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Internet of Things (IOT) plays fundamental role in connecting the surrounding environmental things to the network and made easy to access things from any distant location. Sophisticated Parking Availability Prediction (SPAP) system is becoming important part of intelligent transportation system due to rapid increase in vehicle density particularly during the peak hours of the day. It is a difficult task for the drivers to find a parking space to park their vehicles. In this paper, we study parking availability and prediction techniques which provide most favorable solution for parking problem in metropolitan cities. SPAP system enables the user to find the nearest parking area and gives availability of parking space in that respective parking area. It mainly focuses on reducing the time in finding the parking space and also it avoids the unnecessary travelling through overflowing parking area. Thus it reduces the fuel consumption which in turn reduces carbon footprints present in the environment.

IOT, SPAP System, ETA, RFID, PRA Algorithm

[1] T. Rajabioun, PA. Ioannou, “On-Street and Off-Street Parking Availability Prediction Using Multivariate Spatiotemporal Models”, IEEE transactions on intellegent transportation systems, Vol.16, Issue.5, pp.2913-2924, 2015.
[2] T. Rajabioun, B. Foster, P. Ioannou , “Intelligent parking assist”, 21st Mediterranean Conference on Control and Automation, Greece, pp. 1156-1161, 2013.
[3] F. Caicedo, C. Blazquez, P. Miranda, “Prediction of parking space availability in real time”, Expert Syst. Appl., Vol.39, Issue. 8, pp. 7281-7290, 2012.
[4] E. Wu, J. Sahoo, C. Liu, M. Jin, S. Lin, “Agile urban parking recommendation service for intelligent vehicular guiding system”, Intell. Transp. Syst. Mag. IEEE, Vol.6, Issue. 1, pp. 35-49, 2014.
[5] E.I. Vlahogianni, J.C. Golias, M.G. Karlaftis, “Short-term traffic forecasting: Overview of objectives and methods”, Transport Reviews, Vol. 24, Issue. 5, pp. 533-557, 2004.
[6] B.L. Smith, B.M.Williams, R.Keith Oswald, “Comparison of parametric and nonparametric models for traffic flow forecasting”, Transportation Research Part C Emerging Technologies , Vol. 10, Issue.4, pp. 303-321, 2002
[7] A. Joshi, K.S. Kharade, V.S. Patil, D.A. Kulkarni , “IoT Based Smart Parking for Metro Cities”, International Journal of Computer Sciences and Engineering, Vol.5, Issue.3, pp.57-59, 2017.
[8] Pranay Kujur and Kiran Gautam, “Smart Interaction of Object on Internet of Things”, International Journal of Computer Sciences and Engineering, Vol.3, Issue.2, pp.15-19, 2015.
[9] Renuka R., S. Dhanalakshmi, “Android Based Smart Parking System Using Slot Allocation & Reservations, “ ARPN Journal of Engineering and Applied Sciences, Vol.10, Issue.7, pp. 3116-3120, 2015.

A.C. Buchade, "Sophisticated Parking Availability Prediction System in IoT Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.132-136, 2017.

With the improvement of distributed computing, outsourcing information through distributed server’s pulls in loads of considerations. To ensure the security and accomplish adaptable fine-grained record access control, (ABE) was proposed and utilized as a part of distributed storage framework. As client repudiation is the essential issue in ABE plans. we proposed an cipher text-arrangement trait-based encryption (CP-ABE) plan with effective client repudiation for distributed storage framework. The concern about client repudiation can be explained productively by presenting the idea of client gathering. At the point when any client leaves, the gathering supervisor will redesign client’s PK with the exception of the individuals, who have been declined. Also, CP-ABE plan has substantial calculation cost, as it becomes straightly with the intricacy to access structure. To diminish the calculation price, utilizing big calculation burden to cloud administration suppliers without spilling document substance and mystery keys. Notably, our plan can withstand conspiracy assault performed by denied clients collaborating with existing clients.

distributed computing, cipher-text attribute-based encryption, Data owner, cloud server, private keys, co-llusion attack

