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VANET is an emerging and attractive technology dedicated to safety and comfort services to the vehicle users. Owing to its high dynamic topology and unpredictable channel distribution, it aspires for a suitablerouting protocol algorithm that can generate a near seamless network connectivity among the vehicular nodes. This opens up one among the major research challenges in VANET. This paper has focused mainly on Dynamic Source Routing (DSR) protocol regarding route cache optimizations. The route cache is used in DSR protocol to store all the routes are learned from the source node and to avoid unnecessary route discovery process. With high mobility environment and high load network traffic stale routes will be generated. These stale routes can mainly affect the performance of DSR protocol which cause long delay, increase the packet loss, increase the overhead, and decrease the performance of TCP protocol. Thus DSR offers many optimization opportunities in cache maintenance. This paper reviews different strategies of the route cache, the drawbacks of the pervious mechanism for updating the routes in the cache of DSR protocol. The paper is concluded with some guidelines which may be helpful to prospective VANET routing protocol designers.
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Mapping of jobs to the resources in grid environment is computed under high degree of heterogeneity of resources due to which heuristic and meta-heuristic approaches are the feasible methods to scheduling in grids. In this paper we enhance the genetic based scheduling in computational grids by a bacterial foraging optimization algorithm in grid environment. This paper analyzes the run-length unit parameter of BFO controls and the exploration of the whole search space and then exploits in the promising areas. The HGS scheduling method is improved in order to minimize the makespan and flow time with Bacterial Forging Optimization algorithm to reduce the cost and complete the process submitted by client. By using this algorithm the scheduler will schedule the job to one of the available resources in which the job can be completed with minimal cost and can further reduce the delay.
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Steganography gained importance in the past few years due to the increasing need for providing secrecy in an open environment like the internet.Steganography is the art of hiding the fact that communication is taking place, by hiding information in other information. Many different carrier file formats can be used, but digital images are the most popular because of their frequency on the internet Steganography is used to conceal the information so that no one can sense its existence. In most algorithm used to secure information both steganography and cryptography are used together to secure a part of information. Steganography has many technical challenges such as high hiding capacity and imperceptibility. In this paper,we try to optimize these two main requriments by proposing a novel technique for hiding data in digital images by combining the use of adaptive hiding capacity function that hides secret data in the integer wavelet coefficients of the cover image with the optimum pixel adjustment (OPA) algorithm.The coefficients used are selected according to a pseudorandom function generator to increase the security of the hidden data.The OPA algorithm is applied after embedding secret message to minimize the embedding error.The proposed system showed high hiding rates with reasonable imperceptibility compared to other steganographic system.
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The World Wide Web today is the promising reusable resource of web data for all kinds of domain. The web users, academicians, researchers, and developers are searching, gathering, and executing millions of web queries retrieving (download) and fetching (upload) the hypertext from the web. This is the universal leading information hub and it has excellent progress since its beginning. This paper explores the overview of background, evolution of the WWW and its comparative emerging trends from web 1.0 to web 4.0. Web 1.0 (Static) as a Web of Information Connections, Web 2.0 (Social) as a Web of People Connections, Web 3.0 (Semantic) as a Web of Knowledge Connections and Web 4.0 (WebOS) as a Web of Intelligence Connections are described as four generations of Web.
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The innovative idea of this paper is to developed a speaker recognition system using amplitude and phase information from LP residual separately using Hilbert Transform (HT). The magnitude of the HT of the signal called as the Hilbert Envelope (HE) of the LP residual and the cosine of the phase, called as the Residual Phase (RP).These two are processed at subsegmental, segmental and suprasegmnetal levels using proposed Ergodic HMM and combine their scores at each level. The HMM based speaker recognition extracted temporal sequence information and also modeled intra-speaker variability effectively. The proposed speaker recognition system is compared with the LP residual at different levels.
