iosr-JCE   Volume-17 ~ Issue-3 ~ May-June 2015

Abstract: The thought to utilize composition examination in restorative imaging has been considered following the mid-1970s. Then again, the energizing advancement of both surface investigation calculations and PC innovation restored scientists' enthusiasm for applications for restorative imaging recently. Specialized advancements in medicinal cross-sectional imaging have opened up new vistas for the investigation of the human body, empowering both high spatial and temporal resolution. On Difficulties Caused by Missing Reference Image Datasets, one of the fundamental issues in the improvement of frameworks for the investigation of restorative pictures or division calculations is the situation of pictures that is utilized for both the advancement and the testing of the framework. This paper proposes a symptomatic plan which takes after a well known two-stage approach.

[1]. Chen DR, Chang RF, Huang YL. Computer-aided diagnosis applied to US of solid breast nodules by using neural networks.
[2]. D R. Brunelli, Template Matching Techniques in Computer Vision: Theory and Practice, Wiley, ISBN
[3]. Fukunaga, Keinosuke. Introduction to Statistical Pattern Recognition (2nd ed.).
[4]. Bishop, Christopher. Pattern Recognition and Machine Learning.
[5]. Godfried T. Toussaint. Computational Morphology. Amsterdam: North-Holland Publishing Company.
[6]. Kulikowski, Casimir A.; Weiss, Sholom M. Computer Systems That Learn: Classification and Prediction Methods from Statistics, Neural Nets, Machine Learning, and Expert Systems. Machine Learning.

Abstract: After decades of effort working on database performance, the quality and the usability of database systems have received more attention in recent years. In particular, answering the why-not questions after a search is made has become more important. In this project, the problem of answering why-not questions on top-k queries and refining the user query is solved. Generally many users love to pose those kinds of queries when they are making multi-criteria decisions. However, they would also want to know why their expected answers do not show up in the query results. The different algorithms are developed to answer such why-not questions efficiently. Top-K dominating questions are those which have more than one or two results.

[1]. Sanjay Agrawal, Surajit Chaudhuri, Gautam Das, ―DBXplorer: A System for Keyword-Based Search over Relational Databases‖ 2010.
[2]. Zhian He, Eric Lo, ―Answering Why-Not Questions on Top-K Queries‖, IEEE transactions on knowledge and data engineering, vol. 26, no. 6, june 2014.
[3]. Islam , M.S ; Rui Zhou ; Chengfei Liu, ―On answering why-not questions in reverse skyline queries‖, IEEE transactions on Mining techniques, April 2013.
[4]. Islam, M.S., ―On answering why and why-not questions in Databases‖, Data Engineering Workshop (ICDEW),IEEE international conference,2013.
[5]. Vermeulen,J; Vanderhulst, G. ; Luyten,K. ; Conix, karin ,― PervasiveCrystal: Asking and Answering Why and Why Not Questions about Pervasive Computing Applications‖, IEEE Conference publications, 2010

Abstract: Today E-orientation systems are interested in helping learners to choose a suitable branch to their skills and preferences. In this context the research center within the University Hassan II Mohammedia Ain-Chock announced the appearance of MMSyOrientation project. This project focuses on the orientation of Moroccan student's education as a concern and a family, social, and political preoccupation. The student remains an educational system focus center, especially at the MMSyOrientationi project level, which requires suggesting a feasible model for this major actor. Thus we should refer to users' models specifically learners' profiles, which deal with these outlines in the EIAH domain (it environments for human learning). These models are generic and not dedicated to the academic E-Orientation systems. The purpose of this article is to propose a direction model to the student on the most appropriate way of their profile. In this context we could implement an ontology entitled (Onto E-orientation) to an E-orientation Moroccan system representing different ways of information on such learning, using OWL language.

Keywords: E-Orientation, MMSyOrientation, EIAH, User model, learner profile, Ontology.

