iosr-JCE   Volume-17 ~ Issue-3 ~ May-June 2015

Abstract:Mobile means moving and Ad Hoc means temporary without any fixed infrastructure, so mobile ad hoc networks (MANETs) are a kind of temporary networks in which nodes are moving without any fixed infrastructure or centralized administration. It is the new emerging technology which enables user to communicate without any physical infrastructure regardless of their geographical location, that's why it is also referred to as an "infrastructure less" network. Unfortunately, ad hoc networks are particularly vulnerable due mainly to their lack of infrastructure.

[1]. C. Siva Ram Murthy and B. S. Manoj, "Ad Hoc Wireless Net-works, Architecture and Protocols," Pearson Education, Fourth Impression, 2009.
[2]. M. Abolhasan, T. Wysocki and E. Dutkiewicz, "A review of Routing Protocols for Mobile Ad Hoc Networks", Ad Hoc Networks, Vol. 2, Issue 1, PP. 122, Jan. 2004.
[3]. Elizabeth M. Royer and Chai-Keong Toh, "A Review of Current Routing Protocols for Ad Hoc Mobile Wireless Networks", IEEE Personal Communications Magazine, Vol. 6, No. 2, PP. 46-55, April 1999.
[4]. Scott Corson and Joseph Macker, "Mobile Ad Hoc Networking (MANET): Routing Protocol Performance Issues and Evaluation Considerations", IETF MANET, RFC 2501, 1999.
[5]. Huafeng Wu1, Chaojian Shi1," A Trust Management Model for P2P File Sharing System", International Conference on Multimedia and Ubiquitous Engineering, IEEE Explore 978-0-7695-3134-2/08, 2008.

Abstract: Collecting, storing and processing of huge amounts of climatic data is necessary for accurate prediction of weather. Meteorological departments use different types of sensors such as temperature, humidity etc. to get the values. Number of sensors and volume and velocity of data in each of the sensors makes the data processing time consuming and complex. Leveraging MapReduce with Hadoop to process the massive amount of data. Hadoop is an open framework suitable for large scale data processing. MapReduce programming model helps to process large data sets in parallel, distributed manner. This project aims to build a data analytical engine for high velocity, huge volume temperature data from sensors using MapReduce on Hadoop.

Keywords – Bigdata, MapReduce, Hadoop, Weather, Analytics, Temperature

[1] Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The Google File System. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, SOSP '03, pages 29–43, New York, NY, USA, October 2003. ACM. ISBN 1-58113-757-5. doi: 10.1145/945445.945450.
[2] Jeffrey Dean and Sanjay Ghemawat. MapReduce: A Flexible Data Processing Tool. Communications of the ACM, 53(1):72–77, January 2010. ISSN 0001-0782. doi: 10.1145/1629175.1629198.
[3] Wiki (2013). Applications and organizations using hadoop. http://wiki.apache.org/hadoop/PoweredBy
[4] Gartner Research Cycle 2014, http://www.gartner .com
[5] K. Morton, M. Balazinska and D. Grossman, "Paratimer: a progress indicator for MapReduce DAGs", In Proceedings of the 2010 international conference on Management of data, 2010, pp.507–518.

Abstract: Measuring the similarity between documents is an important operation in the text processing field. Text categorization (also known as text classification, or topic spotting) is the task of automatically sorting a set of documents into categories from a predefine set [1]. TEXT categorization (TC) is the task of automatically classifying unlabeled natural language documents into a predefined set of semantic categories [2]. The term weighting methods assign appropriate weights to the terms to improve the performance of text categorization [1]. The traditional term weighting methods borrowed from information retrieval(IR), such as binary, term frequency (tf), tf:idf, and its various variants, belong to the unsupervised term weighting methods as the calculation of these weighting methods do not make use of the information on the category membership of training documents. Generally, the supervised term weighting methods adopt this known information in several ways. Therefore, the fundamental question arise here.

