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Handwritten character recognition is a subject of importance in these days. Artificial Neural Networks (ANNs) are very much in demand in order to accomplish the task and that is why mass research is also going on in this field. This paper is an approach to identify handwritten characters by observing the gradient of the pixel densities at different segments of the handwritten characters. Different segments of the characters are observed carefully with the help of generated computer programs and rigorous experiments. It is found that the pixel densities at various segments of the character image matrix of different alphabets vary. The gradient of the pixel densities in these segments are used to form unique codes for different alphabets, which are found standard for different variations of same alphabet. Generation of unique codes actually extracts out common features of a particular alphabet written by one or more individuals at different instants of time. The unique codes formed for different alphabets are used to recognize different test alphabets. The method developed in this paper is a feature extraction technique which uses self organizing neural network, where supervised learning is not required.

Artificial Neural Networks; Pixel Density Gradient; Segments; Handwritten Character

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R.K. Mandal, N.R. Manna, "Handwritten English Character Recognition using Pixel Density Gradient Method," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.1-8, 2014.

Image filtering algorithms are applied on images to remove the noise that are either present in the image during capturing in to the image during transmission. These Gaussian noise, speckle noise and salt and pepper noise are occurred when captured the image. Five different image filtering algorithms are compared with three different noises types. The Peak Signal to Noise Ratio (PSNR) and Mean Square Error (MSE) are compared by filters. The median filter gives desirable results in two parameters for the three different noises.

Adaptive filter, Mean filter, Median filter, Kalman filter, Gaussian filter, Additive noise, Salt and pepper noise, Modified Spatial Median filter for Gaussian noise, Peak Signal to Noise Ratio and Mean Square Error.

[1]. Chitresh Saraswat, Amit Kumar,� An Efficient Automatic Attendance System using Fingerprint Verification Technique�, International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 264-269.
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Banupriya S, Karnan M, Sivakumar R, "Performance Analysis of Various Filtering Algorithm for Biometrics Image," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.9-13, 2014.

A Mobile ad hoc network is a collection of mobile nodes connected through wireless links forming an temporary network without fixed topology, centralized access point, infrastructure. In such a network, each node can act as a router and host simultaneously, it can move out or join in the network freely as required. Various routing protocols have been discussed so far in this paper a brief comparison of two reactive protocols DSR and AODV along with proactive protocol DSDV will be done. Detail study of the network performance such as throughput, packet delivery ratio, energy consumption. The simulations are carried out using NS-2 simulator. The results presented specify the importance in careful evaluation and implementation of routing protocols in an ad hoc environment.

MANETs, Ad -Hoc Network, Energy consumptions, Analysis, AODV, DSR, DSDV

[1]. C.E. Perkins and P. Bhagwat, �Highly dynamic destination sequenced distance vector routing (DSDV) for mobile computers,� Proc. of ACM SIGCOMM`94, 1994
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[10]. C. Siva Ram Murthy and B. S. Manoj, �Ad Hoc Wireless Networks: Architectures andProtocols,� Prentice Hall Communication Engineering and Emerging Technologies Series, 2004.
[11]. J. Broch, D Johnson and D. Maltz, �The Dynamic Source Routing Protocol for Mobile Ad Hoc Networks (DSR),� IETF Internet draft, 19 July 2004
[12]. A. Boukerche, �Performance Evaluation of Routing Protocols for Ad Hoc WirelessNetworks,� Mobile Networks and Applications, 2004
[13]. Arun Kumar B.R, LokanathaC.Reddy, Prakash S.Hiremath, IJCSNS International journal of Computer Science and Network Security, Vol.8 No.6, June 2008 .
[14]. Arun Kumar B. R., Lokanatha C. Reddy, Prakash.S. Hiremath, �mobile ad hoc networks: issues, research trends and experiments,� International Engineering &Technology (IETECH) Journal of Communication Techniques, Vol. 2, No. 2, 057-063, 2008.

N. Kishore, S. Singh, R. Dhir, "Energy Based Evaluation of Routing Protocol for MANETs," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.14-17, 2014.

