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This Project work would primarily think about the Mobile unplanned networks area unit shaped by wireless nodes that move freely and haven`t any mounted infrastructure. The shared channel is sculptured as a information measure resource outlined by top cliques of mutual meddling links. We have a tendency to propose a unique resource allocation algorithmic rule that employs associate degree auction mechanism within which flows are bidding for resources. The bids rely each on the flow’s utility operate and therefore the as such derived shadow costs. I then mix the admission management theme with a utility aware on-demand shortest path routing algorithmic rule wherever shadow costs are used as a natural distance metric. As a baseline for analysis, I show that downside the matter is developed as a applied mathematics (LP) problem. Thus, we are able to compare the performance of our distributed theme to the centralized phonograph recording resolution, registering results terribly near the optimum. Next, I isolate the performance of price-based routing and show its blessings in hotspot situations, associate degreed conjointly propose an asynchronous version that`s additional possible for impromptu environments. Additional experimental analysis compares our theme with the state of the art derived from Kelly’s utility maximization framework and shows that our approach exhibits superior performance for networks with magnified quality or less frequent allocations. The contributions of this project are as follows: we have a tendency to propose and judge a combined routing, admission management, and resource allocation theme that aims to maximise the aggregate utility of the system. As a part of this theme, 2 novel utility-based algorithms are bestowed. The core of the theme may be a distributed, QoS-aware, price- based allocation algorithmic rule that allocates information measure to flows mistreatment solely regionally offered data. A complementary price-based routing algorithmic rule for selecting the foremost advantageous path for the flows is additionally projected.

