iosr-JCE   Volume-14 ~ Issue-2

Abstract: There are patterns buried within the mass of data in the various editions of population census figures in this country. These are patterns that will be impossible for humans working with bare eyes and hands, to uncover without computer system to give geo-spatial distribution of population in that area. This paper is an effort towards harnessing the power of data-mining technique to develop mining model applicable to the analysis of census data that could uncover some hidden patterns to get their geo-spatial distribution. This could help better-informed business decisions and provide government with the intelligence for strategic planning, tactical decision-making and better policy formulation. Decision tree learning is a method for approximating discrete-valued target function, in which the leaned function is represented by a decision tree. Decision tree algorithm was used to predict some basic attributes of population in the census database. Structured System Analysis and Design Methodology were used.

Key words: Census, Data-mining and GIS.

[1]. Berry, M.J.A & Linoff (2000). Mastering Data-mining .Wiley Press: New York.
[2]. Crow, M.C &Giudici. (2003).Applied Data-mining :Statistical Method For Business And Industry. John Wiley and Sons .West Sussex, England.
[3]. Folorunso, O. & Ogunde, A.O. (2004). Data-mining as a Technique for Knowledge in Business Process Redesign. The Electronic Journal of Knowledge Management Volume 2 issue 1, pp, 33-44, available on line at www.ejkm.com .
[4]. Gajendra, S. (2008). Data-mining, Data-ware housing and OLAP. Kataria & sons: New Delhi.
[5]. Koh, Chye, H. & Kee, C.L (2004). Going concern prediction using Data-mining Techniques Managerial Auditing Journal, 19:3.
[6]. Kwedlo, W. & Kretowski, M. (2001). Learning Decision Rules using a Distributed Evolutionary Algorithm. Gdansk Press: Poland.
[7]. Mena, K.C (2005). Data mining and Statistics: Guild Form press: New York.
[8]. Pregibon,D.(1997).Data-mining "Statistical Computing and Graphics, pp 7-8."
[9]. RedLands,C.A.(1990).Understanding GIS. Environmental System Research Institute Oxford University Press: New York.
[10]. Rambaldi, G., and J. Callosa (2000). Manual on Participatory 3-Dimensional Modeling fo Natural Resource Management (Volume 7). NIPAP, PAWB-DENR: Philippines Department of Environment and Natural Resources.

Abstract: Analysis of routing in Mesh Network reveals that Proactive routes are fast but suffers vulnerability of route failure under high mobility. Reactive routes on the other hand add extra overhead in the network for obtaining a route before every communication session. As Link state routing provides a Network Map at each node, It is well suited for a mesh network. However due to uncertain demand in the mesh network, link states needs frequent updation and refreshing. A refreshing phase halts current session thus adding latency to the session. In order to avoid this pitfall we propose a unique Cross layer Based Link State Routing for Mesh network. Nodes keep monitoring the link quality as an when a packet is received in a link. Signal to Noise Ratio and Received power is measured at the MAC layer. Any change in the stored value raises an event which is read directly by the network layer. Once Network layer gets the notification, it automatically updates the route table entry with new metric values. Thus there is no specific refresh phase and nodes automatically update the links and route cache. Once a node realizes that the link quality with its next hop has degraded and a better one is available, it opts a handover of the connection called vertical handover. Thus proposed system provides seamless connectivity under varying load and mobility.

Keywords: Cross layer routing, Link state routing protocol, Wireless mesh networks (WMNs).

[1] J. Ren, "Wireless mesh network resource allocation and congestion control algorithm research," Ph.D. dissertation, Beijing Jiaotong University, Beijing, 2010 (in Chinese).

[2] S. Waharte, R. Boutaba, Y. Iraqi, and B. Ishibashi, "Routing protocols in wireless mesh networks: challenges and design considerations," Multimedia Tools and Applications, vol. 29, no. 3, pp. 285−303, 2006.

[3] T.-H. Liu and W.-J. Liao, "Capacity-aware routing in multi-channel multi-rate wireless mesh networks," in Proc. of 2006 IEEE International Conf. on Communications, Istanbul, 2006, pp. 1971−1976.