Storage Area Network provides (SAN) matures larger and wide complexity to implement secure and robust communication between host and storage device in storage infrastructure to consolidate data and utilizing storage resources effectively. To adapt this virtualization in storage makes SAN to enhanced productivity, assets utilization and better management of storage infrastructure. Storage virtualization simplifies resource management by pooling and sharing resources for physical infrastructure to logical-view infrastructure by storage utilization and modifying storage without affecting an application�s availability and non-disruptive Data mitigation to access and storage while migration of data-exchange in progress.

Data Mining, Data Mitigation, RAID, SAN, NAS, Disruptive Data

[1]. Mark Blunden �IBM: Storage Networking Virtualization What�s it all about�, Milk Berx-Debeys, February, 2008 , ISBN 0738421367
[2]. G. Somasundaram �Information Storage and Management�, Alok Shrivastava EMC Education Services, Wiley, December 2009, Page No(212-215)
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[5]. Gunjan Khanna �Application Performance Management in Virtualized Server Environments�, Kirk Beaty, Gautam Kar, Andrzej Kochut IBM T.J. Watson Research Center, Network Operations and Management Symposium, 2006. NOMS 2006. 10th IEEE/IFIP, ISSN: 1542-1201, Page No( 373-381).
[6]. Adrian De Luca �Reinventing Storage Virtualization� Director, Storage Management & Data Protection Asia Pacific, Singapore, Hitachi Storage Solutions April, 2009, Media Buzz
[7]. Daniel A. Menasc�e �Virtualization: Concepts, Applications, and Performance Modeling� Fairfax, VA 22030, USA, CMG Journal 2005
[8]. R.P. Goldberg, �Survey of Virtual Machine Research,� IEEE Computer , October 2012, Page No(34�45), ISSN: 0018-9162
[9]. EMC Corporation, http://www.emc.com, 2014
[10]. Rob Peglar: SNIA Education �Storage virtualization I What, where ,ehy and How� Xiotech Corporation Tutorial 2009
[11]. Shiv Raj Singh �Virtualization and Information Security A Virtualized DMZ Design Consideration Using VMware ESXi 4.1� Thesis Unitec Institute of Technology, New Zealand, 2012
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[14]. Pradnyesh Bhisikar �Security in Data Storage and Transmission in Cloud Computing� International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 3, March 2013
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[16]. Aliabadi, M.R. �A proposed storage virtualization architecture for efficient information management� 538-543, May 2010, IEEE
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[18]. Singh, A. �Server-storage virtualization: Integration and load balancing in data centers� Korupolu, M. Mohapatra, 1 � 12, Nov. 2008, IEEE

P. Manchanda, R. Jain, "Non-disruptive Data Mitigation through Storage Virtualization," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.18-21, 2014.

Wireless access points are today popularly used for the convenience of mobile users. The growing acceptance of wireless local area networks (WLAN) presented different risks of wireless security attacks. Now a day�s in many public places like bus stations, restaurant, malls etc. provides Wi-Fi connectivity to the users with free of cost. These public places having a device like wireless access point through which they provide service to the end users. The growing acceptance of wireless local area network causes a risk of wireless security attacks. The attacker creates a malicious access point to attract the users and perform attacks on user devices through WLAN. Malicious access point is one of the serious threats in wireless local area network. In this paper we have presented survey on recent different malicious access point detection solutions. We identified and compared their advantages and weaknesses.

