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Image processing has been proved to be an effective tool for analysis in various fields and applications. Agriculture sector where the parameters like canopy, yield, quality of the product were the important measures from the farmers� point of view. This paper intends to focus on the survey of application of image processing in agriculture field such as imaging techniques, yield mapping, robotic harvesting, fruit grading, weed detection, and leaves disease detection.

Color Features; Texture Features; Classifier; Machine Vision

[1] J. Blasco, N. Aleixos, J. Gomez, and E. Molto, �Citrus sorting by identification of the most common defects using multispectral computer vision,� Journal of Food Engineering, vol. 83(3), pp. 384�393, 2007.
[2] J. Blasco, N. Aleixos, J. Gomez-Sanchis, and E. Molto, �Recognition and classification of external skin damage in citrus fruits using multispectral data and morphological features,� Biosystems Engineering, vol. 103, pp. 137�145, 2009.
[3] K. Vijayarekha, and R. Govindaraj, �Citrus fruit external defect classification using wavelet packet transform textures and ANN,� IEEE international conference on industrial technology, pp. 2872-2877. Doi: 10.1109/ICIT.2006.372646.
[4] R. Renu, and D. V. Chidanand, �Internal Quality Classification of agricultural produceusing Non-destructive Image Processing Technologies (soft X-ray)� International Journal of Latest Trends in Engineering and Technology, vol. 2(4), pp. 535-543, 2013.
[5] M.M. Blanke, �Prediction of apple yields in Europe-present and new approaches in research,� In L. Smith (Ed.), Proceedings 106th annual meeting Washington state horticultural association, pp. 68-75. Yakima, WA: WSHA Publishing, 2011.
[6] D.M. Bulanon, T. Kataoka, Y. Ota, and T. Hiroma, �A segmentation algorithm for the automatic recognition of fuji apples,� Biosystems Engineering. Vol. 83, pp. 405�412, 2002.
[7] M.W. Hanan, T.F. Burks, and D.M. Bulanon, �A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection,� CIGR Ejournal Vol. XI, 2009.
[8] R. Zhou, L. Damerow, Y.Sun and M. M. Blanke,� Using colour features of cv. �Gala� apple fruits in an orchard in image processing to predict yield,� Precision Agric. DOI 10.1007/s11119-01209269-2, vol. 13, pp. 568-580, 2012.
[9] D. Stajnko, J. Rakun and M. Blanke, �Modelling Apple Fruit Yield Using Image Analysis for Fruit Colour,� Shape and Texture,Europ.J.Hort.Sci,ISSN 1611-4426. � Verlag Eugen Ulmer KG, Statgart, vol. 74(6), pp. 260-267, 2009.
[10] Y. Sarig, �Mechanized fruit harvesting-Site Specific Solutions,� Information and Technology for Sustainable Fruit and Vegetable Production, FRUTIC 05: pp. 237-247, 2005.
[11] M.W. Hanan, T.F. Burks, and D.M. Bulanon, �A machine vision algorithm combining adaptive segmentation and shape analysis for orange fruit detection,� CIGR Ejournal Vol. XI, 2009.
[12] Y. Wang, X. Zhu, and C. Ji, �Machine vision based cotton recognition for cotton harvesting robot,� Computer and Computing Technologies in Agriculture, vol. 2, pp. 1421-1425, 2008.
