Volume-5 , Issue-10 , Oct - 2017 Go Back

Deep web harvesting is the necessity of the time. Deep web, also known as invisible web or hidden web is that portion of the World Wide Web from where the contents present cannot be fetched or indexed by traditional search engines such as Google, Yahoo, Excite, Bing etc. due to some technical limitations in them (Devine and Egger-Sider, 2001). A subset of deep web is the dark web. A Dark web is an overlay network that can only be accessed with specific software, configurations, or authorization, often using non-standard communications protocols and ports. Dark web is the stuff of crime novels. It is a hotbed of criminal activity where anything can be bought and sold. The Dark web is a dangerous place where illicit or underground activities are conducted. Dark web is a pool of black market which includes web sites that allow selling and purchasing of items such as hacking software/malware, counterfeit money, drugs, guns etc. The deep web and the dark web are estimated to be literally thousands of time larger than the visible web. This paper initially defines different layers of the web. The paper also explores the threats that the dark web possess to the modern world. In addition to this, the paper also elucidates the importance of deep web harvesting in our daily lives and explores the tools that could be used to find useful resources buried inside the deep web.

Deep Web, Dark Web, Surface Web, Bitcoins, Bright Planet, Silk Roads, Tor Browser

[1] Bergman, Michael K. (2001). “The deep Web: Surfacing hidden value.” White paper BrightPlanet Available: www.brightplanet.com/images/stories/pdf/deepwebwhite paper. pdf
[2] http://www.worldwidewebsize.com/
[3] Pedley, Paul. "Finding the Hidden Treasure." The Future Just Happened: Black Holes in Cyberspace: The Invisible Web. Annabel Colley and Matthew McDonnell. BBC News. 2001. Web. 26 Jan. 2009.
[4] Bright Planet, Deep Web: A Primer,
http://www.brightplanet.com/deep-web-university-2/deep-web-a-primer/
[5] https://www.comparitech.com/blog/vpn-privacy/how-to-access-the-deep-web-and-darknet/
[6] https://blog.avast.com/diving-into-the-darknet
[7] Jerry Brito, Andrea Castillo (2013). "Bitcoin: A Primer for Policymakers". Mercatus Center. George Mason University. Archived from the original on 21, September 2013. Retrieved 22, October 2013.
[8] http://www.popsci.com/dark-web-revealed
[9] http://www.iflscience.com/technology/what-dark-web/
[10] https://darkwebnews.com/dark-web-market-list/
[11] Department of Justice, United States Attorney’s Office, “Manhattan U.S. Attorney Announces Seizure Of Additional $28 Million Worth Of Bitcoins Belonging To Ross William Ulbricht, Alleged Owner And Operator Of “Silk Road” Website,” press release, October 25, 2013.
[12] https://www.torproject.org/about/overview.html.en
[13] http://www.dpespune.com/wp-content/uploads/2017/03/ Dark-Web.pdf

Hardeep Singh, "Intimidations and Assistances of Deep and Dark Web in Modern Era," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.314-319, 2017.

The problem of securing multiple communications in large-scale network files systems supporting parallel access to multiple storage devices. That is, we consider a model of communication where there are a high number of clients accessing multiple remote and distributed storage devices in parallel. Particularly, focusing on exchange key materials and establishing secure parallel sessions between the clients and the storage devices in the parallel Network File System (pNFS) the current Internet standard is in an scalable and efficient manner. The development of pNFS is driven by Netapp, Panasas, Sun, EMC, IBM, and UMich/CITI, and thus it shares many common features and is compatible with many existing commercial/proprietary network file systems. Networking is considered as the practice of linking multiple computing devices together for sharing of resources. Without the implementation of network, businesses, government agencies, and schools would be unable to operate as efficiently as they do. An organization is able to connect dozens of computers to a single printer is a seemingly simple, yet extremely useful capability. Perhaps even more valuable is the ability to access the same data files from various devices throughout a building. This is incredibly useful for companies that may have files that require access by various employees daily. By the use of network, a same file is made available to several employees on separate computers simultaneously, which improves the efficiency of the organization. Our objective is to implement visual cryptography for strong authentication for parallel network file systems.

Authentication, Networks, Visual Cryptography, Cryptography, Network File System

[1] Hoon Wei Lim and Guomin Yang, “ Authenticated Key Exchange Protocols for Parallel Network File Systems”, IEEE transactions on parallel and distributed systems, vol 27, No. 1, January 2016.
[2] Stuti Nathaniel, Syed Imran Ali, Sujeet Singh, "A Review of Authenticated Key Exchange Protocol Using Random Key Selection with Minimum Space Complexity", International Journal of Computer Sciences and Engineering, Vol.4, Issue.6, pp.191-196, 2016.
[3] C. Adams, “The simple public-key GSS-API mechanism (SPKM),” Internet Eng. Task Force (IETF), RFC 2025, Oct. 2014.
[4] Vinod. L. B and Nithyanada. C. R, "Visual Cryptographic Authentication for Online Payment System", International Journal of Computer Sciences and Engineering, Vol.3, Issue.8, pp.109-114, 2015.
[5] M. K. Aguilera, M. Ji, M. Lillibridge, J. MacCormick, E. Oertli,D. G. Andersen, M. Burrows, T. Mann, and C. A. Thekkath,“Block-level security for network-attached disks,” in 2nd International Conference in File Storage Technology, pp. 159–174, Mar. 2003.
[6] D. Boneh, C. Gentry, and B. Waters, “Collusion resistant broadcast encryption with short ciphertexts and private keys,” in 25th Annual International Conference of Advanced Cryptology, pp. 258–275, Aug. 2005.

