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Data sets used to fuel the recently popular concept of �business intelligence� are becoming increasingly large. Conventional database management software is no longer efficient enough however; parallel database management systems and massive data-scale processing systems like MapReduce indeed look promising. Although, MapReduce is a good option, it is difficult to work with, as the programmer would have to think at the mapper and reducer level. In this paper, we present a simple yet efficient way to mine useful information where a program can be written as a series of steps. We have queried a supercomputer log file using Apache�s Hadoop and PIG, obtained results as to when and why the supercomputer had failed and compared these results to that of a traditional program.

Big Data, Parallel Processing, Hadoop, MapReduce, Data Mining, Business Intelligence, PIG, Log file analysis, Supercomputer

[1]. T. White, Hadoop: The Definitive Guide. Yahoo Press,2010.
[2]. Chuck Lam,Pig:Hadoop in Action.
[3]. J. Dean and S. Ghemawat, �Mapreduce: Simplified Data Processing on Large Clusters,� Comm. of the ACM,Vol. 51, no. 1, pp. 107�113, 2008.
[4]. C. Olston, B. Reed, U. Srivastava, R. Kumar, and A. Tomkins, �Pig latin: A Not-So-Foreign Language for Data Processing,� Proc. of the 2008 ACM SIGMOD international conferenceon Management of Data, 2008, pp. 1099�1110.
[5]. Thomas Reidemeister, Mohammad Ahmad Munawar, Miao Jiang, Paul A.S.Ward, "Diagnosis of Recurrent Faults using Log Files," Proc. of the 2009 Conference of the Center for Advanced Studies on Collaborative Research,November 2009, pp. 12-23 .
[6]. Apache. Hadoop: Open-source implementation of MapReduce. http://hadoop.apache.org.
[7]. Apache. Pig: High-level data ow system for Hadoop. http://www.pig.apache.org
[8]. Michael Cardosa, Chenyu Wang, Anshuman Nangia, Abhishek Chandra, Jon Weissman,"Exploring MapReduce efficiency with highly-distributed data" Proc. of the second international workshop on MapReduce and its applications",June 2011, pp. 27-34.
[9]. H.-C. Yang, A. Dasdan, R.-L. Hsiao, and D. S. Parker, �Map-reducemerge: simplified relational data processing on large clusters,�proc. of the SIGMOD Conference, 2007, pp. 1029�1040.
[10]. A. Gates, O. Natkovich, S. Chopra, P. Kamath, S. Narayanam, C. Olston, B. Reed, S. Srinivasan, and U. Srivastava., �Building a High-Level Dataflow System on Top of Map-Reduce: The Pig Experience.� Proc. of the VLDB Endowment, vol. 2,no. 2, 2009.
[11]. A tutorial on pig: http://www.pig-tutorial.blogspot.in/

M.S. Palle, K. Jyothsna, B. Anusha, "Analyzing Failures of a Semi-Structured Supercomputer Log File Efficiently by Using PIG on Hadoop," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.1-5, 2014.

Automatic recognition of human faces is considered to be a challenging task despite significant progress in both computer vision and pattern recognition. A facial recognition system is a computer application of automatically identifying or verifying a person from a digital image or a video frame from a video source. Often, variations such as in-depth pose changes or illumination variations increase the dissimilarity of two face images of the same person more than the dissimilarity of different persons� face images. In this paper, we have proposed a novel method for three dimensional (3D) face recognition using Radon transform and Symbolic Factorial Discriminant Analysis (Symbolic FDA) is proposed. In this method, the Symbolic Factorial Discriminant Analysis (Symbolic FDA) based feature computation takes into account of face image variations to a larger extent and has the advantage of dimensionality reduction. The experimental results have yielded 99.80% recognition performance with reduced computational cost, which compares well with other state-of-the-art methods.

