Improved LEACH Protocol for Wireless Sensor Networks”, IEEE 2011.
[5] Reshma I. Tandel , “Leach Protocol in Wireless Sensor Network: A Survey”, International Journal of Computer Science and Information Technologies, pp. 1894-1896, 2016.
[6] B. Zhenshan, X. Bo and Z. Wenbo. “HT-LEACH: An Improved Energy Efficient Algorithm Based on LEACH”, International Conference on Mechatronic Sciences, Electric Engineering and Computer (MEC), 2013.
[7] M. Pejanović Đurišić , Z. Tafa and G. Dimić , “A survey of military applications of wireless sensor networks” Mediterranean Conference on Embedded Computing (MECO), IEEE 2012.
[8] K. JeongGil, L. Chenyang, B. Mani,. Srivastava , A. John, Stankovic and Andreas , “ Wireless Sensor Networks for Healthcare”, Proceedings of the IEEE, pp. 1947 – 1960, Nov. 2010.
[9] Y. Dunfan, D. Gong and W. Wang, “Application of wireless sensor networks in environmental monitoring”, 2nd International Conference on Power Electronics and Intelligent Transportation System (PEITS), IEEE 2009.
[10] L. Ruiz-Garcia ,L. Lunadei, P. Barreiro and I. Robla, “A Review of Wireless Sensor Technologies and Applications in Agriculture and Food Industry: State of the Art and Current Trends” , pp.4728-4750,2009.
[11] Nikolas, A. Pantazis, Stefanos, A. Nikolidakis and Dimitrios D. Vergados, “Energy – Efficient Routing Protocols in Wireless Sensor Networks: A Survey”, IEEE Communications Surveys & Tutorials.
[12] A. Gupta and A. Nayyar, “A Comprehensive Review of Cluster-Based Energy Efficient Routing Protocols in Wireless Sensor Networks”, International Journal of Research in Computer and Communication Technology, 2014.
[13] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam and E. Cayirci, “Wireless sensor networks: a survey”, Computer Networks,pp. 393–422, 2002.
[14] W. R. Heinzelman, A. Chandrakasan, and H. Balakrishnan, “Energy efficient communication protocol for wireless microsensor networks,” in 33rd Annual Hawaii International Conference on System Sciences, pp. 3005 – 3014,2000.
[15]R. Patel, S. Pariyani and V. Ukani,” Energy and hroughput Analysis of Hierarchical Routing Protocol(LEACH) for Wireless Sensor Networks”, International Journal of Computer Applications, 2011.
[16] Y. Ren Tsai, “Coverage Preserving Routing Protocols for Randomly Distributed Wireless Sensor Networks”, IEEE Transactions on Wireless Communications, 2007.
[17] J. FENG YAN, “ Improved LEACH Routing Protocol For Large Scale Wireless Sensor Networks”, YUAN-LIU LIU School of Computer Science & Technology Soochow university Soochow 215006, China, IEEE 2011.
[18] Nejla Rouissia and Hamza Gharsellaouib, “Improved Hybrid LEACH Based Approach for Preserving Secured Integrity in Wireless Sensor Networks”, International Conference on Knowledge Based and Intelligent Information and Engineering Systems, pp. 1429-1438, 2017.
[19] B. Djallel Eddine, S. Boubiche and A. Bilami, “A Cross-Layer Watermarking-Based Mechanism for Data Aggregation Integrity in Heterogeneous WSNs” Communications Letters, IEEE, 2015.
[20] G. Kaur, N. Kumar, “Secure and Efficient Data Collection in WSN”, International Journal of Advanced Research in Computer Science and Software Engineering, 2015.
[21] DG. Costa ,S. Figuerłdo and G. Oliveira, “Cryptography in Wireless Multimedia Sensor Networks: A Survey and Research Directions”, 2017.
[22].A.Singh, M.Marwaha, B.Singh, S.Singh, “Comparative Study of DES, 3DES, AES and RSA”, International Journal of Computers and Technology, 2013.
[23]P.Pritto Paul, N.Sankar Ram and M.Usha, “Symmetric Key Encryption for Secure Communication Using Wireless Hart in Wireless Sensor Networks (WSN)”, Australian Journal of Basic and Applied Sciences, 2016.
[24] Zhang, J., R. Shankaran, M.A. Orgun, V. Varadharajan, A. Sattar, “A trust management architecture for hierarchical wireless sensor networks. In Local Computer Networks (LCN)”, IEEE 35th Conference ,pp: 264-267,2010.
[25] H. P. Keeler, P. G. Taylor, “A stochastic analysis of a greedy routing scheme in sensor networks”, SIAM Journal on Applied Mathematics, pp. 2214-2238, 2010.
[26] W. R. Heinzelman, J. Kulik, H. Balakrishnan, “Adaptive protocols for information dissemination in wireless sensor networks”, in Proceedings of the 5th annual ACM/IEEE international conference on Mobile computing and networking, Seattle, Washington, USA, pp. 174-185, 1999.
[27] X. Liu, “A Survey on Clustering Routing Protocols in Wireless Sensor Networks”, Sensors, pp. 11113-11153, 2012.
[28`] S. Naeimi, H. Ghafghazi, C.-O. Chow, H. Ishii, “A Survey on the Taxonomy of Cluster-Based Routing Protocols for Homogeneous Wireless Sensor Networks”, Sensors, 2012.
[29] J. Grover, Shikha, M. Sharma, “Location based protocols in Wireless Sensor Network: A review”, in Proceedings of the International Conference on Computing, Communication and Networking Technologies (ICCCNT), pp. 1-5,2014.
[30] Z. Kai, T. Libiao, L. Wenjun., “Location-Based Routing Algorithms for Wireless Sensor Network”.Emerging Technologies of Future Multimedia Coding, Analysis and Transmission,2009.
[31] Y. B. Ko, N. H. Vaidya, “Location Aided Routing (LAR) in mobile ad hoc networks”, Wireless networks, pp. 307-321, 2000.
[32] 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.
[33] M. Bhardwaj, "Faulty Link Detection in Cluster based Energy Efficient Wireless Sensor Networks", International Journal of Scientific Research in Network Security and Communication, Vol.5, Issue.3, pp.1-8, 2017

