iosr-JCE   Volume-15 ~ Issue-5

Abstract: Face detection and Facial recognition technology has emerged as a striking solution to address many contemporary prerequisites for identification and the verification of identity prerogatives. It brings together the potential of supplementary biometric systems, which attempt to link identity to individually distinctive features of the body, and the more acquainted functionality of visual surveillance systems. In current decades face recognition has experienced significant consideration from both research communities and the marketplace, conversely still remained very electrifying in real applications. The assignment of face detection and recognition has been dynamically researched in current eternities. This paper offers a conversant evaluation of foremost human face recognition research. We first present a summary of face detection, face recognition and its solicitations. Then, a literature review of the predominantly used face recognition techniques is accessible. Clarification and restrictions of the performance of these face recognition algorithms are specified. Here we present a vital assessment of the current researches concomitant with the face recognition process. In this paper, we present a broad range review of major researches on face recognition process based on various circumstances. In addition, we present a summarizing description of Face detection and recognition process and development along with the techniques connected with the various influences that affects the face recognition process.

Keywords: Face Detection, Face Recognition System, Biometric System, Review Research.

[1] Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, ―Detecting Faces in Images: A Survey‖, IEEE Trans. PAMI, vol. 24, No.1, pp. 1-25, Jan 2002.
[2] Craw, H. Ellis, and J. R. Lishman, Automatic extraction of face-feature, Pattern Recog. Lett. Feb. 1987, 183–187.
[3] H. Graf, E. Cosatto, and T. Ezzat, Face analysis for the synthesis of photo-realistic talking heads, in Proceedings Fourth IEEE International Conference on Automatic Face and Gesture Recognition, 2000.
[4] H. P. Graf, T. Chen, E. Petajan, and E. Cosatto, Locating faces and facial parts, in IEEE Proc. of Int. WorkshoponAutomatic Face-and Gesture-Recognition, Zurich, Switzerland, Jun. 1995, pp. 41– 45.
[5] Sudipta Roy ,Prof. Samir K. Bandyopadhyay, ―Detection and Quantification of Brain Tumor from MRI of Brain and it‗s Symmetric Analysis‖ ,International Journal of Information and Communication Technology Research(IJICTR), pp. 477-483,Volume 2, Number 6, June 2012. [ISSN : 2223-4985]
[6] Ming-Hsuan Yang, David J. Kriegman, Narendra Ahuja, ―Detecting Faces in Images: A Survey‖, IEEE Trans. PAMI, vol. 24, No.1, pp. 1-25, Jan 2002.
[7] H. P. Graf, E. Cosatto, D. Gibson, E. Petajan, and M. Kocheisen, Multi-modal system for locating heads and faces, in IEEE Proc. of 2nd Int. Conf. on Automatic Face and Gesture Recognition, Vermont, Oct. 1996, pp. 277–282.
[8] Sudipta Roy, Prof. Samir K. Bandyopadhyay, ―Contour Detection of Human Knee‖, International Journal of Computer Science Engineering and Technology (IJCSIT) ,September 2011 , Vol 1, Issue 8,pp. 484-487.
[9] Govindaraju, Locating human faces in photographs, Int. J. Comput. Vision 19, 1996.
[10] Y. Gao and K.H. Leung, ―Face recognition using line edge map,‖ IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 24, no. 6, June 2002.

Abstract: The aim of this paper is to draw attention for the evaluation of the accessibility and usability of Automated Teller Machine (ATM) systems from the point of view of visually impaired persons. It suggests that there is a need for a more precise application of assistive technologies in order to develop user friendly ATMs that will aid effective accessibility without the requirement of assistance by ordinary people who may take advantage of their visual challenges and project them to all odds of insecurity for their cash. The paper suggests that, while ATMs, as opposed to across the counter services, are now a widely accepted means of easing long queues in the banks and as a time saving factor for the banking services to the consumers. An accepted feature of the ATMs is to incorporate assistive technologies which still remain a challenge even with the formative years of ATM applications. It proposes possible contexts that may benefit the visually impaired from far-reaching exploration and incorporation of relevant assistive technologies. The paper should be of interest to financial institutions whose passion is to render better services to their customers in this era of technology considering that there are those customers who wish to transact with them and even need privacy with the ATMs but cannot access such benefits because of their cognitive challenges and specifically the visually impaired. Finally the paper offers a snap-shot of some of the widely-used assistive technologies for the visually impaired when using ATMs and their associated procedures and potential pitfalls.

