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In this paper the status of various micronutrients like Zink, Copper, Iron and Manganese in soil were calculated using mathematical modeling. Also the steady state level of these micronutrients in soil was estimated for the long term application of particular fertilizer practices. We also discussed the behavior of soil micronutrients level under the application of different level of phosphorus fertilizer and manure applications.

Mathematical model, Micronutrient, Phosphorus

[1]. L. Bortolon and C. Gianello, “Disponibilidade de cobre e zinco em solos do Sul do Brasil”, R. Bras. Ci. Solo, 33, pp. 647- 658, 2009.
[2]. L. Hangsheng, W. Dan, B. Jay, and W. Larry, “Assessment of soil spatial variability at multiple scales”, Ecological Modeiling, 182 pp. 271-290, 2005.
[3]. Y. Wang, X. Zhang, and C. Huang, “Spatial variability of soil total nitrogen and soil total phosphorus under different land uses in a small watershed on the Loess Plateau”, China.Geoderma, 150, pp. 141- 149, 2009.
[4]. S. D. Lopes and C. A. Abreu, “Micronutrientes na agricultura brasileira: Evolução histórica e future”, In: NOVAIS, R.F.; ALVAREZ V., V.H. & SCHAEFER, C.E.G.R., eds. Tópicos em ciência do solo. Viçosa, MG, Sociedade Brasileira em Ciência do Solo, pp.265-298, 2000.
[5]. J. I. Wear and A. L. Sommer, “Acid extractable zinc of soils in relation to the occurrence of zinc deficiency symptoms of corn: A method of analysis”, Soil Sci. Soc. Am. Proc., 12, pp. 143- 144, 1948.
[6]. W. L. Lindsay and W. A. Norvell, “Development of a DTPA soil test for zinc, iron, manganese and copper”, Soil Sci. Soc. Am. J., 42, pp. 421-427, 1978.
[7]. M. Dhanapriya, R. Maheswari, “Estimation of micro and macro nutrients in the soil of remote areas”, Journal of Chemical and Pharmaceutical Sciences, Special Issue 10, 67-73, 2015.
[8]. G. Shukla, G. C. Mishra and S. K. Singh, “Estimation Of Micronutrients In The Soil”, The Bioscan, 10(1), pp. 01-04, 2015.
[9]. M. Khajanchi, R. Sharma and U. Pancholi, “Mathematical Model To Predict The Soil Macronutrients Status Under The Influence Of Phosphorus And Manure For Continuous Cropping System", International Journal of Computer Sciences and Engineering, Vol.-6(5), pp. 926-933, 2018.
[10]. R. W. Leggett and L. R. Williams, “A Reliability Index for Models”, Ecological Modelling, Vol. 13, pp. 303-312, 1981.
[11]. D. S. Rathore, Integrated nutrient management in blackgram, doctoral diss., Maharana Pratap University of agriculture and technology, Udaipur, India, 2008.

M. Khajanchi, R. Sharma, U. Pancholi, "Analysis of Soil Micronutrients Status Using Mathematical Modeling," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.720-726, 2018.

In this article, we have investigated the balanced divided square difference cordial behavior of some path related graphs such as fan graph, half gear graph, triangular snake, double triangular snake, alternate triangular snake, V_D (P_n ).

fan graph, half gear graph, triangular snake, double triangular snake, alternate triangular snake.

