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In this paper using Semi-Markovian approach a reliability model of single unit is develop. Unit works in operative mode initially and goes for preventive maintenance after a pre-specific time of operation. Single repairperson that may be appear and disappear randomly does every repair work including preventive maintenance. New one replaces the failed unit in case its repair is not possible by the server in a given fixed repair time. The repair activities and repairperson (server) are perfect. The random variables associated with failure time of the unit and different repair activities are independent to each other. The failure time and maximum operation time of the unit are exponentially distributed while the distributions of time of appearance and disappearance of server, repair and replacement of the unit are taken as arbitrary with different probability density functions. Applying regenerative point techniques, different reliability attributes are obtained to enhance the performance of system. Numerical results of Mean time to system failure (MTSF), Availability and profit function that are very much helpful to system engineer have also been analysed.

Single Unit, Random Appearance, Reliability, Preventive Maintenance

[1] Al-Ali. Abdul Ameer, K. Murari, “One unit reliability system subject to random shocks and preventive maintenance”, Microelectronics Reliability, 28 (3), 373–377, 1988.
[2] S. Chander, “MTSF and profit evaluation of an electric transformer with inspection for on-line repair and replacement, Journal of Indian Society for Statistics and Operations Research, XXVIII (1-4), 33-43, 2007.
[3] M. S. Kadyan., Promila , “Reliability and cost-benefit analysis of a single unit system with degradation and inspection at different stages of failure subject to weather conditions”, International Journal of Computer Applications, 55(6), 33-38, 2012.
[4] S.C. Malik, M.S. Barak, “Reliability and economic analysis of a system operating under different weather conditions”, J Proc Natl Acad Sci, 79 (Pt II), 205–213, 2009.
[5] S.C. Malik, Gitanjali., “Analysis of a parallel system with maximum repair time and single server who appears and disappears randomly”, International Journal of Mathematical Archive-3(11), 4700-470, 2012.
[6] T. Nakagawa, S. Osaki, “Reliability analysis of a one-unit system with unrepairable spare units and its optimization applications”, Quarterly Operations Research, 27(1), 101-110, 1976.
[7] D. Pawar, D, S. C. Malik, “Performance measure of a single-unit system subject to different failure modes with operation in abnormal weather”, International Journal of Engineering Science and Technology (IJEST), 3(5), 4084-4089, 2011.
[8] Liu. Renbin, Liu. Zaiming, “Reliability analysis of a one-unit system with finite vacations”. In the Proceedings of the MSIE 2011 International Conference on Management Science Industrial Engineering (MSIE), 248-252, 2011.
[9] S.K. Singh, “Profit evaluation of a two-unit cold standby system with random appearance and disappearance time of service facility”, Microelectron. Reliab., Vol. 29, No. 1, pp.21-24, 1989.

Gitanjali, "Stochastic Behaviour of Single Unit System with Preventive Maintenance subject to Random Appearance of Server," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.68-73, 2017.

Advancement in technology and web based activities has increased the size of data sets which may cause the risk of re-identification about individual’s information. Multifarious techniques have been suggested for anonymizing the data sets. Aforesaid techniques ensure the individual’s identity to remain anonymous. As a result of that, privacy preservation in the field of data publishing has become an active area for research. In this paper an evaluation of various k-anonymity algorithms has been carried out with the objective of identifying the value of discernibility that occurs due to anonymization. An experiment has been performed to determine the value of discernibility based on the type of attribute(s) on three publically available data sets that carries different dimensions.

Metrics, Discernibility Metric(DM) , Equivalence Class, Privacy Preserving Data Publishing (PPDP), Quasi identifier (QID), American Time Use Survey (ATUS)

