Volume-2 , Issue-12 , Dec - 2014 Go Back

The biometrics a trading technology to recognize a person based on the physiological or behavioral characteristics. Emotion Recognition system is a part of Human Computer Interaction. The database has been developed to recognize the emotions of human based on speech trait. While recording speech samples different Marathi words and phrases were considered. These words were very useful to identify the emotions of human being. The emotions such as happy, sad, surprise, angry, disgust, fear and neutral were recorded. In the proposed work seven basic emotions were considered such as happy, sad, angry, surprise, fear, disgust, and neutral. According to this, the words like “Are deva”, “Aare re”, “kiti chan”, “Mast”, “Gapre”, “khatarnak” etc. were used to decide what kind of emotion that human being gave through speech. There were total of 50 speakers out of which 25 male and 25 female speakers were recorded. . In this paper for each emotion four Marathi emotional words were recorded. In this way there were total 28 words by One speaker and total 1400 words (emotional words/phrases) recorded by 50 speakers including male and female speaker were recorded. This paper discusses about the development of Speech database developed at School of Computational Sciences, Solapur University, Solapur.

Emotion Recognition, Marathi speech, HCI, Biometric

[1] Mina Hamidi, Maharram Mansoorizade, “Emotion Recognition from Persian Speech with neural network”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.5, Sept.2012.
[2] Bhoomika Panda, Debananda Padhi, Kashamamayee Dash, Prof. Sanghamitra Mohanty, “Use of SVM Classifier & MFCC in Speech Emotion Recognition System”, International Journl of Advanced Research in
Computer Science and Software Engineering, Vol.2, Issue 3, March 2012, ISSN: 2277-128X.
[3] Mina Hamidi, Maharram Mansoorizade, “Emotion Recognition from Persian Speech with neural network”, International Journal of Artificial Intelligence & Applications (IJAIA), Vol.3, No.5, Sept.2012.
[4] Thurid Vogt, Elisabeth Andr´e, and Johanne Wagner,” Automatic Recognition of Emotions from Speech: A Review of the Literature and Recommendations for Practical Realisation”,” Affect and Emotion in HCI, LNCS 4868, Pp. 75–91, 2008. Springer-Verlag Berlin Heidelberg
2008
[5] Donn Morrison, Ruili Wang , Liyanage C. De Silva,” Ensemble methods for spoken emotion recognition in call-centres”, Speech Communication 49 (2007) 98–112.
[6] Yixiong Pan, Peipei Shen and Liping Shen, “Speech Emotion Recognition Using Suppor Vector Machine”, International Journal of Smart Home Vol. 6, No. 2, April, 2012.
[7] Giovanni Costantini, Iacopo Iadarola, Andrea Paoloni, Massimiliano Todisco, “EMOVO Corpus: an Italian Emotional Speech Database”
[8] Julien Epps, Roddy Cowie, Shrikanth Narayanan, Björn Schuller and Jianhua Tao,” Emotion and mental state recognition from speech”, Epps et al. EURASIP Journal on Advances in Signal Processing 2012, 2012:15.
[9] Mina Hamidiand Muharram Mansoorizade,” emotion recognition from persian speech with neural network”, “International Journal of Artificial Intelligence & Applications (IJAIA)”, Vol.3, No.5, September 2012.
[10] Sharad B. Jadhav, Shriram D. Raut, Rajivkumar S. Mente, Ashok R. Shinde, “A Research on Contactless Palm print Recognition System”, International Journal of Research in Computer Science and Information Technology, Vol 02, issue 2, Pp. 45-48, ISSN: 2319-5010, March 2014.

Shinde A.R., Agnihotri P. P. and Khanale P.B., "Development of Standard Speech Database in Marathi Language for Emotion Recognition System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.47-49, 2014.

Machine Login Monitoring and Tracking Security System (MLMTSS) were used to monitor all illegal and malicious attempts of the system user. The system should be able to provide a complete log of text entered such as passwords, emails send, websites visited. The user's login time was send by SMS to the admin. This system tracks all the keystrokes, processes opened and all operations performed by the user and send it through e-mail. The system takes screenshots within the specified interval of time and sends it with the tracked information to the administrator's mail. It also runs invisibly so that the person who is using the computer doesn't even have to be aware that it is running.

Machine Login Monitoring , Tracking Security System, Emails Send, Website Visited, Keystrokes, Password

[1] Preeti Tuli and Priyanka Sahu, “System Monitoring and Security Using Keylogger”, IJCSMC, Vol.2, Issue.3, March 2013, pg.106-111.
[2] Kenneth Revett,Sergio Tenreiro de Magalhaes and Henrique M.D. Santos, “Enhancing Login Security Through the Use of Keystroke Input Dynamics”, © Springer, Part of Springer Science+Business Media.
[3] Masaya Sato and Toshihiro Yamauchi, “Virtual Machine Based logging Scheme for Prevention of Tampering and Loss”, © Springer, Part of Springer Science+Business Media.
[4] S. Sagiroglu and G. Canbek, “Keyloggers”, IEEE Technology and Society Magazine, Vol.28, no.3, pp.10-17, fall 2009.
[5] T. Olzak, “Keystroke logging (keylogging)”, Adventures in Security, April 2008 (accessed May 8, 2010), http://adventuresinsecurity.com/images/Keystroke_Logging.pdf.
[6] Preeti Tuli, Priyanka Sahu and Mahesh Sahu, “System Monitoring and Logging of Information and Crime” IJARCSSE, Vol.2, Issue 9, September 2012, pg.375-379.