3D face recognition, Range image, Radon transform, Symbolic factorial discriminant analysis (Symbolic FDA)

[1] Zhao, W.; Chellappa, R.; Phillips, P. J. & Rosenfeld, A. �Face recognition: A literature survey�, ACM Comput. Surv., ACM, (2003), No. 35, pp:399-458.
[2] Bowyer, K. W.; Chang, K. & Flynn, P. �A survey of approaches and challenges in 3D and multi-modal 3D + 2D face recognition�, Comput. Vis. Image Underst., Elsevier Science Inc., (2006), No.101, pp:1-15.
[3] Chang, K. I.; Bowyer, K. W. & Flynn, P. J. �An Evaluation of Multimodal 2D+3D Face Biometrics�, IEEE Trans. Pattern Anal. Mach. Intell., IEEE Computer Society, (2005), No.27, pp:619-624.
[4] Abate, A. F.; Nappi, M.; Riccio, D. &Sabatino, G., �2D and 3D face recognition: A survey�, Pattern Recogn. Lett., Elsevier Science Inc., (2007), No.28, 1885-1906.
[5] Kachare, N. B. &Inamdar, V. S., �Survey of Face Recognition Techniques International�, Journal of Computer Applications, (2010), No.1, pp:29-33.
[6] Chellappa R. Wilson, C. S. S., �Human and machine recognition of faces: a survey�, (1995), No.83, pp:705-741.
[7] Chang, K.; Bowyer, K. & Bowyer, K., �Multimodal 2D and 3D biometrics for face recognition Analysis and Modeling of Faces and Gestures�, 2003. AMFG 2003. IEEE International Workshop on, (2003), pp:187-194.
[8] S. Gupta, M. K. Markey, A. C. Bovik, "Anthropometric 3D Face Recognition", International Journal of Computer Vision, (2010), Volume 90, No. 3, pp:331-349.
[9] S. Gupta, K. R. Castleman, M. K. Markey, A. C. Bovik, "Texas 3D Face Recognition Database", IEEE Southwest Symposium on Image Analysis and Interpretation, May (2010), pp: 97-100, Austin, TX.
[10] S. Gupta, K. R. Castleman, M. K. Markey, A. C. Bovik, "Texas 3D Face Recognition Database", URL: http://live.ece.utexas.edu/research/texas3dfr/index.htm.
[11] A. Savran, O. �eliktutan, A. Akyol, J. Trojanova, H. Dibeklioğlu, S. Esenlik, N. Bozkurt, C. Demirkır, E. Akag�nd�z, K. �alışkan, N.Aly�z, B.Sankur, İ. Ulusoy, L. Akarun, T. M. Sezgin, "3D Face Recognition Performance Under Adversorial Conditions", in Proc. eNTERFACE�07 Workshop on Multimodal Interfaces, Istanbul, Turkey, July 2007.
[12] N. Aly�z, B. G�kberk, H. Dibeklioğlu, L. Akarun, "Component-based Registration with Curvature Desciptors for Expression Insensitive 3D Face Recognition", 8th IEEE International Conference on Automatic Face and Gesture Recognition, Amsterdam, The Netherlands, September 2008.