[1] A.Alfred Leo, R.Vikramaprasad, R.Dhavaseelan; Divided square difference cordial labeling graphs, International Journal of Mechanical Engineering and Technology, vol.9,Issue 1, pp.1137 – 1144, 2018.
[2] A.Alfred Leo, R.Vikramaprasad, Cycle related balanced divided square difference cordial graphs, Accepted in Journal of Computer and Mathematical Sciences.
[3] I. Cahit, “Cordial graphs: a weaker version of graceful and harmonious graphs,” Ars Combinatoria, vol.23, pp. 201 – 207, 1987.
[4] S.N.Daoud, Edge odd graceful labeling of some path and cycle related graphs, AKCE International Journal of Graphs and Combinatorics, vol.14, pp.178 – 203, 2017.
[5] R.Dhavaseelan, R.Vikramaprasad, S.Abhirami; A new notions of cordial labeling graphs, Global Journal of Pure and Applied Mathematics, vol.11, Issue 4, pp.1767 – 1774, 2015.
[6] J. A. Gallian, A dynamic survey of graph labeling, Electronic J. Combin. vol.15, DS6, pp.1 – 190, 2008.
[7] F. Harary, “Graph theory”, Addison-Wesley, Reading, MA. 1969.
[8] V.J.Kaneria, Kalpesh M.Patadiya, Jeydev R.Teraiya, Balanced cordial labeling and its applications to produce new cordial families, International Journal of Mathematics and its Applications, vol.4,Issue 1-C,pp.65 – 68, 2016.
[9] A. Rosa, On certain valuations of the vertices of a graph, Theory of Graphs (Internat. Symposium, Rome, July 1966), Gordon and Breach, N. Y. and Dunod Paris (1967), pp.349 – 355.
[10] S.S.Sandhya, S.Somasundaram, S.Anusha, Some more results on root square mean graphs, Journal of Mathematics Research,Vol.7, Issue.1,pp.72 – 81, 2015.
[11] R.Varatharajan, S.Navaneethakrishnan, K.Nagarajan, Divisor cordial graphs, International J.Math. Combin, Vol.4, pp.15 – 25, 2011.

A. Alfred Leo, R. Vikramaprasad, "Path Related Balanced Divided Square Difference Cordial Graphs," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.727-731, 2018.

A Social Network is a network which comprises of countless interactions among people whether they are personal or professional. Social networks provide us with a platform to interconnect with each other through text or media in the name of messages, comments, pictures, posts, tags etc. Nowadays, the number of social networks in on an exponential rise, Facebook and Twitter etc. being the renowned ones. Social networks are vast networks which store and maintain information with respect to each user in their databases. That information is mostly contributed by the users only. That contribution being a voluntary one or an uneducated one is entirely another matter. This was the first key motive for our research. Enforcing a secure and comprehensive login process to secure the access of social accounts was the second one. In this paper, we propose a tool to evaluate the vulnerability of Facebook accounts w.r.t the privacy options provided by the social network. We also propose a reinvented login process with an aim to eradicate the perils of unauthorized access to the accounts.

Social networks, SNS, Privacy, Data-exposure, Privacy check, Login Process, Reinvented