[1]. Dehors S. « QBLS : web sémantique de formation pour un apprentissage par questionnement», EIAH Montpellier 2005.
[2]. Razmerita, L., User modeling and personalization of the Knowledge Management Systems,book chapter,in Adaptable and Adaptive Hypermedia, by Idea Group Publishing, 2005.
[3]. De Bra P., Nejdl W., « Adaptive Hypermedia and Adaptive Web-Based Systems », Third International Conference, AH 2004, Eindhoven, The Netherlands, August 23- 26, 2004.
[4]. Mohammed TADLAOUI, Azzedine CHIKH, Karim Bouamrane1, ALEM: Un Modèle de Référence pour les Applications Web Adaptatif Educatif, In: LEAFA 2010 The First International Conference on e-Learning For All, 3-5 June 2010 , Hammamet, Tunisia.
[5]. Duitama F., Defude B., Bouzeghoub A., and Carpentier C., « A framework for the generation of adaptive courses based on semantic metadata », Multimedia Tools and Applications, 2005.
[6]. Guerss Fatima zahra & Aitdaoud Mohammed, Douzi Khadija, Talbi Mohammed, Namir Abdelouahed «Towards a computerized system of pedagogical orientation to succeed in Morocco university», 4th WORLD CONFERENCE ON EDUCATIONAL SCIENCES, WCES- 2014

Abstract: The Border Gateway Protocol (BGP) is the only inter-domain routing protocol.Routing information among Autonomous Systems (AS) is exchanged using BGP. BGP protocol does not provide any security mechanism, so it is weak to provide security for AS path, verification of AS number ownership as well as network prefix. Due to lack of security measures, BGP remains vulnerable to various types of misconfiguration and attacks.

[1]. S. Kent, C. Lynn, and K. Seo" Secure Border Gateway Protocol (S-BGP)" IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATION, VOL. 18, NO. 4, APRIL 2000.
[2]. R. White "Architecture and Deployment Considerations for Secure Origin BGP (so-BGP)" , IETF ID draft-white-sobgp-architecture-02, June 2006.
[3]. G. Goodell,W. Aiello, T. Griffin, J. Ioannidis, P. McDaniel, and A. Rubin "Working around BGP: An incremental approach to improving security and accuracy of inter-domain routing" in Proc. ISOC NDSS'03, San Diego, CA, Feb. 2003.
[4]. Heng Yin, Bo Sheng, Haining Wang, Jianping Pan "Keychain-Based Signatures for Securing BGP" IEEE JOURNAL ON SELECTED AREAS IN COMMUNICATIONS, VOL. 28, NO. 8, OCTOBER 2010.

Abstract: The recent development of digital representation has stimulated the development of automatic extraction of topographic and hydrologic information from Digital Elevation Model (DEM). A DEM is used to create hydrologic models which can be used for various purposes such as predicting stream discharges, river network definition, estimating flood extent and timing, locating areas contributing pollutants to a stream, and simulating the effects of landscape alterations on surface water runoff. DEM is processed to produce accurate stream delineation. The process includes filling of sinks and depressions, treating flat areas and determining flow direction at every pixel. Depressions (or pits) and flat surfaces (or flats) are general types of terrain in raster digital elevation models..

[1]. Quinn, P.F., Beven, P., Chevallier, P., Planchon, O."The prediction of hillslope flow paths for distributed hydrological modelling using digital terrain models." Hydrological processes, Volume 5, Issue 1, pages 59–79, 1991.
[2]. Tarboton, D.G., 1997. "A new method for the determination of flow directions and upslope areas in grid digital elevation models." Water Resources Research, VOL. 33, no. 2, pages-309-319, February 1997.
[3]. Kun Hou, Jigui Sun, Wei Yang, Tieli Sun, Ying Wang, and Sijun Ma, "Extraction Algorithms for Using a Regular Grid DEMs", Jinggangshan, P. R. China, 2-4, April. 2010, pp. 112-115.
[4]. N. Senevirathne a and G. Willgoose, "A Comparison of the Performance of Digital Elevation Model Pit Filling Algorithms for Hydrology", 20th International Congress on Modelling and Simulation, Adelaide, Australia, 1–6 December 2013.
[5]. O. Planchon and F. Darboux, "A fast, simple and versatile algorithm to fill the depressions of digital elevation models", Catena, vol. 46(2-3), 2001 , pp. 1 59-176.

Abstract: The Web Services are the emerging paradigm of Service Oriented Architecture (SOA) in the modern enterprise computing to achieve interconnection of related applications in an organization in terms of services. A service is a Software component which fulfills a defined functionality and does not depend on the context of other services. In programming terms, the service is called as web method which is a function that accepts input parameters and returns the output. The data types of input/output parameters may be simple or complex. In SOA architecture, the list of web methods that a web service offers and the data types of their input/output parameters are described using Web Services Description Language (WSDL) standard and published to Universal Description.