Aiswarya Raj, Meenu Manoharan, Melbin Alex, Pravya Prasad, Majo John, "Machine Login Monitoring and Tracking Security System," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.50-52, 2014.

A covert channel is any methodology of communication that’s acquainted illicitly transfer data, so breaking the security policy of a system. A network covert channel is a covert statement by hiding covert messages in to explicit network packets. Any shared resource will be probably used as a covert channel. In recent years with the growth of various hiding methods, network covert channel has become a new kind of intimidation for network security. A covert channel is an unplanned design within authentic communication whose axiom is to leak information as a part of undeveloped protocols. In fact, most detection systems can detect hidden data in the payload, but struggle to survive with data hidden in the IP and TCP packet headers. The huge number of protocols in internet seems ideal as a high-bandwidth vehicle for covert communication. Due to unwanted and malevolent nature of covert channel applications and as it poses a serious security threat to network, it is recommended to detect covert channels efficiently. This paper presents a criticism of TCP/IP covert channel design and their detection scheme and presents a proposed method based on Naive-Bayesian classifier to detect covert channels in TCP ISN and IP ID fields of TCP/IP packet.

TCP/IP covert channel, TCP, IP, network security

[1] R. J. Anderson and F. A. P. Petitcolas, “On the limits of steganography,” IEEE J. Sel. Areas Commun., vol. 16, no. 4, pp. 474–481, May1998.
[2] S. Attallah, “Trusted Computer System Evaluation Criteria”, Tech. Rep. DOD 5200. 28-STD, 1985 [Online]. Available: http:// csrc.nist.gov/ publistications/history/dod85.pdf.
[3] V. Forte, C.Maruti, M. R. Vetturi, and M. Zambelli, “SecSyslog: An approach to secure logging based on covert channels,” in Proc. First Int. Wksp. Systematic Approaches to Digital Forensic Engineering, pp. 248–263, Nov. 2005.
[4] Transmission Control Protocol (TCP), Information Sciences Institute, University of Southern California, RFC 793, Sep. 1981.
[5] Internet Protocol (IP), Information Sciences Institute, University of Southern California, RFC 791, Sep. 1981.
[6] M. Owens, “A Discussion of Covert Channels and Steganography”, SANS (SysAdmin, Audit, Network, Security) Institute, 2002.
[7] K.Szczypiorski, “Steganography in TCP/IP Networks. State of the Art and a Proposal of a New System HICCUPS Institute of Telecommunications Seminar [Online], Retrieved Jun. 2010
[8] T. Sohn, J. S. , and J. Moon, “A study on covert channel detection of TCP/IP header using support vector machine,” in Proc. 5th Int. Conf. Information and Communication Security (ICICS 2003), pp. 313–324, Oct. 2003.

Apurva N. Mahajan and I. R. shaikh, "Review on covert channel detection methods of TCP/IP header," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.53-56, 2014.

Cloud computing is the future of information technology. It demonstrates all the big trends in the design and use of computer architectures. With cloud computing, it is necessary for data to be not only stored in the cloud but also shared across multiple users, when the data are sharing privacy breach of the data can occur. In cloud computing preserving data privacy is an important task because the data itself contains sensitive information. A basic solution for preserving data privacy is to encrypt the data, and then upload the encrypted data into the cloud. As next give a data access notification to the data owner, providing complete permission of control to the user over the data. Designing an efficient and secure data sharing scheme for groups in the cloud is not an easy task. Oruta is the first privacy-preserving mechanism for shared data stored in the cloud. By using homomorphic authenticable ring structures, it gives privacy preserving public auditing for secure clouds to storage system. This survey analyzes various techniques for preserving shared data privacy in the cloud.