QOS, Computing, resource allocation

[1] I.F. Akyildiz, W. Su, Y. Sankarasubramaniam, and E. Cayirci, “Wireless Sensor Networks: A Survey,” Computer Networks, vol. 38, no. 4, pp. 393-422, 2002.
[2] B. Bamba, L. Liu, P. Pesti, and T. Wang, “Supporting Anonymous Location Queries in Mobile Environments with Privacygrid,” Proc. Int’l Conf. World Wide Web (WWW ’08), 2008.
[3] BlueRadios Inc., “Order and Price Info,” http://www.blueradios. com/orderinfo.htm, Feb. 2006.
[4] B. Bollobas, D. Gamarnik, O. Riordan, and B. Sudakov, “On the Value of a Random Minimum Weight Steiner Tree,” Combinatorica, vol. 24, no. 2, pp. 187-207, 2004.
[5] H. Chan, A. Perrig, and D. Song, “Random Key Predistribution Schemes for Sensor Networks,” Proc. IEEE Symp. Security and Privacy (S&P ’03), pp. 197-213, May 2003.
[6] J. Deng, R. Han, and S. Mishra, “Enhancing Base Station Security in Wireless Sensor Networks,” Technical Report CU-CS-951-03, Univ. of Colorado, Dept. of Computer Science, 2003.
[7] J. Deng, R. Han, and S. Mishra, “Intrusion Tolerance and Anti- Traffic Analysis Strategies for Wireless Sensor Networks,” Proc. Int’l Conf. Dependable Systems and Networks (DSN ’04), June 2004.
[8] J. Deng, R. Han, and S. Mishra, “Decorrelating Wireless Sensor Network Traffic to Inhibit Traffic Analysis Attacks,” Pervasive and Mobile Computing J., Special Issue on Security in Wireless Mobile Computing Systems, vol. 2, pp. 159-186, Apr. 2006.
[9] L. Eschenauer and V.D. Gligor, “A Key-Management Scheme for Distributed Sensor Networks,” Proc. ACM Conf. Computer and Comm. Security (CCS ’02), Nov. 2002.
[10] G. Ghinita, P. Kalnis, A. Khoshgozaran, C. Shahabi, and K.L. Tan, “Private Queries in Location Based Services: Anonymizers are not Necessary,” Proc. ACM SIGMOD Int’l Conf. Management of Data (SIGMOD ’08), 2008.
[11] H. Gupta, Z. Zhou, S. Das, and Q. Gu, “Connected Sensor Cover: Self-Organization of Sensor Networks for Efficient Query Execution,” IEEE/ACM Trans. Networking, vol. 14, no. 1, pp. 55- 67, Feb. 2006.
[12] J. Hill, M. Horton, R. Kling, and L. Krishnamurthy, “The Platforms Enabling Wireless Sensor Networks,” Comm. ACM, vol. 47, no. 6, pp. 41-46, 2004.
[13] Y. Jian, S. Chen, Z. Zhang, and L. Zhang, “Protecting Receiver- Location Privacy in Wireless Sensor Networks,” Proc. IEEE INFOCOM, pp. 1955-1963, May 2007.
[14] D.B. Johnson, D.A. Maltz, Y. Hu, and J.G. Jetcheva, “The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR),” IETF Internet draft, Feb. 2002.
[15] P. Kamat, Y. Zhang, W. Trappe, and C. Ozturk, “Enhancing Source-Location Privacy in Sensor Network Routing,” Proc. Int’l Conf. Distributed Computing Systems (ICDCS ’05), June 2005.
[16] D. Liu and P. Ning, “Establishing Pairwise Keys in Distributed Sensor Networks,” Proc. ACM Conf. Computer and Comm. Security (CCS ’03), Oct. 2003.
[17] K. Mehta, D. Liu, and M. Wright, “Location Privacy in Sensor Networks against a Global Eavesdropper,” Proc. IEEE Int’l Conf. Network Protocols (ICNP ’07), 2007.
[18] D. Niculescu and B. Nath, “Ad Hoc Positioning System (APS) Using AoA,” Proc. IEEE INFOCOM, pp. 1734-1743, Apr. 2003.
[19] Y. Ouyang, Z. Le, G. Chen, J. Ford, and F. Makedon, “Entrapping Adversaries for Source Protection in Sensor Networks,” Proc. Int’l Conf. World of Wireless, Mobile, and Multimedia Networking (WoWMoM ’06), June 2006.
[20] C. Ozturk, Y. Zhang, and W. Trappe, “Source-Location Privacy in Energy-Constrained Sensor Network Routing,” Proc. Workshop Security of Ad Hoc and Sensor Networks (SASN ’04), Oct. 2004.
[21] V. Paruchuri, A. Duressi, M. Duressi, and L. Barolli, “Routing through Backbone Structures in Sensor Networks,” Proc. 11th Int’l Conf. Parallel and Distributed Systems (ICPADS ’05), 2005.
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[25] A. Savvides, C. Han, and M. Srivastava, “Dynamic Fine-Grained Localization in Ad-Hoc Networks of Sensors,” Proc. ACM MobiCom, July 2001.
[26] M. Shao, Y. Yang, S. Zhu, and G. Cao, “Towards Statistically Strong Source Anonymity for Sensor Networks,” Proc. IEEE INFOCOM, 2008.
[27] V. Srinivasan, J. Stankovic, and K. Whitehouse, “Protecting Your Daily In-Home Activity Information from a Wireless Snooping Attack,” Proc. Int’l Conf. Ubiquitous Computing (UbiComp ’08), 2008.
[28] H. Takahashi and A. Matsuyama, “An Approximate Solution for the Steiner Problem in Graphs,” Math. Japonica, vol. 24, pp. 573- 577, 1980.
[29] Y. Yang, M. Shao, S. Zhu, B. Urgaonkar, and G. Cao, “Towards Event Source Unobservability with Minimum Network Traffic in Sensor Networks,” Proc. ACM Conf. Wireless Network Security (WiSec ’08), 2008.

V. Mahesh , "Distributed Bid Construction Algorithm for Resource Allocation in Ad-Hoc Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.193-197, 2019.

Web search engine has long been the most important portal for ordinary people looking for useful information on the web. However, users may fail when search engines offer inappropriate results that do not meet their real intentions. Personalized Web Search (PWS) is a general category of searching for personalized search results, which is suitable for personal user needs. To protect user privacy in PWS based on profile, researchers need to consider two important and conflicting issues during search process. The first problem is that they try to improve the search quality with the application to customize the user profile. The existing methodology, to protect user privacy in PWS based on profile, researchers need to consider two important and conflicting issues during search process. The first problem is that they try to improve the search quality with the application to customize the user profile. In the proposed work, the information retrieval process may be split into two tasks, the retrieval of items and the ranking of the retrieved items. The retrieval is often performed using an inverted index, which contains of all the indexed terms. There are numerous advantages that can be taken from personalization content particularly to advertisers that need to build deals profit out of suggestions.

