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An internet based computing where number of servers situated remotely are inter-connected to each other and access to some services are allowed online is framed as Cloud Computing. But the existing systems require very expensive hardware and the power consumption is also high. Also the existing systems are quite complex and usually require larger area to install along with high maintenance cost. So the idea is to propose a system where users can store their data over the cloud and retrieve their data from any part of the world. The proposed system not only provides reliable file storage but also provides high security. This proposed system uses Raspberry Pi which is a single-board computer; small in size to develop a cloud server which will be secured as well as less expensive. For security, the proposed system uses Kerberos authentication protocol which will act as a third-party service that will be used to authenticate the user’s identity. It will act as a bridge between the user and the cloud storage.

Raspberry Pi, Kerberos Authentication protocol, Cloud Server, Security, Client Server Authentication

[1] “Cloud: Research problems in data center networks,” ACM SIGCOMM Computer Communication Review, vol. 39, no. 1, January 2009.
[2] Fung Po Tso, David R. White, Simon Jouet, Jeremy Singer, Dimitros P.Pezaros “The Galsgow Raspberry Pi Cloud: A Scale Model for Cloud Computing Services” School of Computing Science, University of Glasgow.
[3] Tim Cox, “Raspberry Pi Cookbook for Python Programmers” Page 19.
[4] Clifford Neuman and Theodore TS‘O “Kerberos: an authentication service for computer networks” inf. Sci. Inst., University of Southern California, Marina Del Rey, CA, US.
[5] www.raspberrypi.org
[6] Andrew K. Dennis, “Raspberry Pi Super Cluster” Page 14.
[7] Chundong Wang, Charonn Feng “Security Analysis and Improvement for Kerberos Based on Dynamic Password and Diffile-Hellman Algorithm” Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, China.

Ahlam Ansari, Tahir Ansari, Faizan Hingora and Mudassir Ansari, "A Secure Cloud Server Using Raspberry Pi and Kerberos Authentication Protocol," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.56-58, 2015.

This report consists of a literature review of papers dealing with visual recognition using different techniques. Several papers that brought contribution to this field are summarized, analysed and compared. Different papers uses different moreover similar concepts for image/object recognition and their work brought average results in this field. By using the novel concept of Object Bank (OB) very good progress over image/object recognition has been done over recent years. Here we are stipulating the concept of Object Bank for high level visual recognition by using different Support Vector Machine (SVM) classifiers.

Object Bank, Image recognition, Image representation, SVM, Semantic information, Feature extraction, Maximum Entropy