[4] W. Song and X.-M. Fang, "Routing with congestion control and load balancing in wireless mesh networks," in Proc. of the 6th International Conf. on ITS Telecommunications, Chengdu, 2006, pp. 719−724.

[5] Y.-F. Wai, Y. Zhang, M. Song, and J. Song, "An improved AODV routing protocol for WiFi mesh networks," Journal of Beijing University of Posts and Telecommunication, vol. 30, no. 4, pp. 120−124, 2007 (in Chinese).

[6] Q. Shen and X.-M. Fang, "An integrated metrics based extended dynamic source routing protocol in wireless mesh networks," in Proc. of International Conf. on Communications, Circuits and Systems, Guilin, 2007, pp. 1457−1461.

[7] Y.-L. Yang, J. Wang, and R. Kravets, "Designing routing metrics for mesh networks," in Proc. of the IEEE Workshop on Wireless Mesh Networks (WiMesh), Santa Clara, 2005, pp. 1−9.

[8] P. Jacquet, P. Muhlethaler, T. Clausen, A. Laouiti, A. Qayyum, and L. Viennot, "Optimized link state routing protocol for ad hoc networks," IEEE International Multi Topoic Conf., Lahore, 2001, pp. 62−68

Abstract: In the information technology ground people are using various tools and software for their official use and for their personal reasons. Nowadays people are worrying to choose data accessing tools and software's at the time of buying and selling the products and they are also worrying about various constraints such as cost, life time of the product, color and size of the product etc. In this paper we generated the solutions to the existing unsolved problems. Here we proposed the algorithm Multidirectional Rank Prediction (MDRP) decision making algorithm in order to take an effective decision at all the levels of data extraction, using the above technique and we analyzed the results at various datasets, finally the results were observed and compared with the existing methods such as PCC and VSS. The result accuracy was higher than the existing rank prediction methods.

Keywords: Knowledge Discovery in Database (KDD), Multidirectional Rank Prediction (MDRP), Pearson's Correlation Coefficient (PCC), VSS (Vector Space Similarity)

[1]. Das,J. Heritage Inst. of Technol., Heritage Acad., Kolkata, India, Voronoi based location aware collaborative filtering.
[2]. Mittal, N. MNIT, Jaipur, India Nayak, R.; Govil, M.C.; Jain, K.C. Recommender System Framework Using Clustering and
Collaborative Filtering.
[3]. Akshi Kumar, Abhilasha Sharma Alleviating Sparsity and Scalability Issues in Collaborative Filtering Based Recommender
Systems.
[4]. Nidhi Gupta, Trends in Collaborative filtering Recommendation Technique.
[5]. E. Thirumaran, Collaborative Filtering Based Recommendation Systems.
[6]. Hemalatha Chandrashekhar Indian Institute of Management Ranchi, India, Personalized Recommender System Using Entropy
Based Collaborative Filtering Technique.
[7]. Sotirios P. Chatzis Department of Electrical Engineering, Computer Engineering and Informatics.
[8]. Cyprus University of Technology, A Coupled Indian Buet Process Model for Collaborative Filtering.
[9]. Xuejun Zhang, John Edwards *, Jenny Harding, Personalised online sales using web usage data mining.
[10]. Seok Kee Lee a,1, Yoon Ho Cho b,*, Soung Hie Kim, Collaborative filtering with ordinal scale-based implicit ratingsfor mobile
music recommendations

Abstract: The main aim of every investor is to identify a stock that has potential to go up so that the investor can maximize possible returns on investment. After identification of stock the second important point of decision making is the time to make entry in that particular stock so that investor can get maximum returns on investment in short period of time. There are many conventional techniques being used and these include technical and fundamental analysis. The main issue with any approach is the proper weighting of criteria to obtain a list of stocks that are suitable for investments. This paper proposes a method for stock picking and finding entry point of investment in stocks using a hybrid method consisting of self-organizing maps and selected technical indicators. The stocks selected using our method has given 37.14% better returns in a period of one and a half month in comparison to NIFTY.