[13] A.S. Ogale, and Y. Aloimonos, �A roadmap to the integration of early visual modules,� International Journal of Computer Vision, vol. 72(1), pp. 9-25, 2007.
[14] D. Al-Bashish, M. Braik, and S. Bani-Ahmad, �Detection and classification of leaf diseases using Kmeans-based segmentation and neural-networks-based classification,� Inform. Technol. J., vol. 10, pp. 267-275. DOI:10.3923/itj.2011.267.275, 2011.
[15] T. Rumpf, A.K. Mahlein, U. Steiner, E.C. Oerke, H.W. Dehne, and L. Plumer, �Early detection and classification of plant diseases with Support Vector Machines based on hyperspectral reflectance,� Computers and Electronics in Agriculture, Vol. 74(1), Pages 91-99, ISSN 0168-1699, DOI:10.1016/j.compag.2010.06.009, 2010.
[16] C. Hillnhuetter, and A.K. Mahlein, �Early detection and localisation of sugar beet diseases: new approaches,� Gesunde Pfianzen vol. 60 (4), pp. 143�149, 2008.
[17] M. S.Prasad Babu, and B. Srinivasa Rao, �Leaves recognition using back-propagation neural network - advice for pest and disease control on crops,� Technical report, Department of Computer Science & Systems Engineering, Andhra University, India. Downloaded from www.indiakisan.net on May 2010.
[18] A. Camargo, and J. S. Smith, �An imageprocessing based algorithm to automatically identify plant disease visual symptoms,� Biosystems Engineering, Vol.102 (1), pp. 9-21, ISSN15375110,DOI:10.1016/j.biosystemseng.2008.09.030, 2009.
[19] S. D. Bauer, F. Korc, and W.Forstner, �Investigation into the classification of diseases of sugar beet leaves using multispectral images,� In: E.J. van Henten, D. Goense and C. Lokhorst: Precision agriculture 09. Wageningen Academic Publishers, p. 229-238. URL:http://www.precision-crop-protection.unibonn.de/gk_research/project.php?project=3_09, 2009.
[20] S. Weizheng, W. Yachun, C. Zhanliang, and W. Hongda, �Grading Method of Leaf Spot Disease Based on Image Processing,� In Proceedings of the 2008 international Conference on Computer Science and Software Engineering, Vol. 06, IEEE Computer Society, Washington, DC, 491-494. DOI= http://dx.doi.org/10.1109/CSSE. 2008.1649, 2008.
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[23] Imran Ahmed, Awais Adnan, Salim Gul, and Md Islam, �Edge based real time weed recognition system for selective herbicides,� Proceedings of IMECS, Vol-1, 2008.
[24] Imran Ahmed, Syed shah, Md Islam, and Awais Adnan,� A Real time specific weed recognition system using statistical methods,� World academy of science, engineering and technology, pp 143-145, 2007.
[25] H. Muhammed Siddiqi, Irshad Ahmed, Suziah Sulaiman, �Weed recognition based erosion and dilation segmentation algorithm,� IEEE International conference on education technology and computer, pp 224-228, 2009.
[26] V. K. Mishra, S. Kumar, and R. K. Gupta, �Design and implementation of image fusion system�, IJCSE, International Journal of Computer Sciences and Engineering, Volume 01, Issue03, Page No (182-186), 2014.