[1]. Felzenszwalb, P., Girshick, R., McAllester, D., & Ramanan, "Object detection with discriminatively trained part based models". Journal of Artificial Intelligence Research, 29, 2007.
[2]. Ferrari, V.,&Zisserman. "Learning visual attributes". In NIPS. ,2007.
[3]. Zhu, L., Chen, Y., & Yuille,. "Unsupervised learning of a probabilistic grammar for object detection and parsing". Advances in neural information processing systems, 19, 1617. 2007.
[4]. Fei-Fei, L., Fergus,R.,&Torralba, A. "Recognizing and learning object categories", 2007.
[5]. Somprasertsri, G.; Lalitrojwong, P., "A maximum entropy model for product feature extraction in online customer reviews," Cybernetics and Intelligent Systems, 2008 IEEE Conference on , vol., no., pp.575,580, 21-24 Sept. 2008.
[6]. Kittikhun Meethongjan, Dzulkifli Mohamad "Maximum Entropy-based Thresholding algorithm for Face image segmentation".,2009.
[7]. Lampert, C. H., Nickisch, H., & Harmeling, S. "Learning to detect unseen by between-class attribute transfer". In CVPR, 2009.
[8]. Felzenszwalb, Girschick, McAllester, "Cascade Object Detection with Deformable Part Models".,2009.
[9]. Torresani, L., Szummer, M., & Fitzgibbon, A. "Efficient object category recognition using classemes". In ECCV.,2010.
[10]. Farhadi, A., Endres, I., & Hoiem, D. "Attribute-centric recognition for cross-category generalization". In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, (pp. 2352–2359). New York: IEEE.
[11]. Torresani, L., Szummer, M., & Fitzgibbon, A. "Efficient object category recognition using classemes". In ECCV.,2010.
[12]. Song, Z., Chen, Q., Huang, Z., Hua, Y., & Yan, S. "Contextualizing object detection and classification". In IEEE Conference on Computer Vision and Pattern Recognition (CVPR).,2011.
[13]. Dixit, M., Rasiwasia, N., & Vasconcelos, N. "Adapted Gaussian models for image classification". In CVPR, 2011.
[14]. Hossein Mobahi, Shankar R. Rao, Allen Y. Yang, Shankar S. Sastry, Yi Ma. "Segmentation of Natural Images by Texture and Boundary Compression". IJCV 2011.
[15]. Tim Althoff, Hyun Oh Song, Trevor Darrell. "Detection Bank: An Object Detection Based Video Representation for Multimedia Event Recognition"., 2011.
[16]. Michael Stark, Robert Patrick, James roch:"Fine-Grained Categorization for 3D Scene understanding" (BMVC 2012).
[17]. Li-Jia Li • Hao Su • Yongwhan Lim • Li Fei-Fei. "Object Bank: An Object-Level Image Representation for High-Level Visual Recognition". International Journal of Computer Vision, 2013.(pp. 630-660).
[18]. Hao su, Li-Jia Li, Yongwhan Lim, Li Fei-Fei:"Objects as Attributes for Scene Classification". ICCV, 2013.
[19]. Hetal J. Vala, Astha Baxi.: "A Review on Otsu Image Segmentation Algorithm": IJARCET, Volume 2, Issue 2, February 2013.
[20]. Ahmed Bassiouny, Motaz El-Saban:"Semantic Segmentation As Image Representation For Scene Recognition". Microsoft Advanced Technology Labs, Cairo, Egypt.
[21]. en.wikipedia.org/wiki/Support_vector_machine.
[22]. Digital Image Processing 2nd Edition by Gonzalez and Woods Pearson Publications.

Amrit Kumar Sharma, "A Review on: Visual Recognition Through Object Bank," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.59-62, 2015.

Lately, the research world has been aggressively exploring techniques and means to develop effective systems for Human Computer Interaction (HCI). Humans interact with computers using different interfaces like Graphical User Interfaces (GUI), Voice User Interfaces (VUI) and Gestures. Among these methods gesture interaction provides a very handy means of dealing with computers. The goal of gesture recognition is to recognize and differentiate the human gestures and make use of these gestures for applications in specific areas. The aim of this paper is to provide a survey on numerous techniques employed for gesture recognition, and to outline preliminary system architecture for implementation.