RAP, WLAN , Man In Middle Attack, Wireless security, Malicious Attacker

[1] Hao Han, Bo Sheng, Chiu C. Tan, Qun Li, Sanglu Lu, "A Timing-Based Scheme for Rogue AP Detection", 2011.
[2] Taebeom Kim,Haemin Park, Hyunchul Jung, Heejo Lee,"Online Detection of Fake Access Points using Received Signal Strengths", 2012.
[3] W.wei,S.Jaiswal,J.Kurose and D.Towsley,Identifying 802.11 traffic from passive measurments using iterative Bayesian inference in Proc. IEEE INFOCOM 06,2006.
[4] L.Watkins,R.Beyah, and C. Corbett, Apassive apporach to rogue access point detection,in Proc. IEEE INFOCOM 06,2006.
[5] Active User-side Evil Twin Access Point Detection Using Statistical Techniques Chao Yang, Yimin Song, and Guofei Gu, Member, IEEE.
[6] Roth, V., Polak, W., Rieffel, E. Turner, T.,"Simple and effective defense against Evil Twin Access Points", WiSec�08, March 31�April 2, 2008, Virginia,USA, 2008.
[7] S. B. Patil, S. M. Deshmukh, Dr. Preeti Patil and Nitin Chavan, �Intrusion Detection Probability Identification in Homogeneous System of Wireless Sensor Network�, International Journal of Computer Engineering & Technology (IJCET), Volume 3, Issue 2, 2012, pp. 12 - 18, ISSN Print: 0976 � 6367, ISSN Online: 0976 � 6375.
[8] Ajay M. Patel, Dr. A. R. Patel and Ms. Hiral R. Patel, �A Comparative Analysis of Data Mining Tools for Performance Mapping of WLAN Data�, International Journal of Computer Engineering & Technology (IJCET), Volume 4, Issue 2, 2013, pp. 241 - 251, ISSN Print: 0976 � 6367, ISSN Online: 0976 � 6375.
[9] Dipali D. Punwatkar and Kapil N. Hande, �A Review of Malicious Node Detection in Mobile Ad-hoc Networks�, International Journal of Computer Sciences & Engineering (IJCSE), Volume 2, Issue 2, 2014, pp. 65-69, ISSN 2347-2693 (Online).

S. Patil, S. Vanjale, "A Survey on Malicious Access Point Detection Methods for Wireless Local Area Network," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.22-25, 2014.

Cloud computing technology is changing the focus of IT world and it is becoming popular because of its great characteristics. Load balancing is one of the main challenges in cloud computing. Load balancing is the methodology to distribute the load across multiple servers or a cluster of servers, databases or other resources. Efficient load balancing helps to optimize the server usage, increase throughput, and decrease response time. The objective of this paper to propose a load balancing algorithm that can provide an efficient load balancing. The results discussed in this paper are based on existing round robin, least connection, throttled, fastest response time and the new proposed algorithm. This new algorithm improves the overall response time and data centre processing time as well as reduce the cost, in comparison to the existing algorithms.