Key Words: Neural Network, Stocks Classification, Technical Analysis, Fundamental Analysis, Self-Organizing Map (SOM).

[1] Mizuno, H., Kosaka, M., Yajima, H. and Komoda N., ―Application of Neural Network to Technical Analysis of Stock Market Prediction‖, Studies in Informatic and Control, 1998, Vol.7, No.3, pp.111-120.

[2] Haykin, Simon, ―Neural Networks: A Comprehensive Foundation‖, Macmillian College Publishing Company, New York, 1994.

[3] Phillip D. Wasserman, Van Nostrand, "Neural Computing: Theory and Practice", Van Nostrand Reinhold, New York, 1989.

[4] Youngohc yoon and George swales, ―Predicting stock price performance: a neural network approach‖, IEEE publishing, 1991.

[5] Shaikh A. Hamid, ―Primer on using neural networks for forecasting market variables‖, in proceedings of the conference at school of business, Southern New Hampshire university, 2004.

[6] Ramon Lawrence, ―Using Neural Networks to Forecast Stock Market Prices‖, Course Project, University of Manitoba Dec. 12, 1997.

[7] Monica Adya and Fred Collopy, ―How Effective are Neural Networks at Forecasting and Prediction? A Review and Evaluation‖, Journal of Forecasting, 1998.

[8] Asif Ullah Khan et al., ―Stock Rate Prediction Using Back Propagation Algorithm: Analyzing the prediction accuracy with different number of hidden layers‖, Glow gift, Bhopal, 2005.

[9] Juha Vesanto and Esa Alhoniemi, ―Clustering of the Self-Organizing Map‖, IEEE Transactions on Neural Networks, Vol. 11, No. 3, May 2000.

[10] Serrano, C., ―Self

Abstract: Cardiovascular disease remains the biggest cause of deaths worldwide. The percentage of premature death from this disease ranges from 4% in high income countries and 42 % in low income countries. This shows the importance of predicting heart disease at the early stage. In this paper, a new unsupervised classification system is adopted for heart attack prediction at the early stage using the patient's medical record. The information in the patient record are preprocessed initially using data mining techniques and then the attributes are classified using a Fuzzy C means classifier. In the classification stage 13 attributes are given as input to the Fuzzy C Means (FCM) classifier to determine the risk of heart attack. FCM is an unsupervised clustering algorithm, which allows one piece of data to belong to two or more clusters. The proposed system will provide an aid for the physicians to diagnosis the disease in a more efficient way. The efficiency of the classifier is tested using the records collected from 270 patients, which gives a classification accuracy of 92%. The result shows that the proposed clustering algorithm can predict the likelihood of patients getting a heart attack in a more efficient and cost effective way than the other well known algorithms.

Keywords: Cardiovascular disease, Clustering, Fuzzy C Means, Heart attack prediction,