Gesture Recognition, Segmentation, Feature Extraction, Gesture Classification

[1] Wikipedia, "Gesture recognition," [Online]. Available: http://en.m.wikipedia.org/wiki/Gesture_recognition.
[2] V. I. Pavlovic, R. Sharma and T. S.Huang, "Visual Interpretation of Hand Gestures for Human-Computer Interaction : A Review," IEEE Transactions on Pattern Analysis And machine Intelligence, vol. 19, pp. 677 - 695, 1997.
[3] M. C. A. Burande, P. R. M. Tugnayat and Prof.Dr. Nitin K. Choudhary, "Advanced Recognition Techniques for Human Computer Interaction," IEEE, vol. 2, pp. 480-483, 2010.
[4] S.Mitra and T.Acharya, "Data Mining:Multimedia, Soft Computing and Bioinformatics".
[5] R. Y.Wang and J. Popovic, "Real-time Hand Tracking with a Color Glove," ACM Transactions on Graphics, 2009.
[6] Z. Ren, J. Meng, J. Yuan and Z. Zhang, "Robust Hand Gesture Recognition with Kinect Sensor," ACM, 2011.
[7] A. S. Ghotkar and G. K. Kharate, "Vision based Real Time Hand Gesture Recognition Techniques for Human Computer Interaction," International Journal of Computer Applications, vol. 70, no. 16, pp. 1-6, 2013.
[8] A. S. Ghotkar and G. K. Kharate, "Hand Segmentation Techniques to Hand Gesture Recognition for Natural Human Computer Interaction," International Journal of Human Computer Interaction, vol. 3, no. 1, pp. 15-25, 2012.
[9] J. Alon, V. Athitsos, Q. Yuan and S. Sclaroff, "A Unified Framework for Gesture Recognition and Spatiotemporal Gesture Segmentation," IEEE Transactions of Pattern Analysis and Machine Intelligence, 2009.
[10] S. S. Al-amri, N. Kalyankar and K. S.D, "Image Segmentation by Using Thershold Techniques," JOURNAL OF COMPUTING, vol. 2, no. 5, pp. 83-86, 2010.
[11] A. Dhawan and V. Honrao, "Implementation of Hand Detection based Techniques for Human Computer Interaction Human Computer Interaction," International Journal of Computer Applications, vol. 72, no. 17, pp. 6-13, 2013.
[12] N. A. Ibraheem, R. Z. Khan and M. M. Hasan, "Comparative Study of Skin Color based Segmentation Techniques," International Journal of Applied Information Systems, vol. 5, no. 10, pp. 25-38, 2013.
[13] C. M. Sharma and S. Saxena, "A Context-aware Approach for Detecting Skin Colored Pixels in Images," International Journal of Computer Applications, vol. 71, no. 17, pp. 8-13, 2013.
[14] P. Campadelli, F. Cusmai and R. Lanzarotti, "A Color-Based Method For Face Detection".
[15] Y. Sriboonruang, P. Kumhom and K. Chamnongthai, "Visual Hand Gesture Interface for Computer Board Game Control," IEEE, 2006.
[16] J. Zhang, F. Zhang and M. Ito, "Image processing based remote control with robot arm simulator," SICE, pp. 2344 - 2348, 2009.
[17] D. K. Ghosh and S. Ari, "A Static Hand Gesture Recognition Algorithm Using K-Mean Radial Basis Function Neural Network," IEEE, 2011.
[18] M. Bhuyan, D. Ghosh and P. Bora, "Designing of Human Computer Interactive Platform for Robotic Applications".
[19] J. L. Raheja, R. Shyam and P. B. P. Umesh Kumar, "Real-time Robotic Hand Control Using Hand Gestures," IEEE, pp. 12-16, 2010.
[20] P.V.V.Kishore and P. Kumar, "Segment, Track, Extract, Recognize and Convert Sign Language Videos to Voice/Text," International Journal of Advanced Computer Science and Applications, vol. 3, no. 6, pp. 35-47, 2012.
[21] L. Dung and M. Mizukawa, "Fast Hand Feature Extraction Based on Connected Component Labeling, Distance Transform and Hough Transform," Journal of Robotics and Mechatronics, vol. 21, no. 6, 2009.
[22] Y. Sriboonruang, P. Kumhom and K. Chamnongthai, "Visual Hand Gesture Interface for Computer Board," IEEE, 2006.
[23] T.-N. Nguyen, H.-H. Huynh and J. Meunier, "Static Hand Gesture Recognition Using PCA Combined with ANN," Journal of Automation and Control Engineering, vol. 3, no. 1, 2015.
[24] R. Gopalan and B. Dariush, "Toward a vision based hand gesture interface for robotic grasping," IEEE, pp. 1452-1459, 2009.
[25] H. A. Jalab, "Static Hand Gesture Recognition for Human Computer Interaction," Information Technology Journal, pp. 1265 - 1271, 2012.
[26] S. Gupta, J. Jaafar, W. F. w. Ahmad and A. Bansal, "Feature Extraction Using MFCC," Signal & Image Processing : An International Journal, vol. 4, no. 4, pp. 101-108, 2013.
[27] H. Hasan and S.Abdul-Kareem, "Static Hand Gesture Recognition using Neural Networks," Springer, 2012.
[28] H. Q. J. G. Jinwen Wei and Y. C. Jinwen Wei, "The Hand Shape Recognition of Human Computer Interaction with Artificial Neural Network," IEEE, p. 3809, 2009.
[29] J. L. Raheja, R. Shyam, U. Kumar and P. B. Prasad, "Real-Time Robotic Hand Control using Hand Gestures," Second International Conference on Machine Learning and Computing- IEEE, pp. 13-16, 2010.
[30] P. P. Dali and H. C.R, "Interpretation of Hand Gestures Using Neural Networks:A Review," International Journal of Computer Science and Information Technologies, pp. 1111-1114, 2015.
[31] V. S. Kulkarni and S.D.Lokhande, "Appearance Based Recognition of American Sign Language Using Gesture Segmentation," International Journal on Computer Science and Engineering, pp. 560-565, 2010.
[32] P. P. D and H. C.R, "Interpretation of Hand Gestures Using Neural Networks:A Review," International Journal of Computer Science and Information Technologies,, vol. 6, no. 2, pp. 1111-1114, 2015.