[1]. WSDL 2.0 Specifications: http://www.w3.org/TR/wsdl20/
[2]. WSDL 1.2 Specification: http://www.w3.org/TR/wsdl
[3]. Danish Jamil and Hassan Zaki, "Security Implication of SOAP and Web-Service Interface to the Cloud Computing System," International Journal of Engineering Science and Technology (IJEST), ISSN : 0975-5462 Vol. 3 No. 4, 2011
[4]. Mirtalebi, Arezoo, and Mohammad Reza Khayyambashi, "Enhancing Security of Web Service against WSDL Threats," 2nd IEEE International Conference on Emergency Management and Management Sciences (ICEMMS), pp. 920-923, IEEE, 2011.
[5]. Mirtalebi, Arezoo, and Mohammad Reza Khayyambashi, "A new Security Framework for Protecting WSDL File of Web Service," Internatinal Journal of Computer Science and Network Security (IJCSNS), Vol. 12, No. 9 , pp. 84-90, 2012.

Abstract: In this paper the effect of different Mobility trajectory is analyzed by making some good sectors and bad sectors for VOD over Wimax. To investigate given effect the SVC codes are considered in simulation environment through opnet modeler 14.5. The performance is examination in the terms of throughput, traffic received and packet end to end delay. The result concerns that SISO is better than MIMO. Simulation also shows that the performance of trajectory model is better for Adaptive and for QPSK, when some nodes are not moving.

Keywords: IEEE 802.16, MIMO, OPNET, SISO, VOD, Wimax, Wireless Network.w direction at every pixel. Depressions (or pits) and flat surfaces (or flats) are general types of terrain in raster digital elevation models..

[1]. IEEE, 802.16e-2005. IEEE Standard for Local and Metropolitan Area Networks. Part 16: Air Interface for Fixed and Mobile Broadband Wireless Access Systems, 2005.
[2]. N. Degrande, K. Laevens, and D. De Vleeschauwer, "Increasing the user perceived quality for IPTV services," IEEE Communs. Mag., vol. 46, no. 2, pp. 94-100, Feb. 2008.
[3]. Chakchai So-In, Jain, R., "Scheduling in IEEE 802.16e mobile WiMAX networks: key issues and a survey," IEEE Journal on (Volume: 27, Issue: 2 ),2009. [4]. M. Chen, and A. Zakhor, "Rate control for streaming video over wireless," IEEE Wireless Comms., vol. 12, no. 4, pp. 32--14, 2005. [5]. Schwarz, H., Heinrich Hertz, "Overview of the Scalable Video Coding Extension of the H.264/AVC Standard",Circuits and Systems for Video Technology, IEEE Transactions on (Volume:17 , Issue: 9 , 2007.

Abstract: Cloudburst is a devastating disaster that usually occurs during rainy seasons at Himalayan regions. The recent floods in the 'Kedarnath' area, Uttarakhand are a classic example of flash floods in the Mandakini River due to cloudburst that devastated the country by killing thousands of people besides livestock. The traditional methods used for cloudburst prediction are weather forecasting, data mining techniques for weather prediction by modelling meteorological data, laser beam atmospheric extinction measurements from manned and unmanned aerospace vehicles. These techniques are more expensive and time consuming along with uncertainty of accurate prediction. The proposed method in this paper is Arduino based cloudburst predetermination system with real time calculation of rainfall intensity.

Keywords: aerospace; disaster; forecasting; intensity; prediction;

[1]. http://www.arduino.cc
[2]. Data mining techniques for weather prediction: a review, International Journal on Recent and Innovation Trends in Computing and Communication, Volume:2, Issue:8ISSN:2321-8169 2184 – 2189
[3]. Kedarnath flash floods: a hydrological and hydraulic simulation study, Research Communications
[4]. National Remote Sensing Centre, Indian Space Research Organisation, Balanagar, Hyderabad
[5]. Arduino, G., Reggiani, P. and Todini, E., Recent advances in flood forecasting and flood risk assessment. Hydrol. Earth Syst. Sci., 2005, 9(4), 280–284.

Abstract: An energy efficient key management scheme is an important aspect to ensure secure services in resource constrained Wireless Sensor Networks (WSNs). There are two parts of key management scheme, i.e. key distribution and key revocation. Key distribution is the task to manage the key in such a way that if two or more sensors want to communicate with each other for the purpose of sharing the data or control messages, the key is to be made available to them only. The scheme should be energy efficient, as the sensor nodes in the network are resource constrained. However, Key revocation is the task of removing the compromised sensors from the network to restrict such nodes to participate in further communications to avoid any kind of interference or disturbance in communication process. In this paper, we first review and summarize the current key revocation schemes. Then, we present an energy efficient key revocation scheme for removing compromised sensor nodes from the networks.