[1] Man lan, Chew lim Tan, senier member, IEEE, Jian Su and Yue Lu, member, IEEE "Supervised and traditional term weighting method for Automatic Text Categorization" IEEE Trans. on pattern Analysis and Machine Intellegence, Vol 31, no 4, April 2009.
[2] Z-H. Deng, S.-W. Tang, D.-Q. Yang, M.- M. Zhang, L.-Y. Li, and K.Q. Xie, "A Comparative Study on Feature Weight in Text Categorization," Proc. Asia-Pacific Web Conf., vol. 3007, pp. 588-597, 2004.
[3] F Debole and F. Sebastini, "Supervisedterm weighting method for automated text categorization" Proc. ACM Symp, Applied Computing, pp. 784-788, 2003.
[4] P.Soucy and G. W. Mineau, " beyond TFIDF weighting for text categorizationin vector space model", Proc. Int'l Joint Conf. Artifial Intelligence. Pp. 1130-1135, 2005.
[5] Thorsten Joachims, "Text Categorization with Support Vector Machines: Learning with many relevant features" Universitat Dortmund informatik ls8, baropar, 44221 Dortmund, Germany.

Abstract: In this paper a pose invariant face recognition using neuro-fuzzy approach is proposed. Here adaptive neuro fuzzy interface system (ANFIS) classifier is used as neuro-fuzzy approach for pose invariant face recognition. In the proposed approach the preprocessing of image is done by using adaptive median filter. It removes the salt pepper noise from the original images. From these denoised images features are extracted. Here Principal component analysis (PCA) is used for extracting the features of an image under test. Then ANFIS classifier is used for face recognition. PCA calculate the principal components and are used by ANFIS for further process. Here in this paper combination of PCA and ANFIS is represented as PCA+ANFIS. In the paper standard ORL face database is used for experimental results. The performance PCA+ANFIS with LDA+ANFIS and ICA+ANFIS is analyzed and compared. From experimental results it is shown that PCA+ANFIS outperforms than other two approaches. PCA+ANFIS is also compared by existing feed forward neural network (FFBNN) approach. The results show that proposed approach gives better outputs in terms of accuracy, sensitivity and specificity.

Keywords - Feature extraction, image denoising, image recognition, neural networks and feedforward neural network.

[1]. Brunelli Roberto And Tomaso Poggio, "Face Recognition: Features Versus Templates," IEEE Transactions On Pattern Analysis And Machine Intelligence, Vol. 15, No. 10, Pp. 1042-1052, 1993.
[2]. Turk Matthew And Alex Pentland, "Eigenface For Recognition," Journal Of Cognitive Neuroscience, Vol. 3, No. 1, Pp. 71-86, 1991.
[3]. Huang, Fu Jie, Zhihua Zhou, Hong-Jiang Zhang, And Tsuhan Chen, "Pose Invariant Face Recognition," In Fourth IEEE International Conference On Automatic Face And Gesture Recognition, 2000, Pp. 245-250.
[4]. Wu-Jun Li, Chong-Jun Wan, Dian-Xiang Xu, And Shi-Fu Chen, "Illumination Invariant Face Recognition Based On Neural Network Ensemble," In Proceedings Of The 16th IEEE International Conference On Tools With Artificial Intelligence (ICTAI 2004), 2004.

Abstract:As we move ahead in technology advancements, from simple data processing, to intelligent computing, one area of research undergoing advancement, is the system of reading text characters on an image. Optical Character Recognition (OCR) system is used for converting text characters on images into computer editable text characters. It includes steps such as Image acquisition, pre-processing of the image, segmentation of lines and characters, recognition of characters, and finally application. The image acquisition step determines the method for obtaining the image. Pre- processing of image includes enhancement of the image to make it suitable for recognition. Segmentation is the extraction of the character part of the image. Recognition is the comparison of the sample image with the template image. This paper focuses on the OCR system and includes information regarding the various operations that may be performed on the image for the recognition of characters.

Keywords: Image acquisition, OCR, Pre-Processing, Recognition, Segmentation, Text Characters.