Keywords: accessibility, Automated Teller Machines, Assistive Technology, usability, visually impaired

[1]. Adams, A. S. and Thieben, K. A. (1991), Automatic teller machines and the older population. Applied Ergonomics, 22, 85 -90.
[2]. Miller, A.W, 2006 [Online], Who invented the ATM machine? Automated Teller
[3]. Most, .M.M., Craddick, S., Crawford, S., Redican, S., Rhodes, D., Rukenbrod, F., Laws, R. (2003). Dietary quality assurance processes of the DASH-Sodium controlled diet study. Journal of the American Dietetic Association, 103(10): 1339-1346.
[4]. Preece, J., Y. Rogers, et al. (2002). Interaction Design: Beyond human-computer interaction. New York, USA, John Wiley & Sons.
[5]. Singh, S. and P. Kotze (2003). An Overview of Systems Design and Development Methodology with Regard to the Involvement of Users and Other Stakeholders. SAICSIT 2003: IT Research in Developing Countries, Fourways, South Africa, SAICSIT.
[6]. Whitney, C.W., Lind, B.K., Wahl, P.W. (1998). Quality assurance and quality control in longitudinal studies. Epidemiologic Reviews, 20(1): 71-80.
[7]. EBU ( April 2012), Response to European Commission Green Paper 'Towards an Integrated European market for card, internet and mobile payments'
[8]. Higham, N. 2001. Designing TV for Granny. See at: http://news.bbc.co.uk/hi/english/entertainment/tv_and_radio/newsid_1689000/1689552.stm
[9]. Counterpoint. 2001. Digital Television – Consumers' Use and Perceptions: A Report on a Research Study See at:. www.oftel.gov.uk/publicaitons/research/2001/digtv0901.pdf
[10]. Silvia Dini, Lucia Ferlino, Anna Gettan, Cristina Martinoli, Michela Ott (2006) Educational software and low vision users: evaluating accessibility factors

Abstract: This paper presents an approach to eradicate all of the confusion which surrounds anyone while preparing for the project and seminar. Also it aims in helping the institution to manage the previous batch's seminars and project. Seminar and project activity mainly deals with effective data searching and keeping pace with emerging technologies. This paper focuses the concepts like keeping data at one place, providing guidance related to Projects and seminars. This paper can represent an application that can be used by anyone for solving queries related to project and seminar.

[1]. A K Munns and B F Bjeirmi, "The role of project management in achieving project success", International Journal of Project Management Vol. 14, No. 2, pp. 81-87, 1996 © 1996 Elsevier Science Ltd and IPMA Printed in Great Britain 0263-7863/96
[2]. M.R. Martínez-Torres, S.L. Toral, F. Barrero, F. Cortés, (2010) "The role of Internet in the development of future software projects", Internet Research, Vol. 20 Iss: 1, pp.72 – 86
[3]. Petter Gottschalk, Jan Terje Karlsen, (2005) "A comparison of leadership roles in internal IT projects versus outsourcing projects", Industrial Management & Data Systems, Vol. 105 Iss: 9, pp.1137 – 1149
[4]. Sharon Markless and David Streatfield "Information Management Associates "The Library and Information Commission 2000 Grant Number: LIC/RE/001 ISBN 0-9538432-2-X ISSN 1466-2949E
[5]. Christina M.Finneran,"Flow in computer mediated environments: Promises and challenges", Communication of the association for the Information system(volume 15,2005),82-101
[6]. Neil Selwn," Faceworking: exploring studentds education related use of facebook" DOI 101080/17439880902923622
[7]. Kathleen M. Eisenhardt, "Building theories from case study research" ,Standford California-94305

Abstract: Documentaries used in the training field constitute the rich sources of information. They have the advantage to associate the elements of knowledge with events which request the episodic memory of the learner.Thus, these documentaries increase the probability of retention of knowledge they convey. However, the logical sequence of knowledge does not guarantee an efficient construction which can be mobilized in situations of action. In this paper, we seek how to benefit from the potential of these documentaries to promote the construction and mobilization of knowledge by the learner in an elearning platform. Particularly, we propose a method allowing segmenting the contents of a documentary to design learning activities. Based on a case study related to the field of mechatronics, we begin by segmenting the content of a documentary in terms of elements of knowledge (facts, concepts, procedures, and rules) then we connect with each of these problems they seek to find answers. We reorganize learning activities to promote the acquisition and mobilization of knowledge by the learner. We conclude by proposing a pedagogical scenario to implement these activities in elearning platform.