[13] ChenghuaXu, Yunhong Wang, Tieniu Tan and Long Quan, Automatic 3D Face Recognition Combining Global Geometric Features with Local Shape Variation Information, Proc. The 6th IEEE International Conference on Automatic Face and Gesture Recognition (FG), pp.308-313, 2004.
[14] Deans, S. R., �The radon transform and some of its applications�, Dover Publications Incorporated, 2007, 295
[15] Alexander, A. &Ramm, G., �The Radon Transformation and Local Tomography�, CRC PressINC, 1996, 485 pages.
[16] Bock, H. H. Diday E. (Eds) : �Analysis of Symbolic Data�, Springer Verlag (2000).
[17] Hiremath P. S. and ManjunathHiremath, �Linear Discriminant Analysis for 3D Face Recognition Using Radon Transform�, Multimedia Processing, Communication and Computing Applications Lecture Notes in Electrical Engineering Volume 213, (2013), pp:103-113.
[18] Hiremath P. S. and ManjunathHiremath, �3D Face Recognition using Radon Transform and Symbolic LDA�, International Journal of Computer Applications (0975 - 8887), Volume 67 - No. 4, April 2013.
[19] T.C. Faltemier, K.W. Bowyer, P.J. Flynn, A region ensemble for 3-D face recognition, IEEE Transactions on Information Forensics and Security 3 (1) (2008) 62�73.
[20] T. Maurer, D. Guigonis, I. Maslov, B. Pesenti, A. Tsaregorodtsev, D. West, G. Medioni, Performance of geometrixActiveID TM 3D face recognition engine on the FRGC data, in: IEEE Workshop on Face Recognition Grand Challenge Experiments, 2005, pp. 154�160
[21] I.A. Kakadiaris, G. Passalis, G. Toderici, N. Murtuza, Y. Lu, N. Karampatziakis, T. Theoharis, 3D face recognition in the presence of facial expressions: an annotated deformable model approach, IEEE Transactions on Pattern Analysis and Machine Intelligence 29 (4) (2007) 640�649.
[22] M. H. usken, M. Brauckmann, S. Gehlen, C. Malsburg, Strategies and benefits of fusion of 2D and 3D face recognition, in: IEEE Workshop on Face Recognition Grand Challenge Experiments, 2005, pp. 174�181.
[23] A.S. Mian, M. Bennamoun, R. Owens, An efficient multimodal 2D�3D hybridapproach to automatic face recognition, IEEE Transactions on Pattern Analysisand Machine Intelligence 29 (11) (2007) 1927�1943.
[24] P.S.Hiremath and ManjunathHiremath, �3D Face Recognition Using Radon Transform and Factorial Discriminant Analysis (FDA)�, International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 7, July 2013, ISSN: 2277 128X. pp-1059-1066.