S. Nagarathinam, T. Ravi, S. Ambalavanan, "Machine Vision Applications of Image Processing in Agriculture: A Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.157-160, 2014.

The main issues of knowledge-based authentication, generally text-based passwords, are well identified. A Password authentication system is a need of any application or any web service. Password authentication system is responsible for authentication of user for making entrance into a system. Users tend to choose memorable passwords that are easy for attackers to guess, but strong system assigned passwords are difficult for users to remember. So that a password authentication should be stronger as compare to current authentication system. We are proposing a graphical password authentication system instead of only text authentication. Whole system is developed using PCCP (Persuasive Cued Click Point) technique. We use persuasion to influence user choice is used in click-based graphical passwords, encouraging users to select more random click-points. Using this application system becomes totally secured because it uses various samples of images for authentication. By selecting points of various images as a password we can provide maximum security for authentication. Attackers will not able to attack on a system which uses this system because of only strong authentication.

PCCP(Persuasive Cued Click-Points); Strong Authentication; Graphical Passwords; Guessing Attacks ;Usable Security

[1] S. Chiasson, E. Stobert, A. Forget, R. Biddle, P. Oorschot, �Persuasive Cued Click-Point: Design, Implementation, and Evaluation of a knowledge-based authentication mechanism�, IEEE Transaction on Dependable and Secure Computing, Volume 09 No 2, Issue: March-April 2012.
[2] M. Patel, Y. Kadam, R. Thombare, H. Patil, �Defences against large scale online password guessing attacks by using persuasive click points�, International Journal of Communication and Engineering, Volume 3 No 3, Issue 1, March 2012.
[3] Iranna A M, Pankaja Patil, �Graphical password authentication using persuasive cued click point�, IJAREEIE Volume 2, Issue 7, July 2013.
[4] K. Hari Krishna, �Persuasive click points based large scale online password guessing attacks�, CSEA Volume 04, Issue: 01-2013
[5] T. Chippy, R. Nagendran, �Defences against large scale online password guessing attacks by using persuasive click points�, IJCE Volume 03, Issue:01 March 2012
[6] F. Towhidi, M. Masrom, �A Servey on recognition based graphical user authentication algorithms�, IJCSIS, Volume 6, No 2, 2009.
[7] Devi Srinivas, M.L.Prasanthi, �Implementation of knowledge based authentication system using persuasive cued click points�, IOSRJCE, volume 12, Issue 2, May-June 2013.
[8] Saurabh Sing, Gaurav Agarwal, �Integration of sound signature in graphical password authentication system�, IJCA, volume 12, No 9, Jan 2011.
[9] D. Anu Radha, �A persuasive cued click point based authentication mechanism with dynamic user blocks�, IJREAT, Volume 1, Issue 1, March 2013.

N. Patil, G. Patil, S. Patil, C. Jadhav, S. Durugkar, "Graphical Password for Authentication," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.161-164, 2014.

The objective of this work is to detect counterfeit banknotes using image pattern classification techniques. The color scanner makes it easier to produce counterfeit banknotes. So it is important to find an efficient method to detect counterfeit banknotes. In this work, a method for automated banknote authentication is proposed, which segments the whole banknote into many regions, and then builds individual classifiers on each region. Firstly, the banknote is segmented into different number of partitions. Then the luminance histogram and texture features are extracted from each partition of the banknote. The features extracted from each partition are then used to classify the banknotes using multiple support vector machines. The result is whether the currency is genuine or counterfeit.

Support Vector Machine, Counterfeit Banknote, Luminance Histogram, Texture Features

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[9] Chi-Yuan Yeh, Wen-Pin su, Shie-Jue Lee, �Employing multiple-kernel support vector machines for counterfeit ban knote recognition", Applied soft computing, Elsevier,2011.
[10] Chin-Chen Chang, Tai-Xing Yu, Hsuan Yen Yen, �Paper currency verification with support vector machines�, signal-image technologies and Internet-based sytem, 2007.

R. Bhavani, A. Karthikeyan, "A Novel Method for Counterfeit Banknote Detection," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.165-167, 2014.

IP packet fragmentation and reassembly is that, a packet is split into several pieces (fragments) that fit into packet size of the link to be traversed and combine (reassemble) these pieces or fragments at the receiving node to form original packet or datagram. In this paper, we consider reassembly of fragments allowed at the intermediate routers based on the Maximum Transmission Unit (MTU). Without waiting for the destination to reassemble it can be done at the intermediate hops where ever needed. The three fields of IP header used for fragmentation and reassemble are the packet identifier, each fragment is attached with the identifier and reassembling of fragments is done based on the identifier of the fragments. The fragment offset field gives the position of the fragment along with More Fragment (MF bit) and Don�t Fragment (DF bit) flags and the total length field. These two fields fragment offset and fragment length are combinedly used to place the fragments of a packet in right order. This paper, the IP packet fragmentation and reassembly at intermediate routers will be an option to reduce the load on routers due to more number of fragmented packets and improves the performance and increase the efficiency of the router.

IP, Packet, Datagram, Fragmentation, Reassembly, MTU size, Router, Source, Destination

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[15] Sharmeen Kaur, Raveena Singh and Shivya Gagneja, �Network Security and Methods Of Encoding and Decoding�, International Journal Of Computer Science and Engineering, Volume - 2 Issue � 2, 2347 � 2693, 2014.

G. Pavithra, D.T. Santosh, "IP Packet Fragmentation and Reassembly at Intermediate Routers," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.168-171, 2014.