[1] A. Acquisti and R. Gross, “Imagined communities: Awareness, information sharing, and privacy on the Facebook,” in International workshop on privacy enhancing technologies, 2006.
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[3] R. Gross and A. Acquisti, “Information revelation and privacy in online social networks,” in Proceedings of the 2005 ACM workshop on Privacy in the electronic society, 2005.
[4] A. Hannak, P. Sapiezynski, A. Molavi Kakhki, B. Krishnamurthy, D. Lazer, A. Mislove and C. Wilson, “Measuring personalization of web search,” in Proceedings of the 22nd international conference on World Wide Web, 2013.
[5] H. R. Lipford, A. Besmer and J. Watson, “Understanding Privacy Settings in Facebook with an Audience View.,” UPSEC, vol. 8, pp. 1-8, 2008.
[6] F. Stutzman and J. Kramer-Duffield, “Friends only: examining a privacy-enhancing behavior in facebook,” in Proceedings of the SIGCHI conference on human factors in computing systems, 2010.
[7] V. Toubiana, A. Narayanan, D. Boneh, H. Nissenbaum and S. Barocas, “Adnostic: Privacy preserving targeted advertising,” 2010.
[8] A. Korolova, “Protecting privacy when mining and sharing user data,” 2012.
[9] Y. Liu, K. P. Gummadi, B. Krishnamurthy and A. Mislove, “Analyzing facebook privacy settings: user expectations vs. reality,” in Proceedings of the 2011 ACM SIGCOMM conference on Internet measurement conference, 2011.
[10] K. Raynes-Goldie, “Aliases, creeping, and wall cleaning: Understanding privacy in the age of Facebook,” First Monday, vol. 15, 2010.
[11] C. Castelluccia, E. De Cristofaro and D. Perito, “Private information disclosure from web searches,” in International Symposium on Privacy Enhancing Technologies Symposium, 2010.
[12] N. Wang, H. Xu and J. Grossklags, “Third-party apps on Facebook: privacy and the illusion of control,” in Proceedings of the 5th ACM symposium on computer human interaction for management of information technology, 2011.
[13] F. Aloul, S. Zahidi and W. El-Hajj, “Two factor authentication using mobile phones,” in Computer Systems and Applications, 2009. AICCSA 2009. IEEE/ACS International Conference on, 2009.
[14] C. F. Austin, X. Wan and A. Wright, Two-factor authentication, Google Patents, 2014.
[15] A. T. B. Jin, D. N. C. Ling and A. Goh, “Biohashing: two factor authentication featuring fingerprint data and tokenised random number,” Pattern recognition, vol. 37, pp. 2245-2255, 2004.
[16] W. McKinney and P. D. Team, “Pandas—Powerful Python Data Analysis Toolkit,” Pandas—Powerful Python Data Analysis Toolkit, p. 1625, 2015.
[17] C. Dwyer, S. Hiltz and K. Passerini, “Trust and privacy concern within social networking sites: A comparison of Facebook and MySpace,” AMCIS 2007 proceedings, p. 339, 2007.
[18] U. Maheswari and S. Balaji, “Privacy Preservation on Online Social Networking Issues and Challenges,” International Journal of Computer Sciences and Engineering, vol. 5, no. 8, pp. 215-217, 2017.
[19] B. Kasab, S. Ubale and V. Pottigar, “Enabling Privacy Preservation Technique to Protect Sensitive Data with Access Control Mechanism Using Anonymity,” International Journal of Computer Sciences and Engineering, vol. 3, no. 10, pp. 61-65, 2015.

Poonam Dabas, Sheeba Sharma, "Data Exposure Check and A Comprehensive Login Procedure," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.732-737, 2018.

Online Recommendation helps users to recommend products friends to their friends or any other but it is quite difficult to recommend something to anyone without knowing his/her interest the same difficulty is occurred while recommending movies to users. Each user has its own interest and thoughts about movies. So for this in this paper a movie recommendation technique is proposed in which collaborative filtering is used to recommend movies according to user’s interests and rating. To implement proposed mechanism Python language is used and to analyze performance of proposed mechanism real dataset is used which is collected from Netflix website.

Online Social Networks, Netflix, Movie, Recommendation and Collaborative Filtering

“Using Visual Features and Latent Factors for Movie Recommendation”, in proceedings of “CBRecSys”, pp: 1-4, Boston, MA, USA, 2016.
[2] Khyati Aggarwal and Yashowardhan Soni, “Movie Recommendations using Hybrid Recommendation Systems”,“International Journal on Recent and Innovation Trends in Computing and Communication” ,Vol. 4 No. 12, pp: 206-209, 2016.
[3] Jiaxin Zhu, Yijun Guo, Jianjun Hao and Jianfeng Li, “Gaussian Mixture Model Based Prediction Method of Movie Rating”, in proceedings of “ 2nd IEEE International Conference on Computer and Communications”, pp: 2114-2118, Chengdu, China, 2016.
[4] Sieg, B. Mobasher, and R. Burke, “Improving the effectiveness of collaborative recommendation with ontology-based user profiles,” in Proceedings of the 1st International Workshop on Information Heterogeneity and Fusion in Recommender Systems, ser. HetRec ’10. New York, NY, USA: ACM, 2010, pp. 39–46.
[5] Jyoti, Sanjeev Dhawan and Kulvinder Singh, “Analysing user ratings for classifying online movie data using various classifiers to generate recommendations”, in proceedings of “IEEE International Conference on Futuristic Trends on Computational Analysis and Knowledge Management(ABLAZE)”, pp: 295-300, Noida, India, 2015.
[6] Sanjeev Dhawan, Kulvinder Singh and Jyoti, “High Rating Recent Preferences Based Recommendation System”, in proceedings of “4th International Conference on Eco-friendly Computing and Communication Systems”, pp: 259-264, Kurukshetra, India, 2015.

