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Last decades witness the remarkable increase in the exchange of digital data over World Wide Web. Copyright protection of the digital media has been the key issue in the last decade. Video watermarking is a technique to protect the video from unauthorized person. Security and robustness is to properties of watermarking algorithm. The skill of protecting the data from unauthorized person is known as security while the resistance offered by the watermarking algorithm to any type of modification in cover content is robustness. This paper present an extensive review work accomplished in video watermarking.

DWT (Discrete Wavelet transform); DCT (Discrete Cosine transform; SVD(Singular Value Decomposition); Watermark

[1] H. Hartung and B. Girod, "Watermarking of Compressed and Un-Compressed Video,” Signal Processing, vol. 66, no.3, pp. 283-301, May 1998.
[2] Cox I, Miller M, Bloom J, Fridrich J and Kalker T 2007 Digital watermarking and steganography San Francisco, CA, USA: Morgan Kaufmann Publishers Inc.
[3] Aree and A. Jamal, Efficient Video Watermarking using Motion Estimation Approach, Proceedings of the 8th IEEE/ACIS International Conference on Computer and Information Science, pp. 593-599, Shanghai, China, 2009.
[4] R. Lancini et al, A robust video watermarking technique in the spatial domain, 4th EURASIP-IEEE Region 8 International Symposium on VIPromCom, pp.251-256, 2002.
[5] P. Meerwald et al, Attack on Watermarking Method Based on Significant Difference of Wavelet Coefficient Quantization, IEEE Multimedia, Vol. 11, No. 5, pp. 1037- 1041, 2009.
[6] X. Kang et al, A DWT-DFT Composite Watermarking Scheme Robust to both Affine Transform and JPEG Compression. IEEE in CirSys Video, Vol. 13, No. 8, pp. 776-786, 2003.
[7] Jadhav, Anita, and Megha Kolhekar. "Digital Watermarking in Video for Copyright Protection." In Electronic Systems, Signal Processing and Computing Technologies (ICESC), 2014 International Conference on, pp. 140-144. IEEE, 2014.
[8] Venugopala, P. S., H. Sarojadevi, Niranjan N. Chiplunkar, and Vani Bhat. "Video Watermarking by Adjusting the Pixel Values and Using Scene Change Detection." In Signal and Image Processing (ICSIP), 2014 Fifth International Conference on, pp. 259-264. IEEE, 2014.
[9] Agarwal, Charu, Anurag Mishra, Arpita Sharma, and Girija Chetty. "A Novel Scene Based Robust Video Watermarking Scheme in DWT Domain Using Extreme Learning Machine." In Extreme Learning Machines 2013: Algorithms and Applications, pp. 209-225. Springer International Publishing, 2014.
[10] Cedillo-Hernandez, Antonio, Manuel Cedillo-Hernandez, Mireya Garcia-Vazquez, Mariko Nakano-Miyatake, Hector Perez-Meana, and Alejandro Ramirez-Acosta. "Transcoding resilient video watermarking scheme based on spatio-temporal HVS and DCT." Signal Processing 97 (2014): 40-54.
[11] Singh, Th Rupachandra, Kh Manglem Singh, and Sudipta Roy. "Video watermarking scheme based on visual cryptography and scene change detection." AEU-International Journal of Electronics and Communications 67, no. 8 (2013): 645-651.
[12] Wassermann, Jakob. "New Robust Video Watermarking Techniques Based on DWT Transform and Spread Spectrum of Basis Images of 2D Hadamard Transform." In Multimedia Communications, Services and Security, pp. 298-308. Springer Berlin Heidelberg, 2013.
[13] Ko, Chien-Chuan, Yung-Lung Kuo, Jeng-muh Hsu, and Bo-Zhi Yang. "A multi-resolution video watermarking scheme integrated with feature detection."Journal of the Chinese Institute of Engineers 36, no. 7 (2013): 878-889.
[14] Masoumi, Majid, and Shervin Amiri. "A blind scene-based watermarking for video copyright protection." AEU-International Journal of Electronics and Communications 67, no. 6 (2013): 528-535.
[15]Lin, Wei-Hung, Yuh-Rau Wang, Shi-Jinn Horng, Tzong-Wann Kao, and Yi Pan. "A blind watermarking method using maximum wavelet coefficient quantization."Expert Systems with Applications 36, no. 9 (2009): 11509-11516.
[16] Sadik Ali M. Al-Taweel, Putra Sumari, Saleh Ali K. Alomari and Anas J.A. Husain “Digital Video Watermarking in the Discrete Cosine Transform Domain” Journal of Computer Science 5 (8): 536-543, 2009 ISSN 1549-3636 c 2009 Science Publications.
[17] Lama Rajab Tahani Al-Khatib Ali Al-Haj “Video Watermarking Algorithms Using the SVD Transform” European Journal of Scientific Research ISSN 1450-216X Vol.30 No.3 (2009), pp.389-401.
[18] Hanane Mirza, Hien Thai, and Zensho Nakao I. Lovrek, R.J. Howlett, and L.C. Jain “Digital Video Watermarking Based on RGB Color Channels and Principal Component Analysis” (Eds.): KES 2008, Part II, LNAI 5178, pp. 125–132, 2008. c_Springer-Verlag Berlin Heidelberg 2008.
[19] Chung, Yuk Ying, and Fang Fei Xu. "A secure digital watermarking scheme for MPEG-2 video copyright protection." In Video and Signal Based Surveillance, 2006. AVSS'06. IEEE International Conference on, pp. 84-84. IEEE, 2006.
[20] Noorkami, Maneli, and Russell M. Mersereau. "Improving Perceptual Quality in Video watermarking using motion estimation." In Image Processing, 2006 IEEE International Conference on, pp. 1389-1392. IEEE, 2006.
[21]Xiamu Niu Martin , Martin Schmucker , Christoph Busch “Video Watermarking Resisting to Rotation, Scaling, and Translation” 2002 Proc. SPIE Security Watermarking of Multimedia Contents IV.

