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Sentiment analysis refers to the use of natural language processing and machine learning techniques to identify and extract subjective information in a source material like product reviews. Due to revolutionary development in web technology and social media reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Across domain sentiment analysis invokes adaptation of learned information of some (labeled) source domain to unlabelled target domain. The method proposed in this project uses an automatically created sentiment sensitive thesaurus(SST) for domain adaptation. Based on the survey conducted on related literature, we identified L1 regularized logistic regression is a good binary classifier for our area of interest.This makes our project more accurate in sentiment classification.We can use this as an application for product reviews. In addition to the previous work we propose the use of senti wordnet and adjective adverb combinations for those effective feature learning.

Sentiment Analysis, SST

[1] Andrea Esuli , Fabrizio Sebastiani, ” SENTIWORDNET: A Pub-licly Available Lexical Resource for Opinion Mining”, Proc. of the 5th Conf. on Language Resources and Evaluation (LREC06), 2006.
[2] Bo Pang, Lillian Lee, and Shivakumar Vaithyanathan.”Thumbs up? sentiment classification using machine learning techniques”. In EMNLP 2002, pages 79-86.
[3] Danushka Bolegalla ,David Weir, John Caroll ”Using Multiple Sources to Construct a Sentiment Sensitive Thesaurus for Cross-Domain Sentiment Classification”.
[4] John Blitzer, Ryan McDonald, and Fernando Pereira.”Domain adaptation with structural correspondence learning”. In EMNLP 2006.
[5] Farah Benamara, Sabatier Irit, Carmine Cesarano, Napoli Fed-erico, Diego Reforgiato, ” Sentiment Analysis: Adjectives and Adverbs are better than Adjectives Alone ”, In Proc of Int Conf on Weblogs and Social Media , 2007.

Megha P.K., Hima K.G., "Cross-Domain Sentiment Classification Using SST," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.249-252, 2017.

As the third party cloud storage services provide fewer maintenance facilities, various enterprises and organizations are attracting towards them. This results in the huge amount of data outsourcing over cloud storage servers. Uncontrolled data proliferation is the huge issue. This increasing backup data volume needs better data management technique to deflate the storage space for cloud servers. Data deduplication is one of the most popular data management approaches, which does not allow storing duplicate data over the storage space. This paper presents the application specific inline data deduplication system on cloud server side along with the efficient and optimized file upload and download operations. The system frames and compares utility based and object map based duplicate content searching techniques on the file and chunk algorithmic levels. Map object plays an important role in quick searching for the duplicates as it evades read operations of the existing files. For downloading the file, the system also provides the functionality of data integrity checking at server side for cloud users to verify the originality of file. The performance of the system is evaluated on random files in the form of flat files, structured files, and unstructured files. The experimental results prove the performance of deduplication system in terms of time and memory usage.