[7] Lakshmi Tharun Ponnam, Sreenivasa Deepak Punyasamudram, Siva Nagaraju Nallagulla and Srikanth Yellamati, “Movie Recommender System Using Item Based Collaborative Filtering Technique”, in proceedings of “International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS)”, pp: 1-5, Pudukkottai, India, 2016.
[8] Kartik Chandra Jena, Sushruta Mishra, Soumya Sahoo and Brojo Kishore Mishra, “Principles, Techniques and Evaluation of Recommendation Systems”, in proceedings of “IEEE International Conference on Inventive Systems and Control”, pp: 1-6, Coimbatore, India, 2017.
[9] Jun Ai, Linzhi Li, Zhan Su and Chunxue Wu, “Online-rating prediction based on an improved opinion spreading approach”, in proceedings of “ 29th Chinese Control And Decision Conference IEEE 2017”, pp: 1457-1460, Chongqing, China, 2017.
[10] Dixon Prem Daniel and Rangaraja P Sundarraj, “A Latent Factor Model based Movie Recommender using Smartphone Browsing History”,in proceedings of“International Conference on Research and Innovation in Information Systems IEEE” 2017, pp: 1-6 , Langkawi, Malaysia, 2017.
[11] Veeresh Belgur, Aniket Karande, Nikhil Kulkarni, Pranil Nalawade and Aniket M. Junghare, “Statistical Analysis on Movie Reviews and Ratings”, “International Journal of Science, Engineering and Technology Research (IJSETR)” Vol. 6, Issue.4, ISSN: 2278 -7798, pp: 508-510, 2017.
[12] Karan Soni, Rinky Goyal, Bhagyashree Vadera and Siddhi More, “A Three Way Hybrid Movie Recommendation System”, “International Journal of Computer Applications”, Vol. 160 , No. 9, pp: 29-32 , 2017.

Sanjeev Dhawan, Kulvinder Singh, Neha Singh, "Collaborative Filtering Based Approach to Recommends Movies in Online Social Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.738-741, 2018.

The datacenters are need to transmit more dataflows towords huge servers are interconnected. The task of transmiting the data is give some difficult because it lead to no of issues are there is to manage work load efficiently and adaption of changing network states and arrival of new requests during transmission. For overcome this problem a dynamically load balancing technique may be implemented in software define networks to degedize the total overall response time and increasing network throughput. This approach work more efficiently when there is only one data center in the same network environment. When there is two or more datacenters in the same network region the conflict may occur which datacenter may be transmit the data flows through the servers, to avoid this type of situation we propose a priority based with round robin algorithm for selecting datacenter from the same network environment. for this first list out the datacenters from network environment and make index of the datacenters based on their priorities usually speed. And by using round robin techique for selecting the datacenter may be performed. For this we propose two algorithms for indexing the datacenters based on priorities and round robin approach for selecting datacenter. basically the dynamical load balancing technique gives high throughput on less response time. By combining the prioritised round robin algorithm with this for selecting datacenters is gives better efficient result than traditional model.