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Quantum Dot Cellular Automata (QCA) is an emerging nanotechnology in the field of nanoscience for the low power consumption and high speed of operational phenomenon. Such type of circuit can be used in many digital applications in sequential and combinational mode of operation, where we are restricted to use different circuits for different digital logic and applications. Reconfigurable QCA mean one circuit can be used for different outputs. Reconfigurable hardware using FPGA are used by different technologist for some digital applications. But FPGA has some limitations in different type of applications such as it has limited size and least efficient use in wire connections. Using QCA a Reconfigurable counter is a new and a novel idea in the field of Nano electronics. In this paper the two most important counters Ring and Johnson counter are designed in the same circuit using QCA and the corresponding simulations are shown with the help of QCADesigner tool. The circuit acts as a Ring counter when the control logic is ‘1000’ and acts as a Johnson counter when the control logic is ‘0111’.So, a single QCA circuit have the twin digital logics with the help of a control signal.
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WiMAX (Worldwide Interoperability for Microwave Access) is an emerging broadband wireless. Technology which provides many of the same fundamental principles as Wi-Fi networks, it also provides a greater signal range more than the 100 feet provided by most usual Wi-Fi modems are used. Instead, WiMAX boasts a 30 mile radius, large enough to cover portions of major cities. In addition, this standard is intended to provide 30 to 40 megabits per second as a transfer rate, with a 2011 update to the standard yielding up to 1 gigabit per second at fixed points. It should be noted, however, that bandwidth on a WiMAX network is not exclusive to users and instead must be split, meaning that while higher speeds may be advertised, the number of users can lower transfer rates in practice. In this paper the focus is to improve the QoS in WiMAX and also a study of WiAMX, its basics, application and about its network architecture.
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Meetings are an integral part of workplace dynamics also an important communication and coordination activity of teams: statuses are discussed, decisions, alternatives are considered, details are explained and ideas are generated. In this work, data mining techniques to detect and analyze frequent interaction patterns. We look forward to discover various types of new knowledge on interactions. An interaction tree pattern mining algorithms was proposed to analyze tree structures and extract interaction flow patterns. In this paper we propose the tree based mining for human interaction flow in a discussion session is represented as a tree. In this work we extend an interaction tree mining algorithm in three ways. First, we propose a mining method to extract frequent patterns of human interaction. Second, we explore embedded subtree mining for hidden interaction pattern discovery. Third, we propose temporal data mining techniques for extracting the temporal patterns from the captured content of time series of different meetings in particular time periods such as month or year. Because of the human integration activities varied based on time and experience of events. For extracting temporal pattern mining we use hidden markov model (HMM) along with tree mining algorithm.
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ROTTWEILER tracker an intelligent multi agent system is used to provide securities to common men. ROTTWEILER is of highly innovative, state-of-the-art solutions for GPS Products. We will provide all the support and services customers require by relying on an open and inter-operable technology. Sufficient number of workforce and workstations across the India to provide high quality solutions by combining technology skills, people competencies and efficient processes. Our each solution is designed and optimized to meet the specific needs and challenges facing in every business. Our product will increase productivity, improve efficiency, reduce overall costs and enhance performance and competitiveness. We provide unique line of services for Vehicle Tracking and Personal Tracking with our unique software’s for the purpose of providing variety of comprehensive solution and services. We live in a world of GPS and GPRS, these technologies are integrated to develop a device that can find your live location and plot it on the map. Human covet for things and their demands are uncanny. We want more and more. We are going to one step further in making this more user friendly and easy for the common man to use it and get most out of it. We have made it like a simple website which we call as Web portal. The user can make his or her account in it as they do in any other website for registration. After this our website provides various options to the user like Live-Tracking, Report, Distance calculator and Geo-fencing.