cloud, application, deduplication, integrity, performance, inline

[1] N. Mandagere, P. Zhou, M.A. Smith, S. Uttamchandani, “Demystifying data de-duplication”, In the Proceedings of the ACM/IFIP/USENIX Middleware’08 Conference Companion, ACM, Belgium, pp. 12-17, 2008.
[2] Y. Jiang, C. Lin,W.Meng, C. Yu, A. M. Cohen, N. R. Smalheiser, “Rule-based deduplication of article records from bibliographic databases”, Database(Oxford)-The Jouornal of Biological Databases and Curation, 2014.
[3] M. Carvalho, A. H. Laender, M.A. Goncalves, A. S. da Silvaet, "A genetic programming approach to record deduplication." IEEE Transactions on Knowledge and Data Engineering, Vol. 24, Issue 3, pp.399-412, 2012
[4] Y. Li, K. Xia, “Fast Video Deduplication via Locality Sensitive Hashing with Similarity Ranking”. In the Proceedings of the 2016 International Conference on Internet Multimedia Computing and Service, ACM, China, pp.94-98, 2016.
[5] O. Murashko, J. Thomson, H. Leather, "Predicting and Optimizing Image Compression." In the Proceedings of the 2016 ACM on Multimedia Conference, Amsterdam, The Netherlands, pp. 665-669, 2016.
[6] D. Kim, S. Song, B.Y. Choi, “SAFE: Structure-aware file and email deduplication for cloud-based storage systems”. In Data Deduplication for Data Optimization for Storage and Network Systems. Springer International Publishing. pp.97-115, 2016.
[7] X. Du, W. Hu, Q. Wang, F. Wang, "ProSy: A similarity based inline deduplication system for primary storage." In the proceedings of 2015 IEEE International Conference on Networking, Architecture, and Storage (NAS) Boston, USA, pp. 195-204, 2015.
[8] A. S. Agrawal, J. Malhotra, “Clustered Outband Deduplication on Primary Data” In the proceedings of 2015 IEEE International Conference on Computing Communication Control and Automation (ICCUBEA 2015), Pune, India, pp. 446-450, 2015.
[9] K. He, J. Chen, R. Du, Q. Wu, G. Xue, X. Zhang, "DeyPoS: Deduplicatable Dynamic Proof of Storage for Multi-User Environments," IEEE Transactions on Computers, Vol. 65, Issue. 12, pp. 3631-3645, 2016.
[10] C. Yang, J. Ren, J. Ma, "Provable ownership of file in de-duplication cloud storage," Security and communications network journal, Vol. 8, Issue. 14, pp. 2457-2468, 2013.
[11] X Yao, Y. Lin, Q. Liu, Y. Zhang, "A secure hierarchical deduplication system in cloud storage," In the proceedings of IEEE/ACM 24th International Symposium on Quality of Service (IWQoS), Beijing, China, pp. 1-10,2016.
[12] Y. Zhou, Y. Deng, Y. Li, J. Xie, "Reducing the read latency of in-line deduplication file system," In the proceedings of IEEE 34th International Performance Computing and Communications Conference (IPCCC), Nanjing, China, pp. 1-2, 2015.
[13] G. Wang, Y. Zhao, X. Xie, L. Liu, "Research on a Clustering Data De-Duplication Mechanism Based on Bloom Filter," In the proceedings of IEEE International Conference on Multimedia Technology(ICMT, 2010), Ningbo, China, pp. 1-5, 2010.
[14] J. Wang, Z. Zhao, Z. Xu, H. Zhang, L. Li, Y. Guo, "I-sieve: An inline high performance deduplication system used in cloud storage." IEEE transactions, Tsinghua Science and Technology, Vol. 20, Issue. 1, pp. 17-27, 2015.
[15] Z. Wen, J. Luo, H. Chen, J. Meng, X. Li, J. Li, "A Verifiable Data Deduplication Scheme in Cloud Computing," In the proceedings of International Conference on Intelligent Networking and Collaborative Systems, Salerno, Italy, pp. 85-90, 2014.
[16] M.S. Sulthana, T. Samatha, V. Sravani, A. Mahendra, “Multiple Auditing Schemes with Integrity and Reliability in Cloud Computing”. International Journal of Computer Sciences and Engineering (IJCSE) Vol. 5, Issue.5, pp. 1-6, 2017.
[17] J. Malhotra, J. Bakal "FiLeD: File Level Deduplication Approach". International Journal of Computer Trends and Technology (IJCTT) Vol. 44, Issue. 2, pp.74-79, 2017.
[18] Network working group, RFC 3174 - US Secure Hash Algorithm-1, September 2001.

Jyoti J. Malhotra, Jagdish W. Bakal, "ASID: Application Specific Time Efficient Inline Deduplication on Cloud Storage," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.253-260, 2017.

To build the productivity of any undertaking, we require a framework that would give high performance along adaptabilities and cost efficiencies for client. Distributed computing, as we are all mindful, has turned out to be extremely prevalent over the previous decade. Distributed computing has three noteworthy sorts, in particular, cluster, grid and cloud. Keeping in mind the end goal to build up a high performance distributed framework, we have to use all the previously mentioned three sorts of computing. In this manuscript, we should first have a presentation of all the three sorts of distributed computing. In this manner analyzing them we should investigate inclines in computing and green supportable computing to upgrade the performance of a distributed framework. At last introducing the future degree, we close the manuscript proposing a way to accomplish a Green high performance distributed framework utilizing cluster, grid and cloud computing.