[1] VG. Pandey, A. Sahai, B. Wates, “Property Based Encryption for Fine-Grained Access Control of Encrypted Data”, ACM Computer and Communications Security, Vol.4, Issue.2, pp.89-98, 2006,
[2] Y. Ming, L. Fan, H. Jing-Li, W. Zhao-Li, “An Efficient Attribute Based Encryption Scheme with Revocation for Outsourced Data Sharing Control”, 2011 First International Conference on Instrumentation, Measurement, Computer, Communication and Control, Beijing, pp. 516-520, 2011.
[3] A. Sharma, RS Thakur, S. Jaloree, “Investigation of Efficient Cryptic Algorithm for Storing Video Files in Cloud”, International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.6, pp.8-14, 2016.
[4] R. Lathwal, V.K. Saroha, “A Study on Biometric Technology and Access Control System: Network Security”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.7, pp.31-35, 2014.
[5] M. Chase, “Multi-authority Attribute Based Encryption”, Proc. 4th Theory of Cryptography Conference, Berlin, pp. 515-534, 2007.
[6] Z. Liu, Z. Cao, Q. Huang, D.S. Wongand, T.H. Yuen, “Fully Secure Multi-Authority Ciphertext-Policy Attribute-Based Encryption with-out Random Oracles”, Proc.16th European Symposium on Research in Computer Security, Berlin, pp. 278-297, 2011.
[7] A. Sahai and B. Waters, “Fuzzy Identity-Based Encryption”, EUROCRYPT-LNCS, Vol. 3494, Issue. 8, pp. 457-473, 2005.
[8] J.G. Han, W. Susilo, Y. Mu, J. Yan, “Privacy-Preserving Decentral-ized Key-Policy Attribute-Based Encryption”, IEEE Transactions on Parallel and Distributed Systems, Vol.23, No.11, pp. 2150-2162, 2012.
[9] D. Boneh, M.K. Franklin, “Identity-Based Encryption from theWeil Pairing”, CRYPTO ’01, LNCS, Vol. 2139, Issue.2, pp. 213-229, 2001.
[10] J. Hr and D. K. Noh, “Attribute-Based Access Control with Efficient Revocation in Data Outsourcing Systems”, IEEE Transactions onParallel and Distributed Systems, Vol. 2, Issue.1, pp. 1214-1221,2011.
[11] P.K. Tyswski, M.A. Haan, “Hybrid Attribute-Based Encryption and Re-Encryption for Scalable Mobile Applications in Clouds”, IEEE Transactions on Cloud Computing, Vol. 5, Issue.8, pp. 172-186, 2013.
[12] J.W. Li, C.F. Jia, J. Li, X.F. Chen, “Outsourcing Encryption of At-tribute-Based Encryption with Mapreduce”, Proc.14th International conference on Information and Communications Security, Berlin, pp.191-201, 2012.
[13] M. Green, S. Hohenberger, B. Waters, “Outsourcing the decryp-tion of ABE ciphertexts”, USENIX, Vol.10, Issue.8, pp. 1-34, 2011.
[14] H.L. Qian, J.G. Li, Y.C. Zhang, “Privacy-Preserving Decentralized Ciphertext-Policy Attribute-Based Encryption with Fully Hidden Ac-cess Structure”, Proc.15th International ConferenceonInformation and Communications Security, Berlin, Vol. 23, Issue. 4, pp.363-372, 2013.
[15] J.T .Ning, Z.F. Cao, X.L. Dong, L.F. Wei, X.D. Lin, “Large Universe Ciphertext-Policy Attribute-Based Encryption with White-Box Trace-ability”, Proc.19th European Symposium on Research inComputer Security, Berlin, pp. 55-72, 2014.

S. Rajput, Iyapparaja M, "Extensible and Fine-Grained Characteristics-Positioned Information Storage in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.137-142, 2017.

The administrator has to know the network bandwidth and other resources that are used for accounting and auditing. This creates an emphasis to monitor network traffic and conduct analysis to ensure smooth operations performed routinely. Time is an important factor, which contributes to the variations of packet flow. flexible and allows powerful filters during or after capture to isolate traffic by specific node, protocol, and packet content In this paper, initially, the packet movement in the network was monitored with only hypertext transfers using HTTP. The same movement was analyzed with respect to other file transfer applications. The number of file downloads between the HTTP server and clients are also varied to follow the behavior of data. We also designed and developed multithreaded socket applications using java which was again monitored and analyzed using the above methodology. We compared the reading and the experimental results shows that the developed application outperforms in terms of packet acceleration compared to the previous ones.

packet, bandwidth, delay, throughput, hypertext, HTTP, server, client, multithread and socket