Keywords: LMS, IMS-LD, Documentary, Design, Elearning

[1] Lejeune (2004), IMS Learning Design, Étude d'un langage de modélisation pédagogique, article soumis à la revue DISTANCES et SAVOIR
[2] P.O. Bishop, Neurophysiology of binocular vision, in J.Houseman (Ed.), Handbook of physiology, 4 (New York: Springer-Verlag, 1970) 342-366.
[3] W.J. Book, Modelling design and control of flexible manipulator arms: A tutorial review, Proc. 29th IEEE Conf. on Decision and Control, San Francisco, CA, 1990, 500-506.
[4] Koper, R,. (2003), Combining re-usable learning, resources and services to pedagogical purposeful units of learning. In A. Littlejohn (Ed.), Reusing Online Resources: A Sustainable Approach to eLearning (pp. 46-59). London: Kogan Page
[5] Koper, R. Olivier B., (2004), Representing the Learning Design of Units of Learning, Educational Technology & Society, Vol. 7, n°3, p. 97-111
[6] Pernin, J.P., (2003) Objets pédagogiques : unités d'apprentissage, activités ou ressources ?, Revue "Sciences et Techniques Educatives", Hors série 2003 " Ressources numériques, XML et éducation", pp 179-210, avril 2003, éditions Hermès.
[7] LOM (2002) LOM specification, Learning Object Metadata http://ltsc.ieee.org/wg12/, dernière consultation, Mars 2013.

Abstract: This paper presents the circuit model of photovoltaic (PV) module. Simulation and modeling is done using MATLAB/ Simulink software package. The proposed model is user friendly and can be used as a common platform for both applied physics scientist and power electronics engineers. A detailed modeling procedure is presented. The designed model is verified by using STP255-20/Wd PV module. The IV and PV characteristics are simulated at different temperature and irradiance conditions and the results are verified. The proposed model is very simple fast and accurate. The designed model can be used for analysis of PV characteristics and for simulation of maximum power point tracking algorithms.

[1] Frede Blabbjerg, Zhe Chen and Soren Baekhoej Kjaer, "Power electronics as efficient interface in dispersed power generation systems," IEEE Trans. of Power Electronics. Vol. 19, No. 5, sept. 2004, pp. 1184-1194.
[2] J.P. Benner and L.Kazmerski, "Photovoltaics gaining greater visibility," IEEE spectrum, vol. 29, pp. 34-42, sept. 1999.
[3] Joung h.R. Enslin, Mario S. Wolf, Daniel B Snyman, "Integrated photovoltaic maximum power point tracking converter," IEEE Trans., Vol 53, No.6, pp. 1036-1047, aug. 2009.
[4] Marcelo Gradella Villalva, Jonas Rafael Gazoli and Ernesto Ruppert Filho, "Comprehensive approach of modelling and simulation of photovoltaic arrays,"! EEE Trans. on power electronics, Vol. 24, No. 5, may2009.
[5] Tsai, H.L., Tu, C.S, Su, Y.J., "Development of generalized photovoltaic model using MATLAB/simulink," Proceedings of the world congress on engineering and computer science, pp.978-988, 2008.
[6] Yu, T.C., Chien, T.S., "analysis and simulation of characteristics and maximum power point tracking for photovoltaic systems," PEDS, pp. 1339-1344, 2009.
[7] N.Pandiarajan and R.Muthu, "Development of power electronic circuit oriented model of photovoltaic module," International journal of advanced engineering technology, IJAET, Vol.II, Issue IV, pp118-127, Oct-Dec 2011.
[8] Athimulam Kalirasu, Subharensu Sekar Dash, (2010), "Simulation of closed loop boost converter for solar installation", Serbian journal of electrical engineering, Vol.21 ,No. 1, pp121-130
[9] P. Tamilvani and K.R. Valluvan "Hybrid Modulation Technique for Cascaded Multilevel Inverter for High Power and High Quality Applications in Renewable Energy Systems," International Journal of Electronic and Electrical Engineering, ISSN 0974-2174 Volume 5, Number 1, pp. 59-68.

Abstract: Security has become an increasingly important feature with the growth of electronic communication which calls for more advanced ways to encrypt the raw data[1]AES-128 is going to be implemented as the encryption algorithm as there are 3 types of AES (AES-128,AES-192,AES-256) but why AES? Because AES is famous for its ciphering strength and its strong defense against any attack for example the brute force attack. Using Brute force attack on AES-128 (smallest key length) is unlikely to be practical in the foreseeable future [3]. According to NIST, "Assuming that one could build a machine that could recover a DES key (DES is an old encryption algorithm which no one uses now in modern days) in a second (i.e.,try 2^55 keys per second), then it would take that machine approximately 149 thousand-billion (149 trillion) years to crack a 128-bit AES key". In this paper AES-128 will be implemented on a microcontroller circuit to give it further security and more speed in both the encryption and decryption of the files. Keywords: AES, Cryptography, DES, NIST, Rijndael, Serpent.