P.S. Hiremath, M. Hiremath, "Symbolic Factorial Discriminant Analysis for 3D Face Recognition," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.6-12, 2014.

In many wireless networks, each node has direct radio link to only a small number of other nodes, called its neighbors. Before efficient routing or other network-level activities are possible, nodes have to discover and identify their neighbors. This is called neighbor discovery. By neighbor discovery, every node tries to determine the set of nodes it can communicate within one wireless hop Fast and efficient discovery of all neighboring nodes by a node new to a neighborhood is Essential and critical to the deployment of wireless networks. The recently emerged technology Cognitive radio (CR) has a promising approach to improve the available spectrum utilization efficiency so as to meet the increased demand for wireless communication, which attempts to search for unused or underutilized channels in the spectrum by scanning the part of wireless spectrum.

Cognitive Radios, Neighbor Discovery, Heterogeneous Network

[1]. Neeraj Mittal, Yanyan Zeng, S.Venkatesan, R. Chandrasekaran, �Randomized Distributed Algorithms for Neighbor Discovery in Multi-Hop Multi-Channel Heterogeneous Wireless Networks� in IEEE 2011 31st International Conference on Distributed Computing Systems.
[2]. Yanyan Zeng, Neeraj Mittal, S. Venkatesan, R. Chandrasekaran �Fast Neighbor Discovery with Lightweight Termination Detection in Heterogeneous Cognitive Radio Networks� @2010 Ninth International Symposium on Parallel and Distributed Computing
[3]. S. Vasudevan, D. Towsley, D. Goeckel, and R. Khalili, �Neighbor Discovery in Wireless Networks and the Coupon Collector�s Problem,� in Proc. 15th ACM Annual International Conference on Mobile Computing and Networking (MobiCom), 2009, pp. 181
[4]. Mr. Pravin Khawse, Prof. S. P. Chhaware �Improved Distributed Heterogeneous Neighbor Discovery In Wireless Networks� in IJRSAT ISSN 2319-2690
[5]. D. Cabric, S. M. Mishra, D. Willkommen, R. W. Brodersen, and A. Wolisz, �A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum,� in Proc. 14th IST Mobile Wireless Communications Summit, Jun. 2005.
[6]. R. W. Broderson, A. Wolisz, D. Cabric, S. M. Mishra, and D. Willkomm, �CORVUS: A Cognitive Radio Approach for Usage of Virtual Unlicensed Spectrum,� available at http://bwrc.eecs.berkeley.edu/Research/MCMA/.
[7]. M. J. McGlynn and S. A. Borbash, �Birthday Protocols for Low Energy Deployment and Flexible Neighbor Discovery in Ad Hoc Wireless Networks,� in Proceedings of the ACM International Symposium on Mobile Ad Hoc Networking and Computing (MobiHoc), 2001.
[8]. S. Vasudevan, D. Towsley, D. Goeckel, and R. Khalili, �Neighbor Discovery in Wireless Networks and the Coupon Collector�s Problem,� in Proceedings of the 14th Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom), 2009.
[9]. Z. Zhang, �Performance of neighbor discovery algorithms in mobile ad hoc self-configuring networks with directional antennas,� in Proc. IEEE Military Communications Conference, vol. 5, pp. 3162�3168, Oct. 2005. D. D.
[10]. Lin and T. J. Lim, �Subspace-based active user identification for a collision-free slotted ad hoc network,� IEEE Trans. Commun., vol. 52, pp. 612�621, Apr. 2004.
[11]. D. Angelosante, E. Biglieri, and M. Lops, �A simple algorithm for neighbor discovery in wireless networks,� in Proc. IEEE Int�l Conf. Acoustics, Speech and Signal Processing, vol. 3, pp. 169�172, April 2007.
[12]. D. Angelosante, E. Biglieri, and M. Lops, �Neighbor discovery in wireless networks: A multiuser-detection.
[13]. Nasipuri A, Castaneda R, Das SR. Performance of multipath routing for on-demand protocols in mobile ad hoc networks. ACM/Kluwer Mobile Networks and Applications.

P. Khawse, S.P. Chhaware, "Neighbor Discovery in Distributed Heterogeneous Wireless Networks using Cognitive Radio," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.13-17, 2014.

Content based Image/Video Retrieval system is a querying system that uses content as a key for the retrieval process. It is a difficult task to design an automatic retrieval system because real world images usually contain very complex objects and color information. In this paper, we discuss some of the key contributions in the current decade related to image retrieval and automated image annotation. We also discuss some of the key challenges involved in the adaptation of existing image retrieval techniques to build useful systems that can handle real-world data. so nowadays the content based image retrieval are becoming a source of exact and fast retrieval. In this paper the techniques of content based image retrieval are discussed, analyzed and compared. It also introduced the feature like visual descriptor and ontology methods. The suggestion for feature methodology`s to overcome the difficulties and improve the result performance. In this paper we provide an overview of approaches to CBIR. Major approaches to improving retrieval effectiveness via relevance feedback in text retrieval systems are discussed.

Inference Mechanisms, Multimedia Databases, Content Based Image Retrieval, Visual Descriptor, Ontology