R. Keerthivasan, K.S. Srivastavan Iyer, Vyshnav A.K, M. Vishal, "Visual Cryptography Based Authentication For Parallel Network File Systems," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.320-323, 2017.

In this paper, we intend a protocol scheme for network mobility (NEMO) in ruinous scenarios assisted by cloud computing based on Ad-hoc networks. The proposed attempt tries to reduce the network burden on the mobile nodes and also minimize the link failures during hand over by availing cloud services while transmitting in between the nodes. This paper describes the route creation for the MANET as well as the route maintenance while maintaining the connectivity within the nodes. It also provides the proper mobility support in Ad Hoc mechanism. The introduction of cloud in MANET improves the quality .Thus an attempt is introduced to achieve the level of stability.

Cloud computing, handover, Ad hoc network

[1] “Number of mobile phones to exceed world population" Accessed October 2016,HTTP://www.digitaltrends.com/ mobile/mobile-phone-world-population-2014/
[2] J H. Christensen, Using RESTful web-services and cloud computing to create next generation mobile applications, Proceedings of the 24th ACM SIGPLAN conference companion on Object oriented programming systems languages and applications, October 25-29, 2009, Orlando, Florida, USA [doi>10.1145/1639950.1639958]
[3] M Armbrust, A Fox, R Griffith, A D. Joseph, R Katz, A Konwinski, G Lee, D Patterson, A Rabkin, I Stoica, M Zaharia VIEW OF CLOUD COMPUTING Communications of the ACM, Vol.53 No. 4, Pages50-5810.1145/1721654.1721672
[4] I Sriram, Ali K-Hosseini RESEARCH AGENDAS IN CLOUD TECHNOLOGY ACM Classes : C.2.4; A.1 Cite as arXiv:1001.3259 [cs.DC]
[5] S.-J. Lee, M. Gerla, "AODV-BR; backup routing in ad hoc networks", Proc. of IEEE Int`l Conf on Computer Communications and Networks (IC3N 2000), October 2000.
[6] S. J. Lee, W. Su, J. Hsu, M. Gerla, and R. Bagrodia, "A Performance Comparison Study of Ad Hoc Wireless Multicast Protocols," Proc. IEEE Infocom`00, Tel-Aviv, Israel, Mar. 2000.
[7] Public Key Cryptography http://en.wikipedia.org/wiki/Public-key_cryptography.
[8] U Somani, K Lakhani and M Mundra, "Implementing Digital Signature with RSA Encryption Algorithm to Enhance the Data Security of Cloud in Cloud Computing", 1st International Conference on Parallel, Distributed and Grid Computing (PDGC-2010).

D. Bhanot, A. Chaudhary, "Enhancement of Ad hoc Networks Using Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.326-328, 2017.

Software development tools are the programs or set of programs which assist the software developers in the development of other programs and applications. This makes their work easier. Feature-Oriented Software Development (FOSD) Paradigm is a software development paradigm especially for the development of large-scale software systems and software product lines. Like every other software development paradigm, the complexity of the creation, debugging and maintenance of a Feature-Oriented software development necessitate the tool support. Software product-line development consists of multiple phases (Domain Analysis, Domain Design and Specification, Domain Implementation, and Product Configuration and Generation), each of which shall be supported by proper tools. There are a large number of tools available for supporting each process of FOSD, but proper classification does not exist. Tools which support one phase will not support the processes of other phases, so it is necessary to study the tools available before using it. In this review, our aim is to collect and classify the available tools based on the processes of each phase of FOSD. As the supporting tool field is too broad and continuously changing, the collective information about tools is not available. This review helps the developers and researchers to get a comprehensive idea of the existing tools and their functions, which help them to make use of this according to their need.

Tools, Integrated Development Environment, Feature-Oriented Software Development(FOSD), Software Product Line, Comprehensive Review