[1] Hans Delfs, Helmut Knebl, "Introduction to Cryptography: Principles and Applications", Second Edition, ISBN: 9783540492436, 2007.

[2] Richard A. Mollin, "An Introduction to Cryptography", Second Edition, ISBN: 1584886188 / 9781584886181, 2005.

[3] AviKak, "AES: The Advanced Encryption Standard, Lecture Notes on "Computer and Network Security"", February, 2013.

[4] Christophe RITZENTHALER, "Cryptology course", 2nd Semester 2006.

[5] ChristofPaar, Jan Pelzl, "Understanding Cryptography", ISBN: 9783642041006, 2010

[6] Microchip. 2006. PIC18F452 Data Sheet. Printed by Microchip Technology, Inc in the United States of America.

[7] Alfred J. Menezes, Paul C. van Oorschot, Scott A. Vanstone,"Hand Book of Applied Cryptography", 1997. [8] AviKak, "AES: The Advanced Encryption Standard, Lecture Notes on "Computer and Network Security"", February, 2013.

Abstract: We analyze the breast Cancer data available from the WBC, WDBC from UCI machine learning with the aim of developing accurate prediction models for breast cancer using data mining techniques. Data mining has, for good reason, recently attracted a lot of attention, it is a new Technology, tackling new problem, with great potential for valuable commercial and scientific discoveries. The experiments are conducted in WEKA. Several data mining classification techniques were used on the proposed data. There are many classification techniques in data mining such as Decision Tree, Rules NNge, Tree random forest, Random Tree, lazy IBK. The aim of this paper is to investigate the performance of different classification techniques. The data breast cancer data with a total 286 rows and 10 columns will be used to test and justify the different between the classification methods and algorithm.

Keywords - Machine learning, data mining Weka, classification, breast cancer

[1] Mitchell, T.M. 1997. Machine Learning. McGraw-Hill Science [2] John, G., Cleary, E. & Leonard, E. 1995. K*: An Instance-based Learner Using an Entropic Distance Measure. In: 12th International Conference on Machine Learning, 108-114. [3] Aha, D. & Kibler, D. 1991. Instance-based learning algorithms. Machine Learning.
[4] American Cancer Societies. Breast Cancer Facts& Figures 2005-2006. Atlanta: American Cancer Society, Inc. (http://www.cancer.org/). [5] National Resource. Cancer Epidemiology Biomarkers & Prevention 1999; 8:1117-1121. [6] Houston, Andrea L. and Chen, et. al.. Medical Data Mining on the Internet: Research on a Cancer Information System. Artificial Intelligence Review 1999; 13:437-466.

Abstract: Cataloging traffic keen on precise network applications is vital for application-aware network organization and it turn into more taxing because modern applications incomprehensible their network behaviors. Whereas port number-based classifiers work merely for a little renowned application and signature-based classifiers are not significant to encrypted packet payloads, researchers are inclined to classify network traffic rooted in behaviors scrutinized in network applications. In this document, a session level Flood Cataloging (SLFC) approach is proposed to organize network Floods as a session, which encompasses of Floods in the equal discussion. SLFC initially classifies flood into the analogous applications by packet size distribution (PSD) and subsequently faction Floods as sessions by port locality. With PSD, each Flood is distorted into a set of points in a two-Dimension space and the remoteness among all Flood and the representatives of preselected applications are calculated. The Flood is predicted as the application having a least distance. Meanwhile, port locality is accustomed to cluster Floods as sessions since an application often uses successive port statistics surrounded by a session. If flood of a session are categorized into diverse applications, an arbitration algorithm is invoked to make the improvement.

Keywords: Flood Cataloging; session grouping; session Cataloging; packet size distribution

[1] C. Fraleigh, S. Moon, B. Lyles, C. Cotton, M. Khan, D. Moll, R. Rockell, T. Seely, C. Diot, "Packet-level traffic measurements from the sprint IP backbone" in IEEE Network, November 2003.
[2] T. Karagiannis, A. Broido, N. Brownlee, K.C. Claffy, M. Faloutsos, "Is P2P dying or just hiding?" in IEEE GLOBECOM , November 2004
[3] T. Karagiannis, A. Broido, M. Faloutsos, K.C. Claffy, K.C." Transport layer Identification of P2P traffic" in Internet Measurement Conference (IMC), October 2004.
[4] S. Sen, O. Spatscheck, and D. Wang, "Accurate, scalable in network identification of P2P traffic using application signatures," in WWW2004, May 2004.
[5] M. Roesch. "SNORT: Lightweight intrusion detection for networks" in LISA '99: Proceedings of the 13th USENIX Conference on Systems Administration, Nov. 1999.
[6] C.N. Lu, C.Y. Huang, Y.D. Lin, Y.C. Lai, "Session Level Flood Cataloging by Packet Size Distribution and Session Grouping," Computer Networks, In Press, 2011.