[1] L. Sweeney. “Achieving k-anonymity privacy protection using generalization and suppression”, International Journal on Uncertainty, Fuzziness, and Knowledge-based Systems, 10(5):571{588, 2002}.
[2] Kristen Lefebvre, David J. DeWitt. “Mondrian Multidimensional K-Anonymity” , In proceeding of 22nd International Conference on Data Engineering, ICDE’06, page 25,2006.
[3] Kristen Lefebvre, David J. DeWitt,Raghu Ramakrishnan. Incognito: “Efficient Full-Domain K-Anonymity”, SIGMOD 2005 June 14-16, 2005, Baltimore, Maryland, USA Copyright 2005 ACM 1-59593-060-4/05/06.
[4] J. Gantz and D. Reinsel. “The digital universe in 2020: Big Data, Bigger Digital Shadows, and Biggest Growth in the Far East”. Technical report, IDC, sponsored by EMC, December, 2012.
[5] M.Manigandan and K. Aravind Kumar, "Secure Multiparty Protocol for Distributed Mining of Association Rules", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.6-10, 2015.
[6] Lamba S. and Abbas S. Qamar, “Model for Privacy Preserving of Sensitive Data”, International Journal of Technical Research and Applications, Vol 1, e-ISSN: 2320-8163, PP 07-11, July-August, 2013.
[7] J. Soria-Comas , J. Domingo-Ferrer, D. Sanchez, and S. Martınez, “Improving the Utility of Differentially Private Data Releases via k-Anonymity” , In Proceedings of the 12th IEEE International Conference on Trust , Security and Privacy in Computing and Communications, TRUSTCOM 13, pages 372–379, 2013.
[8] P. Samarati. “Protecting respondents` identities in microdata release”, IEEE Trans. on Knowledge and Data Engineering, 13(6), 2001.
[9] A. Machanavajjhala, J. Gehrke, D. Kifer,and M. Venkitasubramaniam”, l-diversity:Privacy beyond k-anonymity”. In proceeding Of the 22nd IEEE International Conference on Data Engineering (ICDE),Atlanta , GA, 2006.
[10] N.Li,T. Li., “t-closeness: Privacy beyond k-anonymity and l- diversity”, Proceedings of 21st IEEE International Con ference on Data Engineering ICDE), Istanbul, Turkey, April 2007.
[11] R.CW. Wong, J. Li,a.WC.Fu, and Ke. “Wang.(α,k)- Anonymity: An Enhanced k-Anonymity Model For Privacy Preserving Data Publishing” , In Proceeeding of 12th International Conference on Knowledge Discovery and Data Mining pp754-759, Philadelphia,PA, 2006.
[12] Bayardo, R. J. and Agrawal, R., “Data Privacy Through Optimal k-Anonymization”, In Proceedings of the 21st International Conference on Data Engineering, ICDE 05, pages 217–228, 2005.
[13] Nergiz, M. E. and Clifton, C. “Thoughts on k-Anonymization”, Data and Knowledge Engineering, 63(3):622–645, 2007.
[14]DataSource:https://drive.google.com/open?id=0B1QMEQlbBZ9zMy1LU0FEaXprem8
[15] Manjusha S. Mirashe, Kapil N. Hande, “Survey on Efficient Technique for Annonymized Microdata Preservation” ,International Journal of Emerging and Development, 2015, Vol.2, Issue 5, ISSN 2249-6149, pp 97-103, March,2015.
[16] UTD Anonymization Toolbox. http://cs.utdallas.edu/dspl/cgi-bin/toolbox/

Deepak Narula, Pardeep Kumar, Shuchita Upadhyaya, "Performance Interpretation of k-Anonymization Algorithms for Discernibility Metric," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.74-78, 2017.

Interstitial Lung Diseases (ILD) effects the lung intestitium part will leads to breathing problems and gradually leads to death. A deep learning technique convolutional neural network have been proposed to aid computer aided diagnosis system which enhances the accuracy of diagnosis of ILDs by physician because automatic tissue characterization is a crucial component of CAD system. Deep Convolutional Neural Network (CNN) concept raise the accuracy of medical image analysis for the lung pattern classification.CNN designed for the interstitial lung diseases, consist of five convolutional layers with 2×2 kernels and LeakyReLU activation functions. The CNN use the Adaptive moment estimation optimizer algorithm as a weight updation mechanism in back propagation a process. Experimental results prove superior performance and efficiency of the proposed approach through the comparative analysis of CNN against previous methods.

Convolutional Neural Network, Computer Aided Diagnosis, Interstitial Lung Diseases, Texture classification.

[1] R.Uppaluri et al.,”Computer recognition of regional lung disease patterns, “Am.j.Respir.Crit.Care Med., vol.160, no.2, pp.648-654, 1999.
[2] K.R Heitmann et.al.”Automatic detection of ground glass opacities on lung HRCT using multiple neural networks, Eur.Radiol” vol.7, no.9, pp.1463-1472, 1997.
[3] M.Demedts and U.Costabel,”ATS/ERS international multidisciplinary consensus classification of the idiopathic interstitial pneumonias,” Eur.Respiratory J., vol.19, no.5, pp.794-796, 2002.
[4] Y.Xu et.al.”Computer aided classification of interstitial lung diseases via MDCT: 3D adaptive multiple feature method (3D AMFM),”Acad.Radiol, vol.13, no.8, pp.969-978, 2006.
[5] P.D.Korfiatis, A.N.Karahaliou, A.D.Kazantzi, C.Kalogeropoulou, and L.I.Costaridou,”Texture based identification and characterization of interstitial pneumonia patterns in lung multidetector CT,” IEEE Trans.Inf.Technol.Biomed., vol.14, no.3, pp.675-680, May 2010.
[6] M.JGanesh et.al.”A texton-based approach for the classification of lung parenchyma in CT images,” in Proc.MICCAI, pp.595-602. 2010.
[7] R.Xu et.al.”Classification of diffuse lung disease patterns on high resolution computed tomography by a bag of words approach,” Proc.MICCAI, vol.14, pt.3, pp.183-90, 2011.
[8] A.Depeursinge et.al.”Near-affine-invariant texture learning for lung tissue analysis using isotropic wavelet frames,” IEEETrans.Inf.Technol.Biomed vol.16, no.4, pp.665-675, Jul.2012.
[9] Y.Song, W.Cai, Y.Zhou, and D.D.Feng,”Feature-based image patch approximation for lung tissue classification,” IEEE Trans.Med.Imag, vol.32, no.4, pp.797-808, Apr.2013.
[10] W.Zhao et al..,”Classification of diffuse lung disease patterns by a sparse representation based method on HRCT images,” in Proc.Int.Conf.IEEE Eng.Med.Biol.Soc., pp.5457-5460,2013,
[11] Q.Li, W.Cai, and D.D.Feng,”Lung image patch classification with automatic feature learning,” in Proc.Annu.Int.Conf.IEEEEng.Med.Biol.Soc., pp.6079-6082,2013.
[12] G.Van Tulder and M.de Bruijne, “Learning features for tissue classification with the classification restricted Boltzmann machine, Med.Comput.Vis” Algorithms for Big Data, pp.47-58, 2014.
[13] M.Gao et.al.”Holistic classification of CT attenuation patterns for interstitial lung diseases via deep convolutional neural networks,” in 1st workshop Deep Learn.Med.Image Anal, pp.41-48, 2015.
[14] Renuka Uppaluri, Eric A Hoffman et.al,”Computer Recognition of Regional Lung diseases Patterns,” in American Journal of Respiratory and Critical care Medicine 160(2):648-54, September 1999.
[15] Marios Anthimopoulos; Stergios Christodoulidis; Lukas Ebner; Andreas Christe Stavroula Mougiakakou”Lung pattern classification for interstitial lung diseases using Deep convolutional Neural Network”,IEEE Trans.Med..vol.35,issue.5,2016
[16] A. Depeursinge et al., “Building a reference multimedia database for interstitial lung diseases,” Comput. Med. Imag. Graph. vol. 36, no. 3, pp. 227–238, 2012.