[1]. Disha Bhattacharjee, Deepti Tripathi, Rubi Debnath, Vivek Hanumante, Sahadev Roy. "A Novel Approach for Character Recognition", International Journal of Engineering Trends and Technology (IJETT) – Volume 10 Number 6 - Apr 2014
[2]. Ayatullah Faruk Mollah, Nabamita Majumder, Subhadip Basu and Mita Nasipuri. "Design of an Optical Character Recognition System for Camera Based Handheld Devices", IJCSI International Journal of Computer Science Issues, Vol. 8, Issue 4, No 1, July 2011
[3]. A. F. Mollah, S. Basu, N. Das, R. Sarkar, M. Nasipuri, M. Kundu, "Text/Graphics Separation and Skew Correction of Text Regions of Business Card Images for Mobile Devices", Journal of Computing, Vol. 2, Issue 2, February 2010, ISSN 2151-9617
[4]. Ravi Kumar, Anurag Anand, Nikunj Sharma, "Recognition of English Characters by Codes Generated Using Neighbour Identification". International Journal of Application or Innovation in Engineering and Management (IJAIEM) – Vol. 2, Issue 4, April 2013, ISSN 2319-4847
[5]. N. Otsu, "A threshold selection method from gray level histogram", IEEE Transactions in Systems, Man, and Cybernetics, Vol. 9, pp. 62 – 66

Abstract: Software's, programs are valuable assets to developer companies. However, the source code of programs can be theft and JavaScript programs whose code is easily available which is a serious threat to the industry. There are techniques like watermarking which prove the ownership of the program but it can be defaced and encryption which changes source code but it may decrypted and also it cannot avoid the source code being copied. In this paper, we use a new technique, software birthmark, to help detect code theft of software or program. A birthmark is a unique characteristic of a program which is used to identify the program. We extract the birthmark of software from the run-time heap by using frequent sub graph mining and search the same in suspected program.

Keywords: Heap graph, software birthmark, frequent sub graph mining..

[1]. A. Monden, H. Iida, K. I.Matsumoto, K. Inoue, and K. Torii, "Watermarking java programs," in Proc. Int. Symp. Future Software Technol., Nanjing, China, 1999.
[2]. C. Collberg, E. Carter, S. Debray, A. Huntwork, J. Kececioglu, C. Linn, and M. Stepp, "Dynamic path-based software watermarking," in Proc. ACM SIGPLAN 2004 Conf. Programming Language Design and Implementation (PLDI '04), New York, 2004, pp. 107–118, ACM.
[3]. X. Wang,Y.-C. Jhi, S. Zhu, and P. Liu, "Behavior based software theft detection," in Proc. 16th ACM Conf. Comput.andCommun. Security (CCS '09), New York, 2009, pp. 280–290, ACM.
[4]. G.Myles and C. Collberg, "Detecting software theft via whole program path birthmarks," in Proc. Inf. Security 7th Int. Conf. (ISC 2004), Palo Alto, CA, Sep. 27–29, 2004, pp. 404–415.
[5]. D. Schuler, V. Dallmeier, and C. Lindig, "A dynamic birthmark for java," in Proc. 22nd IEEE/ACM Int. Conf. Automated Software Eng. (ASE '07), New York, 2007, pp. 274–283, ACM.

Abstract: Cloud computing enables the process of storing documents, files photos online instead of using home computer, or webmail or a social networking site. If in an organization, it is desired to use an online invoicing service instead of updating the in-house one, which has been used since many years, then that online invoicing service is a "cloud computing" service. This paper briefs about Cloud computing that refers to the delivery of computing resources over the Internet. Also instead of keeping data on one's own hard drive or updating applications for one's needs, it is suggested to use a service over the Internet, at another location, to store the information or use its applications.

Keyword: cloud, online, internet, virtualization, data centre, business application.