Abstract: The World Wide Web (WWW) is global information medium, where users can read and write using computers over internet. Web is one of the services available on internet. The Web was created in 1989 by Sir Tim Berners-Lee. Since then a great refinement has done in the web usage and development of its applications. In this paper we would like to present different stages of web growth starting from its inception to the present web in terms of its technologies and applications.

[1]. World Wide Web Consortium (W3C). Introduction to Web Accessibility. Available at http://www.w3.org/WAI/intro/accessibility.php (Accessed on 04/01/2008)
[2]. http://en.wikipedia.org/wiki/Craigslist
[3]. http://computer.howstuffworks.com/web-10.htm
[4]. http://www.yvoschaap.com/weblog/rip_a_tribute_to_web_10
[5]. "Key difference between Web 1.0 and Web 2.0", Graham Cormode and Balachander Krishnamurthy.
[6]. http://technology.guardian.co.uk/online/story/0,3605,1145689,00.html [last accessed 14/02/07].
[7]. Fichter Darlene, What Is a Mashup? http://books.infotoday.com/books/Engard/Engard-Sample-Chapter.pdf ( retrieved 12 august 2013)

Abstract: Lentils are designated into two classes, Red Lentils and Lentils other than red. The method of determining the class of a lentil is by seed coat color. Red lentils may be confirmed by the cotyledon color. Lentil varieties may have a wide range of seed coat colors from green, red, speckled green, black and tan. The cotyledon color may be red, yellow or green. The size and color of each Indian Lentil type (i.e. Red, Green and Yellow) is determined to be large or Medium or small, then size and color becomes part of the grade name. An Intelligent system is used to identify the type of Indian lentils from bulk samples. The samples were acquired from the proposed image acquisition system with image resolution 640x480. The proposed system facilitating kernels size and color measurement using image processing techniques. These Lentil size measurements, when combined with color attributes of the sample, classify three lentil varieties commonly grown in India with an accuracy approaching 97.08%.

Key Words: Size and Color measurement, Lentil, Image Processing, Artificial Neural Network

[1] M.A. Shahin and S.J. Symons, A machine vision system for grading lentils Canadian Biosystems Engineering Volume 43 2001

[2] N.S. Visen1, D.S. Jayas, J. Paliwal1 and N.D.G. White, Comparison of two neural network architectures for classification of singulated cereal grains, Canadian Biosystems Engineering Volume 46 2004

[3] Da-Wen-Sun Computer Vision Technology for Food Quality Evaluation", Food Science and Technology, International series, Elsevier Inc., 2008.

[4] B S Anami and D G Savakar Improved Method for Identification and Classification of Foreign Bodies mixed Food Grains Image analysis, ICGST-AIML Journal, ISSN 1687-4846, Vol. 9(1), 2009

Abstract: Image Processing is an advancement in the Signal processing domain where the input is an image or video and output can be image, video or in any other forms. With daily advances in Human-Computer interaction, Image Processing, along with computer vision has become an integral part of any intelligent system. They are mainly based on the workings of the visual perception function of the human brain. As such most of the algorithms are designed based around this central concept. The major application of Computer Vision is in the field of intelligent machines and robotic visions, where machines are made intelligent by being able to sense their surrounding visually and optimize themselves accordingly. In case of robots, through computer vision, they are made capable of sensing their surroundings, just like a human.

[1] Richard Szeliski,Computer Vision algorithms and applications, (2010)

[2] R. Fisher , K Dawson-Howe, A. Fitzgibbon, C.Robertson, E. Trucco, John Wiley, Dictionary of Computer Vision and Image Processing, 2005

[3] F. Tanner, B. Colder, C. Pullen, D. Heagy, C. Oertel, & P. Sallee, Overhead Imagery Research Data Set (OIRDS) – an annotated data library and tools to aid in the development of computer vision algorithms, June 2009
[4] Liming Wang, Jianbosh Shi, Gang Song, I-fan Shen, Object Detection Combining recognition and Segmentation, Fudan University, Shanghai, PRC, 200433, obj_det_liming_accv07.pdf