[1]. Demner-Fushman D, Antani SK, Thoma GR, "Automatically Finding Images for Clinical Decision Support," Proceedings of Workshop on Data Mining in Medicine, 7th IEEE Intl Conf on DataMining 2007.
[2]. Demner-Fushman D, Antani SK, Simpson M, Thoma GR, "Combining Medical Domain Ontological Knowledge and Low-level Image Features for Multimedia Indexing," Proc. 2nd International "Language Resources for Content-Based Image Retrieval" Workshop (OntoImage 2008), part of 6th Language Resources and Evaluation Conference (LREC 2008). 20083.
[3]. Daekeun You , Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju , George R. Thoma,� Biomedical Article Retrieval Using Multimodal Features and Image Annotations in Region-based CBIR�.2010
[4]. Antani SK, Demner-Fushman D, Li J, Srinivasan BV, Thoma GR, "Exploring use of images in clinical articles for decision support in Evidence-Based Medicine," Proc. SPIE-IS&T Electronic Imaging. San Jose, CA. January 2008.
[5]. Deserno TM, Antani S, Long R, "Ontology of Gaps in Content-Based Image Retrieval," Journal of Digital Imaging. 2009
[6]. Antani S, Long R, Thoma GR. �Content-Based Image Retrieval for Large Biomedical Image Archives�, Medinfo,San Francisco, CAs, pp.829-33,2004.
[7]. Daekeun You, Emilia Apostolova, Sameer Antani, Dina Demner-Fushman, George R. Thoma,� Figure Content Analysis for Improved Biomedical Article Retrieval�, College of Computing and Digital Media, Vol. 7247, 2009.
[8]. Ryan McDonald,R. Scott Winters,Claire K. Ankuda,Joan A. Murphy,Amy E. Rogers,Fernando Pereira,Marc S. Greenblatt, and Peter S. White,� An Automated Procedure to Identify Biomedical Articles That Contain Cancer-Associated Gene Variants�, 2006
[9]. Manabu Torii, Hongfang Liu,� Classifier ensemble for biomedical document retrieval�,2008.
[10]. Beibei Cheng, Sameer Antani, R. Joe Stanley, Dina Demner-Fushman, George R. Thoma,� Automatic segmentation of subfigure image panels for multimodal biomedical document retrieval�,2011
[11]. Daekeun You , Sameer Antani, Dina Demner-Fushman, Md Mahmudur Rahman, Venu Govindaraju , George R. Thoma, � Automatic identification of ROI infigure images toward improving hybrid (text and image) biomedical document retrieval�,2011.
[12]. H. B. Kekre and Dhirendra Mishra, �Four Walsh Transform Sectors Feature Vectors for Image Retrieval from Image Databases," International Journal of Computer Science and Information Technologies, Vol. 1, No.2, pp 33-37, 2010.
[13]. Serge Belongie, Chad Carson, Hayit Greenspan and Jitendra Malik, �Color and Texture-Based Segmentation using EM and its Application to Content-Based Image Retrieval,� In Proc. of the Sixth International Conference on Computer Vision, Vol. 10, pp. 675-682, Jan 1998.
[14]. Jorma Laaksonen, Erkki Oja and Sami Brandt, �Statistical Shape Features in Content-Based Image Retrieval,� In Proc. Of the 15th International Conference on Pattern Recognition, Vol.2, pp. 1062 - 1065, Sep 2000.
[15]. Yong Rui, and Thomas S. Huang, "Image Retrieval: Current Techniques, Promising Directions, and Open Issues," Journal of Visual Communication and Image Representation, Vol. 10, pp. 39-62, Jan 1999.
[16]. Ying Liu, Dengsheng Zhang, Guojun Lu and Wei-Ying Ma, �A Survey of Content-Based Image Retrieval with High-level Semantics,� Journal of Pattern Recognition, Vol. 40, No. 1, pp. 262-282, Jan 2007.
[17]. Amit Jain, Ramanathan Muthuganapathy and Karthik Ramani, �Content-Based Image Retrieval Using Shape and Depth from an Engineering Database,� In Proc. of the Third International Conference on Advances in Visual Computing, Vol.2, pp. 255-264, 2007.
[18]. Yong Rui, Huang T.S, Ortega M and Mehrotra, "Relevance feedback: a power tool for interactive content-based image retrieval," IEEE Transactions on Circuits and Systems for Video Technology, Vol. 8, No. 5, pp. 644-655, Sep 1998.
[19]. B. Sathyabama, S.Mohana valli, S.Raju and V.Abhai Kumar, "Content Based Leaf Image Retrieval (CBLIR) Using Shape, Color and Texture Features,� Indian Journal of Computer Science and Engineering (IJCSE), Vol. 2, No. 2, May 2011
[20]. Ch. Srinivasa Rao, S.Srinivas Kumar and B.Chandra Mohan, �Content Based Image Retrieval Using Exact Lengendre Moments And Support Vector Machine," The International Journal of Multimedia & Its Applications, Vol. 2, No. 2, pp. 69- 79, May 2010

