iosr-JCE   Volume-16 ~ Issue-5 ~ Sep-Oct 2014

Abstract: In present era, where every person has become addicted to wireless networks, MANET'S has fulfilled the demand of the people by removing the dependency of fixed network. Mobile ad-hoc networks have extended the range of mobile nodes beyond their level. Now when we talk about wireless communication, routing is the backbone of any communication. Ad hoc on demand multipath distance vector routing algorithm (AOMDV) is a very reactive protocol in MANET's. We have selected AOMDV algorithm for routing purpose as there have already been done several work with AODV. Also, in AOMDV the end to end delay is reduced by utilising several parallel paths. Various routing attacks have been identified in single path routing but here we have introduced worm hole attack in multi path routing i.e. AOMDV routing algorithm. In this paper, we have studied the performance of AOMDV algorithm under worm hole attack. Also, we have provided security and authentication to each and every node by the use of public key and private key. Different metrics of the proposed protocol has been evaluated from simulation on NS2 on different scenarios i.e. with worm hole attack and without worm hole attack and there has been a noticeable improvement in the throughput and energy consumption is also reduced. Keywords: AODV; AOMDV; Worm hole attack; black hole attack; security; authentication; protecting

[1]. Y.-C. Hu, A. Perrig, D. B. Johnson; "Wormhole Attacks in Wireless Networks"; IEEE Journal on Selected Areas of Communications, vol. 24, numb. 2, pp. 370-380, 2006
[2]. Debdutta Barman Roy, Rituparna chaki, Nabendu chaki; "A new cluster based wormhole intrusion detection algorithm for mobile adhoc networks"; IJNSA, Vol 1, No 1, April 2009
[3]. Reshmi Maulik, Nabendu chaki; "A study on worm hole attacks in MANET"; International Journal of computer Information systems and Industrial Management applications, ISSN 2150-7988 Vol 3 (2011)
[4]. Bhavneet Kaur, Sandeep Singh Kang;" A distance based scheme for defending against Wormhole attack in Wireless sensor networks"; IJCNWMC ISSN 2250-1568 Vol-3 (2013)
[5]. Y-C Hu, A. Perrig, D. Johnson; "Rushing Attacks and Defense in Wireless Ad Hoc Network Routing Protocols"; Proc. of WISE 2003, September 19, San Diego, California, USA, 2003

Abstract: Datasets in the horizontal aggregated layout are preferred by most of data mining algorithms, machine learning algorithm. Major efforts are required to compute data in the horizontal aggregated format. There are many inbuilt aggregation functions in SQL, namely, minimum, maximum, average, sum and count. These aggregation functions are used with a query evaluation method to retrieve data in the horizontal aggregation format. Optimization techniques used for vertical aggregation is not appropriate for horizontal aggregation. Standard aggregations are hard to interpret when there are many result rows, especially when grouping attributes having high cardinalities. That is why we proposed C4.5 classification algorithm and K-means clustering algorithm with query evaluation method and aggregation function for optimizing horizontal aggregation. Horizontal aggregation is a method which generates SQL code to return aggregated columns in the horizontal tabular layout. It returns a set of numbers instead of one number per row. There are various applications where the horizontal aggregation is used such as electrical billing, banks, hospital management system, pharmacy, and online library etc. [6].

Keywords: C4.5 Algorithm, CASE, Horizontal Aggregation, K-means, OLAP, PIVOT, SPJ

[1] C. C. Ordonez, and Zhibo Chen, Horizontal Aggregation in SQL to prepare Data Sets for Data Mining Analysis," IEEE Transactions on Knowledge and Data Engineering (TKDE), April 2012.
[2] C. Cunningham, G. Graefe, and C.A. Galindo-Legaria, PIVOT and UNPIVOT:Optimization and Execution Strategies in an RDBMS, Proc. 13th Int‟l Conf. Very Large Data Bases (VLDB ‟04), pp. 998-1009, 2004.
[3] Venkatadri.m, Lokanatha C. Reddy,"A Comparative Study On Decision Tree Classification Algorithms In Data Mining" ISSN: 0974-3596, April ‟10 – Sept ‟10, Volume 2 : Issue 2, Page: 24.
[4] R. Rakesh Kumar, A. Bhanu Prasad," K Means Clustering Algorithm for Partitioning Data Sets Evaluated From Horizontal Aggregations, IOSR Journal of Computer Engineering (IOSR-JCE) e-ISSN: 2278-0661, p- ISSN: 2278-8727Volume 12, Issue 5 (Jul. - Aug. 2013), PP 45-48.
[5] Anuja Priyam, Abhijeet, Rahul Gupta, Anju Rathee, and Saurabh Srivastava," Comparative Analysis of Decision Tree Classification Algorithms" International Journal of Current Engineering and Technology, ISSN 2277 – 4106, Vol.3, No.2 (1June 2013)

Abstract: The key concept of my redundancy management is to exploit the tradeoff between energy consumption vs. to the gain of reliability, timeliness, and also security for maximizing the system useful lifetime in this paper I am proposing to explore more extensive malicious attacks in addition to packet dropping and bad mouthing attacks, each and every with some different implications for energy, security and also reliability, and the investigate intrusion detection and multipath routing based tolerance protocols to react to these attacks. Intrusion detection is a security mechanism which is used to identify those who are trying to break and misuse the system without authorization and also those who have legitimate access but misusing their privileges.

Keywords: Cyber physical system, Heterogeneous Wireless Sensor Networks, Intrusion detection system, Multipath routing

[1] O. Younis and S. Fahmy, "HEED: a hybrid, energy-efficient, distributed clustering approach for ad hoc sensor networks,"IEEE Trans. Mobile Comput., vol. 3, no. 4, pp. 366–379, 2004.
[2] E. Felemban, L. Chang-Gun, and E. Ekici, "MMSPEED: multipath multi-SPEED protocol for QoS guarantee of reliability and timeliness in wireless sensor networks," IEEE Trans. Mobile Comput.,vol.5,no. 6, pp. 738–754, 2006.
[3] I. R. Chen, A. P. Speer, and M. Eltoweissy, "Adaptive fault-tolerant
[4] QoS control algorithms for maximizing system lifetime of query-based wireless sensor networks,"IEEE Trans. Dependable Secure Computing, vol. 8, no. 2, pp. 161–176, 2011.
[5] M. Yarvis, N. Kushalnagar, H. Singh, A. Rangarajan, Y. Liu, and S. Singh, "Exploiting heterogeneity in sensor networks," inProc. 2005 IEEE Conf. Computer Commun., vol. 2, pp. 878–890.