Sruthy P S, Sanoj S R, "A Deep Learning Approach For the Detection and Classification of Interstitial Lung Diseases Using Convolutional Neural Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.79-82, 2017.

We present a theoretical approach to come up with an effective mechanism for flood relief in terms of facility location problem with certain constraints. Facility location problem is a well studied economical decision problem to locate limited facility on demand points to cover maximum demand. Let consider a facility network under link failure. The problem is to locate emergency response facilities on a network with links that are subject to a failure model, called vulnerability − based dependency. We address the MAX-EXP-COVER-R problem in the facility network subjects to VB-dependency failure model. The MAX-EXP-COVER-R problem is known to be NP-hard. Let the distance factor R is relaxed from MAX-EXP-COVER-R problem, then it becomes MAX-EXP-COVER problem. The MAX-EXP-COVER problem can be solved in linear time using greedy algorithm. The MAX-EXP-COVER problem is further reduced into an instance of full binary tree, and then run MAX-WT-K-LEAF-SUBTREE problem on the tree will outputs the solution for MAX-EXP-COVER problem. Let the distance factor R = 1, then MAX-EXP-COVER-R problem is equivalent to budgeted dominating set problem with budget k and the induced subgraph of the solution set is need not to be connected.

Facility location, Disaster management, Approximation algorithm, NP-Hardness, Dominating sets, Link failure model, Graph theory, Hardness.

[1] Gerard Cornuejols, Marshall L. Fisher, and George L. Nemhauser. “location of bank accounts to optimize float: An analytic study of exact and approximate algorithms.” Management Science, 23(8):789–810, 1977.
[2] Michael R. Garey and David S. Johnson. “Computers and Intractability: A Guide to the Theory of NP-Completeness.” W.H. Freeman and Co., San Francisco, CA, 1979.
[3] S. Guha and S. Khuller. “Approximation algorithms for connected dominating sets”, Algorithmica, 20(4):374–387, 1998.
[4] Refael Hassin, R. Ravi, and F. Sibel Salman. “Tractable Cases of Facility Location on a Network with a Linear Reliability Order of Links”, pages 275–276. Springer Berlin Heidelberg, Berlin, Heidelberg, 2009.
[5] Hervé Kerivin and A. Ridha Mahjoub. “Design of survivable networks: A survey”, Netw., 46(1):1–21, August 2005.
[6] Samir Khuller, Manish Purohit, and Kanthi K. Sarpatwar. “Analyzing the optimal neighborhood: Algorithms for budgeted and partial connected dominating set problems”, In Proceedings of the Twenty-Fifth Annual ACM-SIAM Symposium on Discrete Algorithms, SODA ’14, pages 1702–1713, Philadelphia, PA, USA, 2014. Society for Industrial and Applied Mathematics.
[7] F Sibel Salman and Eda Yücel. “Emergency facility location under random network damage: Insights from the istanbul case”, Computers & Operations Research, 62:266–281, 2015.

Vairaprakash Gurusamy, K. Nandhini, "Facility Location: A Theoretical Approach for Flood Relief," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.83-89, 2017.

The Education system is taking a new dimension, the ICT based teaching and learning. ICT is one of the contemporary factors which shapes the education system and has the ability to transform the system of education. To study the impact of ICT, acceptance level from the measure of intention of the users (i.e. students) has to be surveyed. Hence, Technology Acceptance Model (TAM) is used with the introduction of a latent variable for improvisation within the university settings. Data is collected using questionnaire, which is prepared to keep in mind all the variables in TAM. Various machine learning techniques have been imposed on the dataset and found that Decision tree has exhibited high accuracy in classification. The results prove that the factors impacting on user acceptance are availability of ICT, perceived ease of use, perceived impact in teaching & learning, perceived inhibitors in teaching & learning and perceived pleasure or arousal, availability, and inhibitor to use the system, which is also affected by inhibitors in the use of ICTs in the universities.