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Grid is an emerging technology for enabling resource sharing and coordinated problem solving in dynamic multi- institutional heterogeneous virtual organizations. However, the management of resources and computational tasks is a critical and complex undertaking as these resources and tasks are geographically distributed. A suitable and efficient scheduling algorithm is needed to schedule user tasks to heterogeneous resources distributed in the grid. We propose a genetic algorithm to solve the scheduling problems in computational grid. The proposed algorithm uses the optimal probing technique of genetic approach and takes different computing capabilities of nodes into consideration. The simulation result shows that proposed algorithm yields better performance when compared with other traditional heuristic approaches.
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Clustering is one of the most important techniques in which the machine learning and data mining tasks. Similar data grouping is performed using clustering techniques. Hierarchical clustering model produces tree structured results. Partitioned clustering produces results in grid format. The documents are projected into a low-dimensional semantic space and then a traditional clustering algorithm is applied to finding document clusters. The Euclidean distance is a dissimilarity measure describes the dissimilarities between the documents. Correlation indicates the strength and direction of a linear relationship between two random variables. A scale-invariant association measure is used to calculate the similarity between two vectors. Correlation preserving index (CPI) based clustering is used for document clustering process. The similarity-measure-based CPI method is used for detecting the intrinsic structure between nearby documents. In CPI method the documents are projected into a low-dimensional semantic space. Correlations between the documents in the local patches are maximized. Correlations between the documents outside these patches are minimized simultaneously. The spectral clustering is applied on the correlation similarity model with nearest neighbor learning process. The Ontology repository is used to manage the term concept relations. Local patch extraction is carried out with Ontology support. Term frequency based weight is replaced with concept weight based model. The document preprocess operations are carried out to extract term information. Stop word elimination and stemming process are applied on the term collection. Porter stemming algorithm is used for suffix analysis. Ontology is used to extract term relationships.
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Increasing dependency on software systems is making way for systems which are more reliable and secure. The ever growing competitive scenario brings in the need to quantify these attributes into numbers which are impressive enough. CIAAAN framework was an attempt in the same direction. The need was felt to implement this framework and then test it as per the current industry. Soon it was realized that for being a part of software industry this process of implementation of CIAAAN had to be automated. It had to be an easy to use and easy to understand process which would encourage the usage of CIAAAN as a default process in the software lifecycle. Another observation was that, just quantifying the security intensity was not enough. People need to know the reasons and wherever possible the steps which can drive their software system towards excellence. This all led to the genesis of SecureSight- a tool which gives a complete insight of security of software system.
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Cryptography is the technique of secret communication between sender and receiver. This is required for secure communication in the presence of a third person (intruder). DNA cryptography is a relatively new field of cryptography which is being explored for advancement in the existing cryptography method. In DNA cryptography makes uses of the concept of DNA computing. This paper presents an overview of DNA based cryptography and one of the methods used in it. It also discuss the various technology used in it and its application. It also shows hardware implementation of DNA based cryptography method.
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Association Rule Mining is process of finding necessary association rules, relationships from large database .Association Rule Mining (ARM) has been implemented for various applications. Market Basket Analysis is one the best example of ARM. Decision making processes in business can be employed with the help of ARM. This Text Mining technique can be applied on various languages to extract association rules .This paper describes the proposed methodology of implementing Association Rule Mining on Punjabi Text.
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Ad-hoc network is a shared wireless network without any infrastructure; mobile nodes are move connected by wireless links. Quality of Service (QoS) is defined as performance agreement between the service provider and user applications based on the connection requirement qualitatively or quantitatively. As an attempt in this direction, the goal of paper is to develop Q-learning scheme, based on QoS parameters. However this approach must take taken into account the QoS parameters in Ad-hoc network: Traffic, Channel Capacity, Energy, Less number of nodes will die out,Cost to achieve Reliability (Confidence factor) .AODV protocol is studied in this aspect. Reactive protocol (AODV) is based on On- demand route discoveries that update the routing tables only for the destination that has traffic going through. The work focuses on study and performance evaluation of these categories using Matlab 7.10.