Rekha Rani, Rajan Manro, "Improved Watermarking Leach Protocol using node level Integrity and Confidentiality in WSN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.597-601, 2018.

The Regression testing is used to retest the component of a system that verifies that after modifications. The test case prioritization is the technique of regression testing which prioritizes the test cases according to the changes which are done in the developed project. This work is based on automated and manual test case prioritization To test the new version of software test case prioritization is applied which prioritize the test cases according to changes and generate maximum number of faults. In this work, technique is been proposed which will traverse the DFD of the project and calculate the function importance which is calculated automated slicing. The functional importance values are given as input to hill climbing algorithm which prioritizes the test cases in the ascending or descending order according to function importance. The algorithm is performed in MATLAB and it is detect more faults in less time period.

Regression Testing, Test Case Prioritization, m-ACO, Automated slicing, FTV(function traversal value)

[1] H. Leung and L. White, “Insight into regression testing”, In Proceeding 27th IEEE International Conference Software Engineering, vol. 20, (1989), pp. 60-69.

[2] S. Khan and A. Awais, “TestFilter: A Statement-Coverage based test case reduction technique”, In Proceeding 12th IEEE International Conference Engineering, vol. 11, (2006), pp. 5-12.
[3] Z. Harman and R. Hierons, “Search algorithms for regression test case prioritization”, In Proceedings 12th IEEE International Journal Software Engineering, vol. 33, (2007), pp. 225-237.
[4] S. Khan and A. Nadeem, “TestFilter: A Statement-Coverage Based Test Case Reduction Technique”, IEEE Conference on multitopic, (INMC’ 06), (2009), pp. 275-280.

[5] J. Daengdej, “Test case prioritization techniques”, Proceedings of IEEE International Journal Software Engineering Knowledge Engineering, vol. 22, (2010), pp. 161-183.

[6] S. Mirarab, “The effect of time constraint on test case prioritization”, IEEE Transaction on Software Engineering, vol. 36, no. 7, (2010), pp. 85-91.

[7] R. Malhotra, A. Kaur and Y. Singh, “A Regression Test Selection and Prioritization Technique”, Journal of Information Processing Systems, vol. 6, no.2, (2010), pp.167-171.

[8] E. Engstrom and P. Runeson, “A Qualitative Survey of Regression Testing Practices”, Springer-Verlag Berlin heidelbreg, LNCS 6156, (2010), pp. 3-16.

[9] D. Hyunsook and S. Mirarab, “The effect of time constraint on test case prioritization”, IEEE Transaction on Software Engineering, vol. 36, (2010), pp. 145-151.