[1]. Kawamoto, K., Houlihan, C.A., Balas, E.A., Lobach, D.F., 2005. "Improving clinical practice using clinical decision support systems: Clinical decision support system: Risk level prediction of heart disease using weighted fuzzy rules 39 a systematic review of trials to identify features critical to success". BMJ 330, 765.
[2]. Shanthakumar B.Patil,Y.S,Kumaraswamy"Intelligent and Effective Heart Attack Prediction System Using Data Mining and Artificial Neural Network". European Journal of Scientific Research Vol. 31, No. 04, 2009, 642-656 .
[3]. Carolas Ordonez "Assosiation Rule Discovery With the Train and Test Approach for Heart Disease Prediction" IEEE Transactions on Information Technology in Biomedicine, Vol. 10, No. 2, April 2006.
[4]. Sellappan Palaniappan,Rafiah Awang, "Intelligent Heart Disease Prediction System Using Data Mining Techniques" IEEE Conference, 2008,pp 108-115.
[5]. K.Srinivas ,B.Kavihta Rani ,Dr. A.Govrdhan "Applications of Data Mining Techniques in Healthcare and Prediction of Heart Attacks" K.Srinivas et al. / (IJCSE) International Journal on Computer Science and Engineering Vol. 02, No. 02, 2010, 250-255.
[6]. Markos G. Tsipouras, Themis P. Exarchos, Dimitrios I. Fotiadis, Anna P. Kotsia, Konstantinos V. Vakalis,Katerina K. Naka, and Lampros K. Michalis" Automated Diagnosis of Coronary Artery Disease Based on Data Mining and Fuzzy Modeling "IEEE Transactions on Information Technology in Biomedicine, Vol. 12, No. 4, July 2008 .
[7]. Dan Li, Hong Gu, Liyong Zhang," A fuzzy c-means clustering algorithm based on nearest-neighbor intervals for incomplete data", Expert Systems with Applications 37 (2010) 6942–6947
[8]. Yang, M.-S., Hwang, P.-Y., Chen, D.-H., 2004. Fuzzy clustering algorithms for mixed feature variables. Fuzzy Sets Systems 141, 301–317.
[9]. Ross, Timothy J., 2004. Fuzzy Logic with Engineering Applications, John Wiley & Sons, Second Edition.
[10]. Lanhai L. Comparison of conventional and fuzzy land classification and evaluation techniques in Oxfordshire England. Int Agric Eng J 1998;7:1–12.

Abstract: A mobile Ad-hoc network (MANET) is a self configuring network of mobile devices connected by wireless links. A dynamic traffic allocation algorithm based on packet delay and hops in Mobile Ad hoc networks is proposed. The algorithm is based on the minimization product of delay and the number of hops in each path and adjusts the traffic adaptively so as to make load-balanced which optimizes network resource utilization. Simulation demonstrated that the algorithm could dynamically balance the traffic allocation between paths. The aim of the minimum utilization of resource in mobile Ad hoc networks can be achieved.

Keywords : MANET, Node Load, Route Weight, Routing Delay, TAODV.

[1] Mahesh KM, and Das SR. On-demand multipath distance vector routing in Ad hoc networks. Ninth International Conference on Network Protocols, Washington D.C., USA, 2001, 14-23.

[2] Dhirendra Kumar Sharma, Chiranjeev Kumar and Sandeep Jain, Neeraj Tyagi, "An Enhancement of AODV Routing Protocol for Wireless Ad Hoc Networks", 1st Int'l Conf. On Recent Advances in Information Technology | RAIT-2012 | IEEE, 2012

[3] Syed Jalal Ahmad, V.S. K. Reddy, A. Damodaram and P. Radha Krishna , "Efficient Path Estimation Routing Protocol for QoS in Long Distance MANETs", IEEE, 2012.

[4] Mustafa Bani Khalaf, Ahmed Y. Al-Dubai and Mourad Abed, "New Velocity Aware Probabilistic Route Discovery Schemes for Mobile Ad hoc Networks", IEEE, 2012.

[5] P. Y. Taifei Zhao, Xizheng Ke, "Position and velocity aided routing protocol in mobile ad-hoc networks," International Journal of Digital Content Technology and its application, vol. 4, pp. 101-109, 2010.

[6] Anh Tai Tran, Myung Kyun Kim , "A real-time communication protocol considering load balancing in Adhoc Network", IEEE, 2013

. [7] Mohd. Ayash, Mohd. Mikki, Kangbin Yim, " Improved Aodv Routing Protocol to Cope with High Overhead in High Mobililty MANETs ", Sixth Int. Conference on Innovative Mobile and Internet Services in Ubiquitous Computing, 2012.

[8] Y. Hassan, M. Abd El-Aziz, and A. Abd El-Radi, "Performance evaluation of mobility speed over MANET routing protocols," International Journal of Network Security, November 2010,Vol. 11, No.3, pp.128-138.

[9] A. Mohammad, M.ould-khaoua, L.Mackenzie, abdulai "Dynamic probabilistic counter based routing in mobile adhoc network,"2nd international conference on Adaptive Science Technology, Jan 2009.