Grid Computing, Cluster Computing, Cloud Computing, High Performance Computing, Distributed Computing

[1] G.Massari, M.Zanella, W.Fornaciari, “Towards Distributed Mobile Computing”, 2016 Mobile System Technologies Workshop (MST), Pages.29 – 35, 2016.
[2] Lalit M.Patnaik, Kailasam Viswanathan Iyer, “Load-leveling in fault-tolerant distributed computing systems”, IEEE Trans.on Software Engineering, Vol.SE-12, Issue.4, Pages.554 – 560, April 1986.
[3] Ofer Shayevitz, “Distributed Computing and the Graph Entropy Region”, IEEE Trans.on Information Theory, Vol.60, Issue.6, Pages.3435 – 3449, June 2014.
[4] Robert J.Calin-Jageman, Paul S.Katz, “A Distributed Computing Tool for Generating Neural Simulation Databases”, Neural Computation, Vol.18, Issue.12, Pages.2923 – 2927, Dec.2006.
[5] Aziz Mohaisen, Huy Tran, Abhishek Chandra, Yongdae Kim, “Trustworthy Distributed Computing on Social Networks”, IEEE Trans.on Services Computing, Vol.7, Issue.3, Pages.333 – 345, Sep 2014.
[6] Zijian Cao, Jin Lin, Can Wan, Yonghua Song, Yi Zhang, Xiaohui Wang, “Optimal Cloud Computing Resource Allocation for Demand Side Management in Smart Grid”, IEEE Trans.on Smart Grid, Vol.8, Issue.4, Pages.1943 – 1955, 2017.
[7] Zizhong Cao, Shivendra S.Panwar, Murali Kodialam, T.V.Lakshman, “Enhancing Mobile Networks With Software Defined Networking and Cloud Computing”, IEEE/ACM Transactions on Networking, Volume.25, Issue.3, Pages.1431 – 1444, 2015
[8] Justin Riley, John Noss, Wes Dillingham, James Cuff, Ignacio M.Llorente, “A High-Availability Cloud for Research Computing”, Computer, Volume.50, Issue.6, Pages.92 – 95, 2017.
[9] Karim Djemame, Django Armstrong, Jordi Guitart, Mario Macias, “A Risk Assessment Framework for Cloud Computing”, IEEE Trans. on Cloud Computing, Volume.4, Issue.3, Pages.265 – 278, 2016.
[10] Jinbo Xiong, Ximeng Liu, Zhiqiang Yao, Jianfeng Ma, Qi Li, Kui Geng, Patrick S.Chen, “A Secure Data Self-Destructing Scheme in Cloud Computing”, IEEE Trans. on Cloud Computing, Volume.2, Issue.4, Pages.448 – 458, 2014.
[11] Sebastián García-Galán, Rocío P.Prado, José Enrique Muñoz Expósito, “Swarm Fuzzy Systems.Knowledge Acquisition in Fuzzy Systems and Its Applications in Grid Computing”, IEEE Trans. on Knowledge and Data Engineering, Volume.26, Issue.7, Pages.1791 – 1804, 2014.
[12] Chris Develder, Marc De Leenheer, Bart Dhoedt, Mario Pickavet, Didier Colle, Filip De Turck, Piet Demeester, “Optical Networks for Grid and Cloud Computing Applications”, Proceedings of the IEEE, Volume.100, Issue.5, Pages.1149 – 1167, 2012.
[13] Jang Uk In, Soocheol Lee, Seungmin Rho, Jong Hyuk Park, “Policy-Based Scheduling and Resource Allocation for Multimedia Communication on Grid Computing Environment” IEEE Systems Journal, Volume.5, Issue.4, Pages.451 – 459, 2011.
[14] Alexandru Iosup, Dick Epema, “Grid Computing Workloads”, IEEE Internet Computing, Volume.15, Issue.2, Pages.19 – 26, 2011.
[15] Shaghahyegh Sharif, Paul Watson, Javid Taheri, Surya Nepal, Albert Y.Zomaya, “Privacy-Aware Scheduling SaaS in High Performance Computing Environments”, IEEE Trans. on Parallel and Distributed Systems, Volume.28, Issue.4, Pages.1176 – 1188. 2017.
[16] Ke Wang, Abhishek Kulkarni, Michael Lang, Dorian Arnold, Ioan Raicu, “Exploring the Design Tradeoffs for Extreme-Scale High-Performance Computing System Software”, IEEE Trans. on Parallel and Distributed Systems, Volume.27, Issue.4, Pages.1070 – 1084, 2016.

P.Meenakshi Sundaram, "A Study of Distributed Computing and Performance Analysis Using Cloud and Grid Computing," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.261-268, 2017.