Priyanka Parvathy Dali and Hema C.R, "Vision Based Gesture Recognition System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.63-67, 2015.

Earlier, we make friendship with our neighbors, colleagues based on geographical area. This is the traditional method of making friends. With the evolution in Internet, a social network comes in existence for connecting with distant people and friends for communicating with them. Existing social network uses social graph and pre-existing relationship between users for recommending friends to user. Such as Facebook uses mutual friends that is friends of friend for recommending friends. This may not be most appropriate method for recommending friends and selection of those by user in real life. We are presenting Friendbook, a social network based friend recommender system, which is based on semantic-based friend recommendation for friend recommendation. Friendbook recommends friends based on users life-style not on social graph. Friendbook discovers the life-style of user, using the user centric data, by taking the advantage sensor rich smart-phones. We model user’s daily life as a life document and extract his/her daily activities inspired by text mining through life document by using Latent Dirichlet Allocation Algorithm. We proposed similarity metric to measure the similarity of life styles between users. Friend matching graph is constructed based on impact ranking which is calculated in terms of users’ life style. Friendbook returns a list of people with highest recommendation scores to query user. We also integrate feedback mechanism with Friendbook to improve the accuracy of recommendation. The result reflects recommendations preferences of users in choosing friends accurately.

Life Styles, Life Document, Recommendations, Impact Ranking

[1] ZhiboWang, Student Member, IEEE, Jilong Liao, Qing Cao, Member, IEEE, Hairong Qi, Senior Member, IEEE, and Zhi Wang, Member, IEEE “Friendbook: A Semantic-based Friend Recommendation System for Social Networks", IEEE 2014 .
[2] Christian Vollmer, Horst-Michael Gross, and Julian P. Eggert. Learning Features for Activity Recognition with Shift-invariant Sparse Coding, Proc. 23. Int. Conf. on Artificial Neural Networks (ICANN 2013), Sofia, Bulgaria, LNCS 8131, pp. 367-374, Springer 2013.
[3] A. Giddens. Modernity and Self-identity: Self and Society in the late Modern Age. Stanford Univ Pr, 1991.

Mukesh C. Warade, Mustak B. Bagwan, Suraj M. Marathe and Sweta Pandey, "Social Network Based Friend Recommender System," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.68-70, 2015.

Master data management helps companies to handle and maintain consistent and complete information of master data. Master data orchestration and knowledge extraction are the two biggest problem related to master data. Due to these two predicaments master data management is necessary for effective organization of master data. Master data is type of organizational data but not meta data and use for decision making and business intelligence. Master data in an enterprise is structured data and flows between different component of an enterprise under one’s control. Common master data domains include person (customers, supplier, and controller), products (item), and location and other specific data. As an outcome of MDM, organizations have higher quality product, reliable data, consistent master data records and probable dedicated and interested consumer. This paper presents an overview of master data’s content, scope, sample, advantages, master data management process, tool and techniques and challenges.