[10] P. Y. Taifei Zhao, Xizheng Ke, "Position and velocity aided routing protocol in mobile ad-hoc networks," International Journal of Digital Content Technology and its application, vol. 4, pp. 101-109, 2010.

Abstract: This paper offers a depth review of numerous Wireless Sensor/detector Systems. Healthcare applications are considered as talented fields for Wireless Sensor Networks, where patients can be watched using wireless medical sensor networks (WMSNs). Present WMSN healthcare research trends center on patient mobility, patient reliable communication and energy-efficient routing. But, installing new technologies in healthcare applications without considering security and safety makes patient privacy in danger. Furthermore, the physiological data of an individual are extremely sensitive. So, security is a supreme requirement of healthcare systems, particularly in the case of patient privacy. This paper discusses the privacy and security issues in healthcare application using WMSNs

Keywords: WMSN, ECIES, Identity Based Encryption, sensor networks,

[1] L. Gatzoulis and I. Iakovidis, "Wearable and portable ehealth systems", IEEE Eng. Med. Biol. Mag., vol. 26, no. 5, pp. 51–56, Sep.–Oct. 2007.

[2] A. Lymperis and A. Dittmar, "Advanced wearable health systems and applications, research and evelopment efforts in the european union," IEEE Eng. Med. Biol. Mag., vol. 26, no. 3, pp. 29–33, May/Jun. 2007.

[3] G. Tr¨oster, "The Agenda of Wearable Healthcare," in IMIA Yearbook of Medical Informatics. Stuttgart, Germany: Schattauer, 2005, pp. 125–138

[4] Y. Hao and R. Foster, "Wireless body sensor networks for health monitoring applications," Phys. Meas., vol. 29, pp. R27–R56, Nov. 2008

[5] C. E. Nishimura and D. M. Conlon, "IUSS dual use: Monitoring whales and earthquakes using SOSUS," Mar. Technol. Soc. J., vol. 27, no. 4, pp. 13–21, 1994

[6] Proceedings of the Distributed Sensor Nets Workshop. Pittsburgh, PA: Dept. Comput. Sci., Carnegie Mellon Univ., 1978.

[7] R. F. Sproull and D. Cohen, "High-level protocols," Proc. IEEE, vol. 66, pp. 1371–1386, Nov. 1978.

[8] P. Nii, E. Feigenbaum, J. Anton, and A. Rockmore, "Signal-tosymbol transformation: HASP/SIAP case study," AI Mag., vol. 3, pp. 23–36, Spring 1982

[9] T. Gao, D. Greenspan, M. Welsh, "Vital sign monitoring and patient tracking over a wireless netowrk", Proceeding of 27th annual international conference of the IEEE EMBS, September 2005.

[10] U. Anliker, J. Ward, P. Lukowicz, "AMON: A wearable multiparameter medical monitoring and alert system" IEEE Transactions on information technology in Biomedicine, Vol. 8, No. 4, Pages 415-427, December 2004.

Abstract: A Distributed network is one in which the data is distributed and the data from source to destination can be transferred through several nodes. When huge amount of packets are transferred through particular node congestion may occur which may result loss of packets and bandwidth also can't utilize. Hence the shortest path is chosen and routing is done dynamically so that the node can't suffer from congestion. Ant based routing techniques is an efficient one in which routing is done on the behavior of the ants and a shortest path is selected such that the packets can be send quickly and bandwidth also utilizes. Here in this paper we compare different ant based techniques for the shortest path selection from source to destination in a distributed network. Here on the basis of different Ant based techniques such as Max-Min, Rank based and Fuzzy rule based ant technique an efficient algorithm of ant technique is implemented which performs better as compared to other existing ant based techniques.

Keywords : ACO, multi congestion, QoS, hierarchical routing, pheromone.

[1]. Le Lu, Shanguo Huang, Wanyi Gu, "A Dynamic Ant Colony Optimization for Load Balancing in MRN/MLN", Communications and Photonics Conference and Exhibition, pp. 1-6, 2011.