[1]. Cloud Computing: Principles and Paradigms edited by RajkumarBuyya, James Broberg, Andrzej M. Goscinski.
[2]. Handbook of Cloud Computing edited by BorkoFurht, Armando Escalante
[3]. http://www.business2community.com/cloud-computing/top-5-examples-cloud-computing-0973287
[4]. http://sbinfocanada.about.com/od/itmanagement/a/Cloud-Computing-Disadvantages.htm
[5]. http://www.aia.org/aiaucmp/groups/aia/documents/pdf/aiab086678.pdf

Abstract: variety of useful applications demonstrate the need for precise motion-based segmentation of image data. Video compression techniques rely heavily on accurate and efficient representation of spatio-temporal regions possessing similar motion models. In addition a variety of cinematographic effects are potentially possible with successful separation of a moving object's image from an arbitrary background. Computer vision systems often depend on the ability to distinguish or describe a moving object in an image sequence..

[1]. B. Han and L. Davis, On-Line Density-Based Appearance Modeling for Object Tracking, ICCV 2005.
[2]. Mittal and N. Paragios, Motion-based Background Substraction using Adaptive Kernel Density Estimation,CVPR, 2004
[3]. Ludwig Schwardt "The Least-Mean-Square Algorithm" University of Stellenbosch, 6th May 2004
[4]. P.R. Giaccone and G.A. Jones. "Segmentation of Global Motion using Temporal Probabilistic Classification." Proceedings of British Machine Vision Conference, pages 619-628, University of Southampton, 1998.
[5]. Wang and Adelson. "Spatio-Temporal Segmentation of Video Data." Proceedings of the SPIE: Image and Video Processing II, vol. 2182, San Jose, CA, February 1994
[6]. Wang and Adelson. "Representing Moving Images with Layers." IEEE Transactions on Image Processing, vol. 3, no. 5, September

Abstract: Orthogonal Frequency Division Multiplexing (OFDM) scheme is also used as a digital multi carrier modulation method .Alarge number of closely spaced orthogonal sub-carrier signals are used to carry data on several parallel data streams or channels. While OFDM is ideally suited to deal with frequency selective channels and AWGN, its performance may be dramatically impacted by the presence of impulse noise. In fact, very strong noise impulses in the time domain might result in the erasure of whole OFDM blocks of symbols at the receiver. BER analysisis done by using various adaptive algorithms and Impulse noise impact can be observed..

[1]. T.Y. Al-Naffouri, A.A Quadeer and G.Caire, "Impulse Noise Estimation and Removal for OFDM Systems".
[2]. Sumi Mathew, PrasanthMurukan, "Periodic Impulse Noise Reduction in OFDM based Power Line Communication"
[3]. AlokPandey, L.D Malviya "Comparitive Study of LMS & NLMS Algorithms in Adaptive Equalize".
[4]. T.Y. Al-Naffouri, A.A Quadeer and G.Caire, "Impulse Noise Estimation and cancellation in DSL using orthogonal clustering".
[5]. Vidya S. Bhosale, V.N. Ghodke "Comparitive Study of RLS&LMS Algorithms for OFDM System".

Abstract: Security plays a vital role in data management application. For this purpose while developing, three different views of applications are modeled namely conceptual, security and GUI model. It formalizes the objects of problem, applications security and Graphical user interface respectively. A model transformation process occurs, which shifts policy specified in security to GUI model. To ensure model's correctness validation process is carried out and as a final step, a code generator will generate a multi-tier application along with access control support. The applications are built using our related tool. Index terms: conceptual model, validation, model driven security, authorization constraints.

[1]. A.Kleppe, W.Bast, J.B.Warmer, and A.Watson,MDA Explained: The Model Driven Architecture-Practice and Promise. Addison-Wesley, 2003
[2]. A.M.R.da Cruz and J.P Faria," A metamodel-based Approach for Automatic user Interface Generation, "proc.13th Int'l Conf. Model driven Eng. Languages and systems:part1(MODELS'10),Oct.2010,pp.256-270
[3]. A.M.R.da Cruz, "Automatic Generation of User Interfaces from Rigorous Domain and Use Case Models," PhD disssertation, Univ.do Porto, Sept.2010.
[4]. C.Kroiss, N.Koch, and A.Knapp, "UWE4JSF: A Model-Driven Generation Approach for Web Applications," Proc. Ninth Int'l Conf.Wenb Engg. pp.493-496, (ICWE'09), 2009
[5]. D.A.Basin,M.Clavel,M.Egea, and M.Schlapter," Automatic generation of Smart,security aware GUI Models,"Proc.Second Int'l Symp.Eng .secure Software and systems(ESSoS'10),Feb.201,pp.201-217