Abstract: In the recent years, cloud computing has gained a lot of attention and big IT giants are providing cloudservices to the clients. As a part of this service user data are stored in remote server machines which are managed by the cloud service provider.This necessitates the encryption of user's sensitive information before outsourcing to the commercial public cloud. Huge amount of information present in the cloud calls for an efficient data retrieval mechanism.We are proposing a novel approach for keyword based search on the encrypted cloud data which facilitates easy retrieval of information from multiple encrypted data sources. Some of the existing approaches for the keyword based search focuses on Boolean search which will consider only the presence of the keyword in the file collection. Some other methods has server-side ranking wherethe encrypted relevance score may maintain some specific order which may lead to leakage of information. This paper uses latest information retrieval procedures along with vector space model and homomorphic encryption to ensure the search accuracy and secrecy while concealing the access pattern and search pattern. It provides a multi-user environment that supports storage of data belonging to multiple data owners andsearch request from multiple users. A salient feature of the suggested method is it allows efficient update operation of the file collection by the data owners. This process avoids the overhead of uploading/calculating the index scores for each of the existing documents from scratch for any file collection change.

Index Terms: Homomorphicencryption,Vector-space model,Secure searchable index

[1]. D. Song, D. Wagner, and A. Perrig, "Practical Techniques for Searches on Encrypted Data," Proc. IEEE Symp. Security and Privacy, 2000.
[2]. D. Boneh, G. Crescenzo, R. Ostrovsky, and G. Persiano, "Public- Key Encryption with Keyword Search," Proc. Int'l Conf. Theory and Applications of Cryptographic Techniques (Eurocrypt), 2004
[3]. C. Wang, N. Cao, J. Li, K. Ren, and W. Lou, "Secure Ranked Keyword Search over Encrypted Cloud Data," Proc. IEEE 30th Int'lConf. Distributed Computing Systems (ICDCS), 2010.
[4]. N. Cao, C. Wang, M. Li, K. Ren, and W. Lou, "Privacy-Preserving Multikeyword Ranked Search over Encrypted Cloud Data," Proc.IEEE INFOCOM, 2011.
[5]. S. Zerr, D. Olmedilla, W. Nejdl, and W. Siberski, "Zerber+r: Top-kRetrieval from a Confidential Index," Proc. 12th Int'l Conf.Extending Database Technology: Advances in Database Technology(EDBT), 2009.

Abstract: More recently, there has been a growing interest in cognitive techniques, which allow devices and even whole networks to monitor the environment in order to dynamically select and use the channel that affects the least interference. This study aims at Simulation of Bluetooth performance with IEEE 802.11b interference and determination of the Bit Error Rate (BER) using Advanced Design System (ADS 2011 C). The study which employed the Agilent Advance Design system 2011 (ADS 2011) as methodology, reveals that the frequency offset and relative interference power of IEEE 802.11b with Bluetooth signal affect the Bluetooth network performance in terms of BER. Thus the study recommends that measurement should be done in order to include the impact of these components on Bluetooth network performance with the interference from IEEE 802.1lb system.

[1]. Howitt, I. (2001). WLAN and WPAN Coexistence in UL Band. Transactions on Vehicular Technology, vol. 50, pp. 1114-1124.
[2]. Dari, T. & Pramoid, V. (2005). Fundamentals of Wireless Communication. Cambridge, MA: Cambridge University Press, Second Edition.
[3]. Golmie, N. & Mouveaux, F. (2001). Interference in the 2.4 GHz ISM band. Impact on the Bluetooth access control performance, vol. 8, pp.2540–2545.
[4]. Simon, M. K. & Alouini, M. S. (2000). Digital Communication over Fading Channels. New York: Wiley, First Edition.
[5]. Fainberg, M. & Goodman, D. (2001). Analysis of the interference between IEEE802.11b and Bluetooth systems. IEEE Vehicular Techn. vol.2, pp.967– 971.
[6]. Proakis, J. G. (2001). Digital Communications. New York: McGraw Hill, Fourth Edition.

Abstract: Glaucoma is a disease in which fluid pressure in the eye increases continously, and damage the optic nerve and leads to vision loss.It is the second leading cause of blindness. For identification of disease in human eyes we are using clinical decision support system which is based on retinal image analysis technique,that used to extract structure,contextual or texture features.Texture features within images which gives accurate and efficicent glaucoma classification.For finding this texture features we use energy distribution over wavelet subband.In this paper we focus on fourteen features which is obtained from daubecies(db3),symlets(sym3),and biorthogonal(bio3.3,bio3.5,and bio3.7).We propose a novel technique to extract this energy signatures using 2-D wavelet transform and passed these signatures to different feature ranking and feature selection strategies.The energy obtained from detailed coefficent are used to classify normal and glaucomatous image with high accuracy. This will be classified using support vector machines, sequential minimal optimization, random forest, naive Bayes and artificial neural network.We observed an accuracy of 94% using the ANN classifier.Performance graph is shown for all classifiers.Finally the defected region founded by segmentation and this will be post processed by morphological processing technique for smoothing operation.
Index Terms: Artificial neural network,data mining,feature extraction,glaucoma,imagetexture,wavelet transforms.

[1]. R. Varma et al., "Disease progression and the need for neuroprotection in glaucoma management," Am. J. Manage Care, vol. 14, pp. S15–S19, 2008.

[2]. R. George, R. S. Ve, and L. Vijaya, "Glaucoma in India: Estimated burden of disease," J. Glaucoma, vol. 19, pp. 391–397, Aug. 2010.