[2]. F. Neumann and C. Witt. "Runtime analysis of a simple ant colony optimization algorithm" 17th International Symposium on Algorithms and Computation (ISAAC '06), pp. 618–627, 2006.

[3]. Y. Zhou. "Runtime analysis of an ant colony optimization algorithm for TSP instances" IEEE Transactions on Evolutionary Computation, pp. 1083–1092, 2009.

[4]. M. Dorigo, Ant colony optimization web page, http://iridia.ulb.ac.be/mdorigo/ACO/ACO.html.

[5]. M. Dorigo, G. Di Caro & L.M. Gambardella. "Ant Algorithms for Discrete Optimization", Artificial Life, pp137-172. 1999.

[6]. A. Colorni, M. Dorigo, and V. Maniezzo, "Distributed optimization by ant colonies", European Conference on Artificial Life ECAL'91, pp. 134-142, 1991.

[7]. M. Dorigo, V. Maniezzo, and A. Colorni, "The ant system: an autocatalytic optimizing process", Technical Report TR91-016, 1991.

[8]. M.Dorigo, "Optimization, learning and natural algorithms", 1992. [9]. M. Dorigo, T. Stützle. "The ant colony optimization metaheuristic: Algorithms, applications and advances", Handbook of Metaheuristics international Series in Operations Research & Management Science, Vol. 57, pp 250-285, 2003.

[10]. B. Doerr, F. Neumann, D. Sudholt, and C. Witt. "On the runtime analysis of the 1-ANT ACO algorithm" Genetic and Evolutionary Computation Conference (GECCO '07), pp. 33–40. 2007.

Abstract: With the recent breakthrough in bioinformatics, demand of more storage space is increasing day by day. With this exponentially increasing data i.e. the big data of bioinformatics sector, the data needs to be handled in more flexible and cost effective manner. With this growth in the volume, variety and velocity of data, cloud computing promises to address big data issues and analysis of challenges of big data in bioinformatics. Big Data of bioinformatics consisting of various sequences (nucleotide or amino acid) which are now showing exponential growth because of high throughput experimental technologies. By the use of various bioinformatics tools demands for large and fast data storage technology is increasing. In this paper, security model is re- drafted for flexible use of model by consumers and various cloud based services and techniques are coined up to make an easy approach for implementing big data of bioinformatics using cloud.

Keywords: Big Data, Bio Cloud, Bioinformatics, Cloud, Cloud Computing, Secure Cloud

[1] Wikipedia found at http://en.wikipedia.org/wiki/Bioinformatics

[2] Schatz MC, Langmead B, Salzberg SL. Cloud computing and the DNA data race. Nat Biotechnol.2010;28(7):691–693. doi: 10.1038/nbt0710-691.

[3] Armbrust M, Fox A, Griffith R, Joseph AD, Katz RH, Konwinski A, Lee G, Patterson DA, Rabkin A,Stoica I, Above the Clouds: A Berkeley View of Cloud Computing. Berkeley: EECS Department, University of California; 2009.

[4] Kharche, Ms Heena, and Mr Deepak Singh Chouhan. "Building Trust In Cloud Using Public Key Infrastructure." International Journal of Advanced Computer Science and Applications 3.3 (2012).

[5] Higdon et al ―Unraveling the complexities of life sciences data‖. Mary Ann Liebert Inc. Vol. 1, No. 1, Big Data.
[6] Wikipedia found at http://en.wikipedia.org/wiki/Human_Genome_Project

[7] Andreas D. Baxevanis, B.F. Francis Quellette Bioinformatics: A practical guide to the analysis of genes and proteins. (NJ: Wiley-Interscience, 2005).

