iosr-JCE   Volume-16 ~ Issue-1 ~ Jan-Feb 2014

Abstract: To hide the communication of users, the anonymity systems pack the application data into equal – sized cells. The size of IP packets in the Tor network can be very dynamic and the IP layer may be repack cells. A new cell-counting attack against Tor allows the attacker to confirm anonymous communication relationship among users very quickly. By varying the number of cells in the target traffic at the malicious exit onion router, the attacker can embed a secrete signal into variation of cell counter of the target traffic and it will be carried and arrive at the malicious entry onion router.

[1] W. Yu, X. Fu, S. Graham, D. Xuan, and W. Zhao, "DSSS-based flow marking technique for invisible traceback," in Proc. IEEE S&P,May 2007, pp. 18–32.
[2] N. B. Amir Houmansadr and N. Kiyavash, "RAINBOW: A robust and invisible non-blind watermark for network flows," in Proc.16thNDSS, Feb. 2009, pp. 1–13.
[3] V. Shmatikov and M.-H. Wang, "Timing analysis in low-latency MIX networks: Attacks and defenses," in Proc. ESORICS, 2006, pp. 18–31.
[4] V. Fusenig, E. Staab, U. Sorger, and T. Engel, "Slotted packet counting attacks on anonymity protocols," in Proc. AISC, 2009, pp. 53–60.
[5] X. Wang, S. Chen, and S. Jajodia, "Tracking anonymous peer-to-peer VoIP calls on the internet," in Proc. 12th ACM CCS, Nov. 2005, pp.81–91.
[6] K. Bauer, D. McCoy, D. Grunwald, T. Kohno, and D. Sicker, "Lowresource routing attacks against anonymous systems," Univ. Colorado Boulder, Boulder, CO, Tech. Rep., Aug. 2007.

Abstract: This paper proposes a Real-Time Link Reliability Routing protocol for wireless sensor networks (WSNs). The protocol achieves to reduce packet deadline miss ratio while considering link reliability, two-hop velocity and power efficiency and utilizes memory and computational effective methods for estimating the link metrics. Numerical results provide insights that the protocol has a lower packet deadline miss ratio and improved sensor network lifetime. The results show that the proposed protocol is a feasible solution to the QoS routing problem in wireless sensor networks that support real-time applications..

[1] F. L. Lewis, D. J. Cook, S. K. Dasm and John Wiley. Wireless Sensor Networks, In Proc. Smart Environment Technologies,
Protocols and Applications, New York, 2004, 1-18.
[2] Tian He, John A. Stankovic, Chenyang Lu, and Tarek F. Abdelzaher. A Spatiotemporal Protocol for Wireless Sensor Network, In
IEEE Transactions on Parallel and Distributed Systems, 16(10), 2005, 995-1006.
[3] E. Felemban, C. G. Lee, and E. Ekici. MM-SPEED: Multipath Multi-Speed Protocol for QoS Quarantee of Reliability and
Timeliness in Wireless Sensor Network, In IEEE Transactions on Mobile Computing, 5(6), 2006, 738–754.
[4] O. Chipara, Z. He, G. Xing, Q. Chen, X.Wang, C. Lu, J. Stankovic, and T. Abdelzaher. Real-Time Power-Aware Routing in Sensor
Network, In Proc. IWQoS, 2006, 83–92.
[5] Y. Li, C. S. Chen and Y.-Q. Song, Enhancing Real-Time Delivery In Wireless Sensor Networks With Two-Hop Information. In
IEEE Transactions On Industrial Informatics, 5(2), 2009, 113–122.
[6] J. Stankovic, T. Abdelzaher, C. Lu, L. Sha and J. Hou. Real-time Communication and Coordination in Embedded Sensor Networks,
In Proc. IEEE, 91(7), 2003, 1002–1022.