Delay-Tolerant Network is a network above networks having unique characteristics like intermittent connectivity, longer delays and constraints on resources. DTN have the capability to survive long delays to achieve interoperability between the regional networks. Since nodes are intermittently connected with each other some new future prediction based routing protocols like Prophet, MaxProp have been proposed in this emerging areas for communication purposes. These future prediction based routing protocols depends on some easily modifiable metrics; attackers can easily forge these parameters to attack the network. This paper discusses one such popular Black-Hole attack on these routing protocols and some of the proposed solutions to mitigate their effect in the network.

Wireless Networks, Delay Tolerant Networks, Security, Black Hole Attack

[1] F. Warthman. �Delay-tolerant networks (dtns): A tutorial�, 2003.
[2] Amin Vahdat and David Becker. �Epidemic routing for partially connected ad hoc networks�, Technical Report CS-2000-06, Department of Computer Science, Duke University, April 2000.
[3] Shikha Jain, Sandhya Aneja. �Spread and Erase: Efficient Routing Algorithm Based on Anti-Message Info Relay Hubs for Delay Tolerant Networks�, In Computer Networks & Communications (NetCom), Vol. 131 (2013), pp. 643-651.
[4] Guoliang Liu; Krishnamani, J.; Sunderraman, R.; Yingshu Li, "Prediction-based routing with packet scheduling under temporal constraint in delay tolerant networks," Performance Computing and Communications Conference (IPCCC), 2013 IEEE 32nd International , vol., no., pp.1,7, 6-8 Dec. 2013
[5] J.Burgess, B.Gallagher, D.Jensen and B.N.Levine, �Maxprop: Routing for vehicle-based disruption-tolerant networking,� in Proceedings of IEEE Infocom, April 2006.
[6] A. Lindgren, A. Doria, and O. Schelen, �Probabilistic routing in intermittently connected networks,� in Mobile Computing and Communications Review, 2003.
[7] A. Balasubramanianm, B. Levine, and A. Venkataramani. �DTN routing as a resource allocation problem�, In Proc. of ACM SIGCOMM, 2007.
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[9] http://www.netlab.tkk.fi/tutkimus/dtn/theone
[10] Yanzhi Ren, Mooi Choo CHuah, Jie Yang, Yingying Chen, �MUTON: Detecting Malicious Nodes in Disruption-Tolerant Networks�.
[11] M. Chuah, P. Yang, and J. Han, "A ferry-based intrusion detection scheme for sparsely connected adhoc networks," in Proceedings of first workshop on security for emerging ubiquotous computing, 2007.
[12] http://www.isi.edu/nsnam/ns
[13] Yanzhi Ren, Mooi Choo Chuah, Jie Yang, Yingying Chen, �Detecting Blackhole attacks in Disruption-Tolerant Networks through packet exchange recording�.
[14] M. E. Mahmoud, M. Barua, X. Shen, �SATS: Secure Data-Forwarding Scheme for Delay-Tolerant Wireless Networks.�

S. Jain, "Black Hole Attack in Delay Tolerant Networks: A Survey," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.172-175, 2014.

Image in-painting is the art of restoring lost and selected parts of an image based on the background information in such a way so that the change is not observed by the observer. Image in-painting techniques is used in many fields like heritage preservation, films etc. In this paper, we are using exemplar based inpainting algorithm, this approach propagates the image information from the known region into the missing region at patch level. We first note that exemplar-based texture synthesis contains the essential process required to replicate both texture and structure; the success of structure propagation, however, is highly dependent on the order in which the filling proceeds. The existing algorithms are combined to improve the efficiency for finding the line association in selected region. Main focus is on data term and confidence term to find association in selected region which is to be inpainted. The region filling is done from that line associated to other section in selected region.