[1] Akash Deepa, K.S. Kahlonb, Harish Kumara, “Survey of Scheduling Algorithms in IEEE 802.16 PMP Networks”, Egyptian Informatics Journal Vol.15, Issue.1, pp.25-36, 2014.
[2] Sol Lim, KJ. Lee, SY. Kim, CS. Chae, “Intae Hwang and Dae Jin Kim* Implementation of IR-UWB MAC Development Tools Based on IEEE 802.15.4a”, International Journal of Control and Automation Vol. 8, No. 4 , pp.275-286, 2015.
[3] Mahesh Kumar, Rakhi Yadav, “TCP and UDP Packets Analysis Using Wire shark”, International Journal of Science, Engineering and Technology Research (IJSETR), Vol.4, Issue.7, 2015.
[4] Niu Xiaoguang, Zhu Ying, Cao Qingqing, Zhang Xining, Xie Wei, Zheng Kun, “An online-traffic prediction based route finding mechanism for smart city”, International Journal of Distributed Sensor Networks, Vol.11, Issue.8, pp.1-12, 2015.
[5] Bast Hannah, Delling Daniel, Goldberg Andrew, Muller Hannemann, Matthias Pajor, Thomas Sanders, Peter Wagner, Doro Thea, Werneck Renato, “Route planning in transportation networks”, Technical Report MSRTR-2014, US, pp-1-68, 2014.
[6] Yang Zhaosheng, Mei Duo, Yang Qingfang, Zhou Huxing, Li Xiaowen, “Traffic flow prediction model for large-scale road network based on cloud computing”, Mathematical Problems in Engineering, Vol.14, Issue.7, pp;23-29, 2014.

Prathap M., A.S. Thanamani, "Real-time Packet Behavior Response in Socket Application," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.143-146, 2017.

Segmentation is the process of dividing a digital image into number of parts of interest. The goal of segmentation is to rearrange and additionally change the representation of an image into something that is more significant and less demanding to study. The aftereffect of picture segmentation is an arrangement of areas that all things considered cover the whole picture, where every pixel in a region is comparative concerning some trademark or registered property, for example, color, intensity, or texture. This paper discusses and reviews the various segmentation techniques like Edge Based, Threshold, Region Based, Clustering and Watershed segmentation used in leaves analysis. This paper shows how different techniques of segmentation used in different application of image processing. Comparative analysis of different methods shown in table and concluded with advantages and disadvantages of segmentation techniques in plant leaf analysis. Edge based and Thresholding techniques are used usually with gray image of plant leaves and Region Based, Clustering and Watershed segmentation technique used with color image of leaves.

Image Segmentation, thresholding, clustering, Region Based, Clustering, Watershed segmentation, plant leaves

[1] Shen Pan, “Edge Detection of Tobacco Leaf Images Based on Fuzzy Mathematical Morphology”, The 1st International Conference on Information Science and Engineering (ICISE2009), Nanjing, pp. 1219-1222, 2009.
[2] P. Umorya, R. Singh, "A Comparative Based Review on Image Segmentation of Medical Image and its Technique", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.71-76, 2017.
[3] Dibya Jyoti Bora, Anil Kumar Gupta, “A New Efficient Color Image Segmentation Approach Based on Combination of Histogram Equalization with Watershed Algorithm”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.156-167, 2016.
[4] Darshana A., Jharna Majumdar, Shilpa Ankalaki, “Segmentation Method for Automatic Leaf Disease Detection”, IJIRCCE, Vol. 3, Issue 7, pp.1-7, 2015.
[5] V. Premalatha, M.G. Sumithra, S. Deepak, P. Rajeswari, “Implementation of Spatial FCM for Leaf Image Segmentation in Pest Detection”, IJARCSSE, Vol.4, Issue.10, pp. 471-477, 014.
[6] K. Singh, A. Kalra, "Improving MRI Segmentation by Fuzzy C Mean Clustering Algorithm Using BBHE Techniques", International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.143-147, 2015.
[7] Azzeddine Riahi, "Image Segmentation Techniques Based on Fuzzy C-Means and Otsu, Applied to the Brain MRI in Tumor Detection", International Journal of Computer Sciences and Engineering, Vol.3, Issue.12, pp.89-101, 2015.
[8] Xiaojing Niu, “Image Segmentation Algorithm for Disease Detection of Wheat Leaves”, 2014 IEEE Proceedings of the 2014 International Conference on Advanced Mechatronic Systems, Japan, pp.270-273, 2014
[9] N. Valliammal, S.N. Geethalakshmi , “ A Novel Approach for Plant Leaf Image Segmentation using Fuzzy Clustering” International Journal of Computer Applications, Vol.44, No.13, pp.10-20, 2012.
[10] Simone Buoncompagni, “Leaf Segmentation under Loosely Controlled Conditions”, BMVC Press, US, pp.1331-13312, 2015.
[11] PR. Hill, CN. Canagarajah, DR. Bull, “Image Segmentation Using a Texture Gradient Based Watershed Transform”, IEEE Transactions on Image Processing, Vol. 12, No. 12, pp.1618-1633, 2003