Cloud computing,Software defined network, Load balance, Data Center, Round-Robin

[1]. T.yang s Senior Member,Can tang,Jie li and Minyi Guo.A.D ynamical and Load-Balancing Flow Scheduling Approach for Big Data Centers in Cloud.IEEE Transaction on Cloud Computng. 2015
[2]. Q.Zhang, M.F.Zhani, Y.Yang et al. PRISM: Fine-Grained Resource-Aware Scheduling for MapReduce. IEEE Transactions on Cloud Computing, Vol.PP, No.99, 2015.
[3]. Z.Z.Cao, M.Kodialam and T.V.Lakshman. Joint Static and Dynamic Traffic Scheduling in Data Center Networks. in Proceedings of IEEE INFOCOM 2014, pp.2445-2553.
[4]. F.Zhang, J.Cao, K.Hwang et al. Adaptive Workflow Scheduling on Cloud Computing Platforms with Iterative Ordinal Optimization. IEEE Transactions on Cloud Computing, Vol.PP, No.99, 2014.
[5]. R.M.Ramos, M.Martinello and E.C.Rothenberg. SlickFlow: Resilient source routing in Data Center Networks unlocked by OpenFlow. Proc. of IEEE 38th Conference on Local Computer Networks (LCN), 2013, pp.606-613.
[6]. K.Greene, TR10: Software-Defined Networking, MIT Technology Review, Retrieved Oct. 7, 2011
[7].T.Feng,B.JunandH.Y.Hu.OpenRouter:OpenFlowextensionand implementation based on a commercial router. Proceedings of 19th IEEE International Conference on Network Protocols (ICNP 2011), 2011, pp.141-142.
[8].M.Schlansker,Y.Turner,J.Tourrilhes,andA.Karp,EnsembleRouting for Datacenter Networks, In ACM ANCS, La Jolla, CA, 2010.
[9]. N.Handigol, S.Seetharaman, M.Flajslik, N.McKeown, and R.Johari, Plug-n-Serve: Load-balancing web traffic using OpenFlow, Demo at ACM SIGCOMM, Aug. 2009.

A.Devi Prasad, K. Thyagarajan, K. Dhasarathrami Reddy, "Dynamical Load Balancing and Priority based Round Robin Algorithm for selecting Data Center in Cloud," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.742-745, 2018.

The prediction of a particular stock price serves as recommendation system for investors. Most of stock prediction studies focus on using macroeconomic indicators to train the prediction model. Due to difficulty in obtaining this data on daily basis, we directly employ the daily prices data to train the model for predicting the stock price. This study focuses on identifying significant inputs among the financial indicators using Principal Component Analysis to construct a model for prediction. A Multilayer Feed-Forward Nonlinear Autoregressive with External (Exogenous) Input (NARX) network is trained and used to predict closing price of a share listed in National Stock Exchange (NSE). Financial indicators of State Bank of India (SBI) are used as case study to train & test the proposed system. NARX network designed for year 2012 and tested for year 2013.

Artificial Neural Network (ANN), Nonlinear Autoregressive with External Input (NARX), Principal Component Analysis (PCA), stock price prediction

[1] Kao, L-J., Chiu, C-C., Lu, C-J. and Yang, J-L. (2013) ‘Integration of nonlinear independent component analysis and support vector regression for stock price forecasting’, NeuroComputing, Vol. 99, No. 1, pp.534–542
[2] Wang, Y. (2014) ‘Stock price direction prediction by directly using prices data: an empirical study on the KOSPI and HSI’, Int. J. Business Intelligence and Data Mining, Vol. 9, No. 2, pp.145–160.
[3] J. Edward Jackson, `A User`s Guide To Principal Components`, A Wiley-Interscience Publication, Page 1
[4] Abhyankar, A. Copeland, L.S., & Wong, W., Uncovering nonlinear structure in real-time stock-market indexes: The S&P 500, the DAX, the Nikkei 225, and the FTSE-100, Journal of Business & Economic Statistics, 15, 1–14., 1997
[5] Mbeledogu N. N., Odoh M. And Umeh M.N., ‘Stock Feature Extraction using Principal Component Analysis’, 2012 International Conference on Computer Technology and Science, IPCSIT vol. 47
[6] Marijana Zekić-Sušac, Nataša Sarlija, and Sania Pfeifer, "Combining PCA Analysis and Artificial Neural Networks in Modelling Entrepreneurial Intentions of Students", Croatian Operational Research Review (CRORR), Vol. 4, 2013
[7] Richard A. Johnson, Dean W. Wichern, `Applied Multivariate Statistical Analysis`, 6th Edition, Pearson Prentice Hall, Page 430
[8] Tsungnan Lin, Bill G. Horne, Peter Tino, C. Lee Giles, Learning long-term dependencies in NARX recurrent neural networks, IEEE Transactions on Neural Networks, Vol. 7, No. 6, 1996, pp. 1329-1351
[9] Yang Gao, Meng Joo Er, NARMAX time series model prediction: feed-forward and recurrent fuzzy neural network approaches, Fuzzy Sets and Systems, Vol. 150, No. 2, 2005, pp.331-350
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[11] www.nseindia.com/products/content/equities/eq_security.htm
[12] Jiawei Han, Micheline Kamber, Jian Pei, Data Mining Concepts and Techniques, MK, 3rd edition, Page 113-114
[13] Support.sas.com/documentation/cdl/en/statug/67523/HTML/default/viewer.htm#statug_princomp_overview.htm
[14] https://en.wikipedia.org/wiki/SAS_(software)