[3]. K. R. Sung et al., "Imaging of the retinal nerve fiber layer with spectral domain optical coherence tomography for glaucoma diagnosis," Br. J. Ophthalmol., 2010.

[4]. J. M. Miquel-Jimenez et al., "Glaucoma detection by wavelet-based anal-ysis of the global flash multifocal electroretinogram," Med. Eng. Phys., vol. 32, pp. 617–622, 2010.

[5]. B. Brown, "Structural and functional imaging of the retina: New ways to diagnose and assess retinal disease," Clin. Exp. Optometry, vol. 91, pp. 504–514, 2008.

Abstract: As described above, network security was developed to protect the physical network and - as with network-layer Virtual Private Network (VPN) solutions - to extend the network. Protecting the network means identifying which packets have access to the protected network and which do not. Extending the network means identifying which remote devices or networks have access to the protected network and which do not. Applications, which use the network for a communications infrastructure, require far more granular control than network security can provide. For example, while literally thousands of applications utilize the UDP protocol, most network security simply prevents the UDP protocol - not individual applications - from traversing the network border. By operating at the network layer, network security has four major limitations when used as the sole enabler of secure application access:

[1]. J. Broch, D.A. Maltz, D.B. Johnson, Y.C. Hu, and J. Jetcheva. A performance comparison of multi-hop wireless ad hoc network routing protocols. In Proceedings of the Fourth Annual ACM/IEEE International Conference on Mobile Computing and Networking (MobiCom98), 1998.
[2]. D. Camara and A.F. Loureiro. Gps/ant-like routing in ad hoc networks. Telecommunication Systems, 18(1–3):85–100, 2001.
[3]. H.S. Chang, W. Gutjahr, J. Yang, and S. Park. An Ant System approach to Markov Decision Processes. Technical Report 2003-10, Department of Statistics and Decision Support Systems, University of Vienna, Vienna, Austria, September 2003.
[4]. C. Cheng, R. Riley, S.P. Kumar, and J.J. Garcia-Luna-Aceves. A loop-free extended bellman-ford routing protocol without bouncing effect. ACM Computer Communication Review (SIGCOMM '89), 18 (4):224–236, 1989.
[5]. L. Chrisman. Reinforcement learning with perceptual aliasing: The perceptual distinctions approach. In Proceedings of the Tenth National Conference on Artificial Intelligence, pages 183–188, 1992.
[6]. I. Cidon, R. Rom, and Y. Shavitt. Multi-path routing combined with resource reservation. In IEEE INFOCOM'97, pages 92–100, 1997

Abstract: Since ARA is a reactive protocol, that is why it is used in such situations where mobility of nodes are higher. In this section, we have proposed the modifications to the algorithm by which the potential of ARA will increase in high mobility scenarios. Pheromone updates play a critical role in the performance of the ant algorithm. In ARA algorithm, initial pheromone value is computed by number hops during the route discovery. This method may not be suitable when nodes are mobile. Pheromone equations are classified in different categories. Two of them are the Classic pheromone filter, where route quality is not taken into consideration, for example the original ARA pheromone equation and the Gamma pheromone filter, which takes time and route quality into consideration.

[1]. Corson. Mobile ad hoc networking (manet): Routing protocol performance issues and evaluation considerations. http://www.ietf.org/rfc/rfc2501.txt.
[2]. Al Huda Amri and et. al. Scalability of manet routing protocols for heterogeneous and homogenous networks. Computers and Electrical Engineering, 2008.
[3]. Rome, Kay, and FriedemannMattern. The design space of wireless sensor networks. IEEE Wireless Communications, 11(6):54–61, December 2004.
[4]. R. Bellman. On a routing problem. Quarterly of Applied Mathematics, 16(1):87–90, 1958.
[5]. 5.F. Mondada, G. C. Pettinaro, A. Guignard, I. W. Kwee, D. Floreano, J.L. Deneubourg, S. Nolfi, L. M.Gambardella, and M. Dorigo. Swarm-Bot: a New Distributed Robotic Concept. Autonomous Robots,17(2–3), 2004.

Abstract: Power is key parameter in communication system. In cognitive radio power of transmitter of primary user and secondary user should be at appropriate level for faithfull reception, at the same time there should be no interference between and among the adjacent users. In this paper power control mechanism is proposed for determining the primary user transmitter power. Interference caused due to primary user to secondary user is white noise by secondary user imply different noise levels in different sub channels. Here OFDM is used to fill the spectral gap for which OFDM channel filling optimization technique is used.

Keywords: Cognitive Radio,OFDM,Primary User, Secondary User,FFT.

[1]. J. Mitola and G. Q. Maguire, "Cognitive radios: Making software radios more personal," IEEE Pers. Commun., vol. 6, no. 4, pp. 13-18, Aug. 1999.
[2]. J. Mitola, "Cognitive radio: An integrated agent architecture for software defined radio," Ph.D. dissertation, KTH, Royal Institute of Technology, Sweden, 2000.
[3]. Fette B "Cognitive radio Technology" ISBN-13: 978-0-7506-7952-7, Newnes 2006.
[4]. S. Haykin. Cognitive Radio: Brain-Empowered Wireless Communications. IEEE J. Sel. Areas Commun., 23(2):201–220, Feb. 2005.
[5]. MohamedGafar Ahmed Elnourani "CR and game theory: overview and simulations" Master's Thesis 2008

Abstract: To export nearly 1,0000 attribute from database to excel of 10-20 column information of students and papers of database format into the target Excel sheet, we developed the intelligent export system which distinguished the simple export system. The system uses Netbeans/Eclipse as the development tool, object-oriented language java as the programming language, MySQL as the background database, POI-HSSF as the apache Library for reading & writing data from excel, used AWT and SWING for design of an application ,and uses the mysql-connector as connection technology to database. The system realizes to export the massive data of database into the Excel format intellectually that convenient for integrating and managing the multiple data required for regular examination process in the institutes and universities. HSSF is the POI Project's pure Java implementation of the Excel '97(-2007) file format. XSSF is the POI Project's pure Java implementation of the Excel 2007 OOXML (.xlsx) file format.HSSF and XSSF provide ways to read spreadsheets create, modify, read and write XLS spreadsheets.
Index Terms: Database, JAVA, POI-HSSF, HSSF, MySQL, XSSF

[1]. http://howtodoinjava.com/2013/06/19/readingwriting-excel-files-in-java-poi-tutorial/, "Reading/writing excel files in java : POI tutorial".