[10] H. Srikanthi and J. Williams, “System test case prioritization of new regression test case”, IEEE Transaction on Software Engineering, vol. 36, no. 2, (2011), pp. 87-94.

[11] A. Kaur and S. Goyal, “A genetic algorithm for regression test case prioritization using code coverage”, International journal on computer science and engineering 3.5, (2011), pp. 1839-1847.

[12] S. Yoo and M. Harman, “Regression testing minimization, selection and prioritization: a survey”, International journal on computer science and engineering, (2012), pp. 67-120.

[13] J. Hwang, “Selection of regression system tests for security policy evolution”, Proceedings of the 27th IEEE/ACM International Conference on Automated Software Engineering. ACM, (2012), pp. 69-74.

[14] X. Zhang and G. Uma, “Factors Oriented Test Case Prioritization Technique in Regression Testing using Genetic Algorithm”, European Journal of Scientific Research, vol. 74, (2012), pp. 34-37.

[15] H. Mei, D. Hao, L. Zhang, L. Zhang, J. Zhou and G. Rothermel, “A Static Approach to Prioritizing Junit Test Cases”, IEEE Transactions on Software Engineering, vol. 38, no. 6, (2012), pp. 1258-1275.
[16] S. Yoo and M. Harman, “Regression Testing Minimisation, Selection and Prioritisation: A Survey”, Software Testing, Verification and Reliability, vol. 22, no. 2, (2012), pp. 67-120.

[17] A. Jatain and G. Sharma, “A systematic review of techniques for Test case prioritization”, International Journal of Computer Applications, vol. 68, (2013), pp. 132-135.

[18] M. Athar and L. Ahmad, “Maximize the Code Coverage for Test Suit by Genetic Algorithm”, International Journal of Computer Science and Information Technologies, vol. 5, (2014), pp. 431-435.

[19] P. Konsaard and L. Ramingwong, “Total Coverage Based Regression Test Case Prioritization using Genetic Algorithm”, Proceeding IEEE International Journal Software Engineering Knowledge Engineering, vol. 24, no. 5, (2015), pp. 24-31.

[20] H. Wang, J. Xing and Q. Yang Q, “Modification Impact Analysis based Test Case Prioritization for Regression Testing of Service-Oriented Workflow Applications”, in Proceedings 39th IEEE International Journal Software Engineering Knowledge Engineering, vol. 30, no. 8, (2015), pp. 67-72.
[21] Saloni Ghai and Sarabjit Kaur, “A Hill-Climbing Approach for Test Case Prioritization” International Journal of Software Engineering and Its Applications,Vol. 11, No. 3 (2017), pp. 13-20
[22] Riza Dhiman and Dr Vinay Chopra, “Modified ACO model for Regression Testing Using Automated Slicing “Journal of Emerging Technologies and Innovative Research,Vol. 5, Issue 6 (June 2018), pp. 180

Riza Dhiman, Vinay Chopra, "Aco for Regression Testing By the Process Automated Slicing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.602-606, 2018.

Nowadays, traditions consistently use a chance to give more significant security. For example, essential capabilities are associated with end dates in structure adventures. Regardless, using time successfully in tradition design is a test, as a consequence of nonappearance of formal conclusions and time-related affirmation frameworks. Along these lines, we propose an aggregate examination framework to formally decide and thus check the organized security traditions. In our framework a parameterized procedure is familiar with manage the time parameters whose characteristics can not be picked in the midst of the tradition arrangement organize. In this article, we at first propose the calculation π associated with time as a formal vernacular to decide security traditions after some time. It supports relentless time showing and the utilization of cryptographic limits. Thus, we describe its formal semantics subject to arranged wise norms, which empowers capable check against various confirmation and riddle properties. Given a parameterized security tradition, our technique conveys a confinement on the time parameters that guarantees the security property met by the tradition or signs a strike that works for any parameter regard. We evaluated our structure with various arranged and non-facilitated security traditions and adequately found an in the past cloud time strike on KerberosV.