[5] Viola, Jones: Robust Real-time Object Detection, IJCV 2001,http://en.wikipedia.org/wiki/Caltech_101#cite_note-Viola_Jones- [6] D. Comaniciu, V. Ramesh and P. Meer, Real-time tracking of non-rigid objects using mean shift. In Proc. Conf. Comp. Vision Pattern Rec., pages II: 142–149, Hilton Head, SC, and June 2000

Abstract: Cloud computing has touched almost every field of the life. Hence number of cloud application consumers is increasing every day and so as the number of application request to the cloud provider. This leads increment of workload in many of the cloud nodes. The motive to use load balancing concepts in cloud environment is to efficiently utilize available resources keeping in mind that no any single system is heavily loaded or not a single system is idle during the active phase of the request completion. Even though cloud computing being a software facility most often, how does it actually performs well in heavily loaded environment at processor level, is discussed in the paper. This paper aims to throw some light on what is cloud load balancing and what is the role of Virtual machine migration in improving it.

[1] Qian Li, Nanqiang Xia, The Utilization of Cloud Computing in Network Collaborative Commerce Chain, Fourth International Conference on Business Intelligence and Financial Engineering, IEEE. 2011.
[2] Soumya Ray, Ajanta De Sarkar, "Execution Analysis Of Load Balancing Algorithms In Cloud Computing Environment". International Journal on Cloud Computing: Services and Architecture (IJCCSA),Vol.2, No.5, October 2012
[3] Punit Gupta, Mayank Kumar Goyal, Prakash Kumar. Trust and Reliability based Load Balancing Algorithm for Cloud IaaS, 3rd IEEE International Advance Computing Conference (IACC), IEEE 2013.
[4] Ali M. Alakeel,A Guide to Dynamic Load Balancing in Distributed Computer Systems, IJCSNS International Journal of Computer Science and Network Security,VOL.10 No.6, June 2010.
[5] Mauro Andreolini, Sara Casolari, Michele Colajanni, Michele Messori, Dynamic load management of virtual machines in cloud architectures, Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering Volume 34, Springer. 2010, pp 201-214.
[6] Varsha P. Patil, G.A. Patil, Migrating Process and Virtual Machine in the Cloud: Load Balancing and Security Perspectives, International Journal of Advanced Computer Science and Information Technology 2012, Volume 1, Issue 1, pp. 11-19, Article ID Tech-21, November 2012.

Abstract: Identifying the stakeholder in requirement engineering process is one of the critical issues. It performs a remarkable part for successful project completion. The software project largely depends on several stakeholders. Stakeholder identification and prioritization is still a challenging part in the software development life cycle. Most of the time, the stakeholders are treated with less importance during the software deployment. Additionally, there is a lack of attempt to think about the right project stakeholder by the development team. In maximum cases, the stakeholder identification technique is performed incorrectly and there is a lack of attempt to mark out them with priority. Besides, there are so many limitations on the existing processes which are used for identifying stakeholders and setting their priority. These limitations pose a negative impact on the development of software project, which should be pointed out by giving deep concern on it. We are aiming to focus on this typical fact, so that we can figure out the actual problem and current work on identifying stakeholders and setting their priority.

1] I.Sommerville, and P.Sawyer, Requirements engineering: A good practice guide (John Wiley and Sons, 1992). Nuseibeh, and Easterbook, Requirements engineering: A roadmap, Proceeding of the ACM Conference on Software Engineering, Limerick, Ireland, pp.35-46.
[2] Sharp, Finkelstein & Galal, Stakeholder identification in the requirements engineering process, Proceedings of 10th International Workshop on Database & Expert Systems Applications (DEXA), 11/12/99, pp. 387-391.
[3] Carla Pacheco, Ivan Garcia, A systematic literature review of stakeholder identification methods in requirements elicitation, The Journal of Systems and Software, 85, 2012, 2171-2181.
[4] Kotonya, G. and Sommerville, Requirements engineering: process and techniques (John Willey, 1998).
[5] Dix, A., Finlay, J.Abowd, G. and Beale, Human-computer interaction (Prentice-Hall, 1993).
[6] Coughlan J, Lycett M, and Robert D Macredie, Communication issues in requirements elicitation: A content analysis of stakeholder experiences, Information & Software Technology, 45(8), 2003, 525–537.