Frequent Pattern Growth (FP-Growth) is a data mining technique, FP-growth algorithm introduced frequent pattern tree (FP-tree), stored as frequent item-sets in a compressed way. It overcomes drawback of candidate generation approach of multiple database scan but at the same time the transaction identifiers can be quite long taking substantial memory space and computation time. An optimised data structure viz. the Multi-Path Graph is used to improve the utilization and increase the efficiency of data mining techniques. Here we will be using graph as a data structure for storing frequent patterns in the memory. The graph structure will help to mine these frequent patterns without constructing FP-trees. However FP-Growth and MP-Graph fail to process extremely vast data-sets optimally. So we will be attempting to compare FP-Growth with MP-Graph as per its efficiency and memory utilization capability using parallelization techniques. We will try to achieve parallelization using CUDA, and bring forth a comparison of both the mining techniques.

Associative rule mining , heterogenous parallel programming , CUDA , frequent pattern mining

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[8]. Marie Fernandes , "Data Mining: A Comparative Study of its Various Techniques and its Process", International Journal of Scientific Research in Computer Science and Engineering, Vol.5, Issue.1, pp.19-23, 2017.
[9]. Jaswant Meena, Ashish Mandloi , "Classification of Data Mining Techniques for Weather Prediction", International Journal of Scientific Research in Computer Science and Engineering, Vol.4, Issue.1, pp.21-24, 2016.
[10]. Deepti Sharma and Vijay B. Aggarwal, "Mapreduce- A Fabric Clustered Approach to Equilibrate the Load", International Journal of Computer Sciences and Engineering, Vol.4, Issue.3, pp.116-123, 2016.

Milind Kamath, Ankit Katariya, Gaurav Bhokare, "An Effective and Optimized Approach to Association Rule Mining using GPGPU," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.269-272, 2017.

The Physical servers utilized as a part of IT are under-used. The better usage of these servers can be accomplished utilizing virtualization innovation. Virtualization strategies make numerous allotments which are secluded with each other called virtual machines. Each virtual machine (visitor) runs their own working framework. The asset apportioned for these VMs may neglect to execute an application in view of asset struggle or un accessibility of assets. This inspires towards live migration of virtual machines. The live migration duplicates the running VM from source host to goal have consistently utilizing TCP as transport protocol. This manuscript assesses execution of TCP in live migration of KVM based virtual machines. The adaptability in UDP which drives the fixation can likewise be utilized for this migration.

Virtualization, Virtual Machines, Live Migration, Transport Protocol, Performance Analysis

[1] Safraz Rampersaud; Daniel Grosu, “Sharing-Aware Online Virtual Machine Packing in Heterogeneous Resource Clouds”, IEEE Transactions on Parallel and Distributed Systems, Year: 2017, Volume: 28, Issue: 7, Pages: 2046 – 2059.
[2] Olve Mo; Salvatore D`Arco; Jon Are Suul, “Evaluation of Virtual Synchronous Machines With Dynamic or Quasi-Stationary Machine Models”, IEEE Transactions on Industrial Electronics, Year: 2017, Volume: 64, Issue: 7, Pages: 5952 – 5962.
[3] Kuo-Yi Chen; J. Morris Chang; Ting-Wei Hou, “An Energy-Efficient Java Virtual Machine”, IEEE Transactions on Cloud Computing, Year: 2017, Volume: 5, Issue: 2, Pages: 263 – 275.
[4] Bo Hu; Shanzhi Chen; Jianye Chen; Zhangfeng Hu, “A Mobility-Oriented Scheme for Virtual Machine Migration in Cloud Data Center Network”, IEEE Access, Year: 2016, Volume: 4, Pages: 8327 – 8337.
[5] Shangguang Wang; Ao Zhou; Ching-Hsien Hsu; Xuanyu Xiao; Fangchun Yang, “Provision of Data-Intensive Services Through Energy- and QoS-Aware Virtual Machine Placement in National Cloud Data Centers”, IEEE Transactions on Emerging Topics in Computing, Year: 2016, Volume: 4, Issue: 2, Pages: 290 – 300.
[6] Dejene Boru Oljira ; Anna Brunstrom ; Javid Taheri ; Karl-Johan Grinnemo, “Analysis of Network Latency in Virtualized Environments”, Global Communications Conference (GLOBECOM), 2016 IEEE.
[7] Luwei Cheng ; Francis C. M. Lau, “Revisiting TCP Congestion Control in a Virtual Cluster Environment”, IEEE/ACM Transactions on Networking ( Volume: 24, Issue: 4, Aug. 2016 ), Page(s): 2154 – 2167.
[8] Ricard Vilalta ; Raül Muñoz ; Arturo Mayoral ; Ramon Casellas ; Ricardo Martínez ; Víctor López ; Diego López, “Transport Network Function Virtualization”, Journal of Lightwave Technology ( Volume: 33, Issue: 8, April15, 15 2015 ), Page(s): 1557 – 1564.
[9] En-Hao Chang ; Chen-Chieh Wang ; Chien-Te Liu ; Kuan-Chung Chen ; Chung-Ho Chen, “Virtualization Technology for TCP/IP Offload Engine”, IEEE Transactions on Cloud Computing ( Volume: 2, Issue: 2, April-June 2014 ), Page(s): 117 – 129.