Abstract: Personal identification based on Biometrics technology is a trend in future. Iris Recognition is regarded as a high accuracy verification technology when compared to traditional approaches. In real-time Iris Recognition, Iris Localization is very important step for Iris Recognition. So, the Iris Masks plays an important role in iris recognition. This Iris Mask indicates which part is occluded or contaminated by noisy image artifacts such as eyelashes, eyelids, eyeglasses frames, and specular reflections. If the Iris Mask is inaccurate, it will decrease its performance in Iris Recognition system. So, we use learning- based algorithms to estimate accurate Iris Masks from Iris Images, which propose to use Figueiredo and Jain's Gaussian Mixture Models (FJ-GMMs) to model the underlying probabilistic distributions of both valid and invalid regions on iris images. We also explored possible features and found that Gabor Filter Bank (GFB) provides the most discriminative information for our goal. Finally, we applied Simulated Annealing (SA) technique to optimize the parameters of GFB in order to achieve the best recognition rate.

Index Terms: Gaussian mixture models, iris mask, iris recognition, iris occlusion estimation, simulated annealing.

[1] J. Daugman, "High Confidence Visual Recognition of Persons by a Test of Statistical Independence," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 15, no. 11, pp. 1148-1161, Nov. 1993.
[2] J. Daugman, "Statistical Richness of Visual Phase Information: Update on Recognizing Persons by Iris Patterns," Int'l J. Computer Vision, vol. 45, no. 1, pp. 25-38, 2001.
[3] J. Daugman, "How Iris Recognition Works," Proc. Int'l Conf. Image Processing, vol. 1, pp. I-33-I-36, 2002.
[4] J. Daugman, "Biometric Personal Identification System Based on Iris Analysis," US Patent 291560, 1994.
[5] J. Daugman, "How Iris Recognition Works," IEEE Trans. Circuits and Systems for Video Technology, vol. 14, no. 1, pp. 21-30, Jan. 2004.
[6] L. Ma, T. Tan, Y. Wang, and D. Zhang, "Personal Identification Based on Iris Texture Analysis," IEEE Trans. Pattern Analysis and Machine Intelligence, vol. 25, no. 12, pp. 1519-1533, Dec. 2003.

Abstract: To facilitate the access to computer systems, computer scientists choose to design interactive mechanisms which are more adapted than traditionally used devices such as keyboard or mouse. The use of gestures modalities has been extensively studied in recent years by the human machine interaction community. Augmented Reality is one of the mechanisms of interaction that allow implementing the human-machine communication. Indeed, its principle is to mix the real world and the virtual world.

[1] Alexander Toshev, Ameesh Makadia, Kostas Daniilidis "Shape-based Object Recognition in Videos Using 3D Synthetic Object Models", IEEE, pp.288-295, 2009.
[2] Pengwei LIU, Huiyuan WANG et al. "Motion Compensation Based Detecting and Tracking Targets in Dynamic Scene", IEEE, pp.703-706, 2010.
[3] Ritika1, Gianetan Singh Sekhon "Path Estimation and Motion Detection of Moving Object in Videos", IOSR Journal of Computer Engineering (IOSRJCE) ISSN: 2278-0661 Volume 2, Issue 4, PP 01-04, July-Aug. 2012.
[4] Mohammed Sayed and Wael Badawy, "A novel motion estimation method for mesh- based video motion tracking", IEEE, 2004, 337-340.
[5] Li-Qun Xu, "Simultaneous Tracking And Segmentation Of Two Free Moving Hands In A Video Conferencing Scenario", IEEE, 2003, 49-52.
[6] Beiji Zou, Xiaoning Peng and Liqin Han, "Particle Filter With Multiple Motion Models For Object Tracking In Diving Video Sequences", IEEE, 2008, 224-228.

Abstract: Current high performance computing (HPC) applications are found in many consumers, industrial and research fields. There is a great deal more to remote sensing data than meets the eye, and extracting that information turns out to be a major computational challenges, so the high performance computing (HPC) infrastructure such as MPP (massive parallel processing), clusters, distributed networks or specialized hardware devices are used to provide important architectural developments to accelerate the computations related with information extraction in remote sensing.