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A topic is defined as a developing event which contains many related events and activities. The convenient storage of documents in the internet leads to the difficult in exploration since there are sequence of documents published by different authors for the particular keyword. Due to the phenomenal growth of a large number of documents in the internet it is difficult for the users to read the entire contents and conclude what exactly present in the document. The situation is even worse if the event is time related. In this study we define a task which summarizes the core content in the chronological order and this makes to readers to understand the content easily. The proposed model, called TECF(Theme Encapsulation and Content Framework)derives themes from the documents on the topic with the help of eigen vectors of the temporal block association matrix, from this the summaries of the document are extracted. Finally the extracted summaries are associated to find the temporal closeness with the help of evolution graph. In the proposed techniques the core content related to the topic is covered in the coherent and consistent manner when compared with the existing separation techniques like human based summaries.
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Fundamental interconnecting structured homogenous network model has a fish shaped topology and proposes a Bypass Flow Splitting Forwarding algorithm to forward packets in network. Reset time is constant in BFF algorithm and there is no provision for optimization of energy while selecting path. Which affects the capability of BFF algorithm to make proper utilization of channel which increases packet loss ratio and controlling the transmission energy consumed for a packet to reach destination. So main objectives of proposed algorithm are :-
• To achieve higher link utilization using dynamic reset time which increases throughput.
• To increase delivery rate and avoid route failures by selecting energy efficient path in Fish network.
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In the medical field, Image Segmentation is an important and challenging factor. This paper describes tumor detection in medical images using genetic algorithm.In the first phase, the brain image is acquired from patients database in which Image Segmentation method which is typically used to locate objects and boundaries in images is applied. After that clustering is applied and then genetic algorithm is introduced along with various stages. Genetic operation like Selection, Crossover and mutation are performed on this clustered image to produce required results.There are number of approaches to detect the tumor over the brain images but it shows the modification of the genetic algorithm. Initially, Genetics is implemented on complete tumor image, because of this the initial population set is quite large but now the size of the population set for the genetics is reduced. For this reduction at first the clustering is performed. The complete work is divided in two main phases, In first phase, the clustering is been implemented to identify the actual area of the brain image that can have cancer. Once this area is identified, it is used as the population set for the genetics. The genetics is implemented on this area and the tumor detection is performed more effectively.
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Linear Programming (LP) is the process of taking various linear inequalities together and find out the “best” optimal solution. Linear programming problems arise in real-life economic situations where profits are to be maximized or costs to be minimized. This paper shows the flexibility of neural networks for modeling and solving diverse linear programming problems. Advantage of using neural networks to solve problems includes powerful computation and less time required. The proposed system is to build the neural network model by using back propagation algorithm for solving linear programming problem. Here the neurons are trained by using back propagation algorithm. This system will provide powerful computation and the computational time required is low.
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The main purpose of this survey is to describe the recent 3D face recognition algorithms and methods. The last few years more and more 2D face recognition algorithms are improved and tested on less than perfect images. However, 3D models hold more information of the face, like surface information, that can be used for face recognition or subject discrimination. Another major advantage is that 3D face recognition is pose invariant. A disadvantage of most presented 3D face recognition methods is that they still treat the human face as a rigid object. This means that the methods aren’t capable of handling facial expressions. Although 2D face recognition still seems to outperform the 3D face recognition methods, it is expected that this will change in the near future.
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Message among companies and clients has been revolutionized by the Internet, Impact of b2b and b2c market places, and innovative approaches of partnership like implicit companies. Many systems with Characterized and independent service, sales and marketing perceptions are accessible these days. In this paper we give an overview of the basic concepts of message, groupware, workflow management and effective projects. This approach of internet based message and internet based software project management, is based on these concepts and can be used for a variety of activities over the Internet.
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Skin detection is the extraction of skin-colored pixels and regions from an image. Skin detection is important part of many applications such as, face detection, face tracking, gesture analysis, biometric, surveillance, facial image coding compression, emotional computing, and face attribute classification and human computer interaction. In this paper, implementation of skin detection is based on the range of pixels of Hue component from HSV color space.