Cloud computing, load balancing, simulation, cloudSim

[1] Buyya, C. S. Yeo, S. Venugopal, J. Broberg, and I. Brandic, Cloud computing and emerging IT platforms: Vision, hype, and reality for delivering computing as the 5th utility, Future Generation Computer Systems, 25:599616, 2009.
[2] Nidhi Jain Kansal, �Cloud Load Balancing Techniques: A Step Towards Green Computing�, IJCSI International Journal Of Computer Science Issues, January 2012, Vol. 9, Issue 1, No 1, Pg No.:238-246, ISSN (Online): 1694-0814.
[3] Rimal B.P., Choi E. and Lumb I. (2009) 5th International Joint Conference on INC, IMS and IDC, 44-51.
[4] Bhathiya, Wickremasinghe.(2010)�Cloud Analyst: A Cloud Sim-based Visual Modeller for Analysing Cloud Computing Environments and Applications�
[5] Zenon Chaczko, Venkatesh Mahadevan, Shahrzad Aslanzadeh, Christopher Mcdermid (2011)�Availabity and Load Balancing in Cloud Computing� International Conference on Computer and Software Modeling IPCSIT vol.14 IACSIT Press, Singapore 2011.
[6] Ram Prasad Padhy (107CS046), PGoutam Prasad Rao (107CS039).�Load balancing in cloud computing system� Department of Computer Science and Engineering National Institute of Technology, Rourkela Rourkela-769 008, Orissa, India May, 2011.
[7] Calheiros Rodrigo N., Rajiv Ranjan, and C�sar A. F. De Rose, Rajkumar Buyya (2009): CloudSim: A Novel Framework for Modeling and Simulation of Cloud Computing Infrastructures and Services CoRR abs/0903.2525: (2009).
[8] Bhathiya Wickremasinghe �Cloud Analyst: A Cloud-Sim-Based Tool For Modeling And Analysis Of Large Scale Cloud Computing Environments. MEDC Project�, Report 2010.
[9] Tackle your client�s security issues with cloud computing in 10steps, http://searchsecuritychannel.techtarget.com/tip/Tackle-your-clients-security-issues-withcloud-computing-in-10-steps.
[10] M. Armbrust, A. Fox, R. Griffith, A. Joseph, R. Katz, A.Konwinski, G. Lee, D. Patterson,A.Rabkin, I. Stoica,M. Zaharia (2009). Above the Clouds: A Berkeley View of Cloudcomputing.TechnicalReport No. UCB/EECS-2009-28, University of California at Berkley, USA, Feb. 10, 2009.
[11] Ko, Soon-Heum; Kim, Nayong; Kim, Joohyun; Thota, Abhinav; Jha, and Shantenu; (2010)"Efficient Runtime Environment for Coupled Multi-physics Simulations: Dynamic Resource Allocation and Load-Balancing" 10th IEEE/ACM International Conference on Cluster, Cloud and Grid Computing (CCGrid), 17-20 May 2010, pp.349-358.
[12] Saroj Hiranwal , Dr. K.C. Roy, �Adaptive Round Robin Scheduling Using Shortest Burst Approach Based On Smart Time Slice� International Journal Of Computer Science And Communication July-December 2011 ,Vol. 2, No. 2 , Pp. 319-323.
[13] Jinhua Hu; Jianhua Gu; Guofei Sun; Tianhai Zhao; (2010) "A Scheduling Strategy on Load Balancing of Virtual Machine Resources in Cloud Computing Environment" Third International Symposium on Parallel Architectures, Algorithms and Programming (PAAP), 18-20 Dec. 2010, pp.89-96
[14] Brain Underdahl, Margaret Lewis and Tim mueting �Cloud computing clusters for dummies� Wiley Publication (2010), [Book].
[15] Roderigo N. Calherios, Bhathiya Wickremasinghe �Cloud Analyst: A Cloud-Sim-Based Visual Modeler For Analyzing Cloud Computing Environments And Applications�. Proc of IEEE International Conference on Advance Information Networking and Applications, 2010.
[16] Sandeep Sharma, Sarabjit Singh, and Meenakshi Sharma �Performance Analysis of Load Balancing Algorithms� World Academy of Science, Engineering and Technology 38 ,2008 page no 269- 272.

M. Sharma, A. Yadav, P. Sharma, "An Optimistic Approach for Load Balancing in Cloud Computing," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.26-30, 2014.

Data mining is the domain which has utility in real world applications. Data sets are prepared from regular transactional databases for the purpose of data mining. However, preparing datasets manually is time consuming and tedious in nature as it involves aggregations, sub queries and joins. Moreover the traditional SQL Structured Query Language) aggregations such as MAX, MIN etc. can generate single row output which is not useful in generating datasets. Therefore it is essential to build horizontal aggregations that can generate datasets in horizontal layout. These data sets can be used further for data mining in the real world applications. This paper focuses on building user-defined horizontal aggregations such as PIVOT, SPJ (SELECT PROJECT JOIN) and CASE whose underlying logic uses SQL queries.