P. Rachana, S. Ranjitha, H.N. Suresh, "Stride Towards Developing an CBIR System Based on Image Annotations and Extensive Multimodal Feature Set," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.18-22, 2014.

Mobile ad hoc network (MANET) is a type of wireless ad hoc network. MANETs are very popular because of its infrastructure less network. Security is a major concern to provide protection between mobile nodes in hostile environment. Certificate revocation is one of the security components in mobile ad hoc networks (MANETs). Certificate revocation scheme, outperforms other techniques in terms of being able to quickly revoke attackers certificates and recover falsely accused certificates. The dynamic and wireless nature of mobile ad hoc network makes them more susceptible to many kinds of malicious attacks. Certificate revocation isolates the attackers from further participating in network activities. Certificates are issued and revoked by trusted party known as Certificate Authority. Certificate revocation invalidates the attacker�s certificate which is essential in keeping the network more secured. Sometimes malicious node will try to remove legitimate nodes from the network by falsely accusing them as attackers. Therefore, the issue of false accusation must be taken into account in designing certificate revocation mechanisms. Clustering approach is able to quickly revoke certificates of accused nodes and also to explicitly distinguish false accusations. Here Warned nodes will also be involved in certificate revocation to make the scheme more efficient and reliable. Cluster based routing protocol is used for revocation of certificates that provides more security in mobile ad hoc network.

Certificate Revocation, Trusted Authority, Cluster Head, Regions.

[1]. Yongguang Zhang, Wenke Lee, �Security in Mobile Ad-Hoc Networks� Springer-Verlag US., pp 249-268, 2005.
[2]. H. Yang, H. Luo, F. Ye, S. Lu, and L. Zhang, �Security in Mobile Ad Hoc Networks: Challenges and Solutions,� IEEE Wireless Comm., vol. 11, no. 1, pp. 38-47, Feb. 2004.
[3]. Weiliu, Nirwan Ansari,�cluster-based certificate revocation with vindication capability for mobile ad hoc networks,� ieee transactions on parallel and distributed systems, vol. 24, no. 2, february 2013.
[4]. Priti Rathi, Parikshit Mahalle, �Certificate Revocation in Mobile Ad Hoc Networks,� International Journal of Application or Innovation in Engineering & Management, Volume 2, Issue 1, January 2013.
[5]. H. Yang, J. Shu, X. Meng, and S. Lu, �SCAN: Self-Organized Network-Layer Security in Mobile Ad Hoc Networks, � IEEE J. Selected Areas in Comm., vol. 24, no. 2, pp. 261-273, Feb. 2006.
[6]. H. Luo, J. Kong, P. Zerfos, S. Lu, and L. Zhang, �URSA: Ubiquitous and Robust Access Control for Mobile Ad Hoc Networks,� IEEE/ACM Trans. Networking, vol. 12, no. 6, pp. 1049-1063, Oct. 2004.
[7]. LIU Xuening, YIN Hao, LIN Chuang, DU Changlai, �Efficient User Authentication and Key Management for Peer-to-Peer Live Streaming Systems�, tsinghua science and technology issnll1007-0214ll13/18llpp234-241 volume 14, number 2, april 2009.
W. Liu, H. Nishiyama, N. Ansari, and N. Kato, �A Study on Certificate Revocation in Mobile Ad Hoc Network,� Proc. IEEE Int�l Conf. Comm. (ICC), June 2011.