[1]. Rohit Vaid, Vijay Kumar, "VLKM: Virtual Location-Based Key Management Scheme for Wireless Sensor Networks" in IEEE International Conference on Parallel, Distributed and Grid Computing (PDGC-14) held at Jaypee University of Information Technology, Waknaghat, Shimla, H.P. , INDIA, pp. 53-61, December 11 – 13 , 2014.
[2]. Ishmanov, Farruh, Sung Won Kim, and Seung Yeob Nam. "A Secure Trust Establishment Scheme for Wireless Sensor Networks." Sensors 14, no. 1 (2014): 1877-1897.
[3]. Renjian Feng, Xiaofeng Xu, Xiang Zhou and Jiangwen Wan, "A Trust Evaluation Algorithm for Wireless Sensor Networks Based on Node Behaviors and D-S Evidence Theory", open access, Sensors 2011, 11, 1345-1360, ISSN 1424-8220, www.mdpi.com/journal/sensors.
[4]. Yong Wang and Byrav Ramamurthy, Xukai Zou and Yuyan Xue, "An Efficient Scheme for Removing Compromised Sensor Nodes from Wireless Sensor Networks" (2007), CSE Technical reports, Computer Science and Engineering, Department of University of Nebraska – Lincoln, Paper 77. http://digitalcommons.unl.edu/csetechreports/77, 9-7-2007.
[5]. Satya Keerthi, A. Manogna, Yasaswini, A. Aparna and S. Ravi Teja, "Behaviour based Trust Management using geometric mean approach for Wireless Sensor Networks", International Journal of Computer Trends and Technology- volume3, Issue2- 2012.

Abstract: As we know that there is a huge crowd in the malls in metro cities. Especially it becomes more crowded on holidays. People purchase different items in the malls and puts them in the trolley. At the cash counter billing process is done using bar code scanner. This is very time consuming process. To avoid this we are developing a system which we called as "Intelligence Billing System Using RFID and ZIGBEE". In this system we are using RFID tags instead of barcodes. This RFID tags will be on the product. Whenever the customer puts a product into trolley it will get scanned by RFID reader and product price and cost will be displayed on LCD display. Like this the process goes on. We are using ZIGBEE transmitter which will be at trolley which is used to transfer data to main computer. At the main computer ZIGBEE receiver will be placed which will receive data from transmitter. To store the products price and total billing memory used will be Atmel AT24C04. LCD used will be 16X2 alphanumeric displays. It will be used to display products names, products cost etc.

Keywords: RFID , ZIGBEE,BARCODE

[1]. ARM-LPC 2138 user manual by NXP Semiconductors.
[2]. Dr.K.V.K.K.Prasad ―Embedded/Real time operating systems‖ Dreamtech Publications 2010 Edition.
[3]. Jay Warrior, Eric Mchenry & Kenneth McGee. They know where you are, IEEE SpectrumVol.40 (7), July 2003. RFID cashless/wireless payment.pdf
[4]. Roussos G, BirkbeckColl.‖ Enabling RFID in retail‖IEEE Computer Magazine,Vol.39 , Issue:3PP: 25 – 30 ,20 March 2006.
[5]. Thorsten Blecker& George Haung ―RFID in Operations and Supply Chain Management‖2008.

Abstract: Today most of vehicle navigational system use latest GPS (Global Positioning System) and GSM (Global System for Mobile Communication) technology for sending vehicle navigational information (latitude and longitude) at server side, but they use several existing techniques (based on time, distance and manually) for sending SMS (Short Message Service) at server side. Addressing that problem, an innovative methodology is designed for transportation i.e. vehicle speed based SMS sending technique. It provides the cost effective solution for sending SMS from navigating vehicle to server side by reducing the quantity of SMS in case of traffic congestion or vehicle damage.

Key words: GPS, GSM, Navigational, SMS, Speed, Vehicle.

[1]. M. AL-Rousan, A. R. AI-Ali and K. Darwish," GSM-Based Mobile Tele-Monitoring and Management System for Inter-Cities Public Transportations", 2004 IEEE International Conference on Industrial Technology (ICIT).
[2]. Kai Jakobs, Carsten Pils, and Michael Wallbaum," Using the Internet in Transport Logistics The Example of a Track & Trace System", P. Lorenz (Ed.): ICN 2001, LNCS 2093, pp. 194–203, © Springer-Verlag Berlin Heidelberg 2001.
[3]. M. AL-Rousan, A. R. AI-Ali and K. Darwish, "GSM-Based Mobile Tele-Monitoring and Management System for Inter-Cities Public Transportations",IEEE International Conference on Industrial Technology (ICIT), 2004.
[4]. Antonio Jose Duarte Santos, Americo Rodrigues Soares, Fernando Manuel de Almeida Redondo, and Nuno Borges Carvalho," Tracking Trains via Radio Frequency Systems", Ieee Transactions on Intelligent Transportation Systems, Vol. 6, No. 2, June 2005.