Abstract:Sequential pattern mining, which discovers the correlation relationships from the ordered list of events, is an important research field in data mining area. In our study, we have developed a Sequential Pattern Tree structure to store both frequent and non-frequent items from sequence database. It requires only one scan of database to build the tree due to storage of non-frequent items which reduce the tree construction time considerably. Then, we have proposed an efficient Sequential Pattern Tree Mining algorithm which can generate frequent sequential patterns from the Sequential Pattern Tree recursively. The main advantage of this algorithm is to mine the complete set of frequent sequential patterns from the Sequential Pattern Tree without generating any intermediate projected tree. Again, it does not generate unnecessary candidate sequences and not require repeated scanning of the original database. We have compared our proposed approach with three existing algorithms and our performance study shows that, our algorithm is much faster than apriori based GSP algorithm and also faster than existing PrefixSpan and Tree Based Mining algorithm which are based on pattern growth approaches.

[1] R. Agrawal and R. Srikant, "Mining sequential patterns," in ICDE, P. S. Yu and A. L. P. Chen, Eds. IEEE Computer Society, 1995, pp. 3-14. [Online] Available: http://doi.ieeecomputersociety.org/10.1109/ICDE.1995.380415
[2] R. Srikant and R. Agrawal, "Mining sequential patterns: Generalizations and performance improvements," in EDBT, ser. Lecture Notes in Computer Science, P. M. G. Apers, M. Bouzeghoub, and G. Gardarin, Eds., vol. 1057. Springer, 1996, pp. 3-17. [Online] Available: http://dx.doi.org/10.1007/BFb0014140
[3] M. J. Zaki, "Spade: An efficient algorithm for mining frequent sequences," Machine Learning, vol. 42, no. 1/2, pp. 31-60, 2001. [Online] Available: http://dx.doi.org/10.1023/A:1007652502315
[4] J. Han, J. Pei, B. Mortazavi-Asl, Q. Chen, U. Dayal, and M. Hsu, "Freespan: frequent pattern-projected sequential pattern mining," in KDD, 2000, pp. 355-359. [Online] Available: http://doi.acm.org/10.1145/347090.347167
[5] J. Pei, J. Han, B. Mortazavi-Asl, H. Pinto, Q. Chen, U. Dayal, and M. Hsu, "Prefixspan: Mining sequential patterns by prefix-projected growth," in ICDE, D. Georgakopoulos and A. Buchmann, Eds. IEEE Computer Society, 2001, pp. 215-224. [Online] Available: http://doi.ieeecomputersociety.org/10.1109/ICDE.2001.914830

Abstract: The main aim of this project is to propose a new method for image segmentation. Image Segmentation is concerned with splitting an image up into segments (also called regions or areas) that each holds some property distinct from their neighbor. Simply, another word for the Object Detection is "Segmentation ". Segmentation is divided into two types they are Supervised Segmentation and Unsupervised Segmentation. Segmentation consists of three types of methods which are divided on the basis of threshold, edge and region. Thresholding is a commonly used enhancement whose goal is to segment an image into object and background. Edge-based segmentations rely on edges found in an image by edge detecting operators. Region based segmentations basic idea is to divide an image into zones of maximum homogeneity, where homogeneity is an important property of regions. Edge detection has been a field of fundamental importance in digital image processing research.

[1]. C.Naga Raju , O.Rama Devi, Sharada Mani "An Improved Ant Colony Optimization Technique by using Fuzzy Inference Rules for Image Classification and Analysis" International Journal of Advanced Engineering & Applications. Jan. 2010.
[2]. C.NagaRaju and Dr.L.S.S.Reddy "Novel Watershed Segmentation Method for Stumpy Boundary Detection for Image Classification" World academy of science, engineering and Technology International Journal of Information Technology and Knowledge Management Volume 2, No. 1, pp. 177-180 January June 2009.
[3]. Dr.LSS Reddy, A.Rama Swami Reddy, C.NagaRaju and B.Sreenivasa Rao" generation of random fields based on matrix-geometric solution method for image analysis and classification" International Journal of Advanced Engineering & Applications, Italy. Jan. 2010.
[4]. Sharada Mani, G. SyamPrasad, O.Rama Devi, C.NagaRaju "A Novel Edge Detection Technique for Color Images" International Journal of Advanced Engineering & Applications Jan. 2010.

Abstract: A Reprogramming service should be efficient, reliable and secured in Wireless sensor network. Wireless reprogramming for wireless sensor network emphasize over the process of changing or improving the functionality of simulation or existing code. For challenging and on demand security purpose, secure and distributed routing protocols such as SDRP and ISDRP were developed. This paper reviews and compares the propagation delay for two reprogramming protocols, SDRP and ISDRP, which based on hierarchy of energies in network. Both are based on identity-based cryptography. But in the improved protocol the keys are distributed to the network as per the sorting and communication capabilities to improve the broadcast or communication nature of the network. Moreover, ISDRP demonstrates the security concepts, which deals over the key encryption properties using heap sort algorithm and the confidentiality parameter is enhanced by changing the private key values after certain interval of time for cluster head in respect to different public keys. The ISDRP shows high efficiency rate clearly with the throughput and propagation results by implementation in practice over SRDP.