Master Data, Master Data Management, People, Place, and Price

[1] A Cleven and F Wortmann, “Uncovering four strategies to Approach Master Data Management”, IEEE 43th HICSS, Jan 2010, pp 1-10.
[2] Master Data Management An Oracle White Paper September 2011
[3] J Kopcke, Master Data Management: Old Problem with a New Urgency
[4] Master Data Management outside of SAP –Standards based MDM for PLMT orbjörn Holm, Eurostep.
[5] A. Dreibelbis, E. Hechler, I. Milman, M. Oberhofer, P.van Run, and D. Wolfson, Enterprise Master Data Management: An SOA Approach to Managing Core Information, IBM Press, 2008.
[6] D. Loshin, Master Data Management, Morgan
[1] Kaufmann, 2008.
[7] R Heikkinen and S Pekkola, “Establishing an Organization’s Master Data Management Function: A Stepwise Approach”, IEEE 46th HICSS, Jan 2013, pp 4719-4728
[8] Rittman, “Introduction to Master Data Management” Mark
[9] Master Data Management (MDM Mastering the Information Ocean) Capgemini white paper.
[10] C. Beasty: "The Master Piece", CRM Magazine, Vol. 12,2008, pp. 39-42.
[11] A. Berson, and L. Dubov, Master Data Management and Customer Data Integration for a Global Enterprise, Mcgraw-Hill, 2007.
[12] A. Zornes: "The Fourth Generation of MDM", DM Review, Vol. 17, 2007, pp. 26-37.
[13] C Imhoff and C White, “Master Data Management: Creating a Single View of the Business”, Copyright 2006, Powell Media, LLC, Intelligent Solutions and BI Research All rights reserved.
[14] J kokemüller, A weisbecker, “master data management: products and research”
[15] C White, “Using Master Data in Business Intelligence”, BI Research March 2007 Sponsored by SAP
[16] “Building the Business Case for Master Data Management”, An Oracle Thought Leadership White Paper April 2009.
[17] “Master Data Management” Book by David Loshin
[18] http://www.uniserv.com/en/data management/master-data-management/
[19] J Kokemüller, A Weisbecker, “ Master Data Management: Products and Research “.
[20] Walter, P., Werth, D. Loos, P. "Peer-to-Peer-Based Model-Management for Cross-Organizational Business Processes", Los Alamitos, CA, USA, June, 2006, pp. 255-260
[21] http://pic.dhe.ibm.com/infocenter/mdm/v11r0/index.jsp?topic=%2Fcom.ibm.mdmhs.overview.doc%2Farchitecture.html
[22] http://help.sap.com/saphelp_nwmdm71/helpdata/en/43/D7AED5058201B4E10000000A11466F/frameset.htm

Saravjeet Singh and Jaiteg Singh, "Master Data Management: A Review," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.71-76, 2015.

Life time of a sensor network is dependent on the power of the battery. In some applications scenarios, it is not possible to replace the dead batteries. Many researches are focusing on designing protocols and algorithms for wireless sensor networks to reduce the energy expenditure. Clustering is one of the best approaches to reduce the energy consumption. The Energy consumption is effected by the distance between sensors. In this Paper, we conducted a survey on the various approaches of clustering to study and design an energy efficient algorithm based on distance to make the clusters.