Abstract: The emotional facial expression is an important element in human communication, along with other forms of non-verbal communication, such as gestures and postures. The human facial expression, with its cunning and moment movements, carries an amazing amount of information that can reflect emotional feelings. Observing people's facial expressions can help a person understand their emotions. The new technology provided today for capturing the facial expressions, with rapid, high resolution image acquisition, helps us to analyze and recognize in real time the true facial emotions. This can be useful in many real time applications, like airport security, trading (the customer's feeling about a product), patient monitoring, and others.In this paper, we focus on the emotion recognition from facial expressions by using Microsoft Kinect for Windows sensor V2 and the face tracking SDK to recognize eight expressions. The implementation of our emotion recognition application was made with Visual Studio 2013 (C++) and Matlab 2014. Keywords: Face expressions, facial features, kinect sensor, face tracking SDK, neural network..

[1]. F. Malawskil,B. Kwolekl,and S. Shinji, "Using Kinect for Facial Expression Recognition under Varying Poses and Illumination," AGH University of Science and Technology 30-059 Krakow, Poland, Nagoya Institute of Technology, Japan.
[2]. Moldoveanu A., Morar A., Asavei V. 3DUPB - The Mixed Reality Campus: A glimpse at how mixed reality systems can shape the future. Revista Română de Interacţiune Om-Calculator 6 (1) 2013, pp. 35-56. ISSN 1843-4460. 2013

Abstract: With immense use of internet over smartphones, it is being a matter of concern for cyber security experts to provide security from ever growing threat of cyber attacks. The motivation of hackers behind developing malware is shifted from joy to a profit earning market. Amongst various forms of malware, mobile based botnet is emerged as a new threat for cyber security experts. Mobile based botnet attacks are increasing day by day and smartphone users are unaware about the harm it can cause. Considering this, the paper gives an overview of working and propagation of mobile based botnet attacks. Common propagation medium of smartphone based botnet are SMS, Bluetooth, NFC and WiFi. The paper proposed a hybrid P2P system for creating botnet using WiFi.

Keywords: Botmaster, C & C server, NFC, Hybrid P2P

[1]. Suresh Ramasubramanian, "ITU Botnet Mitigation Toolkit Background Information", ICT Applications and Cybersecurity Division, Policies and Strategies Department, International Telecommunication Union, Geneva, Switzerland, January 2008.
[2]. Meisam Eslahi, Rosli Salleh and Nor Badrul Anuar,"Bots and Botnets: An Overview of Characteristics, Detection and Challenges", IEEE International Conference on Control System, Computing and Engineering, Penang, Malaysia, pp. 349-354, 23 – 25 November 2012.
[3]. Ihsan Ullah, Naveed Khan and Hatim A. Aboalsamh, "Survey on botnet: its architecture, detection, prevention and mitigation", IEEE Transactions, pp. 660 -665, 2013.
[4]. Abdullahi Arabo and Bernardi Pranggono, "Mobile Malware and Smart Device Security: Trends, Challenges and Solutions", 19th International Conference on Control Systems and Computer Science, IEEE Computer Society, pp. 526-531, 10 – 11 September 2013.
[5]. Joany Boutet and Lori Homsher, "Malicious Android Applications: Risks and Exploitation, How I Met All Your Friends, A Spyware story about Android Application and Reverse Engineering", Information Security Reading Room, The SANS Institute, pp. 1 – 5, 2 March 2010.