Cloud computing, Data sharing, Privacy Preserving

[1] http://en.wikipedia.org/wiki/Cloud_computing
[2] Rong C, Nguyen S T et al. (2013). “Beyond lightning: A sur¬vey on security challenges in cloud computing”, Computers & Electrical Engineering, vol 39(1), 47–54.
[3] DananThilakanathan, Shiping Chen, Surya Nepal and Rafael A. Calvo “Secure Data Sharing in the Cloud”
[4] Wang J, Zhao Y et al. (2009).” Providing Privacy preserving in cloud computing”, International Conference on Test and Measurement, vol 2, 213–216.
[5] Greveler U, Justus b et al. (2011). “A Privacy Preserving System for Cloud Computing”, 11th IEEE International Conference on Computer and Information Technology, 648–653.
[6] Zhou M, Mu Y et al. (2011).” Privacy-Preserved Access Control for Cloud Computing”, International Joint Conference of IEEE TrustCom-11/IEEE ICESS-11/FCST-11, 83–90.
[7] Chadwick D W, and Fatema K (2012). “A privacy preserving authorization system for the cloud”, Journal of Computer and System Sciences, vol 78(5), 1359–1373.
[8] Sayi T J V R K M K, Krishna R K N S et al. (2012) “Data Outsourcing in Cloud Environments: A Privacy Preserving Approach”, 9th International Conference on Information Technology- New Generations, 361–366
[9] Rahaman S M, and Farhatullah M (2012). “PccP: A Model for Preserving Cloud Computing Privacy”, International Conference on Data Science & Engineering (ICDSE), 166–170.
[10] Wang C, Wang Q et al. (2010). “Privacy-Preserving Public Auditing for Storage Security in Cloud Computing”, Proceedings IEEE INFOCOM’
[11] Wang C, Chow S S M et al. (2013). “Privacy-Preserving Public Auditing for Secure Cloud Storage” IEEE Transactions on Computers, vol 62(2), 362–375.
[12] Wang B, Li B et al. (2012).”Oruta: Privacy-Preserving Public Auditing for Shared Data in the Cloud”, IEEE Fifth International Conference on Cloud Computing, 295-302
[13] Jithin, S., and P. Sujatha. "An Analysis on Privacy Preserving in Cloud Computing."
[14] Onankunju, Bibin K. "Access Control in Cloud Computing."
[15] T. Jothi Neela, and N. Saravanan, Privacy Preserving Approaches in Cloud: A survey, IJST, vol.6.

Bincy Paul and M. Azath, "Survey on Preserving Data Privacy in Cloud," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.57-61, 2014.

This paper mainly presents different methods for the person authentication.The survey mainly includes the following methods; a high performance system with features obtained from human retinal images, retina verification using feature point based biometric pattern, then using the digital retinal images, retinal fundus biometric analysis, score level fusion of left and right irises, using 2-D matched filters, then using 2-D morlet wavelet method and finally using the biometric graph matching algorithm.

Retinal feature extraction, retina vessel detection, multigraph attributes, biometric graph matching, person verification.

[1] H. Farzin, H. Abrishami-Moghaddam, and M. S. Moin, “A novel retinal identification system”, EURASIP Journal of Advances Signal Processing, Volume-2008, Page No (1–10), April 2008.
[2] M. Ortega, M. G. Penedo, J. Rouco, N. Barreira, and M. J. Carreira, “Retinal verification using a feature points-based biometric pattern”, EURASIP Journal of Advanced Signal Processing, Volume-2009, Page No (1–13), March 2009.
[3] C. Mariño, M. G. Penedo, M. Penas, M. J. Carreira, and F. González, “Personal authentication using digital retinal images”, Journal of Pattern Analysis and Applications, Volume-9, No-1, Page No (21–33), 2006.
[4] V. Bevilacqua, L. Cariello, D. Columbo, M. D. Fabiano, M. Giannini, G. Mastronardi, and M. Castellano, “Retinal fundus biometric analysis for personal identifications”, Proceedings of International Conference of Intelligent Computing, Page No (1229–1237), September 2008.
[5] L. Latha and S. Thangasamy, “A robust person authentication system based on score level fusion of left and right irises and retinal features”, Proceedings of Computer Science, Volume-2, Page No (111–120), January 2010.
[6] S. Chaudhuri, S. Chatterjee, N. Katz, M. Nelson, and M. Goldbaum, “Detection of blood vessels in retinal images using two-dimensional matched filters”, IEEE Transaction in Medical Imaging, Volume-8, No-3, Page No(263–269), September 1989.
[7] M. Z. C. Azemin, D. K. Kumar, and H. R. Wu, “Shape signature for retinal biometrics”,Proceedings of International Conference of Digital Image Computer Technology and Applications,Page No (381–386), December 2009.
[8] J. Soares, J. Leandro, R. Cesar, H. Jelinek, and M. Cree, “Retinal vessel segmentation using the 2-d Gabor wavelet and supervised classification,” IEEE Transaction in Medical Imaging, Volume- 25, No-9,Page No (1214–1222), September 2006.
[9] Seyed Mehdi Lajevardi, Member, IEEE, Arathi Arakala, Stephen A. Davis, and Kathy J. Horadam “Retina Verification System Based on Biometric Graph Matching” IEEE Transaction in Image Pocessing, Volume-22, No-9, September 2013.
[10] R. C. Gonzalez, R. E. Woods, and S. L. Eddins, “Digital Image Processing Using MATLAB”, Upper Saddle River, NJ, USA: Prentice-Hall, 2003.
[11] B. Ulery, W. Fellner, P. Hallinan, A. Hicklin, and C. Watson, “Studies of biometric fusion,” Department of Commerce, National Institute of Standards Technology, Gaithersburg, MD, USA, Tech. Rep.IR 7346, Sep. 2006.
[12] VARPA. “Varpa Retinal Images for Authentication Database”, http://www.varpa.es/varia.html, 2006
[13] R. Hill, “Biometrics: Personal Identification in Networked Society”. New York, NY, USA: Springer-Verlag, Chapter-6, Page No (123–141), 1999.