[2]. http://poi.apache.org/spreadsheet/quick-guide.html "Busy Developers' Guide to HSSF and XSSF Features".

[3]. http://www.vogella.com/articles/JavaExcel/article.html,"Excel and Java - Read and Write Excel with Java"

[4]. http://mrbool.com/reading-excel-file-with-java/24562,"Reading Excel file with Java".

[5]. http://www.javacoderanch.com/how-to-read-excel-file.html,"How to read Excel file ?".

[6]. http:// java-read-write-excel-file-apache,"Read / Write Excel File In Java Using Apache POI"

Abstract: The amount of textual information available electronically has made it difficult for many users to find and access the right information within acceptable time. Research communities in the natural language processing (NLP) field are developing tools and techniques to alleviate these problems and help users in exploiting these vast resources. These techniques include Information Retrieval (IR) and Information Extraction (IE). The work described in this thesis concerns IE and more specifically, named entity extraction in English. The English language is of significant interest to the NLP community mainly due to its political and economic significance, but also due to its interesting characteristics. Text usually contains all kinds of names such as person names, company names, city and country names, sports teams, chemicals and lots of other names from specific domains. These names are called Named Entities (NE) and Named Entity Recognition (NER), one of the main tasks of IE systems, seeks to locate and classify automatically these names into predefined categories. NER systems are developed for different applications and can be beneficial to other information management technologies as it can be built over an IR system or can be used as the base module of a Data Mining application. In this thesis we propose an efficient and effective framework for extracting Arabic NEs from text using a rule based approach. Our approach makes use of English contextual and morphological information to extract named entities. The context is represented by means of words that are used as clues for each named entity type. Morphological information is used to detect the part of speech of each word given to the morphological analyzer. Subsequently we developed and implemented our rules in order to recognize each position of the named entity. Finally, our system implementation, evaluation metrics and experimental results are presented. We Present our Methodology by this Paper. Which use Hybrid approach of NlP and Machine Learning. This paper is a Review paper and Introduce Our Methodlogy.

Key Terms: Natural Language Processing, Machine Translation, Name Entity Recognition, Different Languages.

[1]. Ronan Collobert, Jason Weston, ―A Unified Architecture for Natural Language Processing: Deep Neural Networks with Multitask Learning‖
[2]. Harjinder Kaur, Dr. Vijay Laxmi, ―A Web Based English to Punjabi MT System for News Headlines,‖ International Journal of Advanced Research in Computer Science and Software Engineering, Volume 3, Issue 6, June 2013.
[3]. Latha R. Nair, David Peter S., ―Machine Translation Systems for Indian Languages,‖ International Journal of Computer Applications (0975 – 8887) Volume 39– No.1, February 2012.
[4]. Vishal Gupta, Gurpreet Singh Lehal, ―Named Entity Recognition for Punjabi Language Text Summarization,‖ International Journal of Computer Applications (0975 – 8887) Volume 33– No.3, November 2011.
[5]. Shubhangi Sharma, Neha Bora and Mitali Halder, ― English-Hindi Transliteration using Statistical Machine Translation in different Notation ,‖ International Conference on Computing and Control Engineering (ICCCE 2012), 12 & 13 April, 2012

Abstract: Mobile Ad Hoc Network is a collection of mobile hosts. The mobility nature of the MANET causes the mobile host to move freely in the network, due to that network disconnections occurs frequently and this causes frequent changes in the network topology. Data in one network cannot be accessed by the host in the another network, the data accessibility from one network to another network becomes impossible, consequently data accessibility become lower in the wireless network than in the conventional fixed network. This leads to performance degradation. Many Replica allocation methods are used to improve the data accessibility by replicating data item on mobile hosts other than the original owner of the data. This improves the data accessibility but some of the mobile hosts in the network may act as selfish nodes. These nodes use the services of other nodes but this does not cooperate with other nodes, this reduces the overall data accessibility. Selfish node detection algorithm is used to detect the selfish nodes and then the data replica allocation method is used for data replication. The simulation result shows that the conducted selfish node detection method and replica allocation techniques outperforms the improvement in data accessibility and reduce the communication cost and query delay. Keywords: Data accessibility, Selfish Nodes, Degree of selfishness, Mobile adhoc network, Replica allocation

[1] K. Balakrishnan, J. Deng, and P.K. Varshney, "TWOACK: Preventing Selfishness in Mobile Ad Hoc Networks", Proc. IEEE Wireless Comm. and Networking, pp. 2137-2142, 2005.
[2] T. Hara, "Effective Replica Allocation in Ad Hoc Networks for Improving Data Accessibility", Proc. IEEE INFOCOM, pp. 1568-1576, 2001.
[3] S. Marti, T. Giuli, K. Lai, and M. Baker, "Mitigating Routing Misbehavior in Mobile Ad hoc Networks", Proc. ACM MobiCom. 255-265, 2000.
[4] H. Miranda and L. Rodrigues, "Friends and Foes: Preventing Selfishness in Open Mobile Ad hoc Networks", Proc. IEEE Int'l Conf. Distributed Computing Systems Workshops, pp. 440-445, 2003.
[5] P. Padmanabhan, L. Gruenwald, A. Vallur, and M. Atiquzzaman, "A Survey of Data Replication Techniques for Mobile Ad Hoc Network Databases", The Int'l J. Very Large Data Bases, vol. 17, no. 5, pp. 1143-1164, 2008.
[6] K. Paul and D. Westhoff, "Context Aware Detection of Selfish Nodes in DSR Based Ad-Hoc Networks", Proc. IEEE Global Telecomm. Conf., pp. 178-182, 2002.
[7] W. Wang, X.-Y. Li, and Y. Wang, "Truthful Multicast Routing in Selfish Wireless Networks", Proc. ACM MobiCom, pp. 245-259, 2004.