[1]. Sven Apel, Don Batory, Christian Kastner, and Gunter Saake, “Feature-Oriented Software Product Lines: Concepts and Implementation”, Springer, 2013.
[2]. Paul Clements, Linda Northrop. “Software Product Lines: Practices and Patterns”, Addison-Wesley, 2001
[3]. Kyo C. Kang, Sholom G. Cohen, James A. Hess, William E. Novak, A. Spencer Peterson, “Feature-Oriented Domain Analysis (FODA) Feasibility Study”, Technical Report CMU/SEI-90-TR-21,Software Engineering Institute, 1990.
[4]. Sven Apel, Christian K¨astner , “An Overview of Feature-Oriented Software development”, Journal of Object Technology, Vol. 8, No. 5, 2009
[5]. Thomas Thum, Sven Apel, Christian Kastner, Ina Schaefer, Gunter Saake, “ A Classification and Survey of Analysis Strategies for Software Product Lines”, CSUR, 2014
[6]. Sven Apel, Alexander von Rhein, Philipp Wendler, Armin Gr¨oßlinger, Dirk Beyer, “ Strategies for Product-Line Verification: Case Studies and Experiments”, ICSE, pp. 482–491, IEEE, 2013.
[7]. Praveen Jayaraman, Jon Whittle, Ahmed M. Elkhodary, Hassan Gomaa, “Model Composition in Product Lines and Feature Interaction Detection Using Critical Pair Analysis”. MODELS, pp. 151–165. Springer, 2007.
[8]. Malte Plath , Mark Ryan, “Feature Integration Using a Feature Construct”, SCP, 41(1):53–84, 2001.
[9]. Leonardo Passos, Jianmei Guo, Leopoldo Teixeira, Krzysztof Czarnecki, Andrzej Wasowski, Paulo Borba, “Coevolution of Variability Models and Related Artifacts: A Case Study from the Linux Kernel”, SPLC, pp. 91–100, ACM, 2013.
[10]. Benjamin C. Pierce, “Types and Programming Languages”. MIT Press, Cambridge, Massachusetts, USA, 2002
[11]. Flemming Nielson, Hanne R. Nielson, Chris Hankin., “ Principles of Program Analysis”, Springer, 2010.
[12]. Edmund M. Clarke, Orna Grumberg, Doron A Peled. “Model Checking”, MIT Press, 1999.
[13]. Andreas Classen, Patrick Heymans, Pierre-Yves Schobbens, Axel Legay, Jean-Fran¸cois Raskin, “ Model Checking Lots of Systems: Efficient Verification of Temporal Properties in Software Product Lines”, ICSE, pp. 335–344, ACM, 2010.
[14]. Alexander Gruler, Martin Leucker, Kathrin Scheidemann, “Modeling and Model Checking Software Product Lines.”, FMOODS, pp. 113–131, Springer, 2008.
[15]. Thomas Thum, Christian Kastner, Sebastian Erdweg, Norbert Siegmund., “Abstract Features in Feature Modeling”, SPLC, pp. 191–200. IEEE, 2011
[16]. Reinhard Tartler, Julio Sincero, Wolfgang Schr¨oder-Preikschat, Daniel Lohmann, “Dead or Alive: Finding Zombie Features in the Linux Kernel”, FOSD, pp. 81–86. ACM, 2009
[17]. Reinhard Tartler, Daniel Lohmann, Julio Sincero, Wolfgang Schr¨oder-Preikschat, “ Feature Consistency in Compile-Time-Configurable System Software: Facing the Linux 10,000 Feature Problem”, In Proceedings of the sixth conference on Computer systems, pp. 47–60. ACM, 2011.
[18]. Suzanne Robertson, James Robertson, “ Mastering the Requirements Process: Getting Requirements Right”, Pearson Education, 2012.
[19]. Timo Asikainen, Tomi Männistö, Timo Soininen, “A Unified Conceptual Foundation for Feature Modeling”, SPLC , IEEE 2006
[20]. C. K¨astner, S. Apel, S. Trujillo, M. Kuhlemann, D. Batory, “Language-Independent Safe Decomposition of Legacy Applications into Features”, Technical Report 02/2008, School of Computer Science, University of Magdeburg, 2008.
[21]. Don Batory, “A Tutorial on Feature-Oriented Programming and the AHEAD Tool Suite”, GTTSE, pp. 3-35, Springer, 2006.
[22]. Ina Schaefer, Lorenzo Bettini, Viviana Bono, Ferruccio Damiani, Nico Tanzarella, “Delta-Oriented Programming of Software Product Lines”, SPLC, pp. 77-91. Springer, 2010.
[23]. Aymeric Hervieu, Benoit Baudry, Arnaud Gotlieb. “Pacogen: Automatic Generation of Pairwise Test Configurations From Feature Models”, ISSRE, pages 120-129, IEEE, 2011.
[24]. Martin Fagereng Johansen, ystein Haugen, Franck Fleurey, “An Algorithm for Generating T-Wise Covering Arrays from Large Feature Models”, SPLC, pp. 46-55. ACM, 2012.
[25]. Reinhard Tartler, Daniel Lohmann, Julio Sincero, Wolfgang Schroder-Preikschat, “Feature Consistency in Compile-Time-Con_gurable System Software: Facing the Linux 10,000 Feature Problem”. In Proceedings of the sixth conference on Computer systems, pp. 47-60. ACM, 2011.
[26]. Christopher Henard, Mike Papadakis, Gilles Perrouin, Jacques Klein, Yves Le Traon, “PLEDGE: A Product Line Editor and Test Generation Tool”, SPLC, pp. 126-129, ACM, 2013
[27]. Faezeh Ensan, Ebrahim Bagheri, Dragan Ga_sevi_c. “Evolutionary Search-Based Test Generation for Software Product Line Feature Models”, CAiSE, volume 7328 of Lecture Notes in Computer Science, pp. 613-628, Springer, 2012.
[28]. Yankui Feng, Xiaodong Liu, Jon Kerridge, “A Product Line Based Aspect-Oriented Generative Unit Testing Approach to Building Quality Components”, COMPSAC, volume 2, pp. 403-408, IEEE, 2007.
[29]. Thomas Thum, Ina Schaefer, Martin Kuhlemann, Sven Apel, “Proof Composition for Deductive Veri_cation of Software Product Lines”, VAST, pp. 270-277, IEEE, 2011.
[30]. Kyungseok Kim, Hyejng Kim, Miyoung Ahn, Minseok Seo, Yeop Chang, Kyo C Kang, “ ASADAL: a Tool System for Co-Development of Software and Test Environment Based on Product Line Engineering”, ICSE, pp. 783-786, ACM, 2006.
[31]. Georg Puschel, Ronny Seiger, Thomas Schlegel, “Test Modeling for Context-aware Ubiquitous Applications with Feature Petri Nets”, In MODIQUITOUS, 2012.