Decision tree, Information Communication Technology, Machine Learning, Technology Acceptance Model

[1] B. Anderson and A. Moore, "AD trees for Fast Counting and for Fast Learning of Association Rules", Knowledge Discovery from Databases, pp. 134-138, 1988.
[2] M.G. Armentano, I. Christensen, and S. Schiaffino, "Applying the Technology Acceptance Model to Evaluation of Recommender System", Polibits, vol. 51, no. 10, pp. 73-79, 2015.
[3] M. Chuttur, "Overview of the Technology Acceptance Model: origins, developments and future directions" Working papers on information systems, vol. 9, no.37, pp. 1-22, 2009.
[4] J. Demsar et al., "Orange: Data Mining Toolbox in Python", Journal of Machine Learning Research, 14(Aug), pp. 2349−2353, 2013.
[5] K. Meena et al., "A Decision tree induction approach to study the population dynamics of Helicoverpa armigera (Hübner) and its natural enemies on cotton", Proceedings of the International Multi Conference of Engineers and Computer Scientists, Hong Kong, 2010, vol. 1, pp. 580-586.
[6] T.M. Mitchell, Machine Learning. McGraw Hill, 1997.
[7] S. Mitra, T. Acharya, Data Mining. Multimedia, Soft Computing, and Bioinformatics. New Jersey, John Wiley & Sons, Inc., 2003.
[8] H. Zhao and S. Ram, “Constrained cascade generalization of decision trees”, IEEE Transactions on Knowledge and Data Engineering, vol. 16, no. 6, pp. 727-739, 2004.

Oruan Memoye Kepeghom, J. Cruz Antony, Igenewari Ipeghan Godpower, Richard Tamunoibuomi, "Impact of ICT and Curriculum Development (University Students from Nigeria) using Modified Technology Acceptance Model -TAM," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.90-93, 2017.

Water contamination is one of the significant apprehensions for the green globalization. Potable water is really a scarce resource to today’s generation. Urbanization, overpopulation, industrialization, has lead to tremendous increase in untreated sewage disposal and industrial effluents. This has lead to spread of life threatening diseases. Water surveillance is an important tool to control the level of contamination in the polluted water. In India, water quality is analyzed by manual water quality surveillance methods which exacerbate water quality deterioration. Therefore, the need of a continuous, real-time, in-situ monitoring system for water quality management has risen. Wireless Sensor Network (WSN) which is real-time, continuous and dynamic system has fascinated us for pro-active water quality management. Though there are a number of research papers available on the working of WSN in different areas, the studies on application of WSN in environmental monitoring remains limited. In this paper, we discuss requirement and suitability of WSN for water quality surveillance. This research work deals with clustering approach which will be energy efficient to increase the lifespan of the network. Then the optimization approach is implemented to optimize the performance of the network.