G. G. Rajput , Bhagwat H. Kaulwar, "Predicting Stock Prices in National Stock Exchange of India using Principal Component Analysis and Neural Networks," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.746-752, 2018.

The interrupted time series data plays a very important role in the evaluation of public health interventions and also to improve hospital antibiotic prescribing. We take the data from a study on the effects of the Italian smoking ban in public places on hospital admissions for Acute Coronary Events (ACEs). In January 2005, Italy introduced regulations to ban smoking in all indoor public places, which the aim of limiting adverse health affects of second hand smoke. The data used here are ACEs in the Sicily region between 2002 and 2006 among those aged 0-69 years. In this paper, we use three models for interrupted time series data. Root Mean Square Error (RMSE) measure is used for selecting the best model. Three models are empirically tested using interrupted time series smoke ban data in the Sicily region, Italy.

ARIMA models, Before Intervention, After Intervention, Adaptive smoothing model, R2, RMSE.

[1] James Lopez Bernal, Steven Cummins, Antonio Gasparrini, “Interrupted Time Series Regression for The Evaluation of Public Health Interventions: a tutorial”, International Journal of Epidemiology, 348-355, 2017.
[2] Ariel Linden, “Conducting Interrupted Time Series Analysis for Single and Multiple Group Comparisions”, The Stata Journal 15, (2), 480-500, 2015.
[3] Evangelos Kontopantelis, Tim Doran, David A Springate, Lain Buchan, David Reeves,“Regression Based Quasi-Experimental Approach When Randomisation is not an option: Interrupted Time Series Analysis”, The BMJ; 350:h2750, 2015.
[4] Monica Taljaard, Joanne E McKenzie, Craig R Ramsay, Jeremy M Grimshaw, “ The Use of Segmented Regression in Analysing Interrupted Time Series Studies: An Exmple in Pre-Hospital Ambulance care”, Implementation Science, 9:77, 2014.
[5] Faranak Ansari, Kiesteen Gray, Dilip Nathwani, Gabby Philips, Simon Ogston, Craig Ramsay, Peter Davely “Outcomes of an Intervention to Improve Hospital Antibiotic Prescribing: Interrupted Time Series with Segmented Regression Analysis”, Journal of Antimicrobial Chemotherapy, 52, 842-848, 2003.
[6] A.K. Wagner, S,B. Soumerai, F.Zhang, D.Ross-Degnan “Segmented Regression Analysis of Interrupted Time Series Studies in Medication Use Research” Journal of Clinical Pharmacy and Therapeutics, 27, 299-309, 2002.
[7] Gene V Glass,”Interrupted Time Series Quasi-Experiments”, American Educational Research Association., 2nd edition 589-608, 1997.
[8] Donald P.Hartmann, John M. Gottman, Richard R. Jones, William Gardner, Alan E. Kazdin, Russell S. Vaught “Interrupted Time Series Analysis and Its Application to Behavioral Data”,Journal of Applied Behavior Analysis,13, 543-559, 1980.
[9] http://ije.oxfordjournals.org/lookup/suppl/doi:10.1093/ije/dyw098/-/DC1.

A. Srinivasulu, B. Sarojamma, "Fitting the Best Model for ACEs Using Interrupted Time Series Data," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.753-760, 2018.