Mobile, Privacy, Preserving, Sensing, System, Security

[1] K. Jain, J. Padhye, V. Padmanabhan, and L. Qiu, “Impact of interference on multihop wireless network performance”, in Proc. ACM MobiCom, pp. 66-80, 2003.
[2] V. Kolar and N. Abu-Ghazaleh, “A multi-commodity flow approach for globally aware routing in multi-hop wireless networks,” in Proc. Fourth Annual IEEE International Conference on Pervasive Computing and Communication, Mar. 2006.
[3] L. X. Cai, L. Cai, X. Shen, J. Mark, and Q. Zhang, “MAC protocol design and optimization for multihop ultra-wideband networks,” IEEE Trans. Wireless Commun., vol. 8, no. 8, pp. 4056-4065, Aug. 2009.
[4] S. Supittayapornpong and P. Saengudomlert, “Joint flow control, routing and medium access control in random access multi-hop wireless networks,” in Proc. IEEE Int. Conf. Commun. (ICC), Dresden, Germany, Jun. 2009, pp. 1-6.
[5] M. Alicherry, R. Bhatia, and L. Li, “Joint channel assignment and routing for throughput optimization in multi-radio wireless mesh networks,” in Proc. ACM MobiCom, pp. 58-72, Aug. 2005.
[6] H. Li, Y. Cheng, C. Zhou, and P. Wan, “Multi-dimensional conflict graph based computing for optimal capacity in MR-MC wireless networks”, in Proc. IEEE ICDCS, Genoa, Italy, June 21-25, 2010.
[7] H. Li, Y. Cheng, X. Tian, and X. Wang, “A generic framework for throughput-optimal control in MR-MC wireless networks,” in Proc. IEEE INFOCOM, Orlando, Florida, Mar. 25-30, 2012
[8] X. Lin and S. Rasool, “Distributed and provably-efficient algorithms for joint channel-assignment, scheduling and routing in multi-channel ad hoc wireless networks,” IEEE/ACM Trans. Networking, vol. 17, no. 6, pp. 1874-1887, Dec. 2009.
[9] M. R. Garey, and D. S. Johnson, “Computers and Intractability: A guide to the theory of NP completness,” W. H. Freeman and Company, 1979.
[10] Y. Cheng, H. Li, and P. Wan. “A theoretical framework for optimal cooperative networking in multiradio multichannel wireless networks.” IEEE Wireless Communications, vol. 19, no. 2, pp. 66-73, 2012.
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[12] S. Ehsan and B. Hamdaoui. “A survey on energy-efficient routing techniques with QoS assurances for wireless multimedia sensor networks.” IEEE Communications Surveys & Tutorials, vol. 14, no. 2, pp. 265-278, 2012.
[13] R. Bolla, R. Bruschi, F. Davoli, and F. Cucchietti. “Energy efficiency in the future internet: a survey of existing approaches and trends in energyaware fixed network infrastructures.” IEEE Communications Surveys & Tutorials, vol. 13, no. 2, pp. 223-244, 2011.
[14] C. Wang, B. Li, and L. Li. “A new collision resolution mechanism to enhance the performance of IEEE 802.11 DCF,” IEEE Trans. on Vehicular Technology, vol. 53, no. 4, pp. 235-1246, July 2004.
[15] V. Namboodiri and L. Gao, “Energy-Efficient VoIP over Wireless LANs,” IEEE Transactions on Mobile Computing, vol. 9, no. 4, pp. 566-581, April 2009.

V. Geetha, V. Akilandeswari, "Optimized Search Model using Sensing and DCG Approach", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.198-203, 2019.

Several algorithms are proposed which uses the basic scheme by predistributing random keys into nodes. The drawback is that one pair wise key may be shared by multiple links. Chan et al. presented two schemes. In their q-composite scheme, multiple keys are required to establish a secure link, which makes a trade-off between connectivity and security. In their random pair wise-key scheme, a unique pair wise key is assigned to each node and every one of a random set. This scheme provides high security but poses an upper bound on network size. Du et al. proposed the pair wise key predistribution scheme based on both the basic scheme and Blom’s scheme, from which it inherits the threshold property. On the contrary, our scheme utilizes Blom’s scheme more smoothly.