Anitha L. and Arunvinodh C., "Diverse Frameworks on Retina Verification," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.62-67, 2014.

In this paper we review the classification algorithm for the Brain Computer Interface. The Characteristics of the EEG signals are changes over time, updating the classifiers of the Brain Computer Interface, (BCI). It will eventually improve the performance of the system. The development of the new adaptive classifiers are not easy because of we cannot predict the intension of the user, in some cases it may be possible to predict the labels of the EEG segments using some information of the state. We briefly present commonly used algorithm and their description of properties. By literature review we presented them in terms of performance. The main point come into Scenario that there is no comprehensive review of signal processing algorithm for EEG Signals. The aim of this review paper is to obtain the limitations and importance of the algorithm is to provide a guideline to the researchers in these fields. Techniques employed for the signal preprocessing, feature extraction and feature classification are discussed, but review focused on signals classification.

BCI, EEG, SSVEP-NIRS Hybrid BCI, EEG-EMG Hybrid BCI , EEG-EOG Hybrid BCI

[1] Yuanqing Li, Jiahui Pan, Fei Wang, and Zhuliang Yu “A Hybrid BCI System Combining P300 and SSVEP and Its Application to Wheelchair Control”, IEEE Trans. Biomed. Eng, vol. 60, no. 11, pp.1-7, Nov. 2013.
[2] Quan Liu ,Kun Chen, Qingsong Ai, sheng quan xie “Review: recent development of signals processing algorithms for SSVEP based brain computer interface”,Journal of Medical and Biological Engineering, , 12 Aug 2013, pp.299-309, Oct.2014
[3] H. Zhang, C. Guan and C. Wang, “Asynchronous P300-based brain computer interfaces: A computational approach with statistical models,” IEEE Transaction on Biomed. Eng., vol. 55, no. 6, pp. 1754–1763, Jun. 2008.
[4] R. Ortner, B. Allison, G. Korisek, H. Gaggl, and G. Pfurtscheller, “An SSVEP BCI to control a hand orthosis for persons with tetraplegia,” IEEE Trans. Neural Syst. Rehabil. Eng., vol. 19, no. 99, pp. 1–5, Feb. 2011.
[5] Xinyi Yong, Mehrdad Fatourechi, Rabab K. Ward and Gary E. Birch “Adaptive Classification in a Self-Paced Hybrid Brain-Computer Interface System”, 34th Annual International Conference of the IEEE EMBS San Diego, California USA, 28 August - 1 September, 2012
[6] Jinyi Long, Yuanqing Li*, Tianyou Yu, and Zhenghui Gu “Target Selection With Hybrid feature for BCI-Based 2-D Cursor Control”, IEEE Trans. Biomed. Eng, vol. 59, no. 1, pp.1-4, Jan. 2012.
[7] M. Rajya Lakshmi, Dr. T. V. Prasad, Dr. V. Chandra Prakash, “ Survey on EEG Signal Processing Methods ”, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 4, Issue 1, January 2014.
[8] Ali Bashashati, Mehrdad Fatourechi, Rabab K Ward and Gary E Birch, “A survey of signal processing algorithms in brain–computer interfaces based on electrical brain signals”, Journal of Neural Eng. 4 (2007) .pp.32–57, sep. 2014
[9] Luis Fernando Nicolas-Alonso * and Jaime Gomez-Gil, “Review Brain Computer Interfaces, a Review”, Sensors 2012, pp. 1211-1279, sept. 2014.
[10] Setare Amiri, Ahmed Rabbi, Leila Azinfar and Reza Fazel-Rezai, “A Review of P300, SSVEP, and Hybrid P300/SSVEP Brain-Computer Interface Systems”, Intechopen human computer interaction, June 2013, pp1-20, sept.2014.
[11] Setare Amiri, Reza Fazel-Rezai, and Vahid Asadpour, “A Review of Hybrid Brain-Computer Interface Systems”, Advances in Human-Computer Interaction Volume 2013 (2013), Article ID 187024, 8 pages.
[12] Tarik al ani and Salila Trad, “signal processing and classification approaches for brain computer interface”,, Intelligent and Biosensors, Vernon S. Somerset (Ed.), ISBN: 978-953-7619-58-9, InTech, DOI: 10.5772/7032.
[13] Ming Cheng et. Al., “Design and Implementation of a Brain-Computer Interface With High Transfer Rates”, IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, VOL. 49, NO. 10, OCTOBER 2002.
[14] F Lotte , M Congedo , A Lécuyer , F Lamarche, “A Review of Classification Algorithms for EEG-based Brain-Computer Interfaces”, citeseerx.
[15] Friday Zinzendoff, Okwonu and Abdul Rahman Othman, “A Model Classification Technique for Linear Discriminant Analysis for Two Groups”, International Journal of Computer Science Issues, Vol. 9, Issue 3, No 2, May 2012.
[16] Minkyu Ahn, Mijin Lee, Jinyoung Choi and Sung Chan Jun, “A Review of Brain-Computer Interface Games and an Opinion Survey from Researchers, Developers and Users”, Sensors 2014.
[17] A. Khorshidtalab, M.J.E. Salami, “EEG Signal Classification for Real-Time Brain-Computer Interface Applications: A Review”, 2011 4th International Conference on Mechatronics (ICOM), 17-19 May 2011
[18] Deon Garrett, David A. Peterson, Charles W. Anderson, and Michael H. Thaut, “Comparison of Linear, Nonlinear, and Feature Selection Methods for EEG Signal Classification” , IEEE transaction. on neural systems and rehabilitation eng, VOL. 11, NO. 2, JUNE 2003.
[19] Mythra H V, Veenakumari H M, Sanjeev Kubakaddi, “ Multi-Class EEG Classification for Brain Computer Interface”, International Journal of Scientific & Engineering Research, Volume 4, Issue 9, September-2013.
[20] Zongyuan Zhao, Shuxiang Xu, Byeong Ho Kang Mir Md Jahangir Kabir, “Investigation of Multilayer Perceptron and Class Imbalance Problems for Credit Rating”, International Journal of Computer and Information Technology ,Volume 03– Issue 04, July 2014