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A Mobile Ad-Hoc Network (MANET) is a collection of mobile nodes. It can be constructed anywhere without any infrastructure. The mobile nodes are equipped with energy-limited batteries. As mobile nodes are battery-operated, an important issue in such a network is to minimize the total power consumption for each operation..Due to limited battery power and transmission bandwidth limitations, in wireless ad hoc networks, it is essential to develop efficient multicast protocols that are optimized for energy consumption and significantly improving network life time. In this paper, we propose a cooperative multicast routing protocol with energy efficiency. COMRoute utilizes cross-layer design by physical-layer cooperative transmission, MAC-layer clustering, and networklayer multicast routing. Also to design an energy efficient algorithm for reducing the amount of overhearing based on RSS value in a Mobile Ad-hoc Network (MANET). A cross layer framework is designed by combining the physical, MAC and network layer. RSS value is taken from the physical layer and it is used for finding the Mobility Parameter (MP). MP value determines the level of overhearing and rebroadcast probability. Simulation results are compared for Packet Delivery Ratio (PDR) and energy consumption.
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Rapid growth of the demand for computational power has led to the creation of large-scale data centers. They consume enormous amounts of electrical power resulting in high operational costs and carbon dioxide emissions. Moreover, modern Cloud computing environments have to provide high Quality of Service (QoS) for their customers resulting in the necessity to deal with powerperformance trade-off. We propose an efficient power management policy for virtualized Cloud data centers. Basically, parametric constraints based automatic power saving system (PCBAPS) is extended form of automatic power saving(APS).In PCBAPS we impose some parametric constraints during virtual machine migration that can be adjusted dynamically to balance the server’s workloads in an efficient way so that migration cost can be improved and energy saving be achieved.
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The digital image processing has been applied in several areas, especially where it is necessary use tools for feature extraction and to get patterns of the studied images. In an initial stage, the segmentation is used to separate the image in parts that represents a interest object, that may be used in a specific study. There are several methods that intends to perform such task, but is difficult to find a method that can easily adapt to different type of images, that often are very complex or specific. To resolve this problem, this project aims to presents a adaptable segmentation method, that can be applied to different type of images, providing an better segmentation. The proposed method is based in a model of automatic multilevel thresholding and considers techniques of group histogram quantization, analysis of the histogram slope percentage and calculation of maximum entropy to define the threshold. The technique was applied to segment the cell core and potential rejection of tissue in myocardial images of biopsies from cardiac transplant. The results are significant in comparison with those provided by one of the best known segmentation methods available in the literature.
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A wireless Sensor Network (WSN) consists of low cost, low power, small in size and multi functional sensor nodes. Routing protocols in WSNs emphasize on data dissemination, limited battery power and bandwidth constraints in order to facilitate efficient working of the network, thereby increasing the lifetime of the network. Routing protocols in WSNs are also application specific which has led to the development of a variety of protocols. This paper presents an energy-efficient wireless sensor network routing algorithm IQEA based on the improved quantum evolutionary algorithm. The algorithm will divide the network into multiple clusters, and in the building cluster process, we consider the residual energy of the node and the distance from the target node, because these are the keys to balance the consumption of whole network energy. Each node within the cluster is formed into a multi-hop route, then by improving the quantum evolutionary algorithm to reduce the total distance of the link, and then come to the best clustering method.