Water; WSN; Water Quality Monitoring

[1] R. Nathiya, S.G. Santhi, "Energy Efficient Routing with Mobile Collector in Wireless Sensor Networks (WSNs)", International Journal of Computer Sciences and Engineering, Vol.2, Issue.2, pp.36-43, 2014.
[2] Lee, H.C.; Banerjee, A.; Fang, Y.M.; Lee, B.J.; King, C.T. “Design of a multifunctional wireless sensor for in situ monitoring of debris flows.” IEEE Transactions on Instrumentation and Measurement, 2010, 59, 2958–2967.
[3] Bayo, A.; Antolín, D.; Medrano, N.; Calvo, B.; Celma, S. “Early Detection and Monitoring of Forest Fire with a Wireless Sensor Network System”. Procedia Engineering 2010, 5, 248–251.
[4] Yunus, E.A.; Ibrahim, K.; Özgür, U. “A framework for use of wireless sensor networks in forest fire detection and monitoring”, Computer Environment Urban Systems. 2012, 36, 614–625.
[5] Aleksandar Milenkovic´, Chris Otto, Emil Jovanov, “Wireless sensor networks for personal health monitoring: Issues and an implementation”, Elsevier, 2006.
[6] Handcock, R.N.; Swain, D.L.; Bishop-Hurley, G.J.; Patison, K.P.; Wark, T.; Valencia, P. “Monitoring Animal Behaviour and Environmental Interactions Using Wireless Sensor Networks, GPS Collars and Satellite Remote Sensing”. Sensors 2009, 9, 3586–3603.
[7] Bahrepour, M.; Meratnia, N.; Poel, M.; Taghikhaki, Z.; Havinga, P.J.M. “Distributed event detection in wireless sensor networks for disaster management.” Proceedings of the 2010 2nd International Conference on Intelligent Networking and Collaborative Systems (INCOS), Thessaloniki, Greece, 24–26 November 2010; pp. 507–512.
[8] Lacono, M.; Romano, E.; Marrone, S. Adaptive monitoring of marine disasters with intelligent mobile sensor networks. In Proceedings of the 2010 IEEE Workshop on Environmental Energy and Structural Monitoring Systems (EESMS), Taranto, Italy, 9 September 2010; pp. 38–45
[9] Qian, H.; Sun, P.; Rong, Y. “Design Proposal of Self-Powered WSN Node for Battle Field Surveillance”. Energy Procedia 2012, 16, 753–757
[10] Padmavathi, G.; Shanmugapriya, D.; Kalaivani, M. “A Study on Vehicle Detection and Tracking Using Wireless Sensor Networks”, Wireless Sensor Networks, 2010, 2, 173–185.
[11] Shahid Siddhik P, S.S.Kumar , “Sea Water Quality Monitoring Using Smart Sensor Network”, International Conference on Control,Instrumentation, Communication and Computational Technologies (lCCICCT), 804-812, 2015
[12] "Guidelines for drinking-water quality, fourth edition". World Health Organization, 2013
[13] Verma, Seema, and Prachi. "Wireless Sensor Network application for water quality monitoring in India", 2012 National Conference on Computing and Communication Systems, 2012
[14] O`Flynn, Brendan, Rafael Martinez-Catala, Sean Harte, C. O`Mathuna, John Cleary, C. Slater, F. Regan, Dermot Diamond, and Heather Murphy. "SmartCoast: a wireless sensor network for water quality monitoring." In 32nd IEEE Conference on Local Computer Networks (LCN 2007), pp. 815-816. Ieee, 2007
[15] Adamo, Francesco, Filippo Attivissimo, Carlo GuarnieriCalò Carducci, and Anna Maria Lucia Lanzolla. "A smart sensor network for sea water quality monitoring." IEEE Sensors Journal 15, no. 5 (2015): 2514-2522
[16] Parmar, Nirav M., and Rahul S. Goradia. "IEEE 802.15. 4 Based Water Quality Monitoring System."
[17] Vijayakumar, N., and R. Ramya. "The real time monitoring of water quality in IoT environment." In Innovations in Information, Embedded and Communication Systems (ICIIECS), 2015 International Conference on, pp. 1-5. IEEE, 2015
[18] Boyne, MsAmruta, and Mr SN Kulkarni. "Build Out of Embedded PLC to Increase Water Supremacy."
[19] Faustine, Anthony, Aloys N. Mvuma, Hector J. Mongi, Maria C. Gabriel, Albino J. Tenge, and Samuel B. Kucel. "Wireless sensor networks for water quality monitoring and control within lake victoria basin: prototype development." Wireless Sensor Network 6, no. 12 (2014): 281.
[20] Zhang, Mingfei, Daoliang Li, Lianzhi Wang, Daokun Ma, and Qisheng Ding. "Design and development of water quality monitoring system based on wireless sensor network in aquaculture." In International Conference on Computer and Computing Technologies in Agriculture, pp. 629-641. Springer Berlin Heidelberg, 2010.
[21] Karuppasamy K, Abinaya B, Sudha R, Arunprasath JP, “Water Quality Monitoring And Control Using Wireless Sensor Networks”, International Research Journal Of Engineering And Technology, pp 1202-1206, 2016.
[22] Barabde M.N., Danve S.R., “Continuous Water Quality Monitoring System for Water Resources at Remote Places”, International Journal of Engineering Research and General Science Volume 3, Issue 3, Part-2 , 172-177, 2015.
[23] Annese V.F., Biccario G.E., Venuto D.D., “Wireless Sensor Network for Real Time Sea Water Quality Monitoring”, doi: 10.13140/RG.2.1.2583.9848
[24] Vasilescu, I., Kotay, K., Rus, D., Corke, P., Dunbabin, M., and Schmidd, P. “Data collection, storage and retrieval with an underwater sensor network, In Proc.”, IEEE SenSys, Diego, California, USA , 2005, pp.154-165.
[25] X. Yang, K. G. Ong, W. R. Dreschel, K. Zeng, C. S. Mungle, C. A. Grimes, “Design of a wireless sensor network for longterm, in-situ monitoring of an aqueous environment,” in Sensors, vol 2, pp. 455-472, 2002
[26] Ji Wang, Xiao-li Ren, Yu-li Shen, Shuang-yin Liu, “A Remote Wireless Sensor Networks for Water Quality Monitoring,” in International Conference on Innovative Computing and Communication and 2010 Asia- Pacific Conference on Information Technology and Ocean Engineering, pp. 7-12, Jan. 2010
[27] M. Zennaro, G. Dogan, Z. Cao, M. Bahader, H. Ntareme and A. Bagula, “On the design of a Water Quality Wireless Sensor Network (WQWSN): an Application to Water Quality Monitoring in Malawi,” in Mieso, Denko, Eds. Proceedings IEEE NGWMN, Vienna, Austria, 2009.

Archna Gupta, Pooja Rani, Yashwant Singh, "Real Time Remote Wireless Sensor Network for Water Quality Monitoring," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.94-99, 2017.