Parag P. Bharne and Deepak Kapgate, "A Review of Classification Techniques in Brain Computer Interface," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.68-72, 2014.

Cued Click points (CCP) is a click-based graphical password scheme, a cued-recall graphical password technique. Users Click on one point per image for a sequence of images. Performance was very good in terms of speed, accuracy, and number of errors. Users preferred CCP to Pass point, saying that selecting and remembering only one point per image was easier, and that seeing each image triggered their memory of where the corresponding point was located, CCP also provides greater security than Pass Points because the number of images increases the workload of attackers.

Cued click points,Graphical password,Performance,Passpoint,Security,Attackers

[1] Sonia Chiasson , P.C van Oorshcot, and Robert ” Graphical Password Authentication Using Cued Click Points “ © Springer-Verlag Heidelberg 2007,ESORICS 2007, LNCS 4734
[2] Tzong-Sun Wu • Ming-Lun Lee • Han-Yu Lin Chao-Yuan Wang “Shoulder-surfing-proof graphical password authentication scheme”© Springer-Verlag Berlin Heidelberg 2013
[3] John Charles Gyorffy , Andrew F. Tappenden ,James Miller “Token-based graphical password authentication “Published online: 2 October 2011 © Springer-Verlag 2011

Greeshma P.Y, Jerry Joe, Jisna V.A, Krishnapriya M.A, Saranya T.G, "Graphical Password Authentication Using Cued Click Points," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.73-75, 2014.

Apache HADOOP is a major novelty in the IT market household last decade. From modest early stages Apache HADOOP has develop a world-wide receipt in figures centers. It brings like dispensation in pointers of regular programmer. As additional figures middles ropes HADOOP platform, it develops authoritative to travel present figures removal procedures onto HADOOP stage for augmented like dispensation efficiency. By the outline of big figures analytics, This trend of transfer of the present figures removal procedures to HADOOP stage has develop rampant. In this review paper, we discover the present transfer doings and tests in migration. This newspaper will leader the booklovers to suggest answers for the present tests in the migration.