Abstract: Multiprocessor task scheduling is considered to be the most important and very difficult issue. Task scheduling is performed to match the resource requirement of the job with the available resources resulting in effective utilization of multiprocessor systems. In this paper, a Multi Objective Genetic algorithm (MOGA) is proposed for static, non- pre-emptive scheduling problem in homogeneous fully connected multiprocessor systems with the objective of minimizing the job completion time. The proposed GA is used to determine suitable priorities that lead to a sub-optimal solution. Our proposed GA for a given job scheduling problem proves that GA results in better sub-optimal solutions.

Keywords: Directed Acyclic Graph, Genetic algorithm, multiprocessing environment, parallel computing, Task scheduling..

[1]. Nasri, Wahid, and Wafa Nafti. "Dept. of Comput. Sci., Higher Sch. of Sci. & Tech. of Tunis, Tunis, Tunisia." In Parallel Distributed and Grid Computing (PDGC), 2012 2nd IEEE International Conference on, pp. 114-119. IEEE, 2012.
[2]. Ravreet Kaur, Dr.Gurvinder Singh "Deptt of Computer Science & Engineering, Guru Nanak Dev University , Amritsar (Punjab), India" .Genetic Algorithm Solution for Scheduling Jobs in Multiprocessor Environment, 978-1-4673-2272 .IEEE,2012.
[3]. (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 4, No.5, 2013 QOS,Comparison of BNP Scheduling Algorithms with Expanded Fuzzy System.A.sharma
[4]. Gupta, Preeti. "A Mapping Heuristic Approach for Scheduling the Tasks on Parallel Multiprocessors." International Journal of Innovative Research and Development 1, no. 8 (2012): 198-206.
[5]. Ahmad, Ishfaq, Yu-Kwong Kwok, and Min-You Wu. "Performance comparison of algorithms for static scheduling of DAGs to multiprocessors." In Second Australasian Conference on Parallel and Real-time Systems, pp. 185-192. 1995.

Abstract: Management of digital images via internet and e-business has witnessed tremendous growth in last decades, resulting in vulnerability to copyright infringement, Manipulation and attacks. This paper reviews some of the current developments in the copyright protection techniques for digital images based on visual cryptography. The approaches reviewed in the paper are lined based on the false positive rate. This paper discusses the main approaches for copyright protection of digital images. This paper also discusses how those approaches can achieve the required security standard. The methods are measured against relevant performance metrics and set according to their respective environments and digital formats being used. Thus contributing sufficient knowledge so as to develop more secure scheme.

Keywords: Copyright Protection, Visual Cryptography.

[1]. M. Naor and A. Shamir, "Visual Cryptography", in Advances in Cryptology – Eurocrypt'94, Vol. 950 of Lecture Notes in Computer Science, Springer-Verlag, Berlin, 1995, pp 1-12.
[2]. R. Hwang, "Digital image copyright protection scheme based on visual cryptography," Tamkang Journal of Science and Engineering, Vol.3, No.2, 2002, pp. 96-106.
[3]. C. S. Hsu and Y. C. Hou, "Copyright protection scheme for digital images using visual cryptography and sampling methods", Optical Engineering, Vol. 44, No. 7, 2005, pp. 1-10.
[4]. M.S. Wang and W.C. Chen, "Digital image copyright protection scheme based on visual cryptography and singular value decomposition", Opt. Eng., 46, Vol. 6, 2007, pp 1-8.
[5]. Y.C Hou and P.H. Huang, "Image Protection based on Visual Cryptography and Statistical Property", IEEE SSP, 2011, pp. 481- 484.

Abstract: This paper discusses the emerging field of pervasive computing applications and its security challenges, the word pervasive or ubiquitous mean "existing everywhere." It produces sevice to anyplace, anywhere, at any time service to small or portable and wearable devices. This technology create new opportunities and challenges for the Information Technology companies to place high-performance computers and sensors in virtually every device, appliance, and piece of equipment in buildings, home care,Intelligent transport system, workplaces, and factories, and even in clothing. Pervasive computing environment or PCE share most of the security issues of traditional networked applications. These include authentication of devices and users, privacy of data or information, defense against malicious code such as viruses, worms, Trojan horses etc, and access control mechanisms. Finally it describes about the future focus for the pervasive computing through the real time applications.