[1] K. Dowd and C. Severance. High Performance Computing. O'Reilly & Associates, Inc., Sebastopol CA, 2nd edition, 1998.
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Press, Cambridge MA,1996.
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2002.
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Mathematics, Philadelphia PA, 2001.
[5] W. Triebel, J. Bissell, and R. Booth. Programming Itanium-based Systems.Intel Press, United States, 2001.
[6] K. R. Wadleigh and I. L. Crawford. Software Optimization for High Performance Computing. Prentice Hall PTR, New Jersey, 2000
[7] Hwang, K. Advance Computer Architecture: Parallelism, Scalability, Programmability, McGraw-Hill, New York, 1993.

Abstract:Routing in delay tolerant networks (DTN) can benefit from the fact that most real life DTN, especially in the context of people-centric networks (e.g. Pocket Switching Networks (PSN)), exhibit some sort of periodicity in their mobility patterns. For example, public transportation networks follow periodic schedules. Even most individuals have fairly repetitive movement patterns, for example, driving to and from work at approximately the same time everyday. This paper proposes a BayesiaLab tool based DTN routing framework that adopts a methodical approach for computing the routing metrics by utilizing the network parameters (e.g. spatial and temporal information at the time of packet forwarding) that capture the periodic behavior of DTN nodes.

[1] J. Burgess, B. Gallagher, D. Jensen, and B. N. Levine. Maxprop: Routing for vehicle-based disruption tolerant networks. In Proceedings of IEEE INFOCOM, April 2006.
[2] H. Dubois-Ferriere, M. Grossglauser, and M. Vetterli. Age matters: Efficient route discovery in mobile ad hoc networks using encounter ages. In Proceedings of ACM MobiHoc, June 2003.
[3] K. Fall. A delay-tolerant network architecture for challenged internets. In Intel Research Technical Report, IRB-TR-03-003, February 2003.
[4] M. Grossglauser and M. Vetterli. Locating nodes with ease: Last encounter routing in ad hoc networks through mobility diffusion. In Proceedings of IEEE INFOCOM, March 2003.
[5] Z. J. Haas and T. Small. A new networking model for biological applications of ad hoc sensor networks. In ACM Transaction on Networking, volume 14, 2006.
[6] J. Han and M. Kamber. Data Mining: Concepts and Techniques. Morgan Kaufmann Publishers, ISBN: 1558609016, 2001.
[7] P. Hui, A. Chaintreau, J. Scott, R. Gass, C. Diot, and J. Crowcroft. Pocket switched networks and human mobility in conference environ- ments. In Proceedings of SIGCOMM, August 2005.
[8] P. Hui and J. Crowcroft. How small labels create big improvements. In Proceedings of International Workshop on Intermittently Connected Mobile Ad hoc Networks, March 2007.

Abstract: Cloud computing has a great tendancy of providing robust computational power to the society at reduced cost. The wide adoption of this promising computation model is prevented by security which is the primary obstacle especially for customers when their confidential data are consumed and produced during the computation. The data stored in the cloud may be frequently updated by the users. It has been reported by many authors on auditing methods to efficiently audit the cloud data storage without challenging the local copy of data, and introduce no additional on-line load to the cloud user.

[1] C. Wang, Q. Wang, K. Ren, and W. Lou, in "Privacy Preserving Public Auditing for Storage Security in Cloud Computing" in Proc. IEEE INFOCOM '10, Mar. 2010.
[2] "Achieving Secure, Scalable, and Fine-grained Data Access Control in Cloud Computing" Shucheng Yu, Cong Wang, Kui Ren, and Wenjing Lou Dept. of ECE, Worcester Polytechnic Institute.
[3] "Dynamic Provable Data Possession" C. Chris Erway Alptekin K upc¸ Charalampos Papamanthou Roberto Tamassia from Brown University, Providence in November 29, 2009
[4] "Cooperative Provable Data Possession for Integrity Verification in Multicloud Storage" Yan Zhu, Member, IEEE, Hongxin Hu, Member, IEEE, Gail-Joon Ahn, Senior Member, IEEE, and Mengyang Yu in "IEEE Transactions on parallel and distributed systems, vol. 23, no. 12, December 2012"
[5] "Detecting and Resolving Firewall Policy Anomalies" Hongxin Hu, Student Member, IEEE, Gail-Joon Ahn, Senior Member, IEEE, and Ketan Kulkarni.