Datamining, Hadoop, Big data

[1] Emanuel, A.W.R. ; Fac. of Inf. Technol., Maranatha Christian Univ., Bandung, Indonesia ; Wardoyo, R. ; Istiyanto, J.E. ; Mustofa, K. “Success factors of OSS projects from sourceforge using Datamining Association Rule” Published in: Distributed Framework and Applications (DFmA), 2010 International Conference on Date of Conference: 2-3 Aug. 2010 Page(s): 1 – 8
[2] Drias, H. ; Comput. Sci. Dept., USTHB, Algiers, Algeria ; Hireche, C. ; Douib, A. “Datamining techniques and swarm intelligence for problem solving: Application to SAT” Published in: Nature and Biologically Inspired Computing (NaBIC), 2013 World Congress on Date of Conference: 12-14 Aug. 2013 Page(s): 200 – 206
[3] Abraham, R. ; Simha, J.B. ; Iyengar, S.S. “Medical Datamining with a New Algorithm for Feature Selection and Naive Bayesian Classifier” Published in: Information Technology, (ICIT 2007). 10th International Conference on Date of Conference: 17-20 Dec. 2007 Page(s): 44 – 49.
[4] Erraguntla, M. ; Ramachandran, S. ; Chang-Nien Wu ; Mayer, R.J. “Avian Influenza Datamining Using Environment, Epidemiology, and Etiology Surveillance and Analysis Toolkit (E3SAT)” Published in: System Sciences (HICSS), 2010 43rd Hawaii International Conference on Date of Conference: 5-8 Jan. 2010 Page(s): 1 – 7
[5] Panah, O. ; Ayatollah Amoli Branch, Comput. Dept., Islamic Azad Univ., Amol, Iran ; Panah, A. ; Panah, A. “Evaluating the datamining techniques and their roles in increasing the search speed data in web” Published in: Computer Science and Information Technology (ICCSIT), 2010 3rd IEEE International Conference on (Volume:9 ) Date of Conference: 9-11 July 2010 Page(s): 806 – 809
[6] Demchenko, Y. ; Syst. & Network Eng. Group, Univ. of Amsterdam, Amsterdam, Netherlands ; de Laat, C. ; Membrey, P. “Defining architecture components of the Big Data Ecosystem” Published in: Collaboration Technologies and Systems (CTS), 2014 International Conference on Date of Conference: 19-23 May 2014 Page(s): 104 – 112
[7] Lei Wang ; State Key Lab. of Comput. Archit., Inst. of Comput. Technol., Beijing, China ; Jianfeng Zhan ; Chunjie Luo ; Yuqing Zhu “BigDataBench: A big data benchmark suite from internet services” Published in: High Performance Computer Architecture (HPCA), 2014 IEEE 20th International Symposium on Date of Conference: 15-19 Feb. 2014 Page(s): 488 – 499
[8] Zibin Zheng ; Dept. of Comput. Sci. & Eng., Chinese Univ. of Hong Kong, Hong Kong, China ; Jieming Zhu ; Lyu, M.R. “Service-Generated Big Data and Big Data-as-a-Service: An Overview” Published in: Big Data (BigData Congress), 2013 IEEE International Congress on Date of Conference: June 27 2013-July 2 2013 Page(s): 403 – 410
[9] Han Hu ; Sch. of Comput., Nat. Univ. of Singapore, Singapore, Singapore ; Yonggang Wen ; Tat-Seng Chua ; Xuelong Li “Toward Scalable Systems for Big Data Analytics: A Technology Tutorial” Published in: Access, IEEE (Volume:2 ) Page(s): 652 – 687
[10] Narayan, S. ; InfoBlox Inc., Santa Clara, CA, USA ; Bailey, S. ; Daga, A. “Hadoop Acceleration in an OpenFlow-Based Cluster Published in: High Performance Computing, Networking, Storage and Analysis (SCC), 2012 SC Companion: Date of Conference: 10-16 Nov. 2012 Page(s): 535 – 538
[11] Jie Zhu ; Dept. of Comput. Sci., Arkansas State Univ., Jonesboro, AR, USA ; Juanjuan Li ; Hardesty, E. ; Hai Jiang “GPU-in-Hadoop: Enabling MapReduce across distributed heterogeneous platforms” Published in: Computer and Information Science (ICIS), 2014 IEEE/ACIS 13th International Conference on Date of Conference: 4-6 June 2014 Page(s): 321 – 326
[12] Mandal, A. ; Renaissance Comput. Inst., Univ. of North Carolina at Chapel Hill, Chapel Hill, NC, USA ; Yufeng Xin ; Baldine, I. ; Ruth, P. “Provisioning and Evaluating Multi-domain Networked Clouds for Hadoop-based Applications” Published in: Cloud Computing Technology and Science (CloudCom), 2011 IEEE Third International Conference on Date of Conference: Nov. 29 2011-Dec. 1 2011 Page(s): 690 – 697
[13] Xiao Yu ; Bo Hong “Bi-Hadoop: Extending Hadoop to Improve Support for Binary-Input Applications” Published in: Cluster, Cloud and Grid Computing (CCGrid), 2013 13th IEEE/ACM International Symposium on Date of Conference: 13-16 May 2013 Page(s): 245 – 252.
[14 Xiaoyi Lu ; Islam, N.S. ; Wasi-ur-Rahman, M. ; Jose, J. “High-Performance Design of Hadoop RPC with RDMA over InfiniBand” Published in: Parallel Processing (ICPP), 2013 42nd International Conference on Date of Conference: 1-4 Oct. 2013 Page(s): 641 – 650
[15] Pandey, S. ; Shri Vaishnav Inst. of Tech. & Sci., Indore, India ; Tokekar, V. “Prominence of MapReduce in Big Data Processing” Published in: Communication Systems and Network Technologies (CSNT), 2014 Fourth International Conference on Date of Conference: 7-9 April 2014 Page(s): 555 – 560.

M.Sharmila Begamr and N.Vetrivelanr, "An Analysis on Data Mining Processes on Big Data Framework," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.76-79, 2014.

Friction Stir Welding (FSW) is a compact national Welding procedure used for Welding like and unlike materials. The procedure is widely used because it produces wide-ranging joins and does not have common problems such as solidification and liquefact cracking associated with the fusion Welding techniques. The FSW of ALUMINIUM and its compounds has been commercialized; and new interest is concentrated on linking unlike materials. However, In order to commercialize the process, investigation educations are required to characterize and establish procedure windows. In particular, FSW has inspired investigators to attempt linking unlike materials such as ALUMINIUM to COPPER which differ In things and wide-ranging joins with none or limited intermetallic mixtures has been produced. In This paper, we review the present investigation national of FSW among ALUMINIUM and COPPER with a focus on the resultant combined microstructure, machine-driven challenging and the utensils hired to harvest the joins and also an insight into upcoming investigation in this field of study.