Energy Consumption; Distance; Hierarchical Clustering

[1] Jenq-Shiou Leu, Tung-Hung Chiang , Min-Chieh Yu, and Kuan-Wu Su, “Energy Efficient Clustering Scheme For Prolonging The Lifetime Of Wireless Sensor Network With Isolated Nodes,” Doi 10.1109/Lcomm.2014.2379715, IEEE Communications Letters.
[2] Ali Chamam and Samuel Pierre, “On The Planning Of Wireless Sensor Networks: Energy-Efficient Clustering Under The Joint Routing And Coverage Constraint,” IEEE Transactions On Mobile Computing, Volume-8, Issue-8, August 2009.
[3] Mehdi Tarhani, Yousef S. Kavian, and Saman Siavoshi, “Seech: Scalable Energy Efficient Clustering Hierarchy Protocol in Wireless Sensor Networks,” IEEE Sensors Journal, Volume-14, Issue-11, November 2014.
[4] Ashok Kumar, Vinod Kumar and Narottam Chand, “Energy Efficient Clustering Scheme for Wireless Sensor Networks,” IJACSA, Volume-3, Issue-5, 2011.
[5] Yan Jin ,Ling Wang ,Yoohwan Kim and Xiao-Zong Yang, “Energy Efficient Non-Uniform Clustering Division Scheme In WSN,” Wireless Pers Commun (2008) 45, Springer Science+Business Media, ISBN: S11277-007-9370-4, Page No (31-43), Llc. 2007.
[6] Sungryoul Lee ,Han Choe , Byoungchang Park,Yukyoung Song and Chong-kwon Kim, “Luca: An Energy-Efficient Unequal Clustering Algorithm Using Location Information For Wireless Sensor Networks,” Wireless Pers Commun (2011) 56, Springer Science+Business Media, ISBN: S11277-009-9842-9, Page No (715-731), Llc. 2009.
[7] Nojeong Heo and Pramod K. Varshney,“An Intelligent Deployment and Clustering Algorithm for A Distributed Mobile Sensor Network”, IEEE, ISBN: 0-7803-7952-7, 2003.
[8] Shio Kumar Singh, M P Singh , and D K Singh., “Energy Efficient Homogenous Clustering Algorithm for Wireless Sensor Networks,” International Journal of Wireless & Mobile Networks (IJWMN), Volume-2, Issue-3, August 2010.
[9] Rumpa Mukherjee, Arindom Mukherjee, “A Survey on Different Approaches for Energy Conservation in Wireless Sensor Networks,” International Journal of Advanced Computer Research (ISSN (Print): 2249-7277 ISSN (Online): 2277-7970) Volume-3, Issue-1, 8 March 2013.
[10] Dali Wei, Yichao Jin, Serdar Vural, Klaus Moessner and Rahim Tafazolli, “Energy-Efficient Clustering Solution For WSN,” IEEE Transactions On Wireless Communications, Volume-10, Issue-11, November 2011.
[11] Surender Kumar, Manish Prateek and Bharat Bhushan, “Energy Efficient (EECP) Clustered Protocol for Heterogeneous Wireless Sensor Network,” IJARCSSE, Volume- 3, Issue- 7, July 2013.
[12 Prakashgoud Patil and Umakant P Kulkarni, “Energy Efficient Aggregation with sink placement for wireless sensor networks,” Ad hoc, Sensor & Ubiquitous Computing (IJASUC), Volume-4, Issue.-, April 2013.
[13] S.Taruna and Sakshi Shringi, “Routing Protocol based on clustering for Prolonging Network Lifetime in Heterogeneous WSNs,” IJRARCSSE, volume-4, Issue-4,april 2013.
[14] Stefanos A. Nikolidakis, Dionisis Kandris, Dimitrios D. Vergados and Christos Douligeris,” Energy Efficient Routing in Wireless Sensor Networks Through Balanced Clustering,” Algorithms 2013, ISSN 1999-4893, Volume-6, Page No (29-42), 2013.
[15] Mansoor-uz-Zafar Dawood, Noor Zaman,Abdul, Raouf Khan and Mohammad Salih, “Designing of energy efficient routing protocol for Wireless Sensor Network Using Location Aware Algorithm,” Journal of Information & Communication Technology, Volume-3,Issue-. 2, 56-70, 2009.
[16] Zhang H. and Shen H., “Balancing Energy Consumption To Maximize Network Lifetime In Data-Gathering Sensor Networks,” IEEE Transactions On Parallel And Distributed Systems, Volume- 2, Issue-10, October 2009.
[17] Animesh Shrivastava and Singh Rajawat,” An Implementation of Hybrid Genetic Algorithm for Clustering based Data for Web Recommendation System,”International journal of computer science and engineering, Volume-2, Issue -4, April 2014

Sakshi Wadhwa and Harpreet Kaur, "Clustering Based Energy Conservation Techniques for Wireless Sensor Network: A Survey," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.77-82, 2015.

The Quality of Service provided by any network is affected by some specific factors. An efficient network achieves its goal by solving all the challenges and satisfies the factors. Many researchers develop several approaches, protocols, techniques, algorithms and methods to improve the performance of network. In this paper, we have conducted a survey to understand the problems of sensor network. The Swarm Optimization Algorithm is reviewed to solve these problems. We analyze that the particle swarm optimization is a capable algorithm to optimize each and every NP hard problems and develops an efficient network.

Performance, Quality of Service, Swarm Optimization Algorithm, Wireless Sensor Network

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[2] Vahe A., Mehdi D., Samaneh H. and Ghazanfari M. N., “PSO Based Node Placement Optimization for Wireless Sensor Networks,” IEEE, Islamic Azad University Tehran, Iran, ISBN: 978-1-4244-8605-2, 2011.
[3] Anthony C. and Gerry D., "An Off-The-Shelf PSO," in Workshop Particle Swarm Optimization, Indianapolis, 2001.
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[5] El-Ghazali T., “Metaheuristics-From Design to Implementation,” John Wiley and Sons publications, Inc., Hoboken, Second (2nd) Edition, ISBN: 978-0-470-27858-1, Page No (95-111), 2009.
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[10] Pradhan P. M., Baghel V. and Bernard M., “Energy Efficient WSN using Multi-Objective Particle Swarm Optimization (MPSO),” IEEE International Advance Computing Conference, Patiala, India, 2009.
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[14] Natalizio E., Guerriero F. and Loscrí V., “Particle Swarm Optimization Schemes Based on Consensus for Wireless Sensor Networks,” 15th ACM International Conference on Modeling, Analysis and Simulation of WMSN, Paphos, Cyprus, 2012.
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Jagmeet Singh and Harpreet Kaur, "Performance of Particle Swarm Optimization for Sensor Networks: A Survey," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.83-87, 2015.