D.Ragupathi, N.Jayaveeran, "The Design & Implementation of Transportation Procedure using Migration Techiques," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.273-278, 2017.

The intensification of Technology has altered the means of people’s communication by means of opinions, views, sentiments and emotions regarding particular product, services, and people on social networking sites .Social Networking sites are defined as a network of reaction, interaction and relations. Many Social Networking sites, like facebook, whatsapp, Twitter, LinkedIn, Google+, YouTube, Pinterest, Instagram, and Tumblr are the medium to convey the user emotions in form of comments for particular topic. But day by day as huge amount of data is generated from these sites. It becomes a challenging task to perform such type of analysis on big data. R is used to perform the analysis of tweets data that are having a size in GBs. Sentiment analysis, subjectivity analysis and opinion mining are the various techniques to process the review .This paper presented an approach to analyze and visualize twitter data with R. Mainly four types of attitudes are connected with each text positive, negative, neutral and uninterested. Each tweet is analyzed for detecting the sentiments attached to it.

Twitter, Data Analysis, Sentiments, Social Media, Emotion Analysis

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[2] Go, R. Bhayani, L.Huang, “Twitter Sentiment Classification Using Distant Supervision”., Stanford University, Technical Paper,2009
[3] A.Pak and P. Paroubek, "Twitter as a Corpus for Sentiment Analysis and Opinion Mining", In Proceedings of the Seventh Conference on International Language Resources and Evaluation, pp.1320-1326,2010.
[4] Bifet and E. Frank, "Sentiment Knowledge Discovery in Twitter Streaming Data", In Proceedings of the 13th International Conference on Discovery Science, Berlin, Germany: Springer, pp. 1-15,2010.
[5] Agarwal, B. Xie, I. Vovsha, O. Rambow, R. Passonneau, “Sentiment Analysis of Twitter Data", In Proceedings of the ACL 2011Workshop on Languages in Social Media, pp. 30-38, 2011.
[6] Po-Wei Liang, Bi-Ru Dai, “Opinion Mining on Social Media Data", IEEE 14th International Conference on Mobile Data Management, Milan, Italy, June 3 - 6, 2013, pp 91-96, 2013.
[7] Vishal A. Kharde, S.S. Sonawane, "Sentiment Analysis of Twitter Data: A Survey of Techniques". International Journal of Computer Applications, Volume 139 – No.11, pp. 5-15, April 2016.
[8] Jahiruddin, "Sentiment Analysis of Twitter Data using Statistical Methods", International Journal of Innovative Research in Engineering & Management (IJIREM), Volume-2, Issue-4, pp. 30-34, July 2015.
[9] Hana Anber, Akram Salah, A. A. Abd El-Aziz3. "A Literature Review on Twitter Data Analysis", International Journal of Computer and Electrical Engineering. Volume 8, Number 3, pp. 241-249, June 2016.
[10] I.Hemalatha, G. P Saradhi Varma, Dr. A. Govardhan. "Preprocessing the Informal Text for efficient Sentiment Analysis", International Journal of Emerging Trends & Technology in Computer Science (IJETTCS). Volume 1, Issue 2, pp.58-61, July – August 2012.
[11] Adam Crymble, "An Analysis of Twitter and Facebook use by the Archival Community" in Archivaria 70, pp. 125-151, 2010.
[12] Chetashri Bhadane, Hardi Dalal and Heenal Doshi, ”Sentiment Analysis-Measuring Opinions”, International Conference on Advanced Computing Technologies and Applications (ICACTA), vol. 45, pp. 808–814, 2015.
[13] Xing Fang and Justin Zhan, “Sentiment Analysis Using Product Review Data”, Journal of Big Data, pp. 1-14, Springer, 2015.
[14] Xia Hu, Jiliang Tang, Huiji Gao and Huan, Liu, ” Unsupervised Sentiment Analysis with Emotional Signals”, Proceedings of the 22nd International Conference on World Wide Web, WWW’13, ACM, pp. 607-617,2013.
[15] Santhi Chinthala, Ramesh Mande, Suneetha Manne and Sindhura Vemuri,” Sentiment Analysis on Twitter Streaming Data”, Springer International Publishing Switzerland, pp. 161-168, 2015.
[16] Pablo Gamallo, Marcos Garcia, “Citius: A Naive-Bayes Strategy for Sentiment Analysis on English Tweets” Proceedings of the 8th International Workshop on Semantic Evaluation, pp: 171–175, Dublin, Ireland, 2014.
[17] Dhanashri Chafale , Amit Pimpalkar, “Review on Developing Corpora for Sentiment Analysis Using Plutchik’s Wheel of Emotions with Fuzzy Logic”, International Journal of Computer Sciences and Engineering”, pp. 14-18, 2014.