Priya Chandrakar and Shahana Gajala Qureshi, "A Review on Video Watermarking," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.48-52, 2015.

Several methods have been developed for text detection and extraction to achieve accuracy for natural scene text and for multi-oriented text. However most of the methods use classifier to improve text detection accuracy. So this paper uses two machine learning classifiers one is to generate candidate region and the other filters nontext. Here connected components (CCs) in images are extracted by using the maximally stable extremal region algorithm. These extracted CCs are partitioned into clusters so that we can generate candidate regions. An AdaBoost classifier is trained to determine the adjacency relationship and cluster CCs by using their pair-wise relations. Since the scale, skew, and color of each candidate can be estimated from CCs, we can develop a text/nontext classifier for normalized images. This classifier will be based on multilayer perceptrons and we can control recall and precision rates with a single free parameter. Finally, the approach can be extended to exploit multichannel information and this method yields the state-of-the-art performance both in speed and accuracy.

Connected Component,Clustering, Extraction, Filtering

[1] K. Jung, “Text information extraction in images and video A survey,” Pattern Recognit., vol. 37, no. 5, pp. 977–997, May 2004.
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[3] S. Lucas, “Icdar 2005 text locating competition results,” in Proc. Int. Conf. Document Anal. Recognit. 2005, pp. 80–84.
[4] Shahab, F. Shafait, and A. Dengel, “ICDAR 2011 robust reading competition challenge 2: Reading text in scene images,” in Proc. Int. Conf. Document Anal. Recognit, 2011, pp. 1491–1496.
[5] Hyung Il Koo and Duck Hoon Kim, “Scene Text Detection via Connected Component Clustering and Nontext Filtering,” IEEE Trans. Image Process., vol. 22, no. 6, pp.2296-2305, June. 2011.
[6] X. Chen and A. Yuille, “Detecting and reading text in natural scenes,” in Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit., 2004, pp. 366–373.
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[9] X. Chen and A. Yuille, “A time-efficient cascade for real-time object detection: With applications for the visually impaired,” in Proc. IEEE Conf. Comput. Vis. Pattern Recognit., Workshops, Jun. 2005, pp. 1–8.
[10] J. Friedman, T. Hastie, and R. Tibshirani, “Additive logistic regression: A statistical view of boosting,” Ann. Stat., vol. 28, no. 2, pp. 337–407, 1998.

S. Elakkiya and T. Kavitha, "Detection of Text Using Connected Component Clustering and Nontext Filtering," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.53-57, 2015.