SS. Lomte, A.P. Janwale, "Plant Leaves Image Segmentation Techniques: A Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.147-150, 2017.

In Ad hoc wireless networks nodes that can move freely have become key research areas in past and continued to dominate now also. Mobile adhoc network are infrastructure-less network in which nodes are mobile but these nodes have limited battery power and signal strength. In mobile adhoc network (MANET) no fixed base station so in MANET nodes behaves like router and host. In this paper Extended Zone Routing Protocol is the extension of Zone Routing Protocol (ZRP). Zone routing protocol combines the benefits of each Proactive and Reactive protocol therefore it`s a hybrid routing protocol. In ZRP routing within the zone is processed by Proactive routing protocols and out of the zone is completed by Reactive routing protocols. Extended Zone Routing Protocol contains additional routing table called Neighbour Routing Table.

Proactive Routing, Reactive Routing, Neighbour routing Table, Bordercast

[1]. AHA. Rahman, ZA Zukarnain, “Performance Comparison of AODV, DSDV and I-DSDV Routing Protocols in Mobile Ad Hoc Networks”, European Journal of Scientific Research, Vol.31, No.4, pp.566-576, 2009.
[2]. S. Sinha, B. Sen, “Effect of Varying Node Density and Routing Zone Radius in ZRP: A Simulation Based Approach”, International Journal on Computer Science and Engineering (IJCSE), vol.4, no.6, pp.1096-1099, 2012.
[3]. UK Singh, SL. Mewada, L. Laddhani, K. Bunkar, “An Overview and Study of Security Issues & Challenges in Mobile Ad-hoc Networks (MANET)”, International Journal of Computer Science and Information Security, Vol.9, No.4, pp.106-111, 2011.
[4]. Haas, Zygmunt J, “Determining the Optimal Configuration for the Zone Routing” IEEE Journal on Selected Areas in Communications, Vol. 17, No. 8, pp. 1395-414, 1999.
[5]. N. Mannan, S. Khurana, "Comparative Analysis of Reactive Protocols in Mobile Ad-Hoc Networks", International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.233-237, 2014.
[6]. UK. Singh, J. Patidar, KC. Phuleriya, "On Mechanism to Prevent Cooperative Black Hole Attack in Mobile Ad Hoc Networks", International Journal of Scientific Research in Computer Science and Engineering, Vol.3, Issue.1, pp.11-15, 2015.
[7]. L. Pal, P. Sharma, N. Kaurav, SL Mewada, "Performance Analysis of Reactive and Proactive Routing Protocols for Mobile Ad-hoc –Networks", International Journal of Scientific Research in Network Security and Communication, Vol.1, Issue.5, pp.1-4, 2013.
[8]. B. Karthikeyan, S. Hari Ganesh, J.G.R. Sathiaseelan,and N.Kanimozhi, “High Level Security with Optimal Time Bound Ad-Hoc On-demand Distance Vector Routing Protocol (HiLeSec-OptiB AODV)”, International Journal of Computer Sciences and Engineering, Vol.4, Issue.4, pp.156-164, 2016.
[9]. S. Mewada, UK. Singh, P. Sharma, “Simulation Based Performance Evaluation of Routing Protocols for Mobile Ad-hoc Networks (MANET)", International Journal of Computer Science, Information Technology and Security, Vol. 2, No. 4, pp.728-732, 2012.
[10]. C. Rakholiya, RD. Joshi, “Performance Enhancement of Zone Routing Protocol in MANET for Reliable Packet Delivery”, InProc. of the Intl. Conf. on Advances in EECSE, USA, pp.1-11, 2012.
[11]. Ritika Kachal, Shrutika Suri, “Comparative Study and Analysis of DSR, DSDVAND ZRP in Mobile Ad-Hoc Networks”, Vol.2 , Issue.5, pp.148-152, 2014.