Abstract: Forest fire is those of the natural disasters that cause devastation to economy and environment. In order determine forest fire many of the detecting approaches have been applied. In this paper, we proposed an approach for detecting forest fire by using land surface temperature satellite imagery. An image processing technique carried out on to identify the regions of interest extracted and mean wavelengths from these images. If the wavelength is greater than 31.8, it treated as forest fire. This approach uses k- mean clustering for segment the images and Haar wavelet transforms coefficient selection is used to modified decomposition of feature extracted regions to get more accuracy.

Keywords: Forest fire; Wavelet coefficient selection; Image processing; k-means clustering; Surface temperature satellite imagery; Temperature scale

[1] Turgay Çelik, Hüseyin Ozkaramanli and Hasan Demirel ,"Fire And Smoke Detection Without Sensors: Image Processing Based approach," 15th European Signal Processing Conference (EUSIPCO), Poznan, Poland, 2007, pp 1794-1798.
[2] T. Hame and Y. Rauste, "Multitemporal satellite data in forest mapping and fire monitoring," EARSeL Advanced Remote Sensing, 1995, pp.93–101.
[3] M.D Flannigan and T.H. Vonder Haar, "Forest Fire Monitoring Using Noaa Satellite Avhrr," Candian Journal Of Forest Research., 1986, pp.975-982.
[4] Yasar Guneri Sahin and Turker Ince, "Early Forest Fire Detection Using Radio-Acoustic Sounding System," Sensors, 2009, pp.1485-1498.
[5] Koji Nakau, Masami Fukuda, Keiji Kushida, Hiroshi Hayasaka, Keiji Kimura and Hiroshi Tani "Forest fire detection based on MODIS satellite imagery, and Comparison of NOAA satellite imagery with fire fighters' information," Institute of Low Temperature Science Hokkaido University, Japan, 2005, pp.18-23.

Abstract: Nowadays, Mobile Wireless Sensor Networks (MWSN) has become most popular and is used to solve challenging problems as industrial sensing and environmental monitoring. MWSN are always ready to be attack by replicas behavior of nodes which would negatively affect the quality of service. Existing schemes in mobile networks incurs efficiency and security problems. In the mobile sensor networks, the node replication attack is a harmful attack where one or more node(s) wrongly claims an identity, are also called clone attack. So, in order to overcome this problem localized algorithms are suggested to resist node replication attacks in mobile sensor networks. The advantages of these algorithms include 1) localized detection; 2) efficiency and effectiveness; 3) network-wide synchronization avoidance; and 4) network-wide revocation avoidance. But the main drawback is high computation complexity and high computation cost. Consequently, the trade-off between security strength and computation overhead has emerged as an area requiring further investigation. So, in order to overcome this trouble an innovative technique is proposed named as Improved Trust based Replica Detection System (ITRDS) which employ clustering algorithm. It is observed that, the proposed scheme decrease the computational complexity, computation cost, computation over head of the network after the detection.

Keywords: MWSN, security, Node Replication attack

[1]. Distributed Detection of Node Replication Attacks in Sensor Networks, Bryan Parno, Adrian Perrig, Carnegie Mellon University; Virgil Gligor, University of Maryland.
[2]. M. Conti, R.D. Pietro, L.V. Mancini, and A. Mei, "A Randomized,Efficient, and Distributed Protocol for the Detection of NodeReplication Attacks in Wireless Sensor Networks," Proc. ACM MobiHoc, pp. 80-89, Sept. 2007.
[3]. D. Braginsky and D. Estrin. Rumor routing algorithm for sensor networks. In Proceedings of ACM Workshop on Wireless Sensor Networks and Applications, 2002.
[4]. A. Hu and S. D. Servetto. Asymptotically optimal time synchronization in dense sensor networks. In Proceed- ings of ACM International Conference on Wireless Sen- sor Networks and Applications, 2003.
[5]. P. Rohatgi. A compact and fast hybrid signature scheme for multicast packet. In Proceedings of ACM Conference on Computer and Communications Security (CCS), Nov. 1999

Abstract: The Energy consumption is a great challenge in the field on Wireless Sensor Network (WSN). It affects the performance of the whole network. There are two basic functions of nodes in the WSN. First is to collect the information from other sensor nodes and second is to pass the information to the base station. A variety of routing protocols have been proposed to reduce the energy consumption of nodes and increase the network lifetime. One of the most popular energy efficient routing protocol is Geographic Adaptive Fidelity (GAF). This is the location based routing protocol which uses the nodes location information to transfer the data. This reduces the use of energy by turning off the radio of some nodes which share the same functionalities. This paper focuses on the GAF protocol and its working. Also this paper reviews the variety of new versions based on GAF protocol to make it better. Keywords: Energy Efficiency, Location, GAF, WSN.

[1] Ridha Soua and Pascale Minet, "A Survey on Energy Efficient Techniques in Wireless Sensor Networks", IEEE Wireless and Mobile Networking Conference (WMNC), pp. 1-9, October 2011.
[2] Pallavi Jindal and Vikas Gupta, "Study of Energy Efficient Routing Protocols of Wireless Sensor Networks and Their Further Researches: A Survey", International Journal of Computer Science and Communication Engineering, Volume 2, Issue 2, pp. 57-62, May 2013.
[3] Sinchan Roychowdhury and Chiranjib Patra, "Geographic Adaptive Fidelity and Geographic Energy Aware Routing in Ad Hoc Routing", Special Issue of International Journal of Computer and Communication Technology (IJCCT), Volume 1, Issue 2, pp. 309-313, August 2010.
[4] Gayatri Prajapati, Palak Parmar, "A Survey on Routing Protocols of Location Aware and Data Centric Routing Protocols in Wireless Sensor Network", International Journal of Science and Research (IJSR), India, Volume 2, Issue 1, pp. 128-133, January 2013.
[5] Parul Tyagi and Surbhi Jain, "Comparative Study of Routing Protocols in Wireless Sensor Network", International Journal of Advanced Research in Computer Science and Software Engineering Research Paper, Volume 2, Issue 9, pp.134-140, September 2012.