To develop data mining techniques to support decision making and discovery of functional group of the connectivity atom for drug effects by analyzing chemical compound data in the form of structured data. Existing studies in data mining mostly focus on hierarchical clustering techniques applied in large and small dataset of pharmaceutical compound and analyse its performance based on time accuracy. In this paper focuses to apply cluster techniques of partition method like Enhanced K-means algorithm and hierarchical method like Birch and Chameleon algorithm used in pharmaceutical compound specifically represented as atom number, atom name like carbon, hydrogen, nitrogen, oxygen with connected atoms. These dataset form a functional group of atoms by functioning in three phases. The performance can be experimented based on time taken to form the estimated cluster, also overall execution time can be reduced by improvement of Enhanced Kmeans algorithm when compared to chameleon and Birch algorithm.

Enhanced K-Mean algorithm; Chameleon algorithm; Birch algorithm

[1] Fahim A.M, Salem A. M, Torkey A and Ramadan M. A, “An Efficient enhanced k-means clustering algorithm,” Journal of Zhejiang University, 10(7):1626–1633, 2006.
[2] Yuan F, Meng Z. H, Zhang H. X and Dong C. R, “A New Algorithm to Get the Initial Centroids,” Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pages 26–29, August 2004.
[3] Daxin Jiang, Chum Tong and Aidong Zhang, “Cluster Analysis for Gene Expression Data,” IEEE Transactions on Data and Knowledge Engineering, 16(11): 1370-1386, 2004.
[4] V. Palanisamy, A. Kumarkombaiya, “Analysing Pharmaceutical Compounds Based On Cluster Techniques”, International Journal of Computer Science Research & Technology, ISSN: 2321-8827, Vol. 1 (03).
[5] Jiawei Han and Micheline Kamber,” Data Mining: Concepts and Techniques”. Publication: ISBN-10: 0123814790 | ISBN-13: 9780123814791, Edition: 3
[6] Zhang, R. Ramakrishnan and M. Livny: BIRCH : “An Efficient Data Clustering Method for Very Large Databases”. SIGMOD “96 6/96 Montreal, CanadaIQ1996ACM0-89791-794-4/96/0006.
[7] Daniel T. Larose , Data Mining Methods and Models, Copyright © 2006 John Wiley and Sons, Inc.
[8] Marjan Kuchaki Rafsanjani, Zahra Asghari Varzaneh Nasibeh Emami Chukanlo, “A survey of hierarchical clustering algorithms”, The Journal of Mathematics and Computer Science Vol .5 No.3 (2012) 229-240.
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[11] Tapas Kanungo, David M. Mount, Nathan S. Netanyahu, Christine D. Piatko, Ruth Silverman, and AngelaY. Wu. An efficient k-means clustering algorithm: Analysis and implementation. IEEE Trans.Pattern Anal. Mach. Intell., 24(7):881–892, 2002.
[12] Yuan F, Meng Z. H, Zhang H. X and Dong C. R, “A New Algorithm to Get the Initial Centroids,” Proc. of the 3rd International Conference on Machine Learning and Cybernetics, pages 26–29, August 2004.

V. Palanisamy and A. Kumarkombaiya, "Designing a Knowledge Discovery of Clustering Techniques in Pharmaceutical Compounds," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.58-63, 2015.

Traditional input devices such as keyboard, mouse and joystick have been around for some while now. With the advancements in the field of Human Computer Interaction, eye tracking or iris tracking is the most promising field. It will fundamentally change the way we interact with computers. The main aim of this project is to develop a low cost application running in an open source environment and a widely used operating system Linux, to replace the traditional computer mouse with the human iris for cursor movement. The target audience majorly consists of handicapped people or people with physical impairment. The system designed aims at detecting the user’s eye movements for navigating the cursor, analyzing the nature and timing of blinks, which in turn is used as an input to the computer as a mouse click. The system consists of a good resolution Logitech C270 HD webcam, as opposed to the otherwise popular infrared cameras available in the market. The existing cameras used for tracking are highly expensive but our system is affordable and easy to use. In the project we have used the Fabian Timm image processing algorithm to achieve iris tracking. With future development, we believe our system has the potential to be used as a fully functional substitute for the mouse pointer.