Timed Security Protocol, Timed Applied π-calculus, Parameterized Verification, Secrecy and Authentication

[1] L. Li, J. Sun, Y. Liu, and J. S. Dong, “Tauth: Verifying timed security protocols,” in ICFEM. Springer, 2014, pp. 300–315.
[2] “Verifying parameterized timed security protocols,” in FM. Springer, 2015, pp. 342–359.
[3] S. Brands and D. Chaum, “Distance-bounding protocols (extended abstract),” in EUROCRYPT, ser. Lecture Notes in Computer Science, vol. 765. Springer, 1993, pp. 344–359.
[4] S. Capkun and J.-P. Hubaux, “Secure positioning in wireless networks,” IEEE Journal on Selected Areas in Communications, vol. 24, no. 2, pp. 221–232, 2006.
[5] N. Sastry, U. Shankar, and D. Wagner, “Secure verification of location claims,” in Workshop on Wireless Security. ACM, 2003, pp. 1–10.
[6] G. Delzanno and P. Ganty, “Automatic verification of time sensitive cryptographic protocols,” in TACAS. Springer, 2004, pp. 342–356.
[7] M. Abadi and C. Fournet, “Mobile values, new names, and secure communication,” in POPL, 2001, pp. 104–115.
[8] R. Bagnara, E. Ricci, E. Zaffanella, and P. M. Hill, “Possibly not closed convex polyhedra and the parma polyhedra library,” in SAS. Springer, 2002, pp. 213–229.
[9] M. Abadi and B. Blanchet, “Analyzing security protocols with secrecy types and logic programs,” J. ACM, vol. 52, no. 1, pp. 102–146, 2005.
[10] B. Blanchet, “An efficient cryptographic protocol verifier based on Prolog rules,” in CSFW. IEEE CS, 2001, pp. 82–96.
[11] D. X. Song, S. Berezin, and A. Perrig, “Athena: a novel approach to efficient automatic security protocol analysis,” Journal of Computer Security, vol. 9, no. 1-2, pp. 47–74, 2001.
[12] [16] C. J. F. Cremers, S. Mauw, and E. P. de Vink, “Injective synchronisation: An extension of the authentication hierarchy,” Theor. Comput. Sci., vol. 367, no. 1-2, pp. 139–161, 2006.
[13] L. Li, J. Pang, Y. Liu, J. Sun, and J. S. Dong, “Symbolic analysis of an electric vehicle charging protocol,” in Proc. 19th International Conference on Engineering of Complex Computer Systems. Springer, 2014, pp. 11–18.
[14] [20] S. Meier, B. Schmidt, C. Cremers, and D. A. Basin, “The Tamarin prover for the symbolic analysis of security protocols,” in CAV. Springer, 2013, pp. 696–701.
[15] C. Cremers, “The Scyther tool: Verification, falsification, and analysis of security protocols,” in CAV. Springer, 2008, pp. 414–418.
[16] T. Chothia, B. Smyth, and C. Staite, “Automatically checking commitment protocols in proverif without false attacks,” in POST, 2015, pp. 137–155.

Yenam Naresh Kumar, Karanam Madhavi, "Specification and Verification Framework with Time Constraints for Security," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.607-613, 2018.

In the today’s world, the data is much important and it is growing rapidly. It requires some intelligent analysis processing that helps to discover some knowledge from it. These massive data can be processed by the some framework like MOA (Massive Online Analysis). It has predefined data stream mining classification techniques which are used to distribute the data depends on its class. Some of these techniques like Hoeffding Tree, Decision Stump or Naive Bayes are well known. Comparative study of these techniques analyzes same type of data and compares the output. Which gives idea about different algorithms can be used for different purpose.

Stream mining, Hoeffding Tree, Decision Stump, Naive Bayes, Classification, Massive Online Analysis (MOA)

[1]. https://moa.cms.waikato.ac.nz/
[2]. S.Muthukrishnan, ―Data streams: Algorithms and Applications‖.Proceeding of the fourteenth annual ACM-SIAM symposium on discrete algorithms, 2003
[3]. Tusharkumar Trambadiya, Praveen Bhanodia, ―A Comparative study of Stream Data mining Algorithms‖ in International Journal of Engineering and Innovative Technology (IJEIT) Volume 2, Issue 3, September 2012.
[4]. https://www.cs.waikato.ac.nz/~abifet/MOA/ StreamMining.pdf
[5]. http://huawei-noah.github.io/stream DM /docs/HDT.html
[6]. Charu C. Aggarwal - A Survey of Stream Classification Algorithms, IBM T. J. Watson Research Center Yorktown Heights, NY 10598.
[7]. Prithvi Bisht, Neeraj Negi, Preeti Mishra, Pushpanjali Chauhan – A comparative study on various data mining tools for intrusion detection in International Journal of Scientific & Engineering Research Volume 9, Issue 5.
[8]. Dasrhana Parikh, Priyanka Tirkha – Data mining & Data stream Mining – open source tool in International journal of innovative research in science, engineering and technology vol.2 Issue 10.