Image Inpainting, Robust-Exemplar Inpainting, Image Segmentation, Object Removal

[1] Shivali Tyagi ,Sachin Singh , �Image Inpainting By Optimized Exemplar Region Filling Algorithm�,IJSCE IISN:2231-2307,Volume -2,Issue-6,January 2013
[2] Muthukumar S, Dr. Krishnan N., Pasupati P., Deepa S., �Analysis of Image inpainting techniques with Exemplar, Poisson and successive elimination and 8 pixel neighbourhood method� IJCA(0975-8887),Volume -9,No.11, November 2010
[3] Pranali Dhabekar, Geeta Salunke, �The Exemplar-based Image Inpainting algorithm through Patch Progation�,IJRTE ISSN:2277-3878,Volume-1, Issue-4,Octomber-2012
[4] Zongben Xu, Jain Sun, �Image Inpainting by Patch Propagation Using Patch Sparsity� IEEE Transactions on Image processing ,vol. 19, No.5,May 2010
[5] R. Mart�ınez-Noriega, A. Roumy, G. Blanchard, �Exemplar-based image inpainting: fast priority and coherent nearest neighbour search�, 2012 IEEE International Workshop on Machine Learning for signal Processing, September.23-26,Satandar, Spain.
[6] Supriya Chhabra, Ruchika Lalit, Dr. S.K. Saxena, �An Analytical Study of Different Image Inpainting Techniques �, Indian Journal of Computer Science and Engineering(IJCSE), Vol. 3 No.3 Jun-Jul 2012.
[7] Jino Lee, Dong-Kyu Lee, Rae-Hong Park, �Robust Exemplar-Based Inpainting Algorithm Using Region Segmentation�, IEEE Transactions on Consumer Electronics, Vol. 58, No. 2, May 2012.
[8] Shruti Garg, G.Sahoo, �Virtual Restoration Of Old Digital Painting�, Indian Journal of Computer Science and Engineering(IJCSE), ISSN 2278-9960 Vol. 2, Issue 3, July 2013, 35-46.

S.R. Gaonkar, P.D. Hire, P.S. Pimple, Y.R. Kotwal, B.A. Ahire, "Image Inpainting Using Robust Exemplar-based Technique," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.176-179, 2014.

Information security has become a major cause of concern because intruders are concerned with reading the information. It is because of electronic dropping security is under threat. This paper deals with the comparative analysis of steganography over coloured images. �Steganography� is a greek word which means �hidden writing�. It is the art of hiding the secret message within a image. The goal of steganography is to avoid drawing suspicious to transmission of hidden message. It serves a better way of securing message than cryptography which provide security to content of message and not the existence. Original message is being hidden within a carrier such that the changes occurred in carrier are not observed. The hidden message in carrier is difficult to detect without retrieval. Different techniques are described in this paper for steganography over coloured images. One of them is spatial steganography. In this technique some bits in the image pixel is used for hiding data. Second technique is Transform Domain Technique which is a more complex way of hiding information in an image. Using Distortion technique, a stego object is created by applying a sequence of modifications to the cover image. The message is encoded at pseudo-randomly chosen pixels. Masking and Filtering technique embed the information in the more significant areas than just hiding it into the noise level. The hidden message is more integral to the cover image. Steganography is efficient, simple and decreases the degree of attack on secret information and improve image quality.

Steganography, Cryptography, Secret Information, Distortion, Spatial, Tranform Domain, Masking and Filtering

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[9] Jagvinder Kaur and Sanjeev Kumar, � Study and Analysis of Various Image Steganography Techniques�Proc. IJCST Vol. 2, Issue 3, September 2011 [10] R.Amirtharajan and R. Akila,� A Comparative Analysis of Image Steganography;� Proc. International Journal of Computer Applications (0975 � 8887) ,Volume 2 � No.3, May 2010.
[11]Video Steganography by LSB Substitution Using Different Polynomial Equations‖, A. Swathi, Dr. S.A.K Jilani, Proc.International Journal of Computational Engineering Research (ijceronline.com) Vol. 2 Issue. 5 [12]Chandramouli, R., Kharrazi, M. & Memon, N., �Image steganography and steganalysis: Concepts and Practice�, Proceedings of the 2 ndInternational Workshop on DigitalWatermarking, October 2003.
[13]Moerland, T., �Steganography and Steganalysis�, Leiden Institute of AdvancedComputing Science,www.liacs.nl/home/tmoerl/privtech.pdf

S. Suri, H. Joshi, V. Mincoha, A. Tyagi, "Comparative Analysis of Steganography for Coloured Images," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.180-184, 2014.