pattern recognition; Human Computer Interface; eye-tracking; Fabian Timm algorithm; blink detection

[1] Rommel Anacan, James Greggory Alcayde, Retchel Antegra, Leah Luna, “Eye-GUIDE (Eye-Gaze User Interface Design) Messaging for Physically-Impaired People”, International Journal of Distributed and Parallel Systems (IJDPS) Vol.4, No.1, January 2013.
[2] Fabian Timm, Erhardt Barth, “Accurate Eye Centre Localisation By Means Of Gradients”, Institute for Neuro- and Bioinformatics, University of L¨ubeck, Ratzeburger Allee 160, D-23538 L¨ubeck, Germany, Pattern Recognition Company GmbH, Innovations Campus L¨ubeck, Maria-Goeppert-Strasse 1, D-23562 L¨ubeck, Germany.
[3] Weston Sewell, Oleg Komogortsev, “Real-Time Eye Gaze Tracking With an Unmodified Commodity Webcam Employing a Neural Network”, Proceedings of ACM Conference on Human Factors in Computing Systems (CHI), Atlanta, GA, 2010.
[4] Reji Mathews, Nidhi Chandra, “Computer Mouse using Eye Tracking System based on Houghman Circle Detection Algorithm with Grid Analysis”, International Journal of Computer Applications (0975 – 8887) Volume 40– No.13, February 2012.
[5] Michael Chau, Margrit Betke, “Real Time Eye Tracking and Blink Detection with USB Cameras”, Boston University Computer Science Technical Report No. 2005-12, May 12, 2005.
[6] Onur Ferhat, Fernando Vilariño, “Eye-Tracking with Webcam-Based Setups: Implementation of a Real-Time System and an Analysis of Factors Affecting Performance, Master in Computer Vision and Artificial Intelligence Report of the Master Project Option: Computer Vision”.
[7] Erna Demjén, Viliam Aboši, Zoltán Tomori, “Eye Tracking Using Artificial Neural Networks For Human Computer Interaction”, Physiological Research Pre Press Article.

Riddhi Chavda,Madhura Barve, Amit Doshi and Ruhina Karani, "Real Time Eye-Tracking Using Web Camera," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.64-67, 2015.

A Network user has to provide susceptible personal information (e.g. name, residence address, credit/debit card number, contact number, driver’s license number and date of birth, etc.) when they are requested by some Web page. This exclusive Personal Identity Information may be used to exclusively 2identify, contact and/or locate a particular user. This information may be exploited and abused if not properly protected. An Identity Management (IDM) system is therefore proposed to overcome this problem and helps to decide the access to this information in a secure manner. The concept of key management has been implemented to achieve the goal of trusted communication. The group public key management scheme, trust of a node.

Personal Identity Information (PII), Identity Management (IDM), Service Provider(SP), Trusted Third Party(TTP)

[1] Pallavi Khatri, "Using Identity and Trust with Key Management for achieving security in Ad hoc Networks" INDIA, _c 2014 IEEE
[2] Lingfang Zeng , Shibin Chen , Qingsong Wei , and Dan Feng, " SeDas: A Self-Destructing Data System Based on Active Storage Framework", China,2013 IEEE
[3] R. Geambasu, T. Kohno, A. Levy, and H. M. Levy, “Vanish: Increasing data privacy with self destructing data,” in Proc. USENIX Security Symp., Montreal, Canada, Aug. 2009, pp. 299–315
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[6] L. Zeng, Z. Shi, S. Xu, and D. Feng, “Safevanish: An improved data self-destruction for protecting data privacy,” in Proc. Second Int. Conf. Cloud Computing Technology and Science (CloudCom), Indianapolis, IN, USA, Dec. 2010, pp. 521–528.
[7] J. R. Douceur, “The sybil attack,” in Proc. IPTPS ’01: Revised Papers From the First Int. Workshop on Peer-to-Peer Systems, 2002.
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[15]. J. Huang and D. Nicol, “A calculus of trust and its application to PKI and identity management,” ACM 8th Symposium on Identity and Trust on the Internet, Gaithersburg, MD, USA, April 2009.
[16]. N. V. Vinh, M.-K. Kim, H. Jun, and N. Q. Tung, “Group-based public-key management for self-securing large mobile ad-hoc networks,” Int’l Forum on Strategic Technology, pp. 250-253, Oct. 2007 .
[17]. B. Poettering, 2006, SSSS: Shamir’s Secret Sharing Scheme [Online]. Available: http://point-at-infinity.org/ssss/
[18]. Khatri, P., Tapaswi, S. & Verma, U.P. (2012). Trust evaluation in wireless ad hoc networks using fuzzy system. In V. Potdar & D. Mukhopadhyay (eds.), pp. 779-783, CUBE 2012.
[19]. L. Qin and D. Feng, “Active storage framework for object-based storage device,” in Proc. IEEE 20th Int. Conf. Advanced Information Networking and Applications (AINA), 2006.
[20]. C. Wang, Q. Wang, K. Ren, and W. Lou, “Privacy-preserving public auditing for storage security in cloud computing,” in Proc. IEEE INFOCOM, 2010.