[8] Costa FF ,Big Data In Biomedicine. Drug Discovery Today. In press (2013)
[9] www.nytimes.com/2012/08/12/business/how-bigdat a- became-so-big-unboxed.html?r=0 [10] Rothberg J. M. et al. ―An integrated semiconductor device enabling non-optical genome sequencing‖: Nature 475(7356): 348–352(2011)

Abstract: The purpose and the goal of the paper is growing substantially demand for water and waste-water infrastructure and that is being met through the available scarce and dwindling water resources. The kingdom of Saudi Arabia (KSA) faces an acute water shortage due to arid climate and absence of permanent lakes and rivers. Ever-increasing imbalances are usually met by increasing water supplies, whereas the concepts of water-demand management have not been given due importance and weight age. Meeting the rapidly rising demand with scarce and depleting resources remains the critical issue. This paper places emphasizes on the urgency of adopting conservation and water-demand management initiatives to maintain demand supply relationship and achieve an acceptable balance between water needs and availability. Demand for water and waste-water infrastructure projects has remained unaffected by the global financial crisis, and it continue to grow exponentially.

Key words: Water Demand, Water Resources, GIS, Highway Street, XML Schema.

[1] Abderrahman, W. A., 2009. Water Demand Management In Saudi Arabia. (Chapter 6). Book Published By International Development Research Centre, Canada. Available At: Http://Www.Idrc.Ca/En/Ev-93954-201-1DO_TOPIC.Html.

[2] Abderrahman, W. A., 1997. The Use Of Closed Water Cycle In Industrial Plants In Saudi Arabia,‖ In Proceedings Of The Conference On Development And Environmental Impact. 21–23 September, Ministry Of Municipal And Rural Affairs, Riyadh.

[3] Al Alawi, M. And M. Abdulrazzak, 1994. Water In The Arabian Peninsula: Problems And Perspectives,‖ In P. Rogers And P. Lydon (Eds.). Water In The Arab World: Perspectives And Progress, Division Of Applied Sciences, Harvard University, Cambridge, Mass.

[4] Al-Ibrahim, A. A., 1990. Water Use In Saudi Arabia: Problems And Policy Implications. Journal Of Water Resources Planning And Management, 116 (3): 375388.

[5] Al-Tukhais, A. S., 1997. Water Resources And Agricultural Production In Saudi Arabia: Present And Future. Proc. Water Resources And Its Utilization In Saudi Arabia: Proceedings Of The First Saudi Conference On Agricultural Sciences, 25– 27 March 1997, College Of Agric., King Saud University, Riyadh, KSA.

[6] Brooks, D. B., 2005. An Operational Definition Of Water Demand Management. Submitted To: International Journal Of Water Resources Development.

[7] Bushnak, A., 1997. Water Desalination And Wastewater Reuse: Review Of The Technology, Economics And Applications In The ESCWA Region. Expert Group Meeting On Development Of Non-Conventional Water Resources And Appropriate Technologies For Groundwater Management In The ESCWA Member Countries. 27–30 October, Manama, Bahrain. Economic And Social Commission Of Western Asia (ESCWA)/UN, Beirut.

[8] Dabbagh, A. E. And W. A. Abderrahman, 1997. Management Of Nroundwater Resources Under Various Irrigation Water Use Scenarios In Saudi Arabia. Arabian Journal Of Science And Engineering 22 (Special Theme Issue On Water Resources In The Arabian Peninsula), Pp. 47-64.

[9] Fao, 1997. Irrigation In The Near East Region In Figures. FAO Country Report Saudi Arabia 9 C. FAO Land And Water Division, Food And Agriculture Organization Of The United Nations, Rome 1997.

[10] Fao, 2009. Irrigation In The Middle East Region In Figures AQUASTAT Survey 2008. FAO Water Report 34, Country Report Saudi Arabia. Edited By Karen Frenken, FAO Land And Water Division, Food And Agriculture Organization Of The United Nations, Rome 2009, Pp. 325-337.