Abstract: Sentiment Analysis (SA) and Summarization is a new and emerging field of research which deals with information extraction and knowledge discovery from text using Natural Language Processing and Data Mining technique, which help to track the mood of public about specific products and social or political event. Sentiments of individuals are extremely useful for people and company owner for making several decisions. However decision based upon some of the online review among the large set of review are not very easy for classifying the sentiment. This report introduce new Hybrid Polarity Detection System for SA of short informal text i.e. Twitter post to compare with state-of-art method which used as analysis of Sentiment Summarization. Additionally proposed Hybrid Polarity Detection System derives high performance with new set of features.

Keywords - Feature Extraction, Machine Learning Method, Opinion Mining, Sentiment Analysis, Sentiment Classification, Subjectivity Classification, Twitter

[1]. Khairullah Khan, Baharum B. Baharudin, Aurangzeb Khan and Fazal-e-Malik, Mining Opinion from Text Documents: A Survey, 3rd IEEE International Conferences on Digital Ecosystem and Technology, 2009.
[2]. Bing Liu, Sentiment Analysis and Opinion Mining, Morgan & Claypool Publishers, May 2012.
[3]. Bo Pang, Lillian Lee and Shivakumar Vaithyanathan, Thumbs up Sentiment Classification using Machine Learning technique, proceedings of EMNLP,2002, 79-86.
[4]. Khairullah Khan, Baharum Baharudin, Aurnagzeb Khan and Ashraf Ullah, Mining opinion components from unstructured reviews: A review, Journal of King Saud University – Computer and Information Sciences, 2014.
[5]. Alec Go, Richa Bhayani and Lei Huang, Twitter Sentiment Classification using Distant Supervision, CS224N project report, Stanford, 2009, 1-12.

Abstract: In Wireless Sensor Network, because of limitation on sensor node energy, the energy efficiency is a major concern which should be deliberated while planning to design a protocol. To overcome this conflict, data grouping and accumulation should be done in this issue to decrease consumption of sensor node energy and cost of communication by aggregating data redundancy at intermediate sensor nodes to reduce number and size of alarming messages in critical event monitoring. The network for design is Ad-Hoc network. In this paper we proposed an improved algorithm Improved-Data Routing for In-Network Aggregation Data (I-DRINA) whose results are compared with existing known protocols as Data Routing for In-Network Aggregation (DRINA) and Low Energy Adaptive Clustering Hierarchy (LEACH). The proposed protocol has designed for improvement in path routing in network and stables the energy consumption in the wireless network to prolong the existence of network for monitoring of an event. Twitter

[1]. Peng Guo, Tao Jiang, Senior Member, IEEE, Qian Zhang, Fellow, IEEE, and Kui Zhang, Sleep Scheduling for Critical Event Monitoring in Wireless Sensor Networks, IEEE Transactions On Parallel And Distributed Systems, Vol. 23, No. 2, February 2012
[2]. Dali Wei, Member, IEEE, et. al., An Energy-Efficient Clustering Solution for Wireless Sensor Networks, IEEE Transactions on Wireless Communications, Vol. 10, No. 11, November 2011
[3]. Yanwei Wu, Member, IEEE, Xiang-Yang Li, Senior Member, IEEE, YunHao Liu, Senior Member, IEEE, and Wei Lou, Energy-Efficient Wake-Up Scheduling for Data Collection and Aggregation, IEEE Transactions On Parallel And Distributed Systems, Vol. 21, No. 2, February 2010
[4]. AbtinKeshavarzian, Huang Lee, LaxmiVenkatraman, Wake up scheduling in wireless sensor networks, Proc. seventh ACM Int'l Conf.Mobile Ad Hoc Networking and Computing, (pp. 322-333).2006
[5]. N. Bouabdallah, M.E. Rivero-Angeles, and B. Sericola, Continuous Monitoring Using Event-Driven Reporting for Cluster-Based Wireless Sensor Networks," IEEE Trans. Vehicular Technology, vol. 58, no. 7, pp. 3460-3479, Sept. 2009.