With the growth of technologies on one side the challenges increased gradually on other sides. When a new technology is introduced there will be a shortage of resource talents in understanding the insight view to take benefits of one from that. There are some very serious challenges the construction industries facing that are motivating new approaches to how we design, operate, and maintain buildings and infrastructure. The new technologies are designed to address challenges in the construction industries especially in both operational and maintenance sectors. Big data is a tool for transformation of manual to automated process uses vast size of data or information that it exceeds the capacity of traditional data management technologies. Inadequate insight knowledge (IK) about generating large datasets is one of the most important constraints in any organizations. This paper focus mainly on construction domain rather than other domains including retail, health care, manufacturing, finance and housing, etc. and their importance of big data technology on construction sites and self evaluating their needs to reduce/mange risks, high returns, intensity, knowledge adequacy using two indices: potential values and ease of capture(EOC).

Insight Knowledge; Potential Values;Ease of Capture

[1] Big Data and the Construction Industry, leopard construction companies leveraging big data analytics, February 5, 2015.
[2] Big data infrastructure covers guiyang, chinadaily.com.cn, 2014-12-17.
[3] Big Data, Predictive Analytics, and Data Visualization in The Construction Engineering December 2013
[4] Perdomo, J., Cavallin, H. (2014) Transforming Building Design Through Integrated Project Delivery in Architectural and Engineering Education, to appear in Proceedings of the ASCE 2014 Construction Research Congress
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[6] Construction Industry Trends, Technology for Construction, www.stratusvue.com, 27 April 2014
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[8] Big data insight, Bigdata.cioreview.com, Aug 19, 2014
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[12] Constructions Big data problem, randy deutsch, January 14, 2014
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[15] Three Reasons Why Construction Companies Fail When Trying to Predict Injuries, Griffin Schultz, and August 2014
[16] Three Ways to Use Big Data Analytics to Your Advantage, proest.com, Mar 12, 2015

J Lokesh, S Sakthivel and T Rajesh, "Big Data in Self Evaluating Construction Domain Using EOC Indices," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.88-92, 2015.

The major disadvantage of Page Rank is that it favors the older pages, because a new page, even a very good one will not have many links unless it is a part of an existing site. Page Rank is a global measure and is query independent. The Rank sinks problem occurs when in a network pages get in infinite link cycles. To improve the search results Topic-Sensitive Page Rank also referred to as TSPR is a context-sensitive ranking algorithm for web search developed by Taher Haveliwala. The disadvantage with topic sensitive page rank algorithm is it uses basis set is small that is it uses 16 top level categories. So we propose to improve topic sensitive page rank algorithm with best set of basis topics. Here we propose to use fine grained set of topics mainly categorized into four categories and sub categories and so on. Almost all paths end at maximum sixth level. This method results efficient results compared page rank and context sensitive topic sensitive page rank algorithms.

Page Rank; Context Sensitive Page Rank ;Biasing; Rank Sink; Link Cycles

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L. Lakshmi and P Bhakara Reddy, "Dynamic Navigation of Query Results Using Biased Topic Sensitive Page Rank Algorithm," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.93-97, 2015.

This paper presents a novel approach used to identify and analyze the features of keystroke dynamics. Keystroke dynamics is a authentication technique which aims to identify the person based on the behavioral characteristic (Typing Rhythms). The keystroke data like Duration, latency and digraph are measured using statistical techniques. The user keystroke patterns are collected and further it is analyzed to identify the quality features that will be given to feature subset selection for selecting the dominant features. The extracted features will be given to the feature subset selection for identifying dominant features.

Biometrics,Feature Extraction , Keystroke Dynamics , Latency, Digraph

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N.Krishnaraj, "Feature Extraction Techniques in Keystroke Dynamics for Securing Personal Devices," International Journal of Computer Sciences and Engineering, Vol.3, Issue.3, pp.98-101, 2015.