Pranav Nair, Swapnaja Gunjal, Archana Jadhav and Ruchita Gadsing, "Achieving Security in Ad hoc Networks Using Identity and Trust with Key Management," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.68-74, 2015.

Resonator fiber optic gyroscope (RFOG) based on the Sagnac effect has the potential to achieve the inertial navigation system condition with a short sensing coil. RFOG based on fiber coupled semiconductor DFB-LD with an FPGA-based digital processor is set up. We evaluate the frequency drift due to the various non-reciprocal noise sources –shot-noise; backscattering, Shupe, Kerr effect and Faraday noises are calculated theoretically with single mode fiber (corning SMF-28)as sensor coil and compared with PM fiber( HB 1500G) as sensing coil with similar parameters. Bias of the gyro which is caused by the noise source is also analysed.

Bias Drift, Digitalization, Noise sources, Optical fiber gyroscope, Optical passive ring-resonator gyro scope, PM fiber, SM Fiber

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Prasada Rao Bobbili, Jagannath Nayak, Prerana dabral Pinnoji and D.V. Rama Koti Reddy, "Theoretical Evaluation of Resonator Fiber Optic Gyroscope Composed of a Polarization-Maintaining and Single Mode fiber with Digital Phase Modulation Technique," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.77-81, 2015.

Along with the modernization of technological era, the technological advancement has also raised concerns about the security of web activities. These activities are in a way or the other are attempted to be compromised by the adversary with the aim of gaining knowledge which may be somehow useful for him/her. In addition, terrorists are also utilizing web for fulfilling their inhuman goals which is currently an utmost concern for security agencies. Although there are many successful attempts have been made to restrict the existence of these illegitimate people, there still is a need for an effective affirmation solution. In respect to this, data mining comes out as a solution by bringing into existence a mining concept named Terrorist Network Mining. Terrorist network mining has proved as the most feasible solution where detection and analysis of terrorists is well performed. Still there were some improvements required to this concept which was efficiently done by combining fuzzy with genetic algorithms with the intrusion detection system (IDS) resulting into significant and efficient detection process. Hence the paper discusses about how well an intrusion detection system performs when combined with fuzzy data mining (reveal patterns whose behavior is intrusive) with genetic algorithm (leads to the success of efficient detection of intruders).

Apriority algorithm, Data mining, Fuzzy logic, Genetic algorithm

[1]. Y.Dhanalakshmi and Dr.I. Ramesh Babu, “Intrusion Detection Using Data Mining Along Fuzzy Logic and Genetic Algorithms”, IJCSNS International Journal of Computer Science and Network Security, VOL.8 No.2,February 2008.
[2]. German Florez,Susan M. Bridges, and Rayford B. Vaughn, “An Improved Algorithm for Fuzzy Data Mining for Intrusion Detection”, Proceedings of Information Processing Society, 2002, Page(s): 457 – 462.
[3]. Jiawei Han & Micheline Kamber (2006) Data Mining;Concepts and Techniques, Second Edition, Morgan Kaufmann Publishers.
[4]. Ci Chen, Shingo Mabu, Chuan Yue, Kaoru Shimada, and Kotaro Hirasawa, “Network Intrusion Detection using Fuzzy Class Association Rule Mining Based on Genetic Network Programming”, Proceedings of the 2009 IEEE International Conference on Systems, October 2009.
[5]. Shingo Mabu,, Ci Chen, Nannan Lu, Kaoru Shimada, and Kotaro Hirasawa, “An Intrusion-Detection Model Based on Fuzzy Class-Association-Rule Mining Using Genetic Network Programming”, Proceedings of IEEE transactions on systems, man, and cybernetics—part c: applications and reviews, vol. 41, no. 1, January 2011.
[6]. Tan Jun-shan, He Wei1, Qing Yan ,” Application of Genetic Algorithm in Data Mining”. 2009 First Int. Workshop on Education Technology and Computer Science. 978-0-7695-3557-9/09 © 2009 IEEE. DOI10.1109/ETCS.2009.340. page 353.page 353.