Abstract: Past research in mathematics, acoustics, and speech technology have provided many methods for converting data that can be considered as information if interpreted correctly. In order to find some statistically relevant information from data, it is important to have mechanisms for reducing the information of each segment in the audio signal into features. These features should describe each segment in such a characteristic way that other similar segments can be grouped together by comparing their features. Preprocessing of speech signals is considered a crucial step in the development of a robust and efficient speech or speaker recognition system. This paper deals with result obtained by MFCC and LPC feature extraction technique and SVM feature matching technique.

Keywords: Speech recognition, Mel Frequency Cepstral Coefficient (MFCC), Linear Predictive Coefficient (LPC), Support Vector Machine (SVM)

[1] M.A.Anusuya,S.K.Katti," Speech Recognition by Machine: A Review", International Journalof Computer Science and Information Security,Vol. 6, No. 3, 2009,pp. 181-205. R.E. Moore, Interval analysis (Englewood Cliffs, NJ: Prentice-Hall, 1966).
[2] Chalapathy Neti, Nitendra Rajput, Ashish Verma, "A Large Vocabulary Continuous Speech Recognition system for Hindi", In Proceedings of the National conference on Communications, Mumbai, 2002, pp. 366-370.
[3] Tejas Godambe and Samudravijaya K., "Speech Data Acquisition for Voice based Agricultural Information Retrieval", presented at the 39th All India DLA Conference, Punjabi University, Patiala, 14-16th June 2011.
[4] Lindasalwa Muda, Mumtaj Begam and I. Elamvazuthi, "Voice Recognition Algorithms using Mel Frequency Cepstral Coefficient (MFCC) and Dynamic Time Warping (DTW) Techniques", JOURNAL OF COMPUTING, VOLUME 2, ISSUE 3, MARCH 2010, ISSN 2151-9617.
[5] Leena R Mehta , S.P.Mahajan , Amol S Dabhade Comparative Study Of MFCC And LPC For Marathi Isolated Word Recognition System" International Journal of Advanced Research in Electrical, Electronics and Instrumentation Engineering Vol. 2, Issue 6, June 2013.

Abstract: Nanotech is the manipulation of the matter on atomic and molecular scale. Nanorobots being an emerging technology used precisely in machines or robots to perform controlled manipulation of objects with nanometre scale dimensions. The Nanorobot components are at a scale of nanometre ( ) ranging from 0.1-10 micrometer.This paper presents a comprehensive survey on novel medical applications by Nanorobots. Recent works on the Nanorobots such as preventing ailments, treating cancer, alleviating pain and progressing human health has been described specifically. The aspect of this paper is on the Nanorobots medical applications and challenges to control its movement in human body.

Keywords: Nanodentistry, Nanomachine, Nanometer, Nanotech, Nanomedicine

[1]. Cavalcanti A, smithJ.A.Claytn, Nanorobots for laparoscopic cancer surgery,International Conference on IEEE/ACIS,2007
[2]. Adriano Cavalcanti, Bijan Shirinzadeh, Tad Hogg, Luis C. Kretly: CMOS-based Nanorobot to Combat Cancer CAN Centre for Automation in Nano biotech Sao Paulo SP 01540, Brazil
[3]. Venkatesan M, D.Y Patil and Jolad B. Nanorobots in cancer treatment, IEEEInternational conference 2010, pp.258 to 264
[4]. Deepa.R.Parmar, Julee.P.soni and Apexa.D. Patel and Dhrubo Jyoti Sen, Nanorobotics in advances in Pharamaceutical sciences, International Journal of Drug Development & Research.Vol.2, Issue2I, june2010
[5]. Neetha J.Shetty, P.Swati and K.David,Nanorobots Future in dentistry, The Saudi Dental Journal v.25 (2) April 2013
[6]. Ana-Maria Dumitrescu,andCristina Dascalu,Dental Nanorobots small instruments with large potential, Romanian journal of oral rehabilitation., Vol.3,No.4,December2011

Abstract: This paper proposed to give in depth survey on types of spoofing attacks. Spoofing is a way of masquerading identity of a person or a computer by providing fake data. In this paper huge variety of spoofing methods which includes brief introduction of Dns, Ip, Email, Arp, Web, Wireless, Mac spoofing. The intention of spoofing is to manipulate, steal data which in turn results in data loss and loss on millions of dollars.

Keywords: spoofing techniques, fake, attacker, network access, invader.

[1]. jin-cherng Lin, men-jue Koo and cheng-sheng wang, A proposal for a schema for ARP spoofing protection Trans tech publication, Switzerland, 284-287, 2013.
[2]. Yangliu, kaikun dong, lan dong, and bin li,research of the ARP spoofing principle and a defensive algorithm, international journal of communication, vol.4, No.4,pp 516-520,2005.
[3]. Edward W. Felten, Dirk Balfanz, Drew Dean, and Dan S. Wallach,Web Spoofing: An Internet Con Game, Technical Report 540–96,1997.
[4]. Golriz Khazan and Mohammad Abdollahi Azgomi, DNS Spoofing Attack Simulation for Model-Based Security Evaluation, IJAST, vol.1,2010.
[5]. Fanglu Guo Jiawu Chen Tzi-cker Chiueh, Spoof Detection for Preventing DoS Attacks against DNS Servers proc of IEEE symposium,2012.

Abstract: Spam mails can be referred as unsolicited bulk email. These messages are used to advertise products and services for phishing purposes or to lead recipients to malicious sites with unethical intentions. Although numerous techniques to block spam e-mails have been developed, we still receive them quite often. The reason behind this is mainly ability of the spammers to manipulate the filters. Therefore we present a method based on Natural Language Processing (NLP) for the filtration of spam emails in order to enhance online security. The technique presented in this paper is a stepwise approach which blocks spam emails based on the sender as well as the content of the mail.

Keywords: Natural Language Processing (NLP), spam detection, online security, spam filtering.