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It is important that programmers and program maintainers understand important properties of the programs they modify and ensure that the changes they make do not alter essential properties in unintended ways. Manually documenting those properties, especially temporal ones that constrain the ordering of events, is difficult and rarely done in practice. We propose an automatic approach to inferring a target system’s temporal properties based on analyzing its event traces. The core of our technique is a set of pre-defined property patterns among a few events. These patterns form a partial order in terms of their strictness. Our approach finds the strictest properties satisfied by a set of events based on the traces. Formal specifications can help with program testing, optimization, refactoring, documentation, and, most importantly, debugging and repair. Unfortunately, formal specifications are difficult to write manually and techniques that infer specifications automatically suffer from 90-99% false positive rates. Consequently, neither option is currently practiced for most software development projects. We present a novel technique that automatically infers partial correctness specifications with a very low false positive rate. We claim that existing specification miners yield false positives because they assign equal weight to all aspects of program behaviour. For example, we grant less credence to duplicate code, infrequently-tested code, and code that has been changed often or recently. By using additional information from the software engineering process, we are able to dramatically reduce this rate. We evaluate our technique in two ways: as a pre-processing step for an existing specification miner and as part of a novel specification inference algorithm. Our technique identifies which traces are most indicative of program behaviour, which allows off-the-shelf mining techniques to learn the same number of specifications using 60% of their original input. To the best of our knowledge, this is the first specification miner with such a low false positive rate, and thus a low associated burden of manual inspection.
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One of the major challenges for the wireless network design is the efficient mobility management, which can be addressed globally (macromobility) and locally (micromobility). Mobile Internet protocol (IP) is a commonly accepted standard to address global mobility of mobile hosts (MHs). It requires the MHs to register with the home agents (HAs) whenever their care-of addresses change. However, such registrations may cause excessive signaling traffic and long service delay. Indeed, a mobile node (MN) within an access network may cause excessive signaling traffic and service disruption due to frequent handoffs. The two latter effects need to be minimized to support quality of service (QoS) requirements of emerging multimedia applications. To solve this problem, we propose a new adaptive micromobility management scheme designed to track efficiently the mobility of nodes so as to minimize both handoff latency and total signaling cost while ensuring the MN’s QoS requirements. We introduce the concept of residing area. Accordingly, the micromobility domain is divided into virtual residing areas where the MN limits its signaling exchanges within this local region instead of communicating with the relatively far away root of the domain at each handoff occurrence. A key distinguishing feature of our solution is its adaptive nature since the virtual residing areas are constructed according to the current network state and the QoS constraints.
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Sensor networks serving mission-critical applications are potential targets for malicious attacks. Although a number of recent research efforts have addressed security issues such as node authentication, data secrecy and integrity, they provide no protection against falsely injected sensing reports once any single node is compromised. In this paper we present a geometric En-route Filtering mechanism (GEF). GEF exploits the sheer scale and dense deployment of large sensor networks. GEF aims at detecting and dropping such falsely injected reports by compromised nodes. It takes advantage of the large scale and dense deployment of sensor networks. GEF’s detection and filtering power increases with the deployment density and the sensor field size.
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Duplication is used to increase data availability in the presence of site or communication failures and to decrease retrieval costs by local access if possible. Our system is completely decentralized and nodes can be removed/added dynamically. P2P is a trendy technology used for file sharing. File duplication and Regularity protection are the methods used in P2P for elevated system performance. File duplication methods indicate replica nodes without thinking about regularity protection which may lead to high overhead for redundant file duplications and regularity protection. Regularity protection methods update files without considering file duplication dynamism which may not give the accuracy of replica regularity. Hence there is a need to think about regularity protection while file duplication to achieve high performance and high availability. When data files are replicated at many nodes, regularity must be maintained among the nodes. In this paper we point out different duplication strategies that are applied P2P systems. This paper presents an Integrated file Duplication and regularity Protection mechanism. Integrated file Duplication and regularity Protection mechanism integrates the two techniques in a systematic and harmonized manner. The security has become one of the major issues for data communication over worldwide networks.