Ashish P. Joshi, Biraj V. Patel, "Comparative Study of Different Classification Algorithms for Stream Data Mining Using MOA," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.614-616, 2018.

In cities especially some areas have garbage regions, So people are affected by several health issues. The main problem is authorities will not clean on time due to lack of information. Sometimes, authorities have also highly impossible to track these areas. Garbage quantification is an important step in improving the cleanliness of the cities. This paper presents one mobile application for garbage images with GPS locations to send authorities directly. When the user clicks the garbage image using through this app, then it will send that image to the server for automatic garbage detection with quantification by using the deep learning in computer vision techniques. Convolutional Neural Network (CNN) algorithms will be used to garbage detection with quantification in an image for accurate results.

Garbage Quantification, Garbage Detection, Deep Learning, Computer Vision, Convolutional neural networks

[1] Mittal, Gaurav, et al. "SpotGarbage: smartphone app to detect garbage using deep learning." Proceedings of the 2016 ACM International Joint Conference on Pervasive and Ubiquitous Computing. ACM, 2016.
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[4] Erhan, Dumitru, et al. "Scalable object detection using deep neural networks." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2014.
[5] Lowe, David G. "Distinctive image features from scale-invariant keypoints." International journal of computer vision60.2 (2004): 91-110.
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[7] Abdel-Hakim, Alaa E., and Aly A. Farag. "CSIFT: A SIFT descriptor with color invariant characteristics." Computer Vision and Pattern Recognition, 2006 IEEE Computer Society Conference on. Vol. 2. IEEE, 2006.
[8] Simard, Patrice Y., Dave Steinkraus, and John C. Platt. "Best practices for convolutional neural networks applied to visual document analysis." null. IEEE, 2003.
[9] Ji, Shuiwang, et al. "3D convolutional neural networks for human action recognition." IEEE transactions on pattern analysis and machine intelligence 35.1 (2013): 221-231.
[10] Kalchbrenner, Nal, Edward Grefenstette, and Phil Blunsom. "A convolutional neural network for modelling sentences." arXiv preprint arXiv:1404.2188 (2014).
[11] Rastegari, Mohammad, et al. "Xnor-net: Imagenet classification using binary convolutional neural networks." European Conference on Computer Vision. Springer, Cham, 2016.
[12] Howard, Andrew G., et al. "Mobilenets: Efficient convolutional neural networks for mobile vision applications." arXivpreprint arXiv:1704.04861 (2017).
[13] Shi, Wenzhe, et al. "Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network." Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2016.
[14] He, Kaiming, et al. "Mask r-cnn." Computer Vision (ICCV), 2017 IEEE International Conference on. IEEE, 2017.
[15] Ren, Shaoqing, et al. "Faster R-CNN: towards real-time object detection with region proposal networks." IEEE Transactions on Pattern Analysis & Machine Intelligence 6 (2017): 1137-1149.
[16] Ren, Shaoqing, et al. "Faster r-cnn: Towards real-time object detection with region proposal networks." Advances in neural information processing systems. 2015.
[17] "Building a Mask R-CNN Model for Detetcting Car Damage " at https://www.analyticsvidhya.com/blog/2018/07/building-mask-r-cnn-model-detecting-damage-cars-python/
[18] "Fully Convolutional Networks (FCN) for 2D segmentation" at http://deeplearning.net/tutorial/fcn_2D_segm.html
[19] "Introduction to SIFT (Scale-Invariant Feature Transform)" at https://docs.opencv.org/3.0-beta/doc/py_tutorials/py_feature2d/py_sift_intro/py_sift_intro.html

B Venugopal, L S Chakravarthy, L Praveen , Ashish Kumar Dwivedi, "A Prototype for Mobile Application of Garbage Quantification using Mask R-CNN," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.617-621, 2018.