Abstract: Tracking object in videos has applications in video surveillance and other main applications. The absence of a commonly adopted performance evaluation Framework is hampering advances in the design of effective video trackers. Tracking an object in a video is a challenging process. The proposed algorithm to track the multiple objects in a video even in conditions such as diversity of targets (head, vehicle and person) and challenges (occlusions, background clutter, pose changes and scale changes).The objects can be tracked based on the compression domain and pixel domain. Using background subtraction method the objects in the fore ground are extracted. The objects in the frame are then detected and then the detected objects are labelled. Performance analysis is done and the tracking accuracy and tracking failures are measured. The proposed system is used to improve the tracking accuracy and decrease the tracking failures in the video tracking process. Finally Performance analysis shows that for different type of trackers our algorithm shows best performance.

Index Terms: Evaluation measure, Performance evaluation, video trackers.

[1] B. Ristic, D. Clark, and B.-T Vo (2011), "A metric for performance evaluation of multi-target tracking algorithms‟, IEEE Transactions on Signal Processing, pp. 3452–3457.
[2] D. Comaniciu, V. Ramesh, and P. Meer (2003), "Kernel-based object tracking‟, IEEE Transactions, pp. 564–577.
[3] D. Scharstein and R. Szeliski (2002), "A taxonomy and evaluation of dense two-frame stereo correspondence algorithms‟, International Journal of Computer Vision, pp. 7–42.
[4] F. Bashir and F. Porikli (2006), "Performance evaluation of object detection and tracking systems‟, Proceeding of IEEE International Workshop, pp. 7–14.
[5] F. Yin, D. Makris, and S. A. Velastin (2007), "Performance evaluation of object tracking algorithms‟, Proceeding of IEEE International Workshop.
[6] H. Li, C. Shen, and Q. Shi (2011), "Real-time visual tracking using sparse Representation‟, Proceeding of IEEE Conference, pp. 1305–13125.
[7] J. Black, T. Ellis, and P. Rosin (2003) , "A novel method for video tracking performance evaluation‟ , Proceeding of IEEE International Workshop.

Abstract: Data aggregation is implemented in wireless sensor networks to reduce data redundancy and to summarize relevant and necessary information without requiring all pieces of the data and to reduce large amount of data transmission. Homomorphic public encryption is the proposed scheme designed for multi-application environment. The base station extracts application-specific data from aggregated cipher texts, and compromised attacks in single application environments, and to degrade damage from unauthorized aggregations. To enhancing a new approach as symmetric key algorithm used where the keys for encryption and decryption are done in same shared secret. These keys are used to provide security in data aggregation and separate the cipher text when it stored and retrieve from database. In Database As a Service (DAS) model, the client has to secure their database. The attacker cannot change the data because it is dynamically created.

Keywords: Aggregation, Homomorphic public Encryption, Symmetric Key, Database Asa Service, Attacker, Cipher text

[1] A. Liu and P. Ning, (2008) "TinyECC: A Configurable Library for Elliptic Curve Cryptography in Wireless Sensor Networks," Proc. Int'l Conf. Information Processing in Sensor Networks (IPSN'08), pp.245-256

. [2] C. Castelluccia, E. Mykletun, and G. Tsudik, (2005) "Efficient Aggregation of Encrypted Data in Wireless Sensor Networks,"Proc.Second Ann. Int'l Conf. Mobile and Ubiquitous Systems: Networking and Services (MobiQuitous'05), pp.109-117.