Wireless, Authentication, Sensor network, Trust management

[1] H.DaiandH.Xu,―Triangle-basedkeymanagement scheme for wireless sensor networks," Frontiers Electrical Electron. Eng. China, vol. 4, no. 3, pp. 300-306,2009.
[2] A.PoornimaandB.Amberker,―Tree-basedkey management scheme for heterogeneous sensor networks," in 16th IEEE International Conf. Netw.,2008.
[3] Y.Zhang,W.Yang,K.Kim,andM.Park,―AnAVLtree- based dynamic key management in hierarchical wireless sensor network," in Proc. International Conf. Intelligent Inf. Hiding Multimedia Signal Process., pp. 298-303,2008.
[4] A.PoornimaandB.Amberker,―Keymanagement schemes for secure communication in heterogeneous sensor networks," International J. Recent Trends Eng.,2009.
[5] A. Das, ―An unconditionally secure key management scheme for largescale heterogeneous wireless sensor networks," in Proc. First International Commun. Syst. Netw. Workshops, pp. 1-10,2009.
[6] Y.Yang,J.Zhou,R.Deng,andF.Bao,―Hierarchicalself- healing key distribution for heterogeneous wireless sensor networks," SecurityPrivacyCommun.Netw., pp. 285-295, 2009.
[7] B. Tian, S. Han, and T. Dillon, ―A key management scheme for heterogeneous sensor networks using keyed-hash chain," in 5th International Conf. Mobile Ad-hoc Sensor Netw.,2010.
[8] J.Kim,J.Lee,andK.Rim,―Energyefficientkey management protocol in wireless sensor networks," International J. Security its Appl.,2010.
[9] M.Wen,Z.Yin,Y.Long,andY.Wang,―Anadaptivekey management framework for the wireless mesh and sensor networks," Wireless Sensor Netw.J.,2010.
[10] H.Jen-Yan,I.Liao,andH.Tang,―Aforward authentication key management scheme for heterogeneous sensor networks," EURASIP J. Wireless Commun.Netw., 2010.
[11] utilizing probabilistic key predistribution, which was improved byChanet al. And Duet al. Recently, Duet al.And Liu and Ning independently proposed to make use of deployment knowledge to further improve the performance of key establishment. Different from all these schemes, LEAP proposed by Zhu et al. assumes a weaker model, that is, there exists a short time interval within which nodes can establish pair wise keys safely after deployment.

S. Hemalatha, "A Study on Trusted Communication in Wireless Networks", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.204-208, 2019.

This paper mainly focuses on the classification of the tweets based on the emotion in which they prompt. The proposed method will extract the tweets on any particular issue and will assist us to analyze the opinion of the people. The tweets can be classified as positive negative or neutral against the particular issue in which the query was made to extract the tweets. The technology which we use is the twitter API, which will assist us in extracting the tweets relevant the particular issue. The next step is to process the tweets i.e. here we will remove all unwanted images, punctuations and special characters. And at last all the tweets will be converted into lowercase for further steps. The classification of final processed tweets will employ a supervised classification method. The basic classifier used in this method is Naive Bayes classifier to classify the emotion of the tweets. The algorithm is trained by all the possible extent. Finally the percentage of the positive and negative tweets will be calculated. Based on the graphical representation we can create a new strategy for the particular issue.