In classic software re-engineering, the user interface are considered to be Platform Specific. Hence were always excluded from the process of software re-engineering. The user interface were re-written for the target platform and integrated with business application in the end. In this paper we propose a Vanilla Model – A Platform Independent Model for Declarative User Interface and algorithm for model transformation using Vanilla Model for Declarative User Interface. This approach will preserve the source artifact user interface will make re-engineering user interface part of main process,. This transformation is then applied to five of the popular libraries such as SWING, HTML5, and more recent libraries of Android and Python- Tkinter.

Model Transformation, Declarative User Interface , Platform Specific Model, Platform Independent Model

[1] A.R. Da Silva, "Model-driven engineering: A survey supported by the unified conceptual model." Computer Languages, Systems & Structures, 43, 139-155. (2015).
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[5] Prince Singha, Aditya, Kunal Dubey, JagadeeswararaoPalli, “Toolkit for Web Development Based on Web Based Information System,” Isroset-Journal (IJSRCSE), 6, no. 5, pp.1-5. 2018..
[6] Shubham, Deepak Chahal, LatikaKharb, “Security for Digital Payments: An Update,” Journal (IJSRNSC), 6, no. 5 , pp. 51-54. 2018.

Smita Agarwal, Alok Aggarwal, S. Dixit, Adarsh Kumart, "Model Transformation of Platform Specific Model to Vanilla Model – A Proposed Platform Independent Model for Declarative User Interface.," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.622-627, 2018.

Clustering is the way toward making a gathering of conceptual items into classes of comparable items. The primary favorable position of bunching over arrangement is that it is versatile to changes and helps single out valuable highlights that recognize diverse gatherings. The real necessities of bunching calculations are Scalability, Ability to manage various types of traits, Discovery of groups with property shape, High dimensionality, Ability to manage uproarious information, Interpretability. The point of the present work is to direct a review on ordinarily utilized grouping approaches alongside its applications.

Clustering, Partition clustering,Heirarchial clustering,Density based clustering,Grid based clustering

[1] K. Chitra & Dr. D.Maheswari,” A Comparative Study of Various Clustering Algorithms in Data Mining”, IJCSMC, Vol. 6, Issue. 8, August 2017.
[2] Han, J. and Kamber, M. Data Mining- Concepts and Techniques, 3rd Edition, 2012, Morgan Kauffman Publishers..
[3] P.Nagpal & P.Mann,” Comparative Study of Density based Clustering Algorithms”, International Journal of Computer Applications (0975 – 8887) Volume 27– No.11, August 2011
[4] Han, J. and Kamber, M. Data Mining- Concepts and Techniques, 3rd Edition, 2012, Morgan Kauffman Publishers.
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Vijay Rai, Pooja Patre, "A Survey on Various Information Clustering Approaches For Efficient Clustering Analysis," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.628-631, 2018.

Cloud Computing is the fastest growing technology in the IT world. It is an architecture which combined the concept of Virtualization technology with several computing paradigms such as Distributed computing, Utility computing, Grid computing etc. to achieve the goal of providing unlimited resources and services over the internet. Cloud computing uses the concept of pay as per use basis where users do not need to pay for infrastructure, installation and its maintenance. Anyone can access the desired service from cloud anytime, anywhere in the world on demand basis. This paper presents an overview of cloud computing along with Root of cloud computing, its evolution and a comparative study of Cloud with several other computing paradigm. It also highlights the characteristic, deployment and service model of cloud computing. The various benefits of cloud with its challenges and applications are also addressed in this paper.

Cloud Computing, Roots of Cloud computing, Evolution, Benefits, Challenges, Applications

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Mohammad Haris, Rafiqul Zaman Khan, "A Systematic Review on Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.632-639, 2018.

The Internet of Things (IOT) consists of, any devices from sensors to smartphones and wearable technology. IoT is all about data. While the IoT grows, data insecurity also increases. The definition involves small devices each with their own Internet Protocol (IP) address connected to other such devices via the internet. Nowadays every home and every business is connected via the Internet of Things, the next level of technology is to connect the whole city in near future. The objective of smart city is to develop an urban city with fully secured from hackers and malware attackers The IOT is a centralized system where, every source information is stored in a centralized manner which is vulnerable to software attacks and system can easily be hacked. This paper proposes building an IOT Smart city using trustworthy decentralized BlockChain technology.

Bitcoin, Blockchain, Consensus, Cryptography, Smart contract

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Anitha Raja, "A Comprehensive Study on Smart City using BlockChain Technology," International Journal of Computer Sciences and Engineering, Vol.6, Issue.11, pp.640-643, 2018.