The wireless sensor network innovation is one of the biggest information preparing and correspondence systems frameworks which consistently produced for dispersed condition in field of ongoing application. There are such huge numbers of factor related with it`s as Data safety, working rate, cost proficiency and extra sensor organize imperatives. Principle thought is about increment the security over existing attacks without influence the execution and many-sided quality of general remote sensor organize. In this research, at first a protected correspondence key trading procedure for WSN called Grid Based Dynamic (GBSD) symmetric keying Protocol. Where in GBSD every sensor in a sensor organize shared symmetric keys to discuss safely with each other. In GBSD, demonstrates the Dynamic keying approach for sensor arranges that necessities to store shared symmetric keys to every sensor. This is near the ideal number of shared symmetric keys for any key dispersion conspire that isn`t defenseless against agreement. In this research achieves low energy consumption, secure way of data transmission, less traffic rate in data transmission.

Public Key Cryptography, Grid Based Dynamic symmetric keying protocol, Wireless Sensor Network

[1] W. Du, J. Deng, Y. S. Han, P. K. Varshney, J. Katz, and A. Khalili, “A pairwise key predistribution scheme for wireless sensor networks," ACM Transactions on Information and System Security (TISSEC), vol. 8, no. 2, pp. 228-258, 2005.
[2] O. Yagan and A. M. Makowski, “Modeling the pairwise key predistribution scheme in the presence of unreliable links," IEEE Transactions on Information Theory, vol. 59, no. 3, pp. 1740-1760, 2013.
[3] F. Yavuz, J. Zhao, O. Yagan, and V. Gligor, “On secure and reliable communications in wireless sensor networks: Towards k-connectivity under a random pairwise key predistribution scheme," in International Symposium on Information Theory (ISIT), IEEE, 2014, pp. 2381-2385.
[4] AzrinaAbd Aziz, “A survey on distributed Topology Control Techniques for Extending the Lifetime of Battery Powered Wireless Sensor network”, IEEE Communications Surveys & Tutorials, vol. 15, no. 1, pp. 1024-1039,2013.
[5] Benazir Fateh, “Joint Scheduling of Tasks and Messages for Energy Minimization in Interference-aware Real-time Sensor Networks”, IEEE Transactions On Mobile Computing, pp. 1123-1136, 2013.
[6] Chatterjea,S&Havinga, P, “A Dynamic data aggregation scheme for Wireless Sensor network,” Research on Integrated Systems and Circuits”, Veldhoven, The Netherlands, Nov. 2003.
[7] Chih-Min Chao, “Design of structure-free and energy-balanced data aggregation in Wireless Sensor network”, ELSEVIER, Journal of Network and Computer Applications vol. 37, no. 5, pp.229– 239,2014.
[8] Chi-Tsun Cheng, “A Delay-Aware Network Structure for Wireless Sensor network With In- Network Data Fusion”, IEEE Sensors Journal, vol. 13, no. 5, pp. 1234-1248,May 2013.
[9] Clausen, T&Jacquet, P, “Optimized Link State Routing Protocol (OLSR)”, http://tools.ietf.org/html/rfc3626, 2003.
[10] Considine, J, Li, F,Kollios, G & Byers, J, “Approximate aggregation techniques for sensor databases,” in Proc. IEEE Int. Conf. Data Engineering (ICDE), 2004.
[11] Cristescu, R,Beferull-Lozano,B&Vetterli, M, “On network correlated data gathering”, IEEE Computer and Communications Societies, vol. 4, pp. 2571–2582, 2004.
[12] Dantu, K &Sukhatme, G, “Connectivity vs. control: Using directional and positional cues to stabilize routing in robot networks”, International Conference on Robot Communication and Coordination (RoboComm’09). pp.1–6,2009.

U. Durai, "A Grid Based Dynamic Symmetric Keying Protocol Based on Routing in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.761-766, 2018.