[1]. Auto-Coding and Natural Language Processing by Richard Wolowitz - 3M Health Information System - White Paper 2011.
[2]. Security Focus Report – Spam in Today‟s Business World by TREND LABS – Global Technical Support and R & D Center of TREND MICRO - White Paper 2011.
[3]. Christoph Karlberger, Gunther Bayler, Christopher Kruegel, and Engin - "Exploiting Redundancy in Natural Language to Penetrate Bayesian Spam Filters" at Kirda Secure Systems Lab Technical University Vienna.
[4]. Shabbir Ahmed and Farzana Mithun – "Word Stemming to Enhance Spam Filtering" at Department of Computer Science & Engineering, University of Dhaka, Bangladesh.
[5]. "Blocking over 98% of Spam using Bayesian Filtering Technology", GFI Software, http://www.secinf.net/anti_spam/Blocking_Spam_Bayesian_Filtering.html, Oct. 2003.

Abstract: Data stream is massive, fast changing and infinite in nature. It is very natural that large amount of unlabeled data and small amount labeled are available in data stream environments. Storing and labeling all data is considered expensive and impractical. The objective is to label small portion of stream data and analyze data online without storing it. Concept drift, concept evolving, stream evolving is also the major challenging problem occurs while working with data stream. Online data stream active learning is needed to tackle these problems. Classification and clustering are two technical areas that are widely used to extract pattern from the large data stream, from that a classification model must endlessly adapt itself to the most recent concept. Hence, this paper gives the overview of various ensemble based classification algorithm techniques in the field of data stream mining.

Keywords: Concept drift, data stream mining, classifier ensemble

[1] Kapil Wankhade, Snehlata Dongre, Ravindra Thool, "New Evolving Ensemble Classifier for Handling Concept Drifting Data Streams", 2nd IEEE International Conference on Parallel, Distributed and Grid Computing, pp. 657-662, 2012
[2] Mayank Pal Singh, "Quantifying Concept Drifting in Network Traffic using ROC Curves from Naive Bayes Classifiers", Nirma University International Conference on Engineering (NUiCONE), 2013
[3] Xingquan Zhu, Xindong Wu, and Ying Yang, "Dynamic Classifier Selection for Effective Mining from Noisy Data Streams", Proceedings of the Fourth IEEE International Conference on Data Mining (ICDM'04)
[4] Gansen Zhao, Ziliu Li, Fujiao Liu and Yong Tang, "A Concept Drifting based Clustering Framework for Data Streams", Fourth International Conference on Emerging Intelligent Data and Web Technologies, 2013.
[5] G. R. Marrs • M. M. Black • R. J. Hickey, "The use of time stamps in handling latency and concept drift in online learning", Springer-Verlag, 2012

Abstract: In this article we considered a cloud computing network model which acts as a tool for dynamic resource allocation problem in cloud computing. Every successful servicing of cloud paradigm need an optimal resource allocation in turn to handle the problem of Bag of tasks (BoT). We assume that the allocation of BoT's are done by two stages of process namely classification according to service level agreement (SLA) of BoT and service providing (SaaS, PaaS and IaaS). After classification the request is routed to any one of the service provider with corresponding probability, Thus the Cloud Computing Networks (CCN) become a general open Jackson Queuing Network system performance measures are obtained to study the efficiency of the CCN.

Keywords: Cloud Computing, Resource Allocation, Jackson Queuing Network

[1] L.M. Vaquero.L.M, L. Rodero-Merino, J. Caceres, and M. Lindner, "A Break in the Clouds: Towards a Cloud Definition," ACM SIGCOMM Computer Comm. Rev., vol. 39, pp. 50-55, Dec. 2008.
[2] Shin-ichiKuribayashi, "Optimal Joint Multiple Resource Allocation Method for Cloud Computing Environments", International Journal of Research and Reviews in Computer Science Vol. 2, March 2011.
[3] Kulkarni, V.G, "Introduction to modeling and analysis of stochastic system" 2 nd edition, Springer text in staticstic, 2011
[4] SaiSowjanya.T, D.Praveen. D, Satish.K, Rahiman. A," The Queueing Theory in Cloud Computing to Reduce the Waiting Time",IJCSET April 2011.
[5] MasoudSalehpour and AsadollahShahbahrami,"Alleviating Dynamic Resource Allocation for Bag of Tasks Applications in Cloud Computing", International Journal of Grid and Distributed Computing Vol. 5, No. 3, September, 2012

Abstract: According to Internet World statistics, todayInternet has 1.7 Billion users, compared with the population of 6.7 billion people.Around 40% of the world population is connected via internet across the globe.By 2020 it is predicted, former will shoot to 5 billion users. Services are rapidly growing on the Internet, generating huge volume of Data likewise, trace logs, service information andtheir respective relationshipsetc. So, there is an urging need to gather such massive amount of Data. This is where the term-"Big Data"comes intopicture. Big data is a catch-phrase or a buzzword, used to describe a massive volume of both structured and unstructured data too large to be processed and handled using traditional database andsoftware technique. Considering, the volume of Data in context and its heterogeneity, its analysis is challenging. Focus of this paper is to do in depth survey as to what exactly is the upcoming technology -Big Data. ALiterature survey has been done to gather information of what has been done in the world of big data so far, present developments in it and the future of data storage under Big Data. The Data sources used for Big-Data analytics in the operational world simplydo not fit into desktop or small-scale database structures and therefore can be hosted using cloud concepts, to achieve this techniques are discussedas to how it is mined more efficiently, with on-premises database architectures.BigData is not a mere term, it's a concern now.

Keywords: Big Data, Hadoop, Map Reduce, Cloud.