Anshul Atre and Rajesh Singh, "Fuzzy Logic and Genetic Algorithm for Data Mining based Intrusion Detection System: A Review Approach," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.82-84, 2015.

Service recommender systems are valuable tools for providing appropriate recommendations to users. In the last decade the rapid growth of the number of customers, services and other online information yields service recommender systems in Big Data environment, some critical challenges .Traditional service recommender systems often suffer from scalability and inefficiency problems when processing or analyzing such large scale data. Moreover, most of the existing service recommender systems present the same ratings and rankings of services to different users without considering diverse users' preferences, and therefore fails to meet users' personalized requirements . KASR(Keyword Aware Service Recommendation System) aims at calculating a personalized rating of each candidate service for a user by extracting keywords from user reviews, and then presenting a personalized service recommendation list and recommending the most appropriate services to users. Various limitations of the current recommendation methods can be reduced by possible extensions that can provide better recommendation capabilities. These extensions include incorporation of the contextual information into the recommendation process. Designing and implementing scalable recommender systems in Big Data environment solve the scalability problem.

Keyword Aware Service Recommendation System , Collaborative Filtering, BigData

[1] Shunmei Meng, Wanchun Dou, Xuyun Zhang, Jinjun Chen, “ KASR: A Keyword Aware Service Recommendation Method on MapReduce for Big Data Applications” IEEE Transactions on Parallel and Distributed Systems, TPDS-2013-12-1141.
[2] Gediminas Adomavicius, and Alexander Tuzhilin “Toward the Next Generation of Recommender Systems: A Survey of the State-of-the-Art and Possible Extensions” IEEE Transactions on Knowledge and Data Engineering, Vol. 17, No. 6, June 2005.
[3] G. Linden, B. Smith, and J. York, “Amazon.com Recommendations: Item-to-Item Collaborative Filtering,” IEEE Internet Computing, Vol. 7, No.1, pp. 76-80, 2003.
[4] K. Lakiotaki, N. F. Matsatsinis, and A. Tsoukis ,“Multi-Criteria User Modeling in Recommender Systems”, IEEE Intelligent Systems, Vol. 26, No. 2, pp. 64-76, 2011.
[5] J. Dean, and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” Communications of the ACM, Vol. 51, No.1, pp. 107-113, 2005.
[6] Daniar Asanov “Algorithms and Methods in Recommender Systems” Berlin Institute of Technology Berlin, Germany.
[7]Osman Khalid Muhammed Usman Shahid Khan, Samee U Khan , Albert Y Zomaya “Omnisuggest: A Ubiquitous Cloud Based Context Aware Recommendation System for Mobile Social Networks ” IEEE Transactions on Services Computing ,2013.
[8] Sang Hyun Choi, Young-Seon Jeong, and Myong K. Jeong “A Hybrid Recommendation Method with Reduced Data for Large-Scale Application” IEEE Transactions on systems, man, and cybernetics—Part C: Applications and Reviews, Vol. 40, No. 5, September 2010
[9] Building Recommendation Platforms with Hadoop, Jayant Shekhar O’Reilly Strata Conference Making Data Work.
[10] “Recommender Systems Handbook” Xavier Amatriain, Alejandro Jaimes, Nuria Oliver, and Josep M. Pujol Springer Science+Business Media, LLC 2011,pp 39-67.
[11] Recommender Systems , Daniel Rodriguez, University of Alcala ,Windsor ,August 5,2013

Shakhy P S and Vidya K S, "A Survey on KASR for Big Data Applications," International Journal of Computer Sciences and Engineering, Vol.3, Issue.4, pp.85-89, 2015.