Network-on-Chips (NoCs) are rapid promising for an on-chip alternative designed in support of many-core System-on-Chips (SoCs). In spite of this, developing an increased overall performance low latency Network on chips using low power overhead has always been a new challenge. Network on Chips (NoCs) by using mesh and torus interconnection topology have become widely used because of the easy construction. A torus structure is nearly the same as the mesh structure, however, has very slighter diameter. The performance of topology can be analyzed based on power and latency; the power consumption and the latency in Network-on-Chip (NoC) are two challenging objectives. In this paper, we proposed on Clustering-Based Topology Generation and disable nodes to construct routers a torus based clustered topology methods for power saving and performances aware on NoC. Experimental results show that the approach saves proposed method consume less power consumption on average in comparison with using torus topology and achieves significant topology performance improvement.

NoCs, Cluster, topology generation, disable notes, Routers

[1] K. Venkatasuresh, K. Bala , "Design and Verification of Five Port Router for Network on Chip Using VERILOG", International Journal of Computer Sciences and Engineering, Vol.2, Issue.9, pp.78-80, 2014.
[2] Abdul Quaiyum Ansari, Mohammad Rashid Ansari, and Mohammad Ayoub Khan. “Performance evaluation of various parameters of network-on-chip for different topologies”.INDICON, Annual IEEE, pages 1–4,2015.
[3] G. Leary, K. Srinivasan, K. Mehta and K. S. Chatha, “Design of network on chip architectures with a genetic algorithm-based technique,” IEEE Trans. on Very Large Scale Integration Systems, vol. 17, no. 5, pp. 674-687, 2009 .
[4] Z. Liu, J. Cai, L. Yao and M. Du, “Application-aware generation and optimization for NoC topology,” in Proc. IEEE Youth Conf. Information, Computing and Telecommunication, pp. 259-262, 2009.
[5] K. C. Chang and T. F. Chen, “Low-power algorithm for automatic topology generation for application- specific networks on chips,” IET Computers & Digital Techniques, vol. 2, no. 3, pp. 239-249, 2008.
[6] Y. Ar, S. Tosun, and H. Kaplan, “TopGen: a new algorithm for automatic topology generation for network on chip architectures to reduce power consumption,” in Proc. AICT, pp.1-5,2009.
[7] F. Ge and N. Wu, “Genetic algorithm based mapping and routing approach for network on chip architectures,” Chinese Journal of Electronics, vol.19, no.1, pp. 91-96, Jan. 2010.
[8] N. Y. Phing, M. N. M. Warip, P. Ehkan, and S. Y. Teo, “Reducing Total Power Consumption and Total Area Techniques for Network-on-Chip Through Disable Cores and Routers Based on Clustering Method,” vol. 10, no. 2, pp. 514–520, 2018.
[9] D. Systems, A. S. Hassan, A. A. Morgan, and M. W. El-kharashi, “Clustered Networks-on-Chip:Simulation and Performance Evaluation,” vol. 2, no. 2, 2017.
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A. Kalimuthu, M. Karthikeyan, "Performance Analysis of Clustering-Based Topology Generation and disable nodes for NoC," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.767-770, 2018.

In this paper, we concentrated on creating productive mining calculation for finding designs from expansive information accumulation. What’s more, scan for helpful and intriguing examples. In the field of text mining, design mining systems can be utilized to discover different text designs, for example, visit itemsets, shut successive itemsets, co-happening terms. This paper shows an imaginative and successful example revelation strategy which incorporates the procedures of example sending an example advancing, to enhance the viability of utilizing and refreshing found examples for finding significant and intriguing data. In proposed framework we can take adequate .txt record as data sources and we apply different calculations and produce expected outcomes. Text-mining alludes by and large to the way toward removing fascinating and non-trifling data and information from unstructured text. A critical contrast with seek is that hunt requires a client to realize what he or she is searching for while text mining endeavors to find data in an example that isn`t known in advance.

Text mining, text classification, pattern mining, pattern evolving, information filtering

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S. Padmavathi, M. Chidambaram, "Implementation of Text Mining in High Utility Itemsets for Pattern Mining," International Journal of Computer Sciences and Engineering, Vol.6, Issue.6, pp.771-775, 2018.