[3] D. Westhoff, J. Girao, and M. Acharya, (2006) "Concealed Data Aggregation for Reverse Multicast Traffic in Sensor Networks: Encryption, Key Distribution, and Routing Adaptation," IEEE Trans. Mobile Computing, vol.5, no. 10, pp. 1417-1431.

[4] E. Mykletun, J. Girao, and D. Westhoff, (2006) "Public Key Based Cryptoschemes for Data Concealment in Wireless Sensor Networks," Proc. IEEE Int'l Conf. Comm. (ICC'06), vol.5.

[5] H. Sanli, S. Ozdemir, and H. Cam, (2004) "SRDA: Secure Reference-based Data Aggregation Protocol for Wireless Sensor Networks," Proc. IEEE 60th Vehicular Technology Conf. (VTC'04-Fall), vol.7.

[6] L. Oliveira, D. Aranha, E. Morais, F. Daguano, J. Lopez, and R. Dahab, (2007) "TinyTate: Computing the Tate Pairing in Resource-Constrained Sensor Nodes," Proc. IEEE Sixth Int'l Symp. Network Computing and Applications (NCA'07), pp.318-323.

Abstract: GIS can manage remotely sensed images, users must have an appropriate digital map that represents lands each one has information according to its owner, status, and some other data. The classification of such lands is a great problem which take long time depending on human efforts. Many kinds of classifications had been used , one of them is the use of supervised multi-layer perceptron with backpropagation neural network classifier and using second order statistics Gray Level Co-occurrence Matrix (GLCM) to calculate eight textural features for each one of three visible bands (RGB) for each land sample. In this research we analyzed the GLCM feature extraction algorithm to detect the appropriate angle that can be chosen ,

[1] K. C. Clearke et al., Geographic Information System and Environmental Modeling, (Prentice Hall, 2009).
[2] F. Harvey, A primer of GIS fundamental geographic and cartographic concepts, (The Guilford Press, 2008).
[3] I. T. Service, Introduction to GIS using ArcGIS, (Durham University, 2006).
[4] A. M. Coleman, An Adaptive Landscape Classification Procedure Using Geoinformatics and Artificial Neural Networks, MSc. Thesis; Faculty of Earth and Life Sciences Vrije University, Amsterdam The Netherlands, MA ,2008.
[5] S. Haykin, Neural Networks and Learning Machines, (PHI Learning Private Limited, 2009).
[6] W. R. Baiee et. Al. , Classification of GIS Image using GLCM and Neural Network , Basra Journal of Science VOL Vol.31(2),110-119, 2013 .
[7] R. Methre et al., Exploring Spatial Information in Spectral Features for Texture Image Retrieval, International Journal of Computer and Network Security Vol. 1; No. 3, 2009.

Abstract: In today's era SMS message is very fast communication way. Communication done through SMS are more faster and simple. So many people are using SMS communication in their day to day life. Also confidential information is exchanged using SMS. So there is need to secure SMS from different threats. The threats are like DoS attack, Message Disclosure, SMS viruses, Phone crashes. Also need to ensure message is send by authorized sender. In order to achieve all these needs this paper describes the solution for SMS security. We describe hybrid compression encryption technique to secure data. This technique firstly encrypt SMS by Elliptic curve encryption technique and after that compress the encrypted SMS using lossless compression technique.

Keywords: Compression; Decryption; Encryption; Security; SMS

[1] A Medani, A Gani, O.Zakaria, A.A. Zaidan, "Review of mobile short message service security issues and techniques towords the solution", Scientific Reaserch and Essays voI.6(6),pp. I 147-1 165,18 March, 201 IJ. Clerk Maxwell, A Treatise on Electricity and Magnetism, 3rd ed., vol. 2.Oxford: Clarendon, 1892, pp.68-73.
[2] R. Ghosal and P. H. Cole, "Elliptic curve cryptography", University of Adelaide Auto-ID Labs, Technical Report.
[3] J. P. A1buja and E. V. Carrera, 'Trusted SMS communication on mobile devices", I Ith Brazilian Workshop on Real-Time and Embedded
[4] R. R. chavan and M. sabnees, "Secuerd mobile messaging", International Conference On Computing, Electronics and Electrical Technologies [ICCEET] 2012.