In networking environment due to increasing demand of resources reliability, load balancing and efficient routing of data are still becoming a major issue. So I am proposing an efficient approach for solving all these issues using an innovative technique called Software Defined Networks(SDN).Software Defined Networks (SDN) has evolved as an emerging research area where it provides efficient network resources by separating data plane from control plane. Network virtualization allows sharing of physical resources by different users where each user executes their applications over its virtual network and its main features are isolation, multitenancy and simplified segmentation. The main component for virtualizing SDN is Hypervisor that abstracts physical SDN’s into isolated virtual SDN’s having its own controller. This paper presents different network hypervisors and its comparison shown will have a great impact on researchers building an efficient network infrastructure.

SDN, Hypervisor, Flowvisor, Multitenancy

[1]. Kilian Rausch, Network Virtualization - An Overview, Network Architectures and Services, July 2011
[2]. H. Wen , Network Virtualization-An Overview, Wireless Virtualization, Springer Briefs in Computer Science,2013.
[3]. N.M. Musharraf, K. Chowdhury, R. Boutaba, A survey on network virtualization. J. Comp.Telecommun. Networking 54(5), 862–876 (2010)
[4]. Gregor Schaffrath,Christoph Werle§ Panagiotis Papadimitriou, Anja Feldmann, Roland Bless, Adam Greenhalgh,Andreas Wundsam, Mario Kind, Olaf Maennel, Laurent Mathy, Network Virtualization Architecture:Proposal and Initial Prototype, ACM, August 17, 2009.
[5]. Raj Jain and Subharthi Paul, Network Virtualization and Software Defined Networking for Cloud Computing: A Survey, IEEE Communication Magazine, November 2013
[6]. Morty Eisen, Introduction to Virtualization, IEEE Circuits and Systems (CAS) Society, April 28,2011.
[7]. Daniel A. Menasce, Virtualization: Concepts, Applications, And Performance Modeling, National Geospatial-Intelligence Agency (NGA)
[8]. Michael Pearce, Sherali Zeadally, Ray Hunt,Virtualization: Issues, Security Threats,andSolutions, ACM, 2013.
[9]. Victor T. Costa, Luís Henrique M. K. Costa, Vulnerabilities and solutions for isolation in FlowVisor-based virtual network environments, Journal of Internet Services and Applications,2015
[10]. Amanpreet Kaur, Sawtantar Singh Khurmi, "A Study Of Cloud Computing Based On Virtualization And Security Threats", International Journal of Computer Sciences and Engineering, Vol.5, Issue.9, pp.108-112, 2017.
[11]. Gabriel Cephas Obasuyi, Arif Sari, Security Challenges of Virtualization Hypervisors in Virtualized Hardware Environment, Scientific research publications, 2015
[12]. Anand More, Priyesh Kanungo, "Use of Cloud Computing for Implementation of e-Governance Services", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.3, pp.115-118, 2017.
[13]. Marisol García-Valls, Tommaso Cucinotta,Chenyang Lu,Challenges in real-time virtualization and predictable cloud computing, Elseiver,2014
[14]. Rob Sherwood, Glen Gibb , Kok-Kiong Yap ,Guido Appenzeller ,Martin Casado , Nick McKeown, Guru Parulkar, Flowvisor: A Network Virtualization layer, Openflow lab, 2009
[15]. Yiannis Yiakoumis, Kok-Kiong Yap, Sachin Katti, Guru Parulkar, Nick McKeown, Slicing home networks, Home nets, 2011.
[16]. Wanqing You, Kai Qian, Xi He, Ying Qian, Towards Security in Virtualization of SDN, International Journal of Computer, Electrical, Automation, Control and Information Engineering , 2014
[17]. Masayoshi Kobayashi, Srini Seetharaman, Guru Parulkar, Guido Appenzeller, Joseph Little, Johan van Reijendam, Paul Weissmann, Nick McKeown, Maturing of OpenFlow and Software Defined Networking through Deployments, Elseiver,2012

P. Sasibhushana Rao, C.Kalyan Chakravarthy, "Network Hypervisors pros and cons: A survey," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.100-104, 2017.

Nowadays, Android Malware is coded so wisely that it has become very difficult to detect them. The static analysis of malicious code is not enough for detection of malware as this malware hides its method call in encrypted form or it can install the method at runtime. The System Calls tracing is an effective dynamic analysis technique for detecting malware as it can analyze the malware at the run time. Moreover, this technique does not require the application code for malware detection. Thus, this can detect that Android malware also which are difficult to detect with static analysis of code. The paper presented the framework of detecting malicious application from 81 malware families by analysis of dynamic feature System Calls Invoked with machine learning algorithms.