Neetu Anand, Tapas Kumar , "Text and Emotion Analysis of Twitter Data," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.279-283, 2017.

A focal issue in sensor network security is that sensors are defenseless to physical catch attacks. Once a sensor is traded off, the foe can without much of a stretch dispatch clone attacks by reproducing the bargained node, dispersing the clones all through the network, and beginning an assortment of insider attacks. Past conflicts with clone attacks experience the ill effects of either a high correspondence/stockpiling overhead or a poor discovery precision. Wireless Sensor Networks (WSNs) offer an incredible chance to screen conditions, and have a great deal of fascinating applications, some of which are very touchy in nature and require full verification secured condition. The security components utilized for wired networks can`t be specifically utilized as a part of sensor networks as there is no user-controlling of every individual node, wireless condition, and all the more significantly, rare vitality assets. In this composition, we address a portion of the extraordinary security dangers and attacks in WSNs. In our proposed work, a novel clone detection framework, called CSI to overcome the previous clone detection problems. We introduce two algorithms are CSI-1 and CSI-2.The CSI-1 (Ordinary Compressed Sensing-Based Approach) algorithm used to construct an aggregation tree. The CSI- 2 (Random Projection-Based Approach) is used to reduce the communication cost. Our proposed CSI method not only achieves lowest communication cost but also manage the network traffic evenly spread over sensor nodes. The presentation and security of CSI will be demonstrated feasibility of clone detection.

WSN, Cloning Attack, Man-in-the-Middle Attack, Zero Knowledge Protocol

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[3] K. Cho, M. Jo, T. Kwon, H.-H. Chen, and D. H. Lee, “Classification and experimental analysis for clone detection approaches in wireless sensor networks,” in IEEE Syst. vol. 7, no. 1, pp. 26–35, Mar. 2013.
[4] M. Conti, R. Di Pietro, L. V. Mancini, and A. Mei, “Distributed detection of clone attacks in wireless sensor networks,” IEEE Trans. Dependable Secure Comput., vol. 8, no. 5, pp. 685–698, Sep./Oct. 2011.
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[10] C.M.Yu, C.S.Lu, and S.Y.Kuo, “CSI: Compressed sensing-based clone identification in sensor networks,” in Proc. IEEE Int. Workshop Sensor Netw. Syst. Pervasive Comput. (PerSeNS), 2012, pp. 290–295.
[11] C.M.Yu, Y.T.Tsou, C.S Lu, and S.Y.Kuo, “Localized algorithms for detection of node replication attacks in mobile sensor networks,” IEEE Trans. Inf. Forensics Sec., vol. 8, no. 5, pp. 754–768, May 2013.
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[13] B. Zhu, S. Setia, S. Jajodia, S. Roy, and L. Wang, “Localized multicast: Efficient and distributed replica detection in large-scale sensor networks,” IEEE Trans. Mobile Comput., vol. 9, no. 7, pp. 913–926, Jul. 2010.
[14] Y. Zeng et al., “Random-walk based approach to detect clone attacks in wireless sensor networks,” IEEE J. Sel. Areas Commun., vol 28, no. 5, pp. 677–691, Jun. 2010.
[15] M. Zhang, V.Khanapure, S.Chen, and X.Xiao, “Memory efficient protocols for detecting node replication attacks in wireless sensor networks,” in Proc. IEEE Int. Conf. Netw. Protocols (ICNP), 2009, pp. 284–293.