Tweets, Supervised Classification,Positive and Negative tweet

[1] V. Kagan, A. Stevens, V. S. Subrahamian, "Using Twitter sentiment to forecast the 2013 Pakistani Election and the 2014 Indian Election", Journal of IEEE Intelligent Systems, vol. 30, pp. 2-5, 2015.
[2] Min Song, MeenChul Kim, Yoo Kyung Jeong, "Analyzing the Political Landscape of 2012 Korean Presidential Election in Twitter", IEEE Computer Society,[online],Available: www.computer.org/intelligent.
[3] Shenghua Liu, Xueqi Cheng, Fuxin Li, Fangtao Li, "TASC:Topic Adaptive Sentiment Classification on Dynamic Tweets", IEEE transactions on knowledge and data engineering, vol. 27, no. 6, pp. 1696-1709, june 2015.
[4] Mark E. Larsen, Tjeerd W. Boonstra, Philip J. Batterham, Bridianne O`Dea, Cecile Paris, Helen Christensen, "We Feel: Mapping Emotion on Twitter", IEEE journal of biomedical and health informatics, vol. 19, no. 4, pp. 1246-1252, july 2015.
[5] Rui Xia, Feng Xu, ChengqingZong, Qianmu Li, Yong Qi, Tao Li, "Dual Sentiment Analysis on tweets: Considering Two Sides of One Review", IEEE transactions on knowledge and data engineering, vol. 27, no. 8, pp. 2120-2133, august 2015.
[6] Shulong Tan, Yang Li, Huan Sun, Ziyu Guan, Xifeng Yan, Jiajun Bu, Chun Chen, Xiaofei He, “Interpreting the Public Sentiment Variations on Twitter”, IEEE Transactions on Knowledge and Data Engineering, vol. 26, pp. 1158-1170, 2014.
[7] HaseSudeepKisan, HaseAnandKisan, AherPriyanka Suresh, “Collective intelligence & sentimental analysis of twitter data by using Standford NLP libraries with software as a service (SaaS)”, IEEE International Conference on Computational Intelligence and Computing Research, pp. 1-4, 2016.
[8] HaseSudeepKisan, HaseAnandKisan, AherPriyanka Suresh, “Sentiment Analysis on Twitter Using Streaming API”, IEEE International Advance Computing Conference (IACC), pp. 915-919, 2017.
[9] MestanFıratÇeliktuğ, “Twitter Sentiment Analysis, 3-Way Classification: Positive, Negative or Neutral”, IEEE International Conference on Big Data (Big Data), pp.2098- 2103, 2018.
[10] Saki Kitaoka,TakashiHasuike, “Where is safe: Analyzing the relationship between the area and emotion using Twitter data”, IEEE Symposium Series on Computational Intelligence (SSCI), pp. 1-8, 2017.
[11] M. Muralidharan,Dr. J. Jeyachidra, "SCARTA: Selection Conscious Approach to Retrieval ofTherapeutic Archives", International Journal of Pure and Applied Mathematics,Volume 119 No. 153461-3469, 2018.
[12] WanxiangChe, Yanyan Zhao, HongleiGuo, Zhong Su, Ting Liu, "Sentence Compression for Aspect-Based Sentiment Analysis", IEEE/ACM transactions on audio speech and language processing, vol. 23, no. 12, pp. 2111-2124, December 2015.
[13] Nan Cao, Conglei Shi, Sabrina Lin, Jie Lu, Yu-Ru Lin, Ching-Yung Lin, "TargetVue: Visual Analysis of Anomalous User Behaviors in Online Communication Systems", IEEE transactions on visualization and computer graphics, vol. 22, no. 1, pp. 280-289, january 2016.
[14] Erik Cambria, BjörnSchuller, Yunqing Xia, Catherine Havasi, "New Avenues in Opinion Mining and Sentiment Analysis", IEEE Computer Society, March-April 2013.
[15] Xin Chen, MihaelaVorvoreanu, Krishna Madhavan, "Mining Social Media Data for Understanding Students` Learning Experiences", IEEE transactions on learning technologies, vol. 7, no. 3, pp. 246-258, july-september 2014.
[16] Desheng Dash Wu, LijuanZheng, David L. Olson, "A Decision Support Approach for Online Stock Forum Sentiment Analysis", IEEE transactions on systems man and cybernetics: systems, vol. 44, no. 8, pp. 1077-1084, august 2014.
[17] M. Muralidharan,V. ValliMayil, "A Study of Natural Language Processing Procedures", IJCSE E-ISSN: 2347-2693, vol. 5, 2017.

N. Vasunthira Devi, R. Ponnusamy, "Emotion Identification in Tweets Using NLP And Classification Procedure", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.209-211, 2019.

Ontology languages play key roles for representing and process info regarding the important world for the rising linguistics net. Efforts are created to develop varied metaphysics languages. Every metaphysics language provides totally different communicatory power and conjointly process quality for reasoning. Metaphysics question languages were developed to question the data outlined by these metaphysics languages and reasoning systems. We have a tendency to conduct a study to match their communicatory power, efficiency, and scalability and best performing arts scenario.

Ontology, RDF, OWL.