[1] The Economist, Nov 2011, "Drowning in numbers – Digital Data will flood the planet and help us understand it better", http://www.economist.com/blogs/dailychart/2011/11/Big-Data-0
[2] 2013IEEEInternationalConferenceonSystems, Man, and Cybernetics,"Big Data Framework "Firat Tekiner1 and John A. Keane
[3] Bonnet L., Laurent A., Sala M., Laurent B., Sicard N., September 2011, "Reduce, You Say: What NoSQL Can Do for Data Aggregation and BI in Large Repositories", dexa, pp.483-488, 22nd International Workshop on Database and Expert Systems Applications, 2011
[4] Mark B., "Gartner Says Solving 'BigData' Challenge Involves More Than Just Managing Volumes of Data". Gartner, June 27, 2011, http://www.gartner.com/newsroom/id/1731916

[5] 2013 IEEE International Symposium on Multimedia,"BigData and NSA Surveillance - Survey of Technology and Legal Issues‟ ,Chanmin Park Taehyung Wang

Abstract: The goals of this study were to develop and evaluate a model for acceptance of biometric-based access control for payment technologies used in commercial Banks. A wide diversity of systems requires reliable personal detection schemes to either authenticate or establish the identity of an entity requesting their services. The rationale of such schemes is to ensure that the rendered services are accessed only by a rightful user and no one else. In this paper, a biometric authentication system was modeled. This scheme eliminates the tension of cards which requires one to memorize theirs difficult passwords and pin numbers. The study centered on establishing how biometric authentication can be used in payment technology to boost e-card security. The target population included customers and DTB staff at Kisii branch. Simple random sampling technique was engaged to select respondents. The necessary data was collected using closed-ended questionnaires for easy of analysis. This data was then tabulated and presented in graphs for interpretation purposes. The researcher used the research study findings to design and develop a model, which was called BAECPT (Biometric- Based Authentication E-Card Payment Technology). The requirements for this model were obtained from the responses that the target population gave. Some research constructs loaded low than the theoretical established threshold values and was therefore dropped, while the ones that loaded higher than these values were retained. These are the ones that were included in the final model.

Keywords: BAECPT, E-Cards, Model, SEM. Abbreviations: Diamond Trust Bank (DTB), TAM (Technology Acceptance Model), PIN (Personal Identification Number), ATM (Automatic Teller Machine), Structured Equation Modeling (SEM).

[1]. G. Diane, (2014), "What Is Fingerprint Authentication?", Conjecture Corporation.

[2]. C. Jensen, (2009), "Identity and Privacy In Internet Age", Springer Journals.

[3]. AdMonsters, (2013), "Putting Money into the Mobile Wallet"

[4]. B. Connelly (2011), "Signalling Theory: A review and Assesment", Journal Of Management.

[5]. F. Dainelli (2011), " Signalling theory and voluntary disclosure to the financial market", Evidence from the profitability indicators published in the annual report, Paper presented at the 34th EAA Annual Congress.

[6]. Elizabeth et al., (2012), "Adoption of E-tax services in Uganda".

[7]. Martin S., (2014), "Instrument Reliability"

Abstract: Collaborative filtering is one of the most promising techniques in recommender systems, providing personalized recommendations to users based on their previously expressed preferences in the form of ratings and those of other similar users. A recommender system uses Collaborative Filtering or Content-Based methods to predict new items of interest for a user. Although both methods have their own and distinct advantages but individually they fail to provide good recommendations in many situations. Incorporating components from collaborative and content based methods, can overcome these challenges like data sparsity, stability, accuracy and correlation of traditional recommender systems. Inadequate ratings lot of time gives poor quality of recommendations in terms of accuracy. Various approaches are used for overcome these issues: i) Firstly, we propose to improve data sparsity and correlation. ii) Secondly, we aim to tackle the problem of rank and relevance and we improve recommender system in novelty & diversity using rank & relevance technique.

Keywords: Recommender systems, recommendation diversity, sparsity, correlation, ranking functions, Collaborative Filtering, Content based filtering.

[1] D. Goldberg, D. Nichols, B. M. Oki, and D. Terry. Using collaborative filtering to weave an information tapestry, volume 35. ACM Press, http://doi.acm.org/10.1145/138859.138867, 1992. 61-70. [cited at p. 1, 10, 13]
[2] Paul Resnick and Hal R. Varian. Recommender systems, volume 40. ACM Press, http://doi.acm.org/10.1145/245108.245121, 1997. [cited at p. 1, 8]
[3] R. Mooney, P. Bennett, and L. Roy. Book recommending using text categorization with extracted information. In Recommender Systems. Papers from 1998 Workshop. Technical Report WS-98, volume 8, 1998.
[4] J. Breese, D. Heckerman, C. Kadie, et al. Empirical analysis of predictive algorithms for collaborative filtering. In Proc. of UAI98, pages 4352, 1998.
[5] J. Herlocker, J. Konstan, and J. Riedl. An algorithmic framework for performing collaborative filtering. In Proc. of SIGIR99, pages 230237. ACM New York, NY,USA, 1999.
[6] G. Linden, B. Smith, and J. York. Amazon. com recommendations: Item-to-item collaborative filtering. Internet Computing, IEEE, 7(1):7680, 2003.

Abstract: In the present circumstances real world databases contain thousands of attributes and relations. Access the information from this corpulent database is a nontrivial task and is an exploring area. Queries are using for database access, but it is a thought-provoking task to the end user because, dearth of familiarity with query language and illiteracy of underlying schema. The new system uses the allowance of query by form methodology. In which the user is granted with a query form that will help the user to iteratively search the database and query enhancement is provided to the user by means of feedback or response. Ad hoc queries can also gratified by using this proposed system. Query enhancement by response is provided by ranking the attribute used in the form. Ranking is mean by precision and recall. To provide the security to the system and make it efficient for private database an encryption is applied. Keywords: Query Interface, Precision, Recall.

[1]. Liang Tang, Tao Li, Yexi Jiang, and Zhiyuan Chen Dynamic Query Forms for Database Queries, in proceedings of TKDE.2013.62,pages 1041-4347,U.S.A,June 2013
[2]. DBPedia. http://DBPedia.org.
[3]. EasyQuery. http://devtools.korzh.com/eq/dotnet/.
[4]. Freebase. http://www.freebase.com.
[5]. C. C. Aggarwal, J. Han, J. Wang, and P. S. Yu. A framework for clustering evolving data streams. In Proceedings of VLDB, pages 81–92, Berlin, Germany, September 2003.