Keywords: Pervasive computing,Devices,Connectivity,Issues,Privacy,User Interfaces..

[1]. http://en.wikipedia.org/wiki/Pervasive_Computing
[2]. Lathies Bhasker T "Pervasive Computing Issues, Challenges andApplications"International Journal Of Engineering And Computer Science ISSN: 2319-7242 Volume 2 Issue 12, Dec.2013.
[3]. "Security Issues with Pervasive Computing Frameworks" Michael CollinsSystems Research Group, School of Computer Science and Informatics,UCD Dublin, Ireand.
[4]. "Issues and challenges in ubiqutious computing" Protocol Engineering & Technology (PET) Lab. Indian Institute of Science,Bangalore.
[5]. https://www.google.co.in/search?q=pervasive+compuing+characteristics&biw.
[6]. A Survey on Pervasive Computing Er. Manita Gorai, Er. Kamna Agarwal , International Journal of Scientific & Engineering Re-search Volume 3, Issue 6, June-2012
[7]. M. Satyanarayanan: Pervasive Computing: Vision and Challenges. IEEE Personel Communications, 2001

Abstract: Our paper proposes a new type of technique for retrieving back the hidden information in an images using mobile communications. The usage of mobile communication in this paper is also termed as a step-2 verification on the receiver's side. By using the histogram modification technique the sender is going to embedded the data into an image, and sends it to the receiver. This paper mainly concentrates on the receiver's side to get back the embedded data using mobile communication.

[1]. Ching-Yu Yang, WU-Chih HU and Chih-Hung Lin, "Reversible data hiding by Coefficient-bias algorithm", Journal of Information Hiding And Multimedia Siganl Processing, Volume 1,November 2, April 2010.
[2]. S.Katzenbeisser, F.A.P, Peticolos, "Information Hiding for Steganography and Digital Watermarking" , Artech House, Norwood,MA,2000.
[3]. Zhicheng Ni, Yun-Qing shi, Nirwan Ansari and Wei su, "Reversible Data Hiding", IEEE Transactions on Circuits And Systems for Video Technology", Volume 16, No:3, March 2006.
[4]. M.Nosrati, R.Karimi, H.Noratti and A.Nosrati, "Embedding Steg-cover in Cover Images Using Linked List Concepts and LSB Technique", Journal Of American Science, Volume 7, No.6, 2011,PP.97-100.
[5]. Wen-Chung Kuo, Dong-Jin Jiang, Yu-Chih Huang, "A Reversible Data Hiding Scheme Based on Block Division", Congress on Image and Signal Processing, Vol. 1, 27-30 May 2008, pp. 365-369 .

Abstract: A wireless sensor network (WSNs) have increased gigantic consideration for their extensive variety of utilization, for example, environmental checking, military consideration, medical services, fiasco administration and so forth .A Vehicular ad hoc network (VANET) utilizes cars as versatile nodes as a part of a MANET to make a portable system. A VANET transforms each partaking car into a remote or switch or node, permitting cars pretty nearly100 to 300 meters of one another to associate and, thus, make a system with a wide range. In wireless networking, the shrouded node issue or concealed terminal issue happens when a node is obvious from a wireless access point(AP), yet not from different nodes communicating with that AP.In our work we will dispose of the issue of concealed terminal node issue.

Keywords: AODV, Hidden terminal node, VANET's, WSN..

[1]. U.R. Bhatt and N.Chouhan, ONU placement in Fiber-Wireless (FiWi) Networks, International Conference on Engineering (NUiCONE), pp. 1 – 6, 2013.
[2]. Z. Cheng and C. Zheng, Analysis and Optimization Model of Cognitive Wireless Mesh Networks, International Conference on Industrial Control and Electronics Engineering (ICICEE), pp. 1426 – 1429, 2012.