S.M. Faisal, A.K. Vajpayee, "Extended Zone Routing Protocol," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.151-154, 2017.

In an aeon where information is precious, all these data need to uncover the relations presented between a set of unlabeled dataset for the purposes of manifold - revise, explore, sort, store, analyze; arises all the more. A very feasible way to explore the relations between data is clustering, an unsupervised data mining technique. Clustering aims to group like data points together in clusters with no similarity between data points of different clusters and leaves behind outliers or points not belonging to any of the clusters. Clustering can be applied to all types of data with varying nature (numeric, categorical, mixed), and dimensions (low, high), however, methodologies and similarity measures that can be applied may vary accordingly. In this manuscript we will discuss about various technologies used for clustering of data like role of distance metrics in clustering, clustering using ensembles and dimensionality reduction/minimaization techniques for modeling complex data relations.

Clustering,Distance Metric Styling, Ensembling ,Large Dimensions

[1] A. Hinneburg. and D. Keim, “An efficient approach to clustering large multimedia databases with noise”, Proceedings of the 4th ACM SIGKDD Conference, US, pp. 58-65, 1998.
[2] A. Rodriguez, A. Laio, “Clustering by fast search and find of density peaks”, Science, Vol. 344, Issue.6191, pp. 1492-1496, 2014.
[3] J. Y. Chen, H.H. He, “A fast density-based data stream clustering algorithm with cluster centers self-determined for mixed data”, Information Sciences, Vol. 345, Issue.6, pp. 271-293, 2016.
[4] A. E. Bayá, P. M. Granitto, “Clustering gene expression data with a penalized graph-based metric”, BMC Bioinformatics, Vol. 12, Issue.1,pp.10-21, 2011.
[5] G. Hinton, S. Roweis, “Stochastic neighbor embedding”, Proceedings of International Conference on Advances in Neural Information Processing Systems, US, pp. 833-840, 2003.
[6] M. Belkin, P. Niyogi, “Laplacian eigenmaps for dimensionality reduction and data representation”, Neural Computation, Vol.15, No. 6, pp. 1373-1396, 2003.
[7] A. Strehl, J. Ghosh, “Cluster ensembles: A knowledge reuse framework for combining multiple partitions”, Journal of Machine Learning Research, vol. 3, Issue.5, pp. 583-617, 2002.
[8] B. Fischer, J. Buhmann, “Bagging for path-based clustering”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 25, no. 11, pp.1411-1415, 2003.
[9] S. Vega-Pons, J. Ruiz-Shulcloper, “Clustering ensemble method for heterogeneous partitions”, Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications, Barlin, pp.481-488 2009.
[10] A. E. Bayá, M. G. Larese, P. M. Granitto, “Clustering using PK-D: A connectivity and density dissimilarity”, Expert Systems with Applications, Vol. 51, Issue.1, pp. 151-160, 2016.
[11] T. F. Cox, M. A. A Cox, “Multidimensional scaling (2nd ed.)”, Chapman & Hall/CRC, USA, pp.1-220, 2000.
[12] S. W. Kim and R. Duin, “An empirical comparison of kernel-based and dissimilarity-based feature spaces”, InJoint IAPR International Workshops on Statistical Techniques in Pattern Recognition (SPR) and Structural and Syntactic Pattern Recognition (SSPR), Berlin, pp. 559-568, 2010.
[13] S. Roweis, L. Saul, “Nonlinear dimensionality reduction by locally linear embedding”, Science, vol.290, no.5500, pp.2323-2326.
[14] A.Fred, A.K. Jain, “Combining multiple clusterings using evidence accumulation”, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 27, no. 6, 2005, pp. 835-850.
[15] E. Pekalska, P. Paclik, R.P.W. Duin, “A generalized kernel approach to dissimilarity-based classification”, Journal of Machine Learning Research, vol. 2, Issue.1, pp. 175-211, 2002.
[16] E. Pekalska, R. Duin, “Beyond traditional kernels: Classification in two dissimilarity-based representation spaces”, IEEE Transactions on Systems, Man and Cybernetics Part C : Applications and Reviews, vol. 38, no. 6, pp. 729-744, 2008.

P. Khandelwal, S. Saxena, "Survey on A Connectivity and Density Dissimilarity Based Clustering," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.155-157, 2017.