Data Mining, Horizontal Aggregations, PIVOT, CASE, SQL, Data Sets

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[4] J.A. Blakeley, V. Rao, I. Kunen, A. Prout, M. Henaire, and C. Kleinerman, �.NET Database Programmability and Extensibility in Microsoft SQL Server,� Proc. ACM SIGMOD Int�l Conf. Management of Data (SIGMOD �08), pp. 1087-1098, 2008.
[5] C. Ordonez. �Integrating K-means clustering with a relational DBMS using SQL,� IEEE Transactions on Knowledge and Data Engineering (TKDE), 18(2):188�201, 2006.
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[7] S. Sarawagi, S. Thomas, and R. Agrawal, �Integrating Association Rule Mining with Relational Database Systems: Alternatives and Implications,� Proc. ACM SIGMOD Int�l Conf. Management of Data (SIGMOD �98), pp. 343-354, 1998.
[8] A. Witkowski, S. Bellamkonda, T. Bozkaya, G. Dorman, N. Folkert, A. Gupta, L. Sheng, and S. Subramanian, �Spreadsheets in RDBMS for OLAP,� Proc. ACM SIGMOD Int�l Conf. Management of Data (SIGMOD �03), pp. 52-63, 2003.
[9] H. Garcia-Molina, J.D. Ullman, and J. Widom, �Database Systems: The Complete Book�, first ed. Prentice Hall, 2001.
[10] C. Galindo-Legaria and A. Rosentahl, �Outer Join Simplification and Reordering for Query Optimization,� ACM Trans. Database Systems, vol.22, no.1, pp.43-73, 1997.
[11] G. Bhargava, P. Goel, and B.R. Iyer, �Hypergraph Based Reorderings of Outer Join Queries with Complex Predicates,� Proc. ACM SIGMOD Int�l Conf. Management of Data (SIGMOD �95), pp. 304-315, 1995.
[12] J. Gray, A. Bosworth, A. Layman, and H. Pirahesh. �Data cube: A relational aggregation operator generalizing group-by, cross-tab and subtotal�. In ICDE Conference, pages 152�159,1996.
[13] G. Graefe, U. Fayyad, and S. Chaudhuri, �On the Efficient Gathering of Sufficient Statistics for Classification from Large SQL Databases,� Proc. ACM Conf. Knowledge Discovery and Data Mining (KDD �98), pp. 204-208, 1998.
[14] J. Clear, D. Dunn, B. Harvey, M.L. Heytens, and P. Lohman, �Non- Stop SQL/MX Primitives for Knowledge Discovery,� Proc. ACM SIGKDD Fifth Int�l Conf. Knowledge Discovery and Data Mining (KDD �99), pp. 425-429, 1999.
[15] C. Cunningham, G. Graefe, and C.A. Galindo-Legeria, �PIVOT AND UNPIVOT: Optimization and Execution Strategies in an RDBMS,�Proc: 13th Int�l Conf. Very Large Data Bases (VLDS�04), pp.998-1009, 2004.
[16] C. Ordonez, �Horizontal Aggregations for Building Tabular Data Sets,� Proc. Ninth ACM SIGMOD Workshop Data Mining and Knowledge Discovery (DMKD �04), pp. 35-42, 2004.
[17] C. Ordonez, �Horizontal Aggregations for Building Tabular Data Sets,� Proc. Ninth ACM SIGMOD Workshop Data Mining and Knowledge Discovery (DMKD �04), pp. 35-42, 2004.
[18] C. Ordonez, �Vertical and Horizontal Percentage Aggregations,� Proc. ACM SIGMOD Int�l Conf. Management of Data (SIGMOD�04), pp. 866-871,2004.
[19] Carlos Ordonez and Zhibo Chen,� Horizontal Aggregations in SQL to Prepare Data Sets for Data Mining Analysis�, IEEE transactions on knowledge and data engineering, vol. 24, no. 4, pp 1-14, April 2012.
[19] G. Luo, J.F. Naughton, C.J. Ellmann, and M. Watzke, �Locking Protocols for Materialized Aggregation Join Views,� IEEE Trans. Knowledge and Data Eng., vol. 17, no.6, pp. 796-807, June 2005.
[20] Jasna S and Manu J Pillai. Article: Preparing Data Sets for the Data Mining Analysis using the Most Efficient Horizontal Aggregation Method in SQL. International Journal of Computer Applications 86(13):32-36, January 2014.

R.S. Nyaykhor, N.T. Deotale, "Horizontal Aggregations In SQL To Generate Data Sets For Data Mining Analysis in An Optimized Manner," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.31-35, 2014.

Cloud server technology is widely used nowadays for huge and secure Data Storage. However a recent study in wireless devices noticed that usage of data traffic is immense in size. It is found that application does not follow the power distribution law with the observations made in popular Social networking & Map navigation applications. Most traffic is due to extraneous data like presence information from GPS, Availability at instantaneous rate will overload the traffic. The transmitted data will have replicate values and it is almost useless to transfer the data repeatedly. This paper proposes the Intellectual Dynamic Audit Service (IDAS) is a superset of Third Party Auditor (TPA), which will instantly help to reduce the notification of traffic data in the client device itself without disturbing the Quality of service (QOS). QOS level is well maintained by usage of Firewall Anomaly Management Environment (FAME) normally used in the verification of packets send from the application to server. Pre-audit for cloud storage is activated for traffic data reduction. Hence the originality of data to be transferred can be checked easily using snapshot created by IDAS.