[32]. Stephan Wei_leder, Dehla Sokenou, Bernd-Holger Schlinglo, “Reusing State Machines for Automatic Test Generation in Product Lines”, MoTiP, pp. 19-28, 2008.
[33]. Chang Hwan Peter Kim, Don Batory, Sarfraz Khurshid, “Reducing Combinatorics in Testing Product Lines”, AOSD, pp. 57-68,ACM, 2011.
[34]. Hung Viet Nguyen, Christian Kastner, Tien N. Nguyen, “Exploring Variability-Aware Execution for Testing Plugin-Based Web Applications”, ICSE, ACM, 2014.
[35]. Christian Kastner, Alexander von Rhein, Sebastian Erdweg, Jonas Pusch, Sven Apel, Tillmann Rendel, Klaus Ostermann, “Toward Variability-Aware Testing.”, FOSD, pp.1-8, ACM, 2012.
[36]. Christian Kastner, Sven Apel, “Virtual Separation of Concerns-a Second Chance for Preprocessors”, Journal of Object Technology, pp.59-78, 2009
[37]. Sven Apel, Sergiy Kolesnikov, Jorg Liebig, Christian Kastner, Martin Kuhlemann, Thomas Leich, “Access Control in Feature-Oriented Programming”, SCP, pp.174-187, 2012.
[38]. Sven Apel, Wolfgang Scholz, Christian Lengauer, Christian Kastner, “Language-Independent Reference Checking in Software Product Lines”, FOSD, pp.65-71, ACM, 2010.
[39]. Sven Apel, Christian Kastner, Christian Lengauer, “Language-Independent and Automated Software Composition: The FeatureHouse Experience”, TSE, pp.63-79, 2013.
[40]. Eric Bodden, Tarsis Toledo, Marcio Ribeiro, Claus Brabrand, Paulo Borba, Mira Mezini, “SPLLIFT: Statically Analyzing Software Product Lines in Minutes Instead of Years”, PLDI, pp.355-364, ACM, 2013.
[41]. Herbert Klaeren, Elke Pulvermueller, Awais Rashid, Andreas Speck,” Aspect Composition Applying the Design by Contract Principle”, GCSE, pp. 57-69, Springer, 2001.
[42]. Thomas Thum, Christian Kastner, Fabian Benduhn, Jens Meinicke, Gunter Saake, Thomas Leich, “FeatureIDE: An Extensible Framework for Feature-Oriented Software Development”, SCP, pp.70-85, 2014.
[43]. Sven Apel, Hendrik Speidel, Philipp Wendler, Alexander von Rhein, Dirk Beyer, “Detection of Feature Interactions Using Feature-Aware Verification”, ASE, pp. 372-375, IEEE, 2011.
[44]. Dirk Beyer , M. Erkan Keremoglu, “ CPAchecker: A Tool for Con_gurable Software Verifcation”, CAV, pp. 184-190, Springer, 2011.
[45]. Micha l Antkiewicz, Kacper B , ak, Alexandr Murashkin, Rafael Olaechea, Jia Hui Jimmy Liang, Krzysztof Czarnecki, “Clafer Tools for Product Line Engineering”, SPLC, pp.130-135, ACM, 2013.
[46]. Jorg Liebig, Christian Kastner, Sven Apel, “Analyzing the Discipline of Preprocessor Annotations in 30 Million Lines of c Code”, AOSD, pp. 191-202, ACM, 2011.
[47]. Flavio Medeiros, Thiago Lima, Francisco Dalton, M_arcio Ribeiro, Rohit Gheyi, Baldoino Fonseca, “Colligens: A tool to support the development of preprocessor-based software product lines in c”, CBSoft, 2013.
[48]. Sven Apel ,Don Batory, “ How AspectJ is Used: An Analysis of Eleven AspectJ Programs” JOT, pp.117-142, 2010.
[50]. Oster, S., Zorcic, I., Markert, F., Lochau, “MoSo-PoLiTe: tool support for pairwise and model-based software product line testing”,In: Proceedings of VAMOS 2011.
[51]. Danilo Beuche, “Systems and software variability management”, Springer, pp. 173-182, 2013.
[52]. Charles Krueger, Paul Clements, “Systems and Software Product Line Engineering with Gears from BigLever Software”, SPLC-14, pp.121-125, ACM, 2014.
[53]. Seidl, Christoph & Schaefer, Ina Assmann, “DeltaEcore-A Model-Based Delta Language Generation Framework”, Lecture Notes in Informatics (LNI), Proceedings - Series of the Gesellschaft fur Informatik (GI), 2014.
[54]. Ribeiro, Márcio & Tolêdo, Társis & Winther, Johnni & Brabrand, Claus & Borba, Paulo, “ Emergo: A Tool for Improving Maintainability of Preprocessor-based Product Lines”, 2012
[55]. Florian Heidenreich, Jan Kopcsek, Christian Wende, “FeatureMapper: Mapping Features to Models”, ICSE Companion’08 Companion of the 30th international conference on Software engineering, pp.943-944, 2008.
[56]. Janet Feigenspan, Maria Papendieck, Christian Kästner, Mathias Frisch, Raimund Dachselt, “FeatureCommander: colorful #ifdef world”, SPLC `11 Proceedings of the 15th International Software Product Line Conference, Volume 2,48, 2011.
[57]. Sven Apel, Dirk Beyer, “Feature Cohesion in Software Product Lines: An Exploratory Study”, ICSE 2011.
[58]. Y. Wong, E. Albert, R. Muschevici, J. Proença, J. Schäfer, R. Schlatte, "The abs tool suite: modeling executing and analysing distributed adaptable object-oriented systems", International Journal on Software Tools for Technology Transfer, vol. 14, no. 5, pp. 567-588, 2012.
[59]. K. Gybels , J. Brichau, “Arranging Language Features for more Robust Pattern-Based Crosscuts”, In Proc. Int’l Conf. Aspect-Oriented Software Development. 2003.
[60]. M. P. Monteiro , J. M. Fernandes, “ Towards a catalog of aspect-oriented refactorings”,. In Proc. of the 4th International Conference on Aspect-Oriented Software Development (AOSD), pages 111-122. ACM Press, March 2005.
[61]. Maxime Cordy, Andreas Classen, Patrick Heymans, Pierre-Yves Schobbens, Axel Legay, “ProVeLines: A Product Line of Verifiers for Software Product Lines”, SPLC 2014
[62]. Maushumi Lahon and Uzzal Sharma, "The Intricacies of Software Component Composition", International Journal of Computer Sciences and Engineering, Vol.03, Issue.01, pp.111-117, 2015.