Chatbot is widely popular now-a-days and catching speed as an application of computer communication. Some programs respond intelligently like human. This type of program is called a Chatbot. This paper addresses the design and implementation of a Chatbot system. We will also study another application where Chatbots could be useful and techniques used while designing a Chatbot.

Chatbot; Communication; Pattern Matching; Request; Response

[1] R. S. Russell, “Language Use, Personality and True Conversational Interfaces”, Project Report of AI and CS-University of Edinburgh, Edinburgh, pp.1-80, 2002.
[2] Y. Zhou, X. Ziyu, A. W. Black, A. I. Rudnicky, “Chatbot Evaluation and Database Expansion via Crowdsourcing”, Proc. of the Chatbot Workshop of LREC, US, pp. 16-19, 2016.
[3] C. R. Anik, C. Jacob, A. Mohanan, “A Survey on Web Based Conversational Bot Design”, JETIR, Vol.3, Issue.10, pp. 96-99, 2016.
[4] R. P. Schumaker, H. Chen, “Leveraging Question Answer Technology to Address Terrorism Inquiry”, Decision Support Systems, Vol.4, Issue.3, pp. 1419-1430, 2007.
[5] B. P. Kiptonui, “Chatbot Technology: A Possible Means of Unlocking Student Potential to Learn How to Learn, Educational Research”, Vol.4, Issue.2, pp. 218-221, 2013.
[6] S. Ghose, J. J. Barua, “Toward the Implementation of a Topic Specific Dialogue Based Natural Language Chatbot as an Undergraduate Advisor”, International Conference on Informatics, Electronics & Vision, India, pp. 1-5, 2013.
[7] J. Jia, “The Study of the Application of a Keywords-based Chatbot System on the Teaching of Foreign Languages”, Report of University of Augsburg, Augsburg, , pp.1-36, 2003.
[8] B. Setiaji, F. W. Wibowo, “Chatbot Using A Knowledge in Database”, IEEE 7th International Conference on Intelligent Systems, Modelling and Simulation, Thailand, pp. 72-77, 2016.

M. Dahiya, "A Tool of Conversation: Chatbot," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.158-161, 2017.

It exhibits a magnificent opportunity to share our perspectives for various trading website give it to buy. Be that as it may, give it confront the knowledge overloading disadvantage. The route to mine significant information from reviews to get a handle on a client`s inclinations and make a right proposal is critical. Old recommender technique examine a few elements, similar to client`s buy records, item class, and geographic area. Amid this work, here we have a trend to propose a social user sentiment prediction technique in recommender technique. Than here we have a trend to propose a social client nostalgic measuring approach and ascertain each client`s notion on things/items. Also, here we have a trend to not exclusively to think a client`s own sentimental attribute however conjointly take social sentimental influence into thought. At that point, we tend to think item name, which may be gathered by the sentimental distribution of a client set that reproduce clients` complete examination. Finally, we tend to circuit 3 variables client supposition similitude, social sentimental influence, and thing`s name likeness into our recommender technique to shape a right evaluating prediction.

Review, Prediction, Item, Sentiment, recommendation, Rating, System

[1] X. Zhao, X. Qian, X. Xie, “User-service rating prediction by exploring social users` rating behaviours”, IEEE Transactions on Multimedia, Vol.18, Issue.3, pp. 496-506, 2016.
[2] H. Nie, Z. Rong,, “Review helpfulness prediction research based on review sentiment feature sets”, New Technology of Library and Information Service, Vol. 31, Issue. 7, pp. 113-121, 2015.
[3] X. Sun, Z. Fu, “Achieving Efficient Cloud Search Services: Multi-Keyword Ranked Search over Encrypted Cloud Data Supporting Parallel Computing”, IEICE Transactions on Communications ,Vol. 98, Issue.1, pp.190-200, 2015.
[4] P. Cui, M. Jiang, Q. Yang , R. Liu , S. Yang, W. Zhu, F. Wang, “Social contextual recommendation”, in proc. 21st ACM Int. CIKM, US, pp. 45-52, 2015.
[5] Z. Liu, Y. Jin, W. Xiong , “Chinese sentiment analysis using appraiser-degree-negation combinations and PSO”, Journal of Computers, Vol.9, Issue.6, pp. 353-359, 2014.
[6] R. Salakhutdinov,“Probabilistic matrix factorization”, NIPS, CA, pp.52-65, 2012.
[7] X. Yang, H. Steck, Y. Liu., “Circle-based recommendation in online social networks”, 18th ACM SIGKDD Int. Conf. KDD, New York, pp. 1267–1275, 2013.
[8] M. Jamali and M. Ester, “A matrix factorization technique with trust propagation for recommendation in social networks”, in Proc. ACM conf. RecSys, Spain, pp. 135-142, 2010.
[9] K. Voll, C. Anthony, M. Taboada, “Methods for creating semantic orientation dictionaries”, in Proc. LREC, US, pp. 427-432, 2006