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The approaches for resemblance search in correlated, high dimensional data sets are derived with in a clustering framework. To conflict the “Curse of Dimensionality” a technique namely Vector approximation by indexing was proposed, employs scalar quantization and necessarily ignores dependencies across dimensions and a source of sub optimality is represented. In contrast inter dimensional correlations are exploited by clustering and thus a more packed in representation of data set. However, existing methods reduce the irrelevant clusters which are based on bounding hyper spheres and bounding rectangles which lack their tightness and compromises the efficiency in nearest neighbor search. In this paper, based on separating hyper plane boundaries of Voronoi clusters we propose a new cluster adaptive distance bound to complement our cluster based index. With a relatively small preprocessing storage overhead a bound is enabled by an efficient spatial filtering and is applicable to similarity measures. Experiments show that our indexing method is scalable with data set size and dimensionality and outperforms with several recent proposed indexes.
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Packet failure causes degradation in the quality of Voice, vidoes,etc… applications. Further Error Correction (FEC) methods which add redundant information to voice packets can be used to minimize the effects of packet failure. Packet failure compensation techniques are divided into receiver-based packet error correction techniques and sender-based error concealment techniques.These techniques are further divided into retransmission-based techniques and Further Error Correction (FEC) techniques.Retransmission-based techniques are only suited for short and very high-Retransmission is avoided in FEC methods.. In media independent FEC based methods, the redundant information can be sent in the form of parity packets. The parity packets are generated from the original packets using a mathematical function and thus the redundant information is independent of the encoding algorithm used. In media specific FEC methods, the redundant information is generated using a different and lower bit rate, encoding algorithm than the algorithm used to encode the original packet. Media specific schemes piggyback information about the present period with later packets
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There are many application areas ranging from multimedia information processing to temporal data mining in temporal data which are all over in the world. There is a high amount of dependency among temporal data and the proper treatment of data dependency. A temporal data clustering framework via a weighted clustering ensemble of multiple partitions is proposed to produce the initial clustering analysis which is based on unlike temporal data representations. In our approach, to reconcile the initial partitions to candidate the consent partitions from different perspectives, a novel weighted consent function guided by clustering validation criteria is proposed and then introduce an agreement function to further reconcile those candidate consensus partitions to a final partition. As a result, for the joint use of various representations, the proposed weighted clustering group algorithm provides an effectual facilitating technique, which reduce the information loss in a single representation and exploits various information sources underlying temporal data. However, our approach tends to confine the essential structure of a data set. Our approach has been evaluated with standard time series, motion path, and timeseries data stream clustering tasks. For a variety of temporal data clustering tasks the results demonstrate that our approach yields favorite results.
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Privacy preserving data publishing is a promising research area since individual privacy has become a major concern. Privacypreserving techniques are often required to reduce the possibility of identifying sensitive information about individuals, when a data set is released to other parties for data analysis. Sensitive information can be a specific location of an individual for spatial data. The study is made on protecting the privacy of persons and data has received from many fields. The information that a user browses certain websites may be considered sensitive in web surfing. We review the research work in privacy-preserving data publishing. Without violating the confidentiality of personal information we explain how an organization can release the data to the public is discussed. However, the simplest solution to protect the sensitive information is not to disclose the information but it will delay the data analysis process. The data must be disclosed under the government regulations in some applications. On the other hand, if the data owner first modifies the data then the modified data can guarantee privacy, such that the modified data retains enough utility and can be released to other parties safely. In this paper, we examine how the data owner can modify the data and how the modified data can preserve privacy and protect sensitive information.
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For data delivery, a wide range of emerging applications confronts the existing techniques to users and applications accessing data from multiple autonomous servers. We develop a framework an alternative and more flexible approach that maximizes user utility for comparing pull based solutions and present dual optimization approaches. It maximizes the user utility while satisfying constraints on the usage of system resources it is the first approach and it satisfies the utility of user profiles while minimizing the usage of system resources it is the second approach. For the latter approach, we present a static optimal solution (SUP) and formally identify sufficient conditions for SUP to be optimal for both. A limitation of static solutions to pullbased delivery is that they cannot adapt to the dynamic behavior of large area applications. Moreover, to improve user utility with only a moderate increase in resource utilization an adaptive algorithm (fbSUP) is proposed and shows how it can incorporate feedback. We can analyze the behavior of SUP and fbSUP under various update models by using real and synthetic data traces. When the estimations of SUP closely estimate the real event stream, we can achieve a high degree of satisfaction of user utility, and has the potential to save a significant amount of system resource is shown in our experiments. We further show that SUP can exploit feed back to improve user utility with only a moderate increase in resource utilization.