Kala K. U., M. Nandhini, "Classification of Tools For Feature-Oriented Software Development: A Comprehensive Review," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.329-337, 2017.

The idea behind the sentiment analysis is to determine the sense trailing the response of product, present in a series of words. It assists us to determine the possible approach mention online. To achieve an idea present in the response of reviews, sentiment analysis is quite useful and defines the overview of public opinion behind the social media elements. Natural language is too complex for machine to follow. To instruct the machine regarding all the feelings, culture, slang and innovation are one of the major challenges for developer. Portray the system to realize the affect of tone is even more difficult. Natural language processing plays a vital role for categorizing the words as ‘positive’ or ‘negative, without having the knowledge regarding the context, it becomes very difficult to analyze the sentiment. In basic way feedback shows the better information about what exactly required, this helps to automate the system using natural language processing

Sentiment Analysis, Methods of Sentiment Analysis, Minimization methods, Benefits of minimization

[1] W. Che, Y. Zhao, H. Guo, Z. Su, and T. Liu “ Sentence Compression for Aspect-Based Sentiment Analysis”, IEEE/ACM Transaction on audio, speech and language Processing, Vol. 23, No. 12, 2015.
[2] K. M. Alhawiti, “Natural Language Processing and its Use in Education”, International Journal of Advanced Computer Science and Applications, Vol. 5, No.12,2014.
[3] J. Clarke and M. Lapata, “Modelling Compression with Discourse Constraints”, Edinburgh EH8 9LW, UK.
[4] E. Marsi, E. Krahmer, I. Hendricks, W. Daelemans,” Is sentence compression an NLG task?” December 2008.
[5] R. Barzilay, L. Lee, “Learning to Paraphrase: An Unsupervised Approach Using Multiple-Sequence Alignment, Proceedings of HLT-NAACL, 2003, pp. 16-23, Edmonton, 2003.
[6] C.S Yang, H.P Shih,” A Rule-Based Approach For Effective Sentiment Analysis”, PACIS 2012.
[7] E. Pitler, ”Methods for Sentence Compression” MS-CIS 10-20, 2010.
[8] A. Sharma, Aakanksha, “A Comparative Study of Sentiment Analysis Using Rule and Support Vector Machine”, International Journal of Advanced Research in Computer and Communication Engineering Vol. 3, Issue 3, March 2014.
[9] S .J.Veeraselvi, M.Deepa, “Survey on Sentiment Analysis and Sentiment Classification”, International Journal of Engineering Research & Technology Vol. 2, Issue 10, October – 2013 pp. 2278-0181.
[10] G. Patil, V. Galande, V .Kekan, K. Dange, “Sentiment Analysis Using Support Vector Machine” International Journal of Innovative Research in Computer an Communication Engineering, pp. 3297: Vol. 2, Issue 1, 2014.
[11] D. Virmani, V. Malhotra, R. Tyagi, “Sentiment Analysis Using Collaborated Opinion Mining” 2012.
[12] B. R Jadhav M. Mahajan, “Review of Dual Sentiment Analysis”, International Journal of Science and Research, pp. 2319-7064.
[13] T. Cohn, C. C Burch, M. Lapata,” Constructing Corpora for the Development and Evaluation of Paraphrase Systems”, Association for Computational Linguistics 2008.
[14] D. R Radev, K. McKeown, “Introduction to the Special Issue on Summarization”, Association for Computational Linguistics, Vol. 28, Issue 4. 1999.
[15] B. Dorough, S. Rock, D. Jurafsky, J. H. Martin “Part- of-Speech Tagging”, Speech and Language Processing. 2017.
[16] X. Zhou, X. Tao, J. Yong, "Sentiment Analysi on Tweets for Social Events" IEEE 17th International Conference on Supported Cooperative Work in Design, Canada, 6581022, 2013.
[17] M. S Neethu, R. Rajasree "Sentiment Analysis in Twitter using Machine Learning Techniques”, IEEE 14th International conference on mobile data Management– 31661, Milan, Italy Vol. 139, 2013.
[18] A. Hassan, A. Abbasi, D. Zing."Twitter Sentiment Analysis: A Bootstrap Ensemble Framework , National Science Foundation IIS-1236970, 2013.
[19] W. Medhat, A. Hassan, H. Korashy,” Sentiment Analysis algorithms and applications: A survey”, Ain Shams Engineering Journal, pp.1093–1113, Issue. 5, 2014.
[20] C. D. Manning, M. Surdeanu, J. Bauer,” The Stanford Core NLP Natural Language Processing Toolkit”, Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations, pp. 23-24, 2014.
[21] S. Saziyabegum, P.S. Sajja, ”Review on Text Summarization Evaluation Methods”, International Journal of Computer Science and Engineering, Vol.8 No. 4, 2017