A. Sharma, Iyapparaja M., "Prediction of Online Products Rating Using Textual Review Social Sentiment," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.162-169, 2017.

Network operators will rely on the security services to protect the IT infrastructures. It describes design, development and implementations of organizational policies using OCS (Open Computer System) software. These policies are enforced automatically in distributed network environment. We aim to reduce human intervention by developing a practical network reconfiguration system, in that system we simply deploy security policies which are handled by the software. The main component is OCS (open computer system) which consists of different modules of SNMP(simple network management protocol) they are SNMP communities, SNMP groups, SNMP target address and SNMP users etc. Network device automated management tools contains many firewalls in dynamic environment. It is necessary to enable network elements by reconfiguring without any human intervention . The main focus of this project is OCS-NG(open computer system next generation) inventory software that contains all related information’s and it is connected to workstations. Finally, the system collects the information efficiently and store the security management information in the database for off-line analysis required for network based policy enforcement.

NetworkManagement, Security, Policyspecification, Managementconsole, VLANconfiguration

[1] Eld G., Hundley K., “Cisco Security Architectures”, McGraw-Hill, New York, pp.1-635, 1999 .
[2] S Gkarafli, AA Economides, “Comparing the proof by knowledge Authentication Techniques”, international Journal of Computer Science and Security (IJCSS), Vol.4, Issue.2, pp.37-55, 2011.
[3] R. Morris, K. Thompson, “ Password security: A case history”, Comm. ACM, Vol.22, no. 11, pp. 594-597, 1979.
[4] Harbittr A., Menasce D.A., “ A methodology for Analyzing the Performance of Authentication Protocols”, ACM, Vol 5, no 4, pp.58-91, 2002.
[5] R. Yavatkar, D. Pendarakis, R. Guerin, “ A Framework for policy based admission control”, IETF, USA, pp.1-20, 2000.
[6] A. Bandara, E. Lupu, J. Moffet, A.,Russo, “A goal-based approach to policy refinement”, In Proceedings of 5th IEEE Workshop on policies for Distributed system and networks(Policy 2004), NewYork,USA, pp.23-28, 2004.
[7] R. Wie. “Policies in Network and Systems Management - Formal definition and architecture”. Journal of Systems and Network Management Vol.2, no.1, pp.63-83, 1994.
[8] Alabady S., “ Design and Implimentation of a Network Security Model using Static VLAN and AAA Server,” In proceeding International Conference on Information & Communication Technologies: from Theory to Applications, US, pp.1-9, 2008.
[9] B.L. Riddle, M.S. Miron, J.A. Semo, “Passwords in use in a university timesharing environment”, Computer and Security, Vol. 8, no.7, pp. 569-579, 1989.
[10] Gholamreza Shahmohammadi, “Scenario-based Evaluation of software Architecture styles from the security viewpoint”, international Journal of Computer Science and Engineering (IJCSE), Vol.4, Issue.4, pp.10-20, 2016 .

Nagaveni, Sujata.Terdal, "Network Security and Policy Enforcement System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.170-174, 2017.

An Image Retrieval (IR) system is used for accessing and retrieving the pictures from big image database. Content means the image features like color ,texture and shape of the image. For color features, we implement the image pyramid for dimension reduction. It distinguish the color information of image pixels and the spatial correlation is the difference of colors, we represent a new algorithm, known as Global Correlation Vector (GCV), To remove color characteristic in picture pyramid. This system is utilized on HSV color space because it is clear to human vision eye. Dominant color Descriptor (DCD) refers to distinct small quantity of dominant color values as good as their statistical house. It provides an effective, scalable and intuitive representation of colors present in an area or picture. Discrete Wavelet Transform (DWT) is used to keep the certain contents of the pictures together with the decrease of the scale of the feature vector and it describes the texture feature of an image. Edges are the significant one as edges signify mainly the local greatness variations. The implementation result based on precision and recall. The proposed precision is reached up to 100%. For classification, Support Vector Machine (SVM) is used. It classifies the data with class labels. The distance is calculated with different similarity metrics like Chebychev, Normalised Euclidean Distance (NED), Manhattan Distance (MD), ED, Canberra, Hamming Distance(HD) and Minkowski.