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[2] Mike Dean, G.S., Sean Bechhofer, Frank van Harmelen, Jim Hendler, Ian Horrocks, Deborah L. McGuinness, Peter F. Patel-Schneider and Lynn Andrea Stein, OWL Web Ontology Language Reference, ed. W3C. 2004.
[3] Ian Horrocks, P.F.P.-S., Harold Boley,SaidTabet,BenjaminGrosof and Mike Dean, SWRL: A Semantic Web Rule Language Combining OWL and RuleML. available at http://www.w3.org/Submission/2004/SUBM-SWRL- 20040521/,2004.
[4] Horrocks, I., DAML+OIL: a Description Logic for the Semantic Web. the IEEE Computer Society Technical Committee on Data Engineering, 2002. 25(1): pp.4-9.
[5] Michael K. Smith, C.W.a.D.L.M., OWL Web Ontology Language Guide. W3C,2004.
[6] Becker, M.Y. Godel`s Completeness Theorem.in
Computer Laboratory University of Cambridge.28.2.2003.
[7] Seaborne, A., Jena Tutorial A Programmer`s Introduction to RDQL. HP Labs,2002.
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Semantic Web.KSL 03-14, 2003.
[9] Voronkov, A.R.a.A., Vampire 1.1. IJCAR, 2001. LNAI 2083: pp.376-380.
[10] Palmer, S.B., The Semantic Web: the introduction. W3C, 2001.39
[11] Frank Manola, E.M.a.B.M., RDF Primer. W3C,2004.
[12] Brian McBride, J.G.a.D.B., RDF Test Cases. W3C, 2004.
[13] ]D. McGuinness and F van Harmelen (eds) OWL Web Ontology Language Overview http://www.w3.org/TR/2003/WD-owl-features-20030331/
[14] M. Dean, G. Schreiber (eds), F. van Harmelen, J. Hendler, I. Horrocks, D. McGuinness, . Patel-Schneider, L. Stein, OWL Web Ontology Language Reference http://www.w3.org/TR/2003/WD-owl-ref-20030331/
[15] M. Smith, C.Welty, D. McGuinness, OWL Web Ontology Language Guide http://www.w3.org/TR/2003/WD-owl-guide-20030331/P. Patel-Schneider, P. Hayes, I. Horrocks, OWL Web Ontology Language Semantics and Abstract Syntax http://www.w3.org/TR/2003/WD-owl-semantics-20030331/

R. Revathy, "A Review on Ontology Languages", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.212-216, 2019.

Endoscopy process to find the bleeding parts in human body is a complicated work. In ancient mechanism they formally use the wired endoscopy to the patients it will leads to several drawbacks. Thus nowadays wired is changed with wireless capsule endoscopy to overcome the situational hazard of the physician. This is further enhanced by the wireless capsule endoscopy through which it can be used. Wireless Capsule Endoscopy (WCE) is in the form of capsule like format that is used for the further identification by the physician. Here the WCE capture videos of the inner organs. While the physician used to point out the issue, they want to focus on the particular video. There may be manual errors may occur. Thus it can be overcome by the proposed word based color histogram. This model is a promising model to compute the WCE video and predict the accurate result without the burden of the phycisian. It is proposed in this system by including a color features. In this model the RGB color feature is used to predict the bleeding frame. To classify the bleeding Frame two classification algorithms is used. They are Support Vector Machine (SVM) and K Nearest Neighbour (KNN) is proposed in this project. Bleeding frame is identification is maintained and performed with the help of proposed algorithm and techniques are simple and efficient.

WCE, Word based color Histogram, SVM, KNN

[1] D. O. Faigel and D. R. Cave, Capsule endoscopy: Saunders Elsevier, 2008.
[2] M. Yu, "M2a (tm) capsule endoscopy: A breakthrough diagnostic tool for small intestine imaging," Gastroenterology Nursing, vol. 25, pp. 24-27, 2002.
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[4] G. Iddan, G. Meron, A. Glukhovsky, and P. Swain, "Wireless capsule endoscopy," Nature, vol. 405, p. 417, 2000.
[5] N. M. Lee and G. M. Eisen, "10 years of capsule endoscopy: an update," 2010.
[6] M. Pennazio, "Capsule endoscopy: Where are we after 6 years of clinical use?," Digestive and Liver Disease, vol. 38, pp. 867-878, 2006.
[7] B. Li and M.-H. Meng, "Computer-aided detection of bleeding regions for capsule endoscopy images," Biomedical Engineering, IEEE Transactions on, vol. 56, pp. 1032-1039, 2009.
[8] R. Francis, "Sensitivity and specificity of the red blood identification (RBIS) in video capsule endoscopy," in The 3rd International Conference on Capsule Endoscopy, 2004.
[9] Y. Fu, W. Zhang, M. Mandal, and M.-H. Meng, "Computer-Aided Bleeding Detection in WCE Video," Biomedical and Health Informatics, IEEE Journal of, vol. 18, pp. 636-642, 2014.
[10] L. Cui, C. Hu, Y. Zou, and M.-H. Meng, "Bleeding detection in wireless capsule endoscopy images by support vector classifier," in Information and Automation (ICIA), 2010 IEEE International Conference on, 2010, pp. 1746-1751.