Aluminium, Copper, Unlike Materials, Intermetallic Compounds, Microstructure

[1] Ming-Der Jean; Inst. of Mechatron. Sci. & Technol., Yung-Ta Inst. of Technol. & Commerce, Ping-Tung, Taiwan; Wei-Jen Chen ; Jeng-Der Lin “Joint properties of tilted angles of spin for 6061 aluminum alloys in friction stir welded experiments” Published in: Consumer Electronics, Communications and Networks (CECNet), 2012 2nd International Conference on Date of Conference: 21-23 April 2012 Page(s): 3296 – 3299.
[2] Arularasu, S. ; Dept. of Production Technol., Anna Univ., Chennai, India ; Jothilingam, A. “Design and development of low cost friction stir welding machine” Published in: Advances in Engineering, Science and Management (ICAESM), 2012 International Conference on Date of Conference: 30-31 March 2012 Page(s): 305 – 311
[3] Binti Sataruddin, N.S.; Mech. Eng. Dept., Univ. Teknol. PETRONAS, Tronoh, Malaysia ; Awang, M. ; Shaari, K.Z.K. “Material flow study during friction stir welding process using computational fluid dynamics simulation” Published in: National Postgraduate Conference (NPC), 2011 Date of Conference: 19-20 Sept. 2011 Page(s): 1 – 4.
[4] Muthukrishnan, M. ; Dept. of Mech. Eng., Tamilnadu Coll. of Eng., Coimbatore, India ; Marimuthu, K. “Some studies on mechanical properties of friction stir butt welded Al-6082-T6 plates” Published in: Frontiers in Automobile and Mechanical Engineering (FAME), 2010 Date of Conference: 25-27 Nov. 2010 Page(s): 269 – 273.
[5] Patthi, U.B. ; Dept. of Mech. Eng., Univ. Teknol. PETRONAS, Tronoh, Malaysia ; Awang, M. “Friction stir welding on aluminum 6092/sic/25p/t6 metal matrix composite: Its microstructure evolution and mechanical properties” Published in: National Postgraduate Conference (NPC), 2011 Date of Conference: 19-20 Sept. 2011 Page(s): 1 – 6.
[6] Joshi, V. ; Dept. of Mech. Eng., Indian Inst. of Technol., Chennai, India ; Balasubramaniam, Krishnan ; Prakash, R.V. “Optimization of Friction stir welding parameter for AA 5083 by radiography and ultrasonic technique” Published in: Ultrasonics Symposium (IUS), 2011 IEEE International Date of Conference: 18-21 Oct. 2011 Page(s): 1920 – 1923
[7] Khansari, N.M. ; Dept. of Aerosp., Univ. of Tehran, Tehran, Iran ; Karimi, N. ; Sabour, M.H. “Optimization of Friction Stir Welding by fuzzy logic” Published in: Fuzzy Systems (IFSC), 2013 13th Iranian Conference on Date of Conference: 27-29 Aug. 2013 Page(s): 1 – 6.
[8] Sugimoto, M. ; Furukawa Electr. Co., Ltd., Tochigi, Japan ; Shimizu, H. ; Tsubouchi, H. ; Okada, T. “Novel Type of Aluminum-Alloy Jacketed Nb3Sn Superconductors Manufactured by Friction Stir Welding Technique” Published in: Applied Superconductivity, IEEE Transactions on (Volume:22 , Issue: 3 )
[9] Yang Li ; Inst. of Adv. Network Technol. & New Services, Univ. of Sci. & Technol. Beijing, Beijing, China ; Wei Huangfu ; Xiaodong Peng ; Yi Xing “A wireless sensor network for the metallurgical gas monitoring” Published in: Computers and Communication (ISCC), 2014 IEEE Symposium on Date of Conference: 23-26 June 2014 Page(s): 1 – 6.
[10] Ningning Wang ; Faculity of Inf. Eng. & Autom., Kunming Univ. of Sci. & Technol., Kunming, China ; Qingrong Huang ; Lihui Feng “Study on soft measurement of SO2 conversion ratio of producing sulfuric acid from metallurgical off-gas based on MLR” Published in: Control and Decision Conference (CCDC), 2013 25th Chinese Date of Conference: 25-27 May 2013 Page(s): 3091 – 3094
[11] Xincheng Li ; Sch. of Mech. Eng., Jiangsu Univ., Zhenjiang, China ; Ding Fei ; Zhang Zhaoyang ; Weixing Zhu “Ultra-Fine Grain Steel's Metallurgical Image Restoration Method Based on Improved Water-Growing” Published in: Image and Signal Processing, 2009. CISP '09. 2nd International Congress on Date of Conference: 17-19 Oct. 2009 Page(s): 1 – 4.
[12] Baake, E. ; Inst. of Electrotechnol., Leibniz Univ. of Hannover, Hannover, Germany ; Umbrashko, A. ; Jakovics, A. “Numerical simulation and analysis of heat and mass transfer processes in metallurgical induction applications” Published in: EUROCON 2009, EUROCON '09. IEEE Date of Conference: 18-23 May 2009 Page(s): 1578 – 1583.
[13] Chen Jian-qiang ; Sch. of Electron. & Inf. Eng., Beijing Jiao tong Univ., Beijing, China ; Xu Hong-ze ; Yu Zhang ; Zhang Lu-ning “Research on the limit capacity of the railway transportation based on the swarm platform in the metallurgical enterprises” Published in: Mechatronic Sciences, Electric Engineering and Computer (MEC), Proceedings 2013 International Conference on Date of Conference: 20-22 Dec. 2013 Page(s): 2442 – 2446
[14] Saxen, H. ; Heat Eng. Lab., Abo Akademi Univ., Turku; Pettersson, F. “A Data Mining Method Applied to a Metallurgical Process” Published in: Computational Intelligence and Data Mining, 2007. CIDM 2007. IEEE Symposium on Date of Conference: March 1 2007-April 5 2007 Page(s): 368 – 375
[15] Yaling Luo ; Coll. of Basic Med., Chongqing Med. Univ., Chongqing, China ; Zhuang Chen ; Liping Wan “Research on the identification algorithm and simulation relating to the radiation coefficient of metallurgical reheating furnace” Published in: Control and Decision Conference (CCDC), 2010 Chinese Date of Conference: 26-28 May 2010 Page(s): 1146 – 1151.