Abstract: prioritizing the test cases helps to increase the rate of fault detection. The difficulty of ensuring the dependability in the growth of the scale of software and software testing in distributed environment a sufficient software testing technique is not possible. It is often difficult to test a parallel and distributed system in the real world deployment. Hence in this paper, after generating test cases using functional requirements, dependency structure prioritization technique is used to prioritize the test cases based on the functional structure of dependency. A new technique namely cosine similarity based clustering approach is used to group the test cases based on the similarity values to form clusters. Each cluster is distributed in the distributed environment for parallel execution in order to reduce the computation time and to improve the rate of fault detection.

Index Terms: Cosine similarity approach, dependency structure prioritization, software testing.

[1] Ryan Carlson, Hyunsook Do, Anne Denton (2011) ‗a Clustering Approach to Improving Test Case prioritization: An Industrial Case Study,' 27th IEEE International Conference on Software Maintenance.
[2] K.P.N.V.Satya sree , Dr.J V R Murthy(2012) ‗ clustering based on cosine similarity measure,' international journal of engineering science and advanced technology.
[3] Shifa-e-Zehra Haidry and Tim Miller (2013) 'Using Dependency Structures for Prioritization of Functional Test suites,' IEEE transaction on software engineering.
[4] Neethi Narayanan, J.E.Judith, Dr.J.Jayakumari (2013) ‗Enhanced Distributed Document Clustering Algorithm Using Different Similarity Measures,' IEEE Conference on Information and Communication Technologies.
[5] Vikas Thada, Dr Vivek Jaglan (2013)‗Comparison of Jaccard, Dice, Cosine Similarity Coefficient To Find Best Fitness Value for Web Retrieved Documents Using Genetic Algorithm,' International Journal of Innovations in Engineering and Technology.
[6] J.Jenny Li (2005)'Prioritize Code for Testing to Improve Code Coverage of Complex Software,' IEEE transaction on software reliability engineering.

Abstract: This study detailed the effect of simultaneous multiple intrinsic and extrinsic factors on the characteristics of Sfax activated sludge wastewater treatment plant (WWTP), located in Southern East Tunisia. The plant performance was evaluated through descriptive and statistical analysis of quantity and quality data of both raw wastewaters and treated effluent over a period of three years (2008 – 2010). Despite the rehabilitation, poor performance was shown to be attributed to raw wastewaters quality, civilization populations, bad functioning of the aerators and the industrial fallouts and deposits.

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2 ème Ed.
[3]. Azri, C., Abida, H. and Medhioub, K. (2007). Performance evaluation of the Wastewater treatment plant of Sfax city (Tunisia):
influence of intrinsic and extrinsic factors. Asian journal of Water, Environment and Pollution, 5 (3), 35 – 47.
[4]. Azri, C., Abida, H. and Medhioub, K. (2009). Geochemical behaviour of the Tunisian background aerosols in sirocco
w…………….. Advances in atmospheric sciences, 26 (3), 390 – 402.
[5]. Azri, C., Maalej, A., Medhioub, K. and Rosset, R. (2007). Evolution of atmospheric pollutants in the citty of Sfax (Tunisia)
(October 1996 – June 1997). Atmosphera, 20 (3), 223 – 242.

Abstract: In this paper, an attempt to design and simulate charge pump phase locked loop method of synchronization in grid connected solar photovoltaic system has been made. Delivery of power with high quality to the grid is of concern in grid connected systems and to ensure the same auto synchronization technique is required. Such a method is proposed and analyzed using MatLab-Simulink and its performance is verified for compliance with IEEE 929 standard prescribed for inverter fed grid systems and IEC 61727 / IEEE 1547 standard for harmonic limitation of grid connected inverters.