E.K. Neena, C. Balakrishnan, "Cluster Based Certificate Revocation of Attacker�s Nodes in MANET," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.23-27, 2014.

A popular and particularly efficient method for making a decision tree for classification from symbolic data is ID3 algorithm. Revised algorithms for numerical data have been proposed, some of which divide a numerical range into several intervals or fuzzy intervals. Their decision trees, however, are not easy to understand. A new version of ID3 algorithm to generate a understandable fuzzy decision tree using fuzzy sets defined by a user. In this paper, first the fuzzy decision tree is constructed for the given data and then fuzzy reasoning is applied in order to predict the class variable.

Fuzzy Technique

[1] J.R. Quinlan (1979}: �Discovering Rules by Induction from large collections of Examples�, in d.Michie (ed.): Expert Systems in the Micro Electronics Age, Edinburgh University Press.
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[5] H. Ichihashi (1993): �Tuning Fuzzy Rules by Neuro-Like Approach�, Journal of Japan Society for Fuzzy Theory and Systems, Vol.5, No.2, pp.191-203 (in Japanese).
[6] F. Kawachi and T. matsuura (1990): �Development of Expert System for Diagnosis by Gas in Oil and Its Evaluation in Practice Usage�, Technical Meeting on electrical Insulation Material (The Institute of Electrical Engineers of Japan), EIM-90-40 (In Japanese).

S.V.S.G. Devi, "Prediction Of A Class Variable In Classification Problem Using Fuzzy Inference Method," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.28-29, 2014.

Predicting the human location-based on the current tracking system is inefficient and costly due to data transmission where the cost is based on the usage of data. The aim is to develop an efficient child awareness system which is essential software that uses the asset tracking algorithm to predict the exact location of the child. This tracking system allows the parents to monitor their child�s mobile phone to trace the location and the mobile events thereby developing an efficient asset tracking solution to preserve valuable mobile assets by means of location and context awareness route learning techniques. All call events and text content can be seen by the parents and location can be traced (through the utilization of GPS and WPS). This system uses android based smart phones for triggering the software that sends alert to the parent when the child moves out of the geographical zone. The centralized server is used to store all notification and updated information of the child. This paper provides tracking approach to create awareness to the parents about their child�s behaviour.

Tracking Management, Asset preservation, Context Adaptation, GPS, Location awareness algorithm

[1] G.D. Bhanage, Y. Zhang, Y. Zhang, W. Trappe, and R.E. Howard, �RollCall : The Design for a Low-Cost and Power Efficient Active RFID Asset Tracking System,� Proc. Int�l Conf. Computer as a Tool,pp. 2521-2528, Sept. 2007.
[2] N. Rajendran, P. Kamal, D. Nayak, and S.A. Rabara, �WATS-SN: A Wireless Asset Tracking System Using Sensor Networks,� Proc.IEEE Int�l Conf. Personal Wireless Comm., pp. 237-243, Jan.2005.
[3] Yanying Gu, Anthony Lo, Senior Member, IEEE, and Ignas Niemegeers, � A Survey of Indoor Positioning Systems for Wireless Personal Networks� vol-3 , No.1, First Quarter, 2009.
[4] A.K. Dey and G.D. Abowd, �Towards a Better Understanding of Context and Context-Awareness,� Proc. Conf. on Human Factors in Computing Systems, Apr. 2000.
[5] G. Gehlen, F. Aijaz, S. Muhammad, and B. Walke, �A Rule Based Publish/Subscribe Context Dissemination Middleware,� Proc.Wireless Comm. and Networking Conf., pp. 2541-2546, Mar. 2007.
[6] G. Chen and D. Kotz, �Policy-Driven Data Dissemination for Context-Aware Applications,� Proc. IEEE Third Int�l Conf. Pervasive Computing and Comm., pp. 283-289, Mar. 2005.
[7] Dineshbalu Balakrishnan, Member, IEEE, and Amiya Nayak, Senior Member, IEEE, �An Efficient Approach for Mobile Asset Tracking using Contexts�, vol-23, No.2, Feb.2012.
[8] Meng-Hsun Tsai, Student Member, IEEE, Yi-Bing Lin, Fellow, IEEE, and Hsiao-Han Wang, �Active Location Reporting for Emergency Call in UMTS IP Multimedia Subsystem�, Vol-8,No.12, Dec-2009.
[9] D. Balakrishnan, A. Nayak, and P. Dhar, �Adaptive and Intelligent Route Learning for Mobile Assets Using Geo-Tracking and Context Profiles,� Proc. IEEE/IFIP Seventh Int�l Conf. Embedded and Ubiquitous Computing, Aug. 2009.
[10] D. Balakrishnan, A. Nayak, P. Dhar, and S. Kaul, �Efficient Geo-Tracking and Adaptive Routing of Mobile Assets,� Proc. IEEE 11th Int�l Conf. High Performance Computing and Comm., June 2009.
[11] G.H. Truelove, M.A. Foster, V.K. Kohli, and T.G. Raslear,�Real-Time Asset Tracking and Monitoring Using Low-Cost Cellular Networks,� Proc. IEEE/ASME Joint Rail Conf., pp. 315-318, Apr. 2006.