S. Martin Vinoth, K.Manonmani and S.Gopi, "FSW of Contradictory Ingredients among Alloy Mixtures and Copper," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.80-86, 2014.

Image authentication is the process of proving an image, is an accurate representation of the original one. Image authentication techniques have recently gained great attention due to its importance of multimedia applications. Through non-secure channels like internet digital images are increasingly transferred. Several methods which include Fragile Water-marking, Semi Fragile Water-Marking, Conventional Cryptography are used to protect the authenticity of images. Methods are classified according to the service they provide, that is strict authentication, localization, selective authentication, tamper detection and reconstruction capabilities and robustness against different desired image processing operation. The main aim of the paper is to present a survey and comparison of emerging techniques for image authentication.

Conventional Cryptography , Fragile Water Marking , Image Authentication, Image Contents , Robust Image Hashing , Semi Fragile Water Marking

[1] K.M.M. Rao, ―Overview Of Image Processing , Medical Image Processing, Proc. of workshop on Medical Image Processing and Applications, 8th October 1995 @ NRSA, Hyderabad-37. 4.
[2] Adil Haouzia & Rita Noumeir, , “Methods of Image Authentication : a Survey,” Published online: 1 August 2007, # Springer Science and Business Media, LLC 2007,Multimedia Tools Appl (2008) 39:1–4.
[3] Reshma Vartak, Smita Deshmukh , “Survey of DigitalImage Authentication Techniques ,” International Journal of Research in Advent Technology, Vol.2, No.7, July 2014 E-ISSN: 2321-9637 .
[4] R. B. Wolfgang and E. J. Delp, "Fragile watermarking using the VW2D watermark," in Security and Watermarking of Multimedia content, VOL.3657of SPIE proceedings, January 1999.
[5] C.Y. Lin and S.F. Chang, "Semi-fragile watermarking for authenticating JPEG visual content," in Proc. SPIE International Conf. on Security and Watermarking of Multimedia Contents, VOL. 3971, January 2000.

[6] Book of Angela D’Angelo, Giacomo Cancelli, and Mauro Barni, “Watermark-based Authentication,” .
[7] Gaurav A. Hiware, Rahul D. Chopade, Prof. Manish S. Nimkar , “A Cryptographic Technique using image based Authentication,” http: // www.ijfeat.org (C) International Journal For Engineering Applications and Technology [62-70] , March ISSN 2321-8134.
[8] H. Yang and A. C. Kot, “Binary image authentication with tampering localization by embedding cryptographic signature and block identifier,” IEEE Signal Processing Letters, VOL. 13, Dec. 2006 .
[9] S.Jothimani, P.Betty , “Survey of Image Authentication Techniques,” International Journal of Engineering Trends and Technology (IJETT) – Volume 7 Number 4- Jan 2014 .
[10] Yan Zhao, Shuozhong Wang, Guorui Feng, Zhenjun Tang,"A Robust Image Hashing Method Based on Zernike Moments ,” Journal of Computational Information Systems6:3(2010) 717-725 .
[11] F. Zernike, Beugungstheorie des schneidenverfahresund seiner verbesserten form[J], der phasenkontrastmethode, Physica, 1934, 1, pp.689-704

Vinitha.C and Dr.M.Azath, "Study On Image Authentication Techniques," International Journal of Computer Sciences and Engineering, Vol.2, Issue.12, pp.87-89, 2014.