Cloud computing, distributed server, Data storage, presence services, cost effective, Mobile computing

[1]. Cong Wang, Sherman S.M. Chow, Qian Wang, Kui Ren, and Wenjing Lou, "Privacy-Preserving Public Auditing for Secure Cloud Storage� IEEE Transactions on Computers, Vol. 62, No. 2, Page no (362- 375), February 2013.
[2]. G. Ateniese, R. Burns, R. Curtmola, J. Herring, L. Kissner, Z. Peterson, and D. Song, �Provable Data Possession at Untrusted Stores,� Proc. 14th ACM Conf. Computer and Comm. Security (CCS �07), Page no (598-609), November 2007.
[3]. Chi-Jen Wu, Jan-Ming Ho, and Ming-Syan Chen, "A Scalable Server Architecture for Mobile Presence Services in Social Network Applications" IEEE Transactions On Mobile Computing, Vol. 12, No. 2, Page No (386-398), Feb 2013.
[4]. M.A. Shah, R. Swaminathan, and M. Baker, �Privacy-Preserving Audit and Extraction of Digital Contents,� Cryptology ePrint Archive, Report No. HPL-2008/186, 2008.
[5]. C. Chi, R. Hao, D. Wang, and Z.-Z. Cao, �IMS Presence Server: Traffic Analysis and Performance Modeling,� Proc. IEEE International Conference on Network Protocols (ICNP), ISBN-1-4244-2507-5, 2008.
[6]. M.A. Shah, M. Baker, J.C. Mogul, and R. Swaminathan, �Auditing to Keep Online Storage Services Honest,� Proc. 11th USENIX Workshop Hot Topics in Operating Systems (HotOS �07), Page No (1-6), 2007.
[7]. Z. Xiao, L. Guo, and J. Tracey, �Understanding Instant Messaging Traffic Characteristics,� Proc. IEEE 27th International Conference on Distributed Computing Systems (ICDCS), ISBN-0-7695-2837-3, Page No (7-14) 2007.
[8]. Mobile device forensics, http://en.wikipedia.org/wiki/Mobile_device_forensics, 2013.
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R.K. Prakash, B. Sivananthan, "Empirical Auditing for Computing in Preserving Manner," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.36-40, 2014.

The present research paper majorly discusses with regard to various issues related to agile software development approach in related to changes mande in diverse background.

Extreme Programming ,Feature driven development ,Focal Point ,Return on Investment(ROI), Scrums

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A. Singhal, T. Chaner, R. Gupta, "Study: Impact of Agile on Current IT Scenario," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.41-45, 2014.

As the location based service makes the users to process their queries from anywhere and at anytime, the spatial query processing that with this provides the processing on the basics of the spatial attributes in the skyline. With thus provides the users to identify the nearest neighbour among their query given with its spatial attributes. With the existing, they have done the skyline query processing based on the range in the spatial attribute for analyzing the nearest among the other skyline data sets. They have done these using two novel algorithms as index and non index algorithm. We are going to concentrate by making with that range query to process on the basis of user�s feedback as their rating for the skyline query processing. Here the processing is done by ranking on the spatial attribute in the skyline data sets. When the user makes the feedback as their rating for the result of the skyline, this will be also analyzed while considering the next query processing for the nearest neighbour in the skyline query processing. Here, we are going to consider the algorithms as nearest neighbour algorithm and branch and bound algorithm in which this makes the analysis on the basics of both nearest and the minimum bound within the skyline.

Spatial data mining, Skyline Query Processing, Interesting Points

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R.P. Steffi, S. Sundaramoorthy , "Top-K Spatial Preference Query with Range Based Skyline Query in Mobile Environment," International Journal of Computer Sciences and Engineering, Vol.2, Issue.3, pp.46-50, 2014.