Abstract: The difficulty of association rule mining for objects in a massive database is to locate huge frequent patterns and relation among objects in a pattern from database entries has been examined with various different algorithms. But Apriori algorithm include plenty of challenges like vast amount of database check for creating big pattern and doing support calculation, great number of candidate findings. These comprised in this paper and impact to these a newer algorithm FPMBM (Frequent Pattern Mining with Boolean Matrix) has been considered. This newer algorithm uses a boolean matrix k-pattern for all patterns materialize in transactions. A list is maintained to short out the number of loops for patterns creation as well as only single database scan is followed in advance stage i.e. at the time of vertical database conversion.

Keywords - Association rule, Frequent pattern mining, Boolean Matrix

[1] Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, MORGAN KAUFMANN PUBLISHER, An Imprint of Elsevier
[2] R. Agrawal and S. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases", Proceedings of the 20th International Conference on Very Large Data Bases, September 1994.
[3] J. Park, M. Chen and Philip Yu, "An Effective Hash-Based Algorithm for Mining Association Rules", Proceedings of ACM Special Interest Group of Management of Data, ACM SIGMOD‟95, 1995.
[4] Han, Pei & Yin, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, Volume 8, Issue 1 , pp 53-87,2004
[5] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, " New Algorithms for Fast Discovery of Association Rules", Proc. 3rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'97, Newport Beach, CA), 283-296 AAAI Press, Menlo Park, CA, USA 1997

Abstract: Competent frequent pattern mining techniques be critical for finding relationship rules. Here, examination of subject of finding association rules for objects into an enormous DB of customer purchase entries is discussed. Ruling huge patterns following DB entry set had guided lots of techniques. Like Apriori, DHP, ECLAT, FP Growth etc. At this point, we projected new technique known as Frequent Pattern Mining with Serialization and De-Serialization (FPMSD), that's proficient for finding frequent patterns. FPMSD utilizes DLI(down-level-index) i.e. patterns which co-found with delegate item can be recognized rapidly and straightly using effortless and quickest method. This would happen to advantageous compare to other frequent pattern mining techniques. Additionally serialization will save produced frequent patterns into a file and de-serialization will pull through saved patterns from file. This Serialization and De-serialization Technique(SDT) takes lesser time for patterns entry set gathering than getting this from scratch. Keywords - Association rule, Frequent pattern mining, DLI, SDT.

[1] Data Mining: Concepts and Techniques, Jiawei Han and Micheline Kamber, MORGAN KAUFMANN PUBLISHER, An Imprint of Elsevier
[2] R. Agrawal and S. Srikant, "Fast Algorithms for Mining Association Rules in Large Databases", Proceedings of the 20th International Conference on Very Large Data Bases, September 1994.
[3] J. Park, M. Chen and Philip Yu, "An Effective Hash-Based Algorithm for Mining Association Rules", Proceedings of ACM Special Interest Group of Management of Data, ACM SIGMOD'95, 1995.
[4] Han, Pei & Yin, "Mining Frequent Patterns without Candidate Generation: A Frequent-Pattern Tree Approach", Data Mining and Knowledge Discovery, Volume 8, Issue 1 , pp 53-87,2004
[5] M. Zaki, S. Parthasarathy, M. Ogihara, and W. Li, " New Algorithms for Fast Discovery of Association Rules", Proc. 3rd ACM SIGKDD Int. Conf. on Knowledge Discovery and Data Mining (KDD'97, Newport Beach, CA), 283-296 AAAI Press, Menlo Park, CA, USA 1997

Abstract: In this paper, we propose a new color visual cryptography scheme which is based on modified visual cryptography. Visual Cryptography is based on cryptography where n images are encoded in a way that only the human visual system can decrypt the hidden message without any cryptographic computations when all shares are stacked together. In this scheme sender can share n-1 natural images and one secrete image of variable size with the server and it make the encryption with extraction of feature of natural images without altering the contents of natural images. When the receiver identification is done that time server make the decryption process and send the secrete image with transmitted by highly secure secret channel. Moreover, this approach avoids the pixel expansion problem and makes it possible to recover secret images without any distortion.

Keywords: Color image, Natural Image, Transmission risk, Visual Cryptograph

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