[1]. Q. Wang, and I. Balasingham, Wireless Sensor Networks - An Introduction, Chapter 1 Wireless Sensor Networks: Application-Centric Design, 2010, 1-13
[2]. Q. Wang, Y. Zhu and L. Cheng, Reprogramming Wireless Sensor Networks: Challenges and Approaches, IEEE network, 20(3), 2006, 48-55
[3]. A. Chlipala, J. Hui, and G.Tolle, Deluge: Data Dissemination for Network Reprogramming at Scale, University of California at Berkeley Computer Science Division, Berkeley,2003
[4]. S. Hyun, P. Ning, A. Liu, and W. Du, Seluge: Secure and DoS-Resistant Code Dissemination in Wireless Sensor Networks, International Conference on Information Processing in Sensor Networks, St. Louis, MO, 2008, 445-456
[5]. D. He, C. Chen, S. Chan and J. Bu, SDRP: A Secure and Distributed Reprogramming Protocol for Wireless Sensor Networks, IEEE Transactions on Industrial Electronics, 59(11), 2012, 4155-4163
[6]. P. E. Lanigan, R. Gandhi, and P. Narasimhan, Sluice: Secure dissemination of code updates in sensor networks, 26thInternational Conference on Distributed Computing Systems, 2006, 53

Abstract: SDN creates a dynamic and flexible network architecture that can change as the business requirements change. The growth of the SDN market and cloud computing are very much connected. As the applications change and the network is abstracted, virtualization become a necessary step and SDN serves as the fundamental building blocks for the network. Traditional networking devices are composed of an embedded control plane that manages switching, routing and traffic engineering activities while the data plane forwards packet/frames based on traffic. In SDN architecture, control plane functions are removed from individual networking devices and embedded in a centralizedserver. The SDN controller makes all traffic related decisions in the network without nodes active participation, as opposed to today's networks.

Keyword-API, cloud computing, IT, middleware, OpenFlow, SDN

[1] IBM Systems and Technology, Software Defined Networking - A new paradigm for virtual, dynamic, flexible networking, October 2012

[2] Kirk Bloede, Software Defined Networking – Moving Towards Mainstream, Electronics Banking Research, August 2012

[3] Brocade VCS Fabrics: The Foundation for Software-Defined Networks

[4] Network Transformation with Software-Defined Networking and Ethernet Fabrics, Brocade Communications Systems, Inc., 2012

[5] Software Defined Networking: What Is It and Why Do You Need It?enterasys secure network

Abstract: Network security and Intrusion Detection Systems (IDS's) is an important security related research area. This paper applies K-star algorithm with filtering analysis in order to build a network intrusion detection system. For our experimental analysis and as a case study, we have used the new NSL-KDD dataset, which is a modified dataset for KDDCup 1999 intrusion detection benchmark dataset. With a split of 66.0% for the training set and the remainder for the testing set a 2 class classifications has been implemented. WEKA which is a java based open source software consists of a collection of machine learning algorithms for Data mining tasks has been used in the testing process. The experimental results show that the proposed approach is very accurate with low false positive rate and high true positive rate and it takes less learning time in comparison with other existing approaches used for efficient network intrusion detection.

Keywords: Information Gain, Intrusion Detection System, Instance-based classifier, K-Star, Weka.

[1] Peyman Kabiri and Ali A. Ghorbani, "Research on Intrusion Detection and Response:A Survey" International Journal of Network Security, Vol.1, No.2, PP.84–102, Sep. 2005.

[2] Bhavin Shah and Bhushan H Trivedi, "Artificial Neural Network based Intrusion Detection System: A Survey" International Journal of Computer Applications (0975 – 8887) Volume 39– No.6, February 2012.

[3] Gaikwad, Sonali Jagtap, Kunal Thakare, and Vaishali Budhawant "Anomaly Based Intrusion Detection System Using Artificial Neural Network and Fuzzy Clustering" International Journal of Engineering Research & Technology (IJERT) Vol. 1 Issue 9, November- 2012, ISSN: 2278-0181.

[4] Sandip Sonawane , Shailendra Pardeshi, and Ganesh Prasad "A survey on intrusion detection techniques" World Journal of Science and Technology 2012, 2(3):127-133, ISSN: 2231 – 2587.