M. Likhar, S. L. Kasar, "Review on Aspect Based Sentiment Analysis Using Sentence Minimization," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.338-341, 2017.

Today, gaining popularity privacy for accessing outsourcing data stored on cloud is a major issue in cloud computing. In cloud storage data is stored on single physical machine and this stored data may shared by multiple users from different machines. User doesn`t have control on accessing outsourced data. For identification of leaked data to everyone data access security or privacy for data is a challenging section. To store as well for sharing data securely Cryptosystem is used. In cryptosystem before storing data on cloud user firstly encrypt data then placed it on cloud. And then data decryption is performed when user want to access it. This task may require multiple keys for data encryption as well as data decryption. In proposed system key-aggregation is implemented on concept on merging or aggregating the encryption and decryption key into single one in cryptosystem for sharing of scalable data. It is very compressed formed of aggregation of key. In proposed approach unique key can hold multiple keys that are required. In proposed system concentrate on sharing data securely, efficiently among multiple users. Sharing data and delegation of data is possible as fixed sized of data blocks are created. In this proposed system implements Shamir secret sharing algorithm for securely, sharing aggregate key for multiple users.

Cloud storage, data sharing, key-aggregate encryption, patient-controlled encryption

[1] Chu, C. K., Chow, S. S., Tzeng, W. G., Zhou, J., & Deng, R. H. (2014). Key-aggregate cryptosystem for scalable data sharing in cloud storage. IEEE transactions on parallel and distributed systems, 25(2), 468-477.
[2] M. J. Atallah, M. Blanton, N. Fazio, and K. B. Frikken, Dynamic and Efficient Key Management for Access Hi-erarchies, ACM Transactions on Information and SystemSecurity (TISSEC), vol. 12, no. 3, 2009. 416432.
[3]J. Benaloh, M. Chase, E. Horvitz, and K. Lauter, Patient Controlled Encryption:, in Proceedings of ACM Work-shopCloud Computing. ACM, 2009, pp. 103114.
[4] F. Guo, Y. Mu, Z. Chen, and L. Xu, Multi-Identity Single-Key Decryption without Random Oracles, in Proceedingsof Information Security and Cryptology (Inscrypt 07), ser.LNCS, vol. 4990.Springer, 2007, pp. 384398.
[5] V. Goyal, O. Pandey, A. Sahai, and B. Waters, Attribute-Based Encryption for Fine-Grained Access Control of En-crypted data, in Proceedings of the 13th ACM conferenceon Computer security. ACM, 2006, pp. 8998.
[6] S. G. Akl and P. D. Taylor, Cryptographic Solution to aProblem of Access Control in a Hierarchy, ACM Trans-actions on Computer Systems (TOCS), vol. 1, no. 3, pp. 239-248, 1983.

Bharati A. Patil, "A Secure and Scalable Data Sharing using Key Aggregate Crypto System," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.342-346, 2017.

Social media is a platform for public around the World to discuss their issues and opinions. Before knowing the actual aspects of social media people must have to know what does social media mean? Social media is a term used to describe the interaction between groups or individuals in which they produce, share, and sometimes exchange ideas, images, videos and many more over the internet and in virtual communities. Children are growing up surrounded by mobile devices and interactive social networking sites such as Twitter, MySpace, and Facebook, Orkut which has made the social media a vital aspect of their life. Social network is transforming the behavior in which youthful people relate with their parents, peers, as well as how they make use of technology. The effects of social networking are twofold.[1] On the positive side, social networks can act as invaluable tools for professionals. They achieve this by assisting young professionals to market their skills and seek business opportunities. Social networking sites may also be used to network efficiently. On the negative side, the internet is laden with a number of risks associated with online communities. Cyber bullying, which means a type of harassment that is perpetrated using electronic technology, is one of the risks. In this paper we cover every aspect of social media with its positive and negative effects. Focus is on the particular field like health, business, education, society and youth. During this paper we explain how these media will influence the society in a broad way.

Social Media, Business, Society, Mobile Devices, Education, Cyber Bullying

[1] Abhimanyu Shankhdhar, JIMS / Social media and businss /
[2] Mahmoudi Sidi Ahmed et al., “Detection of Abnormal Motions in Multimedia”, Chania ICMI-MIAUCE’08 workshop, Crete, Greece, 2008.
[3] S.Shabnoor,S.Tajinder,Social Media its Impact with Positive and NegativeAspects IJCATR, Volume 5– Issue 2, 71 - 75, 2016
[4] Bin Zhao et al., “Online Detection of Unusual Events in Multimedia via Dynamic Sparse Coding”, 2011.
[5] Nagar, Himanshu, Chetna Dabas, and J. P. Gupta. "Navie Bayes and K-Means Hybrid Analysis for Extracting Extremist
Tweets", ACM Conference, pp 27-32.
[6] S. Willium, “Network Security and Communication”, IEEE Transaction, Vol.31, Issue.4, pp.123-141, 2012.
[7] A.T.M Shahjahan, K.Chisty, “Social Media research and its effect on our society” International journal of Information 7 communication Engineering , Vol:8, No:6,2014
[8] W.Tariq, M.Mehboob, M.A.Khan, F.Ullah “The Impact of social Media and Social Networking on education and Students of Pakistan” international Journal of Computer sciences issues, Vol:9,No:3,July 2012

W. Akram, R. Kumar, "A Study on Positive and Negative Effects of Social Media on Society," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.351-354, 2017.