Keywords: Charge Pump Phase locked loop, IEC 61727, IEEE 929, IEEE 1547, MatLab, Solar photovoltaic array, Synchronization

[1] G. N. Tiwari, Solar Energy, Fundamentals, Design, Modelling and Applications (Narosa Publishing House Pvt. Ltd., New Delhi, 2002).
[2] Jasvir Singh, Study and design of grid connected solar photovoltaic system at Patiala, Punjab, Master of Engineering, Thapar University, Patiala, India, July 2010.
[3] Pavan Kumar Hanumolu, Karthikeya Mayaram, Analysis of charge-pump phase-locked loops, IEEE Transactions on circuits and systems, vol. 51, No. 9, Sep 2004.
[4] BHARAT HEAVY ELECTRICALS LIMITED, Handbook of Switchgears (Tata McGraw-Hill Education Private Limited, 2005).
[5] Miss. Sangita R Nandurkar, Mrs. Mini Rajeev, Design and simulation of three phase inverter for grid connected photovoltaic systems, Proceedings of third biennial national conference, NCNTE – 2012, Feb 24-25.

Abstract:Data mining is the process of analyzing data from different perspectives and summarizing it into useful information. Automated data collection allows users to analyze data from many different dimensions or angles, categorize it and summarize the relationships identified. Discrimination is of two types, Direct and Indirect discrimination. Direct discrimination occurs when decisions are made based on sensitive attributes.

[1]. Sara Hajian and Josep Domingo"A methodology for Direct and Indirect Discrimination Prevention in Data Mining" IEEE Transaction on Knowledge and Data Engineering Vol.25. No.7. July 2013. [2]. D.Pedreschi, S.Ruggieri and F.Turini," Discrimination-Aware Data Mining," Proc. 14th ACM Int'l Conf. Knowledge Discovery and Data Mining.
[3]. Faisal Kamiran, Toon Calders "Classification with no Discrimination by Preferential Sampling"Eindhoven University of Technology, The Netherlands.
[4]. Dino Pedreschi Salvatore Ruggieri Franco Turini Discrimination-aware Data Mining Dipartimento di Informatica, Università di Pisa L.go B. Pontecorvo 3, 56127 Pisa, Italy

Abstract: Semantic Web Service is an emerging technology which is growing popularly, due to the increased level of machine understandability. In this paper we propose semantic web service discovery framework using Sense based match making mechanism that provides flexibility for searching exact word. Our work is mainly focused on to overcome the disambiguation problem and also for making the use of semantic web easier. It incorporates NLP techniques for disambiguating words, meanings and establishing a context for a set of words. This mechanism improves the quality of discovery process which enhances the user selection and also helps to express the desired semantic Web services. The experimental test shows the positive impact on the discovery process.

Keywords: Semantic Web Service, Ontology, Match Making, Natural Language Processing.

[1] A.A. Patil, S.A. Oundhakar, A.P. Shethand, and K. Verma, "METEOR-S Web Services annotation Framework," Proc.13thInt'L Conf. World Wide Web (WWW), 2004.
[2] AabhasV.Paliwal, JaideepVaidya, Nabila dam, "Semantic –Based Automated Service Discovery "IEEE Transactions on Service Computing, 2012.
[3] Fangfang Liu, Yuliang Shi2 Jie Yu, Tianhong Wang, Jingzhe Wu, "Measuring Similarity of Web Services Based on WSDL", IEEE International Conference on Web Services, 2010.
[4] Farrag, T. A., Saleh, A. I., Ali, H. A.," Towards SWSs Discovery: Mapping from WSDL to OWL-S Based on Ontology Search and Standardization Engine". IEEE Transactions on Knowledge and Data Engineering, Vol. 25, no. 5, May 2013.
[5] G. Meditskos and N. Bassiliades, "Object-Oriented Similarity Measures for Semantic Web Service Matchmaking," Proc. Fifth European Conf. Web Services (ECOWS '07), 2007.