Information communication between two people can be done using various medium. These may be linguistic or gestures. Gestures recognition means identification and recognition of gestures originating from any type of body motion but only originate from face or hand. It is a process by which gestures made by users are used to convey the information. It provides important aspects of human interaction, both interpersonally and in the context of human - computer interfaces. There are several approaches available for recognizing gesture, some of them being MATLAB, Artificial Neural Networks, etc. This paper is a comprehensive evaluation of how gesture can be recognized in a more natural way using neural networks. It consists of 3 stages: image acquisition, feature extraction and recognition. In first stage the image is captured using a webcam, digital camera in approximate frame rate. In the second stage features are extracted using input image. The features may be angle made between fingers, no of fingers that are opened or closed or semi closed and identification of each finger. Finally neural network is used for recognition of the image.

Gesture Recognition, Artificial Neural Network, MATLAB, Image Acquisition, Feature Extraction

[1].Rajesh Mapari,, Dr. Govind Kharat, �Hand Gesture Recognition using Neural Network�, International Journal of Computer Science and Network (IJCSN) Volume 1, Issue 6, Page No (56-60), December 2012
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[3]. Prateem Chakraborty, Prashant Sarawgi, Ankit Mehrotra, Gaurav Agarwal, Ratika Pradhan, �Hand Gesture Recognition: A Comparative Study�, International MultiConference of Engineers and Computer Scientists 2008, Volume I IMECS 2008, Page No ( 19-21) [4].Sushmita Mitra, Tinku Acharya, �Gesture Recognition: A Survey�, IEEE Transactions on systems, man and cybernetics-Part C: Applications and Reviews, Volume 37, Page No (311-324), MAY 2007
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[7].Tin HninnHninnMaung, �Real-Time Hand Tracking and Gesture Recognition System Using Neural Networks�, World Academy of Science, Engineering and Technology 50, Page No (466-470), 2009
[8]. E. Stergiopoulou, N. Papamarkos, �Hand gesture recognition using a neural network shape fitting technique�, Engineering Applications of Artificial Intelligence 22, Page No (1141-1158), 2009
[9]. Pushpaja V. Saudagare, D.S. Chaudhari, �Facial Expression Recognition using Neural Network �An Overview�, International Journal of Soft Computing and Engineering (IJSCE), Volume-2, Issue-1,Page No (224-227), March 2012.
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[11] Sujeet D.Gawande, Prof. Nitin .R. Chopde, � Neural Network based Hand Gesture Recognition�, International Journal of Emerging Research in Management &Technology, ISSN: 2278-9359 ,Volume-2, Issue-3, Page No(34-38), March 2013.
[12] Ashis Pradhana, M.K.Ghosea and Mohan Pradhana, �A Hand Gesture Recognition using Feature Extraction�, International Journal of Current Engineering and Technology, ISSN 2277 � 4106, Volume-2,Issue-4, Page No(323-327),Dec 2012.

K. Arora, S. Suri, D. Arora, V. Pandey, "Gesture Recognition Using Artificial Neural Network," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.185-189, 2014.

As the number of Internet users and the number of accessible Web pages grows, it is becoming increasingly difficult for users to find documents that are relevant to their particular needs. The key factors for the success of the World Wide Web are its large size and the lack of a centralized control over its contents. Users must either browse through a large hierarchy of concepts to find the information for which they are looking or submit a query to a publicly available search engine and wade through hundreds of results, most of them irrelevant[5]. Web crawling is the process used by search engines to collect pages from the Web. Web crawlers are one of the most crucial components in search engines and their optimization would have a great effect on improving the searching efficiency. This paper, introduces web crawler that uses a concept of irrelevant pages for improving its crawling performance. [5] Despite their conceptual simplicity, implementing high-performance web crawlers poses major engineering challenges due to the scale of the web. This crawler computes the weights for the pages we come across during the crawling process and hence decide how much a particular page is important to us. Both issues are also the most important source of problems for locating information. The Web is a context in which traditional Information Retrieval methods are challenged, and given the volume of the Web and its speed of change, the coverage of modern search engines is relatively small. Moreover, the distribution of quality is very skewed, and interesting pages are scarce in comparison with the rest of the content.