With increasing demand of security, user authentication occupies prior in information security and plays a prominent role in protecting user`s privacy, which has become a critical issue nowadays. In this digital world the usage of online transactions became very common and at the same time various attacks are performed behind this. The most common method used for authentication in online banking is by providing text password, which is the combination of letters, sequence of characters and special symbols. Authentication system, based on text passwords is widely used but they can easily compromise with attacks. Users normally select simple passwords because they can easily memorize at the time of login. So providing more security than existing assists in protecting resources against unauthorized access. Image-based-authentication is a good alternative to traditional password system. Apart from many conventional cryptographic methods, visual cryptographic techniques have also been used for providing security to data. In this proposed work, Visual cryptographic scheme is used that encrypts a secret image by breaking into image shares. Along with text based password login, image based authentication using visual cryptography is included. This authentication system is useful for various sectors like industries, online banking and shopping.

Information Security, Virtual cryptography, authentication, online banking, encryption

[1] B.V.Prasanthi ,”Cyber Forensics Tools : A Review” International Journal of Engineering Trends and Technology (IJETT),vol 41 no 5,pp.266-271,2016.
[2] Prasanthi, B. V., Prathyusha Kanakam, and S. Mahaboob Hussain. "Cyber Forensic Science to Diagnose Digital Crimes-A study."International Journal of Scientific Research in Network Security and communication (IJSRNSC), vol 50 no 2,pp.107-113,2017.
[3] Prasanthi, B. V., et al. "Security Enhancement of ATM System with Fingerprint and DNA Data." International Journal of Advanced Research in Computer Science and Software Engineering, vol. 4, no. 12, pp. 477–479, Dec. 2014.
[4] Prasanthi, B. V., et al. "Palm Vein Biometric Technology: An Approach to Upgrade Security in ATM Transactions." International Journal of Computer Applications ,vol 112 no 9,2015.
[5] P. S. Revenkar, Anisa Anjum and W. Z. Gandhare. ”Survey of Visual Cryptographic Schemes”. International Journal of Security and Its Applications Vol. 4, No. 2, April, 2010.
[6] Feng Liu, Chuankun Wu, Xijun Lin. ”Step Construction of Visual Cryptography Schemes”. IEEE transactions on information forensics and security, vol. 5, no. 1, march 2010.
[7] A. Parakh and S.kak .”A Recursive Threshold Visual Cryptography Scheme ”. Department of Computer Science, Oklahoma State University Stillwater, OK 74078.
[8] N. Askari, H.M. Heys, and C.R. Moloney. ”An extended visual cryptography scheme without pixel expansion for halftone images”. 26th annual ieee canadian conference on electrical and computer engineering year 2013.
[9] Zhi Zhou, Gonzalo R. Arce and Giovanni Di Crescenzo. ”Halftone Visual Cryptography”. IEEE transactions on image processing, vol. 15, no. 8, august 2006.
[10] D. Jena and S. Jena . ”A Novel Visual Cryptography Scheme”. 978-07695-3516-6/08 2008IEE DOI 10.1109/ICACC.2009.109
[11] Kulvinder Kaur and Vineeta Khemchandani. ”Securing Visual Cryptographic Shares using Public Key Encryption”.3rd IEEE International Advance Computing Conference (IACC),pp.1108-1113,2013.
[12] Bhuyan, Sangeeta.” Image Security using Visual Cryptography”. Diss. 2015.
[13] Bonthu, Sridevi, B. V. Prasanthi, and K. Himabindu. "Automation Of Pre-Processing Of Students Data",vol 8 no 3,pp.241-245,2016.

B.V.Prasanthi, Sridevi Bonthu, "Resisting Cyber-attacks in Digital Banking using Visual Cryptography," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.355-359, 2017.

Orthogonal-frequency division-multiplexing(OFDM) is a standout amongst many commended different access framework, which is capable of performing both modulation and multiplexing in both wired and wireless communication specially used to achieve the today‟s need of high speed communication beyond 4g-Long term evolution. OFDM was originally adapted by 3GPP association for the development of LTE. It has many advantageous characteristics such as higher spectral efficiency, higher bit rate, and lower multipath distortion etc. Even with these many advantages OFDM still suffers from few critical issues. One among them is OFDM signal‟s higher peak power ratio (PAPR). Which drives power amplifier into a nonlinear region hence results toout of band radiation and in band distortion which is a reason for degradation of performanceof the system in terms ofBER. Here we will demonstrate the reenactment of the OFDM with various supporting adjustment systems, for example: QPSK, QAM. Additionally, here will demonstrate an endeavor to decrease the PAPR utilizing distinctive PAPR lessening procedures to enhance the nature of the OFDM signal along withsimulation results.