S. Renuka, A. Annadhason, "A Novel Detection of Bleeding Frame and Region in the Wireless Capsule Endoscopy Video", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.217-221, 2019.

The objective of this project is to predict the accident zone in kumbakonam city. This will be very useful to transport department as well as administration department of kumbakonam city and take decision easily about the accident zone. This project will give the references about the accidents places in kumbakonam city. It is very useful to reduce the accidents in the kumbakonam city. This project contains two major modules namely Administrator and User. Both administrator and user may be a police. Administrator must be a higher authority. The privilege of the administrator is to create, update and delete the users’ account. Administrator can view and delete the accident data’s which was updated by the user. Major operation by an administrator is to maintain accident data’s and user data’s. The privilege of the user is to add and update accident data’s. Those accident data’s can be used to generate the report about the accidents happened in kumbakonam city. User can view their profile and also view the accident data.

Accident Zone, Prediction, Road Traffic, Data Mining, Analysis

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[3] Beshah, T., A. Abraham, et al. (2005). "Rule Mining and Classification Of Road Traffic Accidents Using Adaptive Regression Trees." Journal Of Simulation 6(10-11).
[4] Chang, L. and W. Chen (2005). "Data mining of tree-based models to analyze freeway accident frequency." Journal of Safety Research 36: 365-375.
[5] Chang, L. and H. Wang (2006). "Analysis of traffic injury severity: An application of non-parametric classification tree techniques Accident analysis and prevention " Accident analysis and prevention 38(5): 1019-1027.
[6] Chong, M., A. A., et al. (2005). "Traffic Accident Analysis Using Machine learning Paradigms." Informatica 29(1).Getnet, M. (2009).
[7] Applying data mining with decision tree and rule induction techniques to identify determinant factors of drivers and vehicles in support of reducing and controlling road traffic accidents: the case of Addis Ababa city. Addis Ababa Addis Ababa University.
[8] Mahdi PakdamanNaeini et.al Stock Market Value Prediction Using Neural Networks2010 International Conference on Computer Information Systems and Industrial Management Applications (CISIM)
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[14] Yahoo hadoopTutorial ,
[15] https://developer .yahoo.com/hadoop/tutorial/
[16]GSOC proposal to implement neural network, https://issues.apache.org/jira/browse/MAHOUT-364.

S. Sterlin, K. Lakshmi, "Predicting Accident Zone in Thanjavur City Using Data Mining Techniques", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.222-224, 2019.

A Wireless Sensor Network (WSN) is a collection of sensor nodes, which builds up a network using radio communication in an autonomous. This spoofing technique can be executed using blackhole or sinkhole attacks, which are used to fetch the streams of data leading to cluster heads or base stations usually. In this paper, we are addressing the issue of a variant of DDoS attack: Selective-Jamming Attack as TDMA is prone to a particularly insidious form of jamming attack, namely Selective Jamming (SJ).

Blackhole, Neuralnetworks, Selective jamming, TDMA

[1] Marco Tiloca, Domenico De Guglielmo, GianlucaDini and Giuseppe Anastasi, “SAD-SJ: a Self-Adaptive Decentralized solution against Selective Jamming attack in Wireless Sensor Networks”, ETFA, vol. 18, pp. 1-8, IEEE,2013.
[2] Md. MonzurMorshed, Md. Rafiqul Islam, “CBSRP: Cluster Based Secure Routing Protocol”, IACC, vol. 3, pp. 571-576, IEEE,2013.
[3] Patrice Seuwou, Dilip Patel, Dave Protheroe, George Ubakanma “Effective Security as an ill-defined Problem in Vehicular Ad hoc Networks (VANETs)”.
[4] Muhammad A. Javed and Jamil Y. Khan “A Geocasting Technique in an IEEE802.11p based Vehicular Ad hoc Network for Road Traffic Management”.(2010).
[5] Chia-Chen Hung, Hope Chan, and Eric Hsiao-Kuang Wu “Mobility Pattern Aware Routing for Heterogeneous Vehicular Networks”( IEEE WCNC 2008).
[6] JoãoA. Dias, João N. Isento, Vasco N. G. J. Soares, FaridFarahmand, and Joel J. P. C. Rodrigues “Testbed-based Performance Evaluation of Routing Protocols for Vehicular Delay-Tolerant Networks” (2011IEEE).
[7] Steffen Moser, Simon Eckert and Frank Slomka “An Approach for the Integration of Smart Antennas in the Design and Simulation of Vehicular Ad-Hoc Networks” 2012 IEEE.