System Call,Malicious application detection,malware families

[1] Schmidt, Aubrey-Derrick, Hans-Gunther Schmidt, Jan Clausen, Kamer A. Yuksel, Osman Kiraz, Ahmet Camtepe, and Sahin Albayrak. "Enhancing security of linux-based android devices." In Proceedings of 15th International Linux Kongress, pp. 1-16. 2008.
[2] Kolbitsch, Clemens, Paolo Milani Comparetti, Christopher Kruegel, Engin Kirda, Xiao-yong Zhou, and XiaoFeng Wang. "Effective and Efficient Malware Detection at the End Host." In USENIX security symposium, pp. 351-366. 2009.
[3] Wang, Xinran, Yoon-Chan Jhi, Sencun Zhu, and Peng Liu. "Detecting software theft via system call based birthmarks." In Computer Security Applications Conference, 2009. ACSAC`09. Annual, pp. 149-158. IEEE, 2009.
[4] Lanzi, Andrea, Davide Balzarotti, Christopher Kruegel, Mihai Christodorescu, and Engin Kirda. "Accessminer: using system-centric models for malware protection." In Proceedings of the 17th ACM conference on Computer and communications security, pp. 399-412. ACM, 2010.
[5] Sanz, Borja, Igor Santos, Carlos Laorden, Xabier Ugarte-Pedrero, and Pablo Garcia Bringas. "On the automatic categorisation of android applications." In Consumer Communications and Networking Conference (CCNC), 2012 IEEE, pp. 149-153. IEEE, 2012.
[6] E.Tchakount, P.Dayang .”System calls analysis of malware on android”. International Journal of Science and Technology. Vol. 2 issue 9,2013
[7] Sato, Ryo, Daiki Chiba, and Shigeki Goto. "Detecting Android malware by analyzing manifest files." Proceedings of the Asia-Pacific Advanced Network 36 (2013): 23-31.
[8] Huang, Chun-Ying, Yi-Ting Tsai, and Chung-Han Hsu. "Performance evaluation on permission-based detection for android malware." In Advances in Intelligent Systems and Applications-Volume 2, pp. 111-120. Springer, Berlin, Heidelberg, 2013.
[9] Canfora, Gerardo, Francesco Mercaldo, and Corrado Aaron Visaggio. "A classifier of malicious android applications." In Availability, Reliability and Security (ARES), 2013 Eighth International Conference on, pp. 607-614. IEEE, 2013.
[10] Liu, Xing, and Jiqiang Liu. "A two-layered permission-based Android malware detection scheme." In Mobile cloud computing, services, and engineering (mobilecloud), 2014 2nd ieee international conference on, pp. 142-148. IEEE, 2014.
[11] Jeong, Youn-sik, Hwan-taek Lee, Seong-je Cho, Sangchul Han, and Minkyu Park. "A kernel-based monitoring approach for analyzing malicious behavior on android." In Proceedings of the 29th Annual ACM Symposium on Applied Computing, pp. 1737-1738. ACM, 2014.
[12] Arp, Daniel, Michael Spreitzenbarth, Malte Hubner, Hugo Gascon, Konrad Rieck, and C. E. R. T. Siemens. "DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket." In NDSS. 2014.
[13] Kang, Hyunjae, Jae-wook Jang, Aziz Mohaisen, and Huy Kang Kim. " Comparative analysis of classification algorithm in EDM for improving student performance." International Journal of Distributed Sensor Networks (2015).
[14] S.Malik and K. Khatter. "AndroData: A Tool for Static & Dynamic Feature Extraction of Android Apps." International Journal of Applied Engineering Research,Vol. 10, issue 94, 2015.
[15] S.Malik and K. Khatter. "System Call Analysis of Android Malware Families." Indian Journal of Science and Technology,Vol. 9, issue 21 ,2016.
[16] B.R. Patel, "Comparative analysis of classification algorithm in EDM for improving student performance", International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.171-175, 2017.

Sapna Malik, "Android System Call Analysis for Malicious Application Detection," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.105-108, 2017.

As of late, the gap between online business and person to person communication has turned out to be m ore and more indistinct. Numerous online businesses reinforce the social sign in system where customers can sign-in on their portals by applying their informal organization characters, like their Twitter or Facebook IDs. Users additionally can announce their recently bought items on social networking or microblogs by mentioning the corresponding product url from the online business sites. In this paper, we put forward an innovative solution for “cross site cold start web product recommendation” to endorse different items from “e-commerce” sites for users at “microblogging or social networking” sites in “cold start positions”, a very rare concept explored before. The foremost task is to utilize the information fetched from microblogging or social interacting. In exact, we propose learning the two client and items element depictions called client embeddings and item embeddings respectively from the information fetched through online commercial sites using “recurrent neural systems”. And later applying “Altered Gradient boosting trees” model to convert user long range informal statement keywords into user embedding’s. We at that point build up a component based “Lattice factorization method” which can be used to learn client embedding’s for “cold-start product recommendation”.

E-commerce, recurrent neural networks, demographic, microblogs, product recommendation.