Web Crawler , Seed , Frontier, Page Weight, Threshold Value

[1] Prashant Dahiwale, Anil Mokhade, M.M. Raghuwanshi, Intelligent Web Crawlers, ICWET, ACM New York, NY, USA, pp. 613-617, 2010.
[2] Brian Pinkerton, Finding what people want: Experiences with the Web Crawler, Proceedings of first World Wide Web conference, Geneva, Switzerland, 1994
[3] Gautam Pant, Padmini Srinivasan, Filippo Menczer, Crawling the Web, pp. 153-178, Mark Levene, Alexandra Poulovassilis (Ed.), Web Dynamics: Adapting to Change in Content, Size, Topology and Use, Springer-Verlag, Berlin, Germany, November 2004.
[4] Christopher Olston, Marc Najork, Web Crawler Architecture, Journal Foundations and Trends in Information Retrieval archive, Volume 4 Issue 3, pp. 175-246, March 2010.
[5] B. Pinkerton, �Finding what people want: Experiences with the WebCrawler,� in Proceedings of the 2nd International World Wide Web Conference ,1994.
[6] en.wikipedia.org/wiki/

P. Dahiwale, A. Dangre, P. Kolpyakwar ,V. Wankhede, P. Akre , "Design and Implementation of Web Crawler," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.190-193, 2014.

This paper will be implement Ontology Based Search Using Semantic Web. The method of web crawling with filter is used. This approach is query based approach using Jena API. The proposed approach solves the problem of revisiting web pages by crawler. The Semantic Web is an extension of the current Web that allows the meaning of information to be precisely described in terms of well-defined vocabularies that are understood by people and computers.

Sementics Web, Metadata, An Ontology,RDF,Search Engine

[1] Raman Kumar Goyal1, Vikas Gupta2, Vipul Sharma3, Pardeep Mittal4, ―Ontology Based Web Retrieval, 1Lecturer (Information Technology), International Journal of Computer Sciences and Engineering RIEIT, Railmajra, 2AP (CSE), RIEIT, Railmajra,3Student, UIET, Panjab University, Chandigarh, 4AP (CSE), BFCET, Bathinda.
[2] Felix Van de Maele, ―Ontology-Based Crawler for the Semantic Web, Faculty of Science, Department of Applied Computer Science, Vrije Universiteit Brussel, May 2006.
[3] Subhendu Kumar Pani,Deepak Mohapatra,Bikram Keshari Ratha,� Integration of Web Mining Web Crawler Relevance &State of Art�,Vol.02,No.3,2010,772-776.
[4] Jan Paralic, Ivan Kostial, ―Ontology Based Information Retrieval‖, Department of Cybernetics and AI, Technical University of Kosice, Letna 9, 040 11 Kosice, Slovakia.
[5] Sriram Raghavan,Hector GarciaMolina, ―Crawling the Hidden Web‖, Computer Science Department, Stanford University, USA.
[6] Chang Su, Yang Gao, Jianmei Yang, Bin Luo ―An Efficient Adaptive Focussed Crawler Based on Ontology Learning, Proceedings of the Fifth International Conference on Hybrid Intelligence Systems- 2005 IEEE.
[7] Debajyoti, Arup Biswas, Sukanta ―A New Approach to Design Domain Specific Ontology Based Web Crawler, 10th InternationalConference on Information Technology � 2007 IEEE. Ringe et. al.
[8] Ganesh S, Jayaraj M, Aghila G ―Ontology Based Web Crawler‖ Information Technology;Coding & Computing, 2004 volume 2, 2004 page (s) -337-341-IEEE.
[9] Yuan X, H Macgregor and J. Harms, �An efficient scheme to remove crawler traffic from the internet.� Proceedings of the 11th International Conference on Computer Communications and Networks, Oct 2002. 14-16, IEEE CS Press, (pp: 90-95).
[10] Kai Song, Yonghong Tian, Tiejun Huang, Wen Gao, �Diversifying theImage Retrieval Results�.

Sheetal, M. Bansal , "Keyword Based Efficient Web Crawler for Next Generation Semantic Web," International Journal of Computer Sciences and Engineering, Vol.2, Issue.4, pp.194-196, 2014.