P. Padmavathy, C. Balakrishnan, "Smart Tracking of Human Location and Events Based on WPS using Android Technology," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.30-34, 2014.

In this paper we are discussing about cloud computing, their types, the problem faced while using cloud computing, and their solutions - mobile cloud computing, we also explain why we use mobile cloud computing ? What is mobile cloud computing? We also study the architecture of mobile cloud computing. In this paper we proposed new techniques how do backup and restore data from mobile to cloud. Here we proposed to apply some compression technique while backup and restore data from Smartphone to cloud and cloud to the Smartphone.

MCC, Cloud Computing, Femtocell, Cloud Model

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Mohd.A. Salam, A.C. Pandey, "Mobile Cloud Computing: Taking Web-Based Mobile Applications to the Cloud," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.35-42, 2014.

Sensors networks are capable of collecting an enormous amount of data over space and time .Often ,the ultimate objective is to �sample, store and forward � that is to sense the data , store it locally and ultimately forward it to accent almost and analyzed. Typical sensor nodes are wireless nodes with limited storage and computational power . Furthermore they are prone to �failure� by going out of wireless range, interference running out of battery etc. The sensor and storage nodes are distributed randomly in some region and cannot maintain routing tables or shared knowledge of network topology .Some nodes might disappear from the network due to failure or battery depletion overall this problem has occurred for overcoming this problem so many techniques are studied in this paper.

Distributed data collection algorithm ,Storage nodes ,Sensor nodes ,WSNs

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M.S. Kukade, K.N. Hande, "Analysis of Uniform Distribution of Storage Nodes in Wireless Sensor Network," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.43-47, 2014.

Recommender systems (RS) are introduced to help users with finding the desired information. Collaborative filtering (CF) approach is one of the most widely used techniques in recommender systems. Prediction is the main part of all recommender systems. In this paper we propose an enhanced prediction formula which could be employed in all CF-based methods. We used Resnick prediction formula as a base because it�s the most well-known and employed formula in CF-based RS. In the formula we have used not only the average of active user�s ratings, but also the collective average of similar users� ratings and the average of all ratings given to the target item. The results are promising and satisfying. We compared the results of enhanced prediction formula to the unenhanced version to verify the effectiveness of our proposed method.

Collaborative Filtering, Recommender Systems, Prediction Formula, Enhancement

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M. Hatami, S. Pashazadeh, "Enhancing Prediction in Collaborative Filtering-Based Recommender Systems," International Journal of Computer Sciences and Engineering, Vol.2, Issue.1, pp.48-51, 2014.