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Today traditional intrusion detection systems are unable to detect intrusion attacks. Huge number of false alarm generated by the system results in financial loss of an organization. The unique features of instant based learning encourage and motivate the researchers to employ this technique in variety of applications and especially in intrusion detection systems. Recently Reinforced Learning (RL) and Artificial Immune System (AIS) has been applied for anomaly based intrusion detection in computer networks. Artificial immune system is a new technique which is applied for solving various problems in the field of information security. In this paper we presents an intrusion detection system based on instant learning technique such as neural network, pattern classification and reinforced learning. In instant based learning reinforced learning play an important role for decision making for predictive event.

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This paper presents a Survey on different Intrusion detection system with different tools use for detection of attack. Intrusion detection system on the internet is heated research field in computer science. This system is capable of detecting unauthorized access, break-ins, penetrations, and other form of computer absue. Intrusion detection system have the ability to analyze network traffic and recognize incoming and on-going network attack. System involves a lots of tools that are used to identify different attack against computer system and network. Various open source and commercial tools are available for detection of intruders.This paper introduce how useful different detection techniques of data mining for detecting attack fastly.

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Parallel data processing has become indispensable for processing applications involving huge data sets. This brings into focus the Graphics Processing Units (GPUs) which emphasize on many-core computing. With the advent of General Purpose GPUs (GPGPU), applications not directly associated with graphics operations can also harness the computation capabilities of GPUs. Hence, it would be beneficial if the computing capabilities of a given GPGPU could be task optimized and made available. This paper describes a client-server framework in which users can choose a processing task and submit large data-sets for processing to a remote GPGPU and receive the results back, using well defined
interfaces. The framework provides extensibility in terms of the number and type of tasks that the client can choose or submit for processing at the remote GPGPU server machine, with complete transparency to the underlying hardware and operating systems. Parallelization of user-submitted tasks on the GPGPU has been achieved using NVIDIA’s Compute Unified Device Architecture (CUDA).

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Wireless Mesh Networks (WMNs) are emerging and promising technology. It could revolutionize the way its next generation wireless network access capability. WMNs are combining with any networks with its backbone network. WMNs are interconnecting between the mesh routers using wireless links and it provides “last mile” connectivity issue with its potential addressing scheme. WMNs use different resource management at different level. These resource managements are facing different issues, including routing. Characteristics of WMNs have a strong impact on routing; existing wireless networks are evaluated and identified routing performance metrics. Performance metrics are serving the basis
for deriving the key design feature for routing in wireless mesh networks. WMNs are combination of static and dynamic nodes, so Routing is one of the challenging task. It has deal with different resource managements like radio technology, power, mobility and etc. Routing is a typical task to satisfy the parameters like packet loss ratio, routing overhead, throughput and end-to-end delay. The Proposed routing protocol, satisfy most of the parameters specified and it uses on-demand table driven technique in routing.
Basically proposed protocol is a hybrid routing protocol. It uses reactive routing at client level and proactive routing use at backbone level. Initially routing generated in WMNs are either Mesh Points (MP) or Mesh Clients (MC), if it is MP, it uses table-driven else it uses on-demand, so we called it as a radio aware routing protocol. We use virtual clustering concept in this protocol, it collects the topology information at backbone level and it reduce the end-toend delay in the network. Expected transmission time (ETT) as
performance metric in the proposed protocol, it reduce the packet loss ratio. Network size increases this proposed protocol performs well, the simulation results have shown this.

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