CBIR, GCV, Sobel Edge Detection, Prewitt Edge Detection, NTSC, YCbCr, DCD, DWT, HSV Histogram, SVM)

[1] Pooja Devi, Mahesh Parmar, “A Survey On Cbir Techniques And Learning Algorithm Comparision”, International Journal of Latest Trends in Engineering and Technology, Vol.8, Issue.1, pp.197-205, 2017.
[2] Devrat Arya, Jaimala Jha, “ Global And Local Descriptor For Cbir And Image Enhancement Using Multi-Feature Fusion Method”, International Journal of Research, Vol.4, Issue.6, pp.170-182, 2016.
[3] S Agarwal, AK. Verma, N. Dixit, “Content Based Image Retrieval using Color Edge Detection and Discrete Wavelet Transform”, International Conference on Issues and Challenges in Intelligent Computing Techniques (ICICT), India, pp.368-372, 2014
[4] Vinay Lowanshi, Shweta Shrivastava, "Two Tier Architecture for Content Based Image Retrieval Using Modified SVM and knn-GA", International Journal of Computer Sciences and Engineering, Vol.2, Issue.10, pp.41-45, 2014.
[5] Lin Feng, Jun Wu, Shenglan Liu, Hongwei Zhang, “Global Correlation Descriptor: a novel imagerepresentation for image retrieval ”, Journal of Visual Communication and Image Representation, Vol.33, Issue.11, pp.104-114, 2015.
[6] P. Rachana, S. Ranjitha, H.N. Suresh, "Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set", International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.18-22, 2014.
[7] Nooby Mariam, Rejiram R, “A Modified Approach in CBIR Based on Combined Edge Detection Color and Discrete Wavelet Transform”, 2015 International Conference on Advances in Computing, Communications and Informatics, Kochi, pp. 2201-2205, 2015.
[8] Gurmeet Kaur, Arshdeep Singh, “Content Based Image Retrieval from Colored Digital Images using Enhanced SVM Technique” International Journal of Advanced Research in Computer Science and Software Engineering, Vol. 5, Issue.7, pp.1-7, 2015.
[9] Chih-Chin Lai, Ying-Chuan Chen, “A User-Oriented Image Retrieval System Based on Interactive Genetic Algorithm”, IEEE Transactions on Instrumentation and Measurement, Vol. 60, Issue.10, pp.3318-3325 , 2011
[10] Ka-man wong, Lai-man Po, Kwok Waicheung “A compact and efficient Color Descriptor for Image Retrieval”, 2007 IEEE International Conference on Multimedia and Expo, Beijing, pp. 611-614, 2007.
[11] Aasish Sipani, Phani Krishna , Sarath Chandra, “Content Based Image Retrieval Using Extended Local Tetra Patterns”, International Journal of Computer Sciences and Engineering, Vol.2, Issue.11, pp.11-17, 2014.
[12] Yogita Mistry, D.T. Ingole, M.D. Ingole, “Efficent Color Based Image Retreival Using Transform and Spatial Feature Level Fusion” c 2015 IEEE,Vol.1, Issue.8, pp.1828-1836, 2015.
[13] A. Agarwal, S.S. Bhadouria, "An Evaluation of Dominant Color descriptor and Wavelet Transform on YCbCr Color Space for CBIR", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.2, pp.56-62, 2017.
[14] Min Huanga,Huazhong Shua, Yaqiong Mab, Qiuping Gongb” Content-based image retrieval technology using multi-feature fusion” 0030-4026/© 2015 Published by Elsevier GmbH,pp.2144-2148.
[15] Bhoomika Gupta, Shilky Shrivastva, Manish Gupta “Optimisation of Image retreival by using HSV color space Zernike moment and DWT Technique”, 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, pp. 1-5, 2015.

P. Devi, M. Parmar, "Object Detection Based Image Retrieval using Edge Detection,GCV Method for YCbCr and NTSC Color Space," International Journal of Computer Sciences and Engineering, Vol.5, Issue.5, pp.175-181, 2017.