G. Vinothini, "A Study of Neural Networks based Blackhole Attack Protection in WSNs", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.225-228, 2019.

E-Iearning is a new topic in education environments and gradually has found its proper place in the recent training methods. But due to the fact that, there is no face to face contact between the teachers and students in e-learning systems, neither the teachers nor the students in the course are aware of each other`s behavior, so in these types of systems, the need of feedback between the students and the professors is felt , this will help improve the teaching and learning process. Although most of these systems can offer a reporting tool" the teachers, in general, cannot provide a clear view about the status of their students. In this paper we investigate efficient query search, as well as global issues, with the aim of solving this problem with a new approach in the design of decision support systems, a system which would enable teachers to answer questions like these in order to understand students` academic achievement using data mining techniques based on the data in the database management system for educational content. Finally, the paper concludes and suggests that teachers of these courses do not require the learning and data mining techniques, but only a model or models are needed to interpret the results of teachers and other educational activities that are essential to help.

Data Mining; Web Mining; Data ware house; ELearning; Distance Education

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[9] Graf, S. ,Kindhuk and Liu, T. Identifying Learning Styles in Learning Management Systems by Using Indications from Students` Behaviour. Proc. of the 8th IEEE International Conference on Advanced Learning Technologies.July, Santander, Spain. 2008.
[10] Han, J. Data mining: Concepts and Techniques. Morgan Kaufmann. 2006.
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[13] Merceron, A. and Yacef, K. . 2008. Interestingness Measuresfor Association Rules in EducationalData. 1st International Conference on Educational Data Mining (EDM08). Montreal, Canada.
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S. VishnuPriya, M. P. Virgin Mary, "The Design of Decision Support System to Improve E-Learning Environments", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.229-230, 2019.

Cloud Computing is a set of IT services that are provided to a customer over a network on a leased basis and with the ability to increase or down their service requirements. Cloud Computing provides the services to the third party provider who owns the infrastructure. It holds the probable to purge the requirements for setting up of high-cost computing infrastructure for IT-based solutions and services that the industry uses. Many industries, banking, healthcare and education are moving towards the cloud due to the efficiency of services provided by the pay-per-use pattern based on the possessions such as processing power used, transactions carried out, bandwidth consumed, data transferred, or storage space occupied. This paper mainly focuses on three cloud service models, frequently referred to as Software as a Service (SaaS), Platform as a Service (PaaS) and Infrastructure as a Service (IaaS). This also discusses with example some major cloud service providers in order to show that how cloud computing will make the business world simpler, more proficient and more specialized.

Cloud Security Threats, Cloud Models, SaaS, PaaS, IaaS

[1] ("AWS Customer Agreement". Amazon Web Services, Inc. Retrieved April 6, 2016.)
[2] Krutz, Ronald L., and Russell Dean Vines. "Cloud Computing Security Architecture." Cloud Security: A Comprehensive Guide to Secure Cloud Computing. Indianapolis, IN: Wiley, 2010. 179-80. Print.
[3] P. Mell and T. Grance, "Draft nist working definition of cloud computing - v15," 21. Aug 2009, 2009.
[4] https://en.wikipedia.org/wiki/Cloud_computing_security#Data_security
[5] https://searchitchannel.techtarget.com/definition/cloud-service-provider-cloud-provider
[6] (http://status.aws. amazon.com/s3-20080720.html
[7] Platform as Service; http://java.dzone.com/articles/whatplatform-service-paas
[8] https://searchaws.techtarget.com/definition/Amazon-Web-Services

J.P. Karthika, P. Vennila, "Amazon: Cloud Computing Services and Security", International Journal of Computer Sciences and Engineering, Vol.07, Issue.04, pp.231-234, 2019.