G.Vinitha, K. Bhuvaneshwari, "A Lightweight and Reliable Routing Approach for in-Network Aggregation in Wireless Sensor Networks," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.284-287, 2017.

Rainfall Prediction is essential for countries which are based on agricultural economy like India. There are several factors are used to predict the rainfall such as temperature, pressure, wind speed, humidity, Mean sea-level etc. The accurate Rainfall Prediction is one of the most challenging problems in the atmospheric research. This paper discuss about Data mining technique which is suitable to predict the rainfall. This was carried out using several Classification algorithms such as Decision tree and Artificial Neural Network. ANN is a non-linear data modelling tool which is used to enhance the capability of Data Mining. It provides high accuracy, flexibility, good robustness, distributed storage and parallel processing. In this paper Back propagation Neural Network, Support Vector Machine is used for rainfall prediction. ANN improves the efficiency of Rainfall prediction by analysing the historical and current facts to make accurate predictions about future. For rainfall prediction, several Data mining techniques are used with ANN and comparison has been done by many researches are discussed.

Rainfall Prediction, Data Mining, Classification algorithms, Artificial Neural Networks, Back Propagation

[1] Stuti Mishra, "Genetic study of advance breeding lines of soybean under Excess Monsoon Rainfall condition in Kymore Plateau zone of Madhya Pradesh", International Journal of Scientific Research in Biological Sciences, Vol.2, Issue.5, pp.4-9, 2015.
[2] S.V.J.S.S. Rajesh, B.S.P. Rao, K. Niranjan, "Remote Sensing Study on Interlinking of rivers From Pennar to Cauvery", International Journal of Computer Sciences and Engineering, Vol.5, Issue.2, pp.100-111, 2017.
[3] Fahad Sheikh, Karthick.S, Malathi. D, Sudarsan.J.S, Arun.C, “Analysis of Data mining Techniques for Weather Prediction”, Indian Journal of Science and Technology, Vol.9, 2016.
[4]Folorunsho Olaiya, Adesesan Barnabas Adeyemo, “Application of Data Mining Techniques in Weather Prediction and Climate Change Studies”, International Journal of Information Engineering and Electronic Business, Vol.1, pp. 51-59, 2012.
[5]A.Geetha, Dr.G.M.Nasira, “Data Mining for Meteorological Applications: Decision Trees for Modelling Rainfall Prediction”, In the Proceedings of the 2014 IEEE International Conference on Computational Intelligence and Computing Research.
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R. Sukanya, K. Prabha, "Comparative Analysis for Prediction of Rainfall using Data Mining Techniques with Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.288-292, 2017.

Low latency is basic for intuitive networked applications. Be that as it may, while we know how proportional frameworks to in-wrinkle limit, decreasing latency particularly the tail of the latency dispersion can be a great deal more troublesome. We contend that the utilization of repetition with regards to the wide-territory Internet is a viable approach to change over a little measure of additional limit into diminished latency. By starting excess operations crosswise over differing assets and utilizing the primary outcome which finishes, repetition enhances a framework`s latency even under extraordinary conditions. We show that repetition can fundamentally diminish latency for little yet basic undertakings, and contend that it is a successful broadly useful methodology even on gadgets like phones where data transfer capacity is generally obliged.

Performance, Reliability, Latency

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V. Geetha, S. Rajalakshmi, "Predicting a Low Latency in Different States of Emergency Events using Web Resources," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.293-299, 2017.

Informatics techniques, for example, content mining and natural language processing, are constantly required in bioinformatics examine. In this review, we talk about content mining and natural language processing techniques in bioinformatics from two points of view. Initially, we plan to scan for information on science, recover references utilizing content mining techniques, and reproduce databases. For instance, protein connections and quality ailment relationship can be mined from PubMed. At that point, we examine the uses of content mining and natural language processing systems in bioinformatics, including foreseeing protein structure and capacity, recognizing noncoding RNA. At last, various strategies and applications, and also their commitments to bioinformatics, are talked about for later use by content mining and natural language processing analysts.

NLP, Bioinformatics, Textmining, Non Coding, RNA Identification

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M. Muralidharan, V.Valli Mayil, "A Study of Natural Language Processing Procedures," International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.300-304, 2017.