[1] B. Xiao as well as I. Benbasat, "Web based business item proposal operators: Use, attributes, and effect" MIS Quarterly, 2007,vol. 31, pp. 137 to 209.
[2] J. Gordon and A. P. Singh, "Social knowledge through aggregate network factorization," in Proceeding’s fourteenth ACM SIGKDD international conference knowledge disclosure data mining, pp. 650 to 658, 2008.
[3] B.Li, X. Xue, and Q. Yang “Can pictures and records collaborate? Cross-domain collaborative clarifying for sparsity lessening,” in Proceedings twenty first international joint Conference artificial intelligence pp. 2052 to 2057, 2009.
[4] M. Gearing, “Trade deals expectation and thing suggestions utilizing client socioeconomics at stock level,” SIGKDD Exploration Newsletter, Dec. 2008. vol. 10, no. 2, pp. 84-89.
[5] Y. Zhang and J. Wang, "Chance demonstrate for E-trade suggestion: Correct item; perfect period," in Processes thirty sixth Institute, ACM SIGIR Conference research, Create Information Recovery, pp. 303 to 312, 2013
[6] Y. Korean, C. Volinsky, "Network factorization strategies for recommender frameworks," Computer, volume 42, number 8, pp. 30 to 37, August 2009.
[7] L. Hong, B. Davison, and A. S. Doumith, "Co-factorization machines: Modelling client interest and foreseeing singular choices in Twitter," in Proc. 6th ACM International. Conf. Web Hunt Data Mining, pp. 557 to 566, 2013.
[8] M. R. Lyu, H. Ma, I. king, and T. C. Zhou, "Enhancing recommender frameworks through consolidating public logical data," ACM Transactional. Information. System, no. 2, 2011.vol. 29.
[9] M. Pennacchiotti and Y. Zhang, “Recommending branded items from social interacting sites,” in Proceeding’s seventh ACM Conference. Recommender Systems. Hong Kong, China, pp. 77 to 84, October 12 to 16, 2013.
[10] Wayne Xin Zhao, Edward y.chang , Ji-oRong Wen, "connecting social media to e-commerce cold start product recommendation using microblogging information" volume 28, number 5, may 2016.

U. Lakshmi Prasanna, A. Revathi, "Web Recommendation Using Microblogging Information," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.109-114, 2017.

Sensor networks that are utilized underwater are usually called Underwater Wireless Sensor Networks (UWSNs). In UWSNs, nodes have limited source of energy, hence energy remains a main issue in underwater network. The lifetime of network decreases due to variation in energy consumption of nodes and creates network holes. This paper presents Energy Efficient Hierarchical Clustered based Routing Protocol (EEHCR). In this paper, the network is divided in regions based on energy, which are high energy nodes, intermediate energy nodes and low energy nodes. The nodes are allowed to perform switching between levels. When the energy of a node in higher level decrease below intermediate level, the node switch to low level and another node from intermediate level switch to higher level. In this way node survival time will be higher and results in enhancing network lifetime.

UWSNs, routing protocol, EEHR, EEHCR

[1] M. Ijaz, N. Javaid, and H. Maqsood, “An Energy Efficient Hybrid Clustering Routing Protocol for Underwater WSNs,”IEEE 3rd Int. Conf. Eco-friendly Comput. Commun. Syst., no. JANUARY, 2016.
[2] M. Muhammad, N. Javaid, S. Hussain, T. Hafeez, H. Maqsood, and S. Zarar, “EEHR: Energy Efficient Hybrid Routing Protocol for Underwater WSNs,”Proc. - 2015 10th Int. Conf. Broadband Wirel. Comput. Commun. Appl. BWCCA 2015, pp. 20–26, 2016.
[3] I. Snigdh, R. Khichar, and N. Gupta, “Lifetime Prolonging Algorithm for Underwater Acoustic Sensor Network 1,” Middle East J. Sci. Res., vol. 13, no. 6, pp. 818–822, 2013.
[4] S. Zarar “Increased throughput DB-EBH protocol in underwater wireless sensor networks,”Proc. - IEEE 30th Int. Conf. Adv. Inf. Netw. Appl. Work. WAINA 2016, no. March, pp. 571–576, 2016.
[5] F. Fazel, M. Fazel, and M. Stojanovic, “Random access compressed sensing for energy-efficient underwater sensor networks,” IEEE J. Sel. Areas Commun., vol. 29, no. 8, pp. 1660–1670, 2011.
[6] A. Sinha and I. Yadav, “Review on Under Water Acoustic Sensor Network,”Int. J. Sci. Eng. Technol. Res., vol. 5, no. 4, pp. 1269–1275, 2016.
[7] A. Rana, M. Bala, Varsha, "Performance Analysis of Energy Efficient Clustering Protocol in WSN", International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.1-5, 2017.
[8] T. Liaqat, N. Javaid, S. M. Ali, M. Imran, and M. Alnuem, “Depth-based energy-balanced hybrid routing protocol for underwater WSNs,”Proc. - 2015 18th Int. Conf. Network-Based Inf. Syst. NBiS 2015, pp. 20–25, 2015.
[9] S. Climent, J. V. Capella, N. Meratnia, and J. J. Serrano, “Underwater sensor networks: A new energy efficient and robust architecture,”Sensors, vol. 12, no. 1, pp. 704–731, 2012.
[10] N. Li, J.-F. Martínez, J. Meneses Chaus, and M. Eckert, “A Survey on Underwater Acoustic Sensor Network Routing Protocols,”Sensors, vol. 16, no. 3, pp. 414, 2016.

K. Bansal, P. Singh, "Energy Eficient Hierarchical Clustered Based Routing for Underwater Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.11, pp.115-119, 2017.