OFDM, PAPR, LTE and QAM

[1] R.W. Chang, ”Synthesis of Band-Limited Orthogonal Signals for Multichannel Data Transmission”, Bell Syst. Tech. J., vol.45, pp. 1775-1796, Dec. 1966.
[2] B.R. Salzberg, “Performance of an efficient parallel data transmission system”, IEEE Trans. Commun. Technol., vol. COM-15, pp. 805-813, Dec. 1967.
[3] S.B. Weinstein and P.M. Ebert, “Data transmission by frequency-division multiplexing using the discrete Fourier transform”, IEEE Trans. Commun. Technol., vol. COM-19, pp. 628-634, Oct. 1971.
[4] Brijesh Ahirwar , Vishal Pasi “A Survey Paper on Inter-carrier Interference Reduction Techniques in OFDM Systems”, IJCSE, Volume-3, Issue-6, Page no. 26-29, Jun-2015
[5] Heung-Gyoon Ryu, Yingshan Li, and Jin-Soo Park, “An Improved ICI Reduction Method in OFDM Communication System,” IEEE Transactions on Broadcasting, vol.51, no.3, pp.395-400, 2013.
[6] Elisha Gray, “Electrical Telegraph for Transmitting Musical Tones,” U.S. Patent 166,095, July 27, 1875.
[7] M. Schwartz, “The Origins of Carrier Multiplexing: Major George Owen Squier and AT&T,” IEEE Commun. Mag., vol. 30 pt. 2, 1911, pp. 1675–80.
[8] Erik Dahlman et al, “3G Evolution HSPA and LTE for Mobile Broadband” academic press, 2007
[9] Yong Soo Cho et al, “Mimo-ofdm wireless communications with matlab” May 2014, Republic of Korea
[10] Shampal Singh1 and Avtar Singh Buttar2 “Carrier Frequency Offset Estimation Techniques in OFDM System: A Survey”, IJCSE Vol-2, Issue-8, 2014.

Satyanarayan. K. Padaganur, Jayashree.D. Mallapur, M. N. Deshmukh, P.S.Patil, "An Approach to Improve the Quality of OFDM Signal Using Papr Reduction Schemes," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.360-366, 2017.

As reuse is gaining popularity, there is challenge for the wastewater reusability also. Now a day’s lots of research work are in boon for such system. In context with this there is need to develop systems which help to decision maker where to use wastewater. Population grows with rising standard of living; more wastewater is generated and disposed to sensitive environments with negative impact on humans and ecosystems. The aim of this research is to develop a DSS by using Machine Learning technique specifically supervised Decision Tree Algorithm, which is intended for applying intelligent decisions supporting reuse of wastewater management. This developed system provides best decision for the reuse of wastewater after the treatment. Further this system is deployed on cloud, as Cloud computing provides a good manageability and fast computational service. Currently its very challenging for exiting system stakeholders specifically ( Municipal Corporation or user) to use this type of cloud based DSS for waste water. To upgrade this management scenario, this research project proposes CBDSSWWM.

Cloud Computing, Waste water management, BOD, COD, PH, COLOR, MPN, TURBIDITY, TSS

[1] B. C. Chamberlain ,G. Carenini , G. Oberg , D. Poole , H. Taheri,”A decision support system for the design and evaluation of sustainable wastewater solutions. IEEE Transactions on Computers. 2014 Jan;63(1):129-41.
[2] S.L.Mewada, U.K. Singh, P. Sharma, "Security Enhancement in Cloud Computing (CC)", International Journal of Scientific Research in Computer Science and Engineering, Vol.1, Issue.1, pp.31-37, 2013.
[3] M. Eman, O.Hegazy, and M. N. El-Dien. "Integration of GIS and cloud computing for emergency system." International Journal Of Engineering and Computer Science 2, no. 10 : 2889-2893. 2013.
[4] N. Qazi, D. Smyth, T. McCarthy,” Towards a GIS-based decision support system on the amazon cloud for the modelling of domestic wastewater treatment solutions in Wexford, Ireland.”, In Computer Modelling and Simulation (UKSim), 2013 UKSim 15th International Conference on 2013 Apr 10 (pp. 236-240). IEEE.
[5] A. Cohen and C. E. Condeluci “ Decision Support systems For Benefits: Framework and Evaluation”, Liazon October, 2014.
[6] A. S. Patil, and N. J. Kulkarni "Decision Support System for Waste Water Management: A Review." International Journal of Innovative Research in Advanced Engineering (IJIRAE) Volume 1 Issue 3, SPECIAL ISSUE 2278-2311 (2014).
[7] B Kele, D.J.Midmore, C. Devitt, and J. Ludlow. "scada operation and monitoring of large scale on-site wastewater treatment and reuse systems." 31st Annual Water Industry Workshop Operations Skills Page No 106 University Central Queensland Campus – Rockhampton, 4 to 6 July, 2006.
[8] v nikolić, ž ćojbašić, i. ćirić, e. petrović ,” Intelligent decision making in wastewater treatment plant scada system”, Facta Universitatis Series: Automatic Control and Robotics Vol. 9, No 1, 2010, pp. 69 - 77

A.K.Bhavsar, J.S. Shah, "Cloud Based Decision Support System for Waste-Water Management using Supervised Decision Tree Algorithm," International Journal of Computer Sciences and Engineering, Vol.5, Issue.10, pp.367-372, 2017.