Abstract: Mobile Ad Hoc Networks (MANET) are wireless networks which do not require any infrastructure support for transferring data packet between two nodes. Mobile ad-hoc network have the attributes such as wireless connection, continuously changing topology, distributed operation and ease of deployment. The mobile nodes perform both as a host and a router forwarding packets to other nodes. Routing in these networks is highly complex. It can be used for various applications areas as sensor applications, disaster management, conferences, rescue operations, military communications, hybrid wireless network architectures and wireless mesh networks and many more. Traditional routing mechanism and protocol of wired network are inapplicable to ad-hoc networks which initiated the need of a dynamic routing mechanism in ad-hoc network. This paper evaluates the performance of AODV, DSR (Reactive) and IERP (Hybrid) routing protocols with respect to varying Constant Bit Rate (CBR). A detailed simulation has been carried out in QualNet Simulator 6.1. The performance analysis is based on different network metrics such as Average End to End delay, Throughput, Average Jitter, Packet Delivery Ratio.

Keywords: AODV, DSR, IERP, Mobile Ad-hoc networks, QualNet 6.1.

[1] R. Paulus, P.D. Kumar, P.C. Philips , A. Kumar "Performance Analysis of Various Ad Hoc Routing Protocols in MANET using Variation in Pause Time and Mobility Speed", International Journal of Computer Applications (0975 – 8887) Volume 73– No.8, July 2013.
[2] P.K Maurya, Rajeev Paulus, A.K. Jaiswal , M. Srivastava , "Performance Analysis of ZRP over AODV, DSR and DYMO for MANET under Various Network Conditions using QualNet Simulator" Volume 66–No.17, IJCA (0975 – 8887), March. 2013
[3] Syed Basha Shaik , Prof. S. P. Setty ," Performance Comparison of AODV, DSR and ANODR for Grid Placement Model" International Journal of Computer Applications (0975 – 8887) Volume 11– No.12, pp 6-9, December 2010.
[4] Perkins, E. Belding-Royer, and S. Das, "Ad hoc On-Demand Distance Vector (AODV) Routing, "draftietf-manet-aodv-13.txt, Feb. 2003.
[5] J.Broch, D. Jhonson, and D.Maltz, "The dynamic source routing protocol for mobile adhoc networks for IPv4" IETF RFC 4728, Feb 2007.
[6] Z J. Haas, M. R. Pearlman, and P. Samar, "The Zone Routing Protocol (ZRP) for Adhoc Networks", draft-ietf-manet-zone-zrp-04.txt.July 2002.
[7] S.R.Das, C.E.Perkins and E. M. Royer, 2000. "Performance Comparison of Two On-Demand Routing Protocols for Ad-Hoc Networks," In Proceedings of INFOCOM 2000.
[8] S. R. Raju and J.Mungara "Performance Evaluation of ZRP over AODV and DSR in Mobile Adhoc Networks Using Qualnet" ISSN 1450-216X Vol.45 No.4 (2010), pp.651-667, European Journal of Scientific Research, 2010.
[9] The QualNet simulator, www.scalablenetworks.com.
[10] C.E. Perkins and E.M. Royer, "Ad-hoc On-Demand Distance Vector Routing", Proceedings of 2nd IEEE Workshop, Mobile Computing System Applications, pp:90-100, 1999.

Abstract: Signature verification models based on personal model have been reported by many researchers in past but the method proposed here is a forensic document examination approach using computational methods, unlike other models which require a considerable number of genuine signatures of the same writer to correctly train the model. This paper proposes a forensic signature verification approach making a robust verification system even when few samples per writer are available. The efficiency of the proposed method is based on results of 150 writers with 10 signatures of each writer.

Keywords: Neural Network Classifier, offline signature verification system, SVM,.

[1] Cesar R. Santos, Flávio Bortolozzi, Luiz S. Oliveira, Edson Justino, 2007 Off-line Signature Verification Based on Forensic Questioned Document Examination Approach Pontifical Catholic University of Parana, SAC'07, March 11-15, Seoul, Korea.

[2] Srihari, A. S., Cha, H. Arora, S. Lee, 2001. "Individuality of Handwriting: A Validity Study", Proc. ICDAR‟01, Seattle (USA), pp 106-109.

[3] Oliveira L. S., Justino, E., Freitas, C., and Sabourin, R. 2005, The Graphology Applied to Signature Verification, 12th Conference of the International Graphonomics Society (IGS 2005), pages 286-290.