One of the most challenging jobs that any manager can take on is the management of a large-scale project and it requires numerous activities throughout the project. A Modern project management has developed various techniques based on network techniques in order to plan the projects processes in time, their costs and resources. Critical Path Method (CPM) is one of the best procedures for the planning, scheduling and optimizing the resource usage. CPM scheduling is a basic project control tool hence it is using in the all type of project. Completing a project on time and within the budget is not an easy task. The project scheduling plays an important role in the time and cost aspects of a project. Each project managers have different systems in planning and scheduling, which is usually consists of Gantt chart or Bar chart. The development of Critical Path Method provides a basic and systematic approach to project managers. This results in the use of software’s like Microsoft Project and Primavera.

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Nowadays the use of credit card has increased, because the amount of online transaction is growing. With the day to day use of credit card for payment online as well as regular purchase, case of fraud associated with it is also rising. To reduce the huge financial loss caused by frauds, a number of modern techniques have been developed for fraud detection which is based on data mining, neural network, genetic algorithm etc. Here a survey of techniques for online credit card fraud detection using Hidden Markov Model, Genetic Algorithm and Hybrid Model, and comparison between them has been shown.

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In this computer age, all the computer applications handle data in the form of text, numbers, symbols and combination of all of them. The primary objective of data compression is to reduce the size of data while data needs to be stored and transmitted in the digital devices. Hence, the data compression plays a vital role in the areas of data storage and data transmission. Golomb code, which is a variable-length integer code, has been used for text compression, image compression, video compression and audio compression. The drawback of Golomb code is that it requires more bits to represent large integers if the divisor is small. Alternatively, Golomb code needs more bits to represent small integers if the divisor is large. This paper proposes Modified Golomb Code based on Golomb Code, Extended Golomb Code to represent small as well as large integers compactly for the chosen divisor. In this work, as an application of Modified Golomb Code, Modified Golomb Code is used with Burrows-Wheeler transform for text compression. The performances of Golomb Code and Modified Golomb Code are evaluated on Calcary corpus dataset. The experimental results show that the proposed code provides better compression rate than Golomb code on an average. The performance of the proposed code is also compared with Extended Golomb Codes (EGC). The comparison results show that the proposed code achieves significant improvement for the binary files of Calgary corpus comparing to EGC.

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We suggest a protocol for secure mining of association rules in horizontally distributed databases. The existing primary protocol is that of Kantarcioglu and Clifton [1]. Our protocol, like theirs, is rely on the Fast Distributed Mining (FDM) algorithm of Cheungetal, which is not a secured distributed version of the Apriori algorithm. The major ingredients in our protocol are two novel safe multi-party algorithms—one that calculates the combination of private subsets that each of the interacting players have, and another that tests the insertion of an element contained by one player in a subset contained by another. Our protocol offers enhanced privacy with respect to the protocol in [1]. In count, it is simpler and is significantly more effective in terms of interaction rounds, communication charge and computational cost. Data mining techniques are used to discover patterns in huge databases of information. But sometimes these patterns can disclose susceptible information about the data holder or persons whose information are the subject of the patterns. The idea of privacy-preserving data mining is to recognize and prohibit such revelations as evident in the kinds of patterns learned using traditional data mining techniques.[5].

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Extended surfaces are used to increase heat transfer rate and this study related to extended surfaces (fin) and heat transfer rate. This review summarizes the previous work on various shapes of fins. In this study, discuss heat transfer in different geometry of fins because the heat transfer rate depends on the changing of the fins materials, fins cross-section, fin profiles, air velocity etc. So this review helps to select the fins according their requirements.

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Wireless Mobile devices has led to the explosion of the computing world. This huge information can be utilized by observing the mobile network’s behavior. The big data thus generated in the mobile networks enable us to gain useful insights into the user patterns by using big data analysis techniques and computing techniques. The proposed model introduces uses data set information, data analysis techniques to categorize the data set in the network communication into two types, one is user based and the other one is network oriented. The essential data is then computed into by the telecommunication operators who are facing the tremendous challenge to provide satisfactory service to mobile users with varying QoS requirement. Thus by including high volume media transmission, huge amount of machine to machine(M2M) connectivity the data is summarized and reviewed to form temporal and spatial analysis to data mining and statistical test. The call measurement and call detail record, respectively, to understand the base station behavior. The users behavior is revealed and predicted by comparing the base stations location and real-world map and determine the behavior of the users.

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Communication networks are rapidly becoming the need of time. Internet being the best example to describe. However, traffic congestion is the major cause in degrading the overall performance of the network.Node usage probability is a concept from a complex network perspective for uniformly distributing the traffic load and frequently choosing a node to relay packets in a network. The concept of node usage probability comprises of effective network design strategies, routing algorithms and resource allocation schemes, which helps improve the overall traffic performance. Node usage probability is a metric used wherein the nodes in the network are used efficiently as the problems arise when the nodes are overused or sometimes not used at all inducing congestion in the network thereby hampering the overall performance. The performance of a minimum-node-usage routing algorithm is compared with that based on other routing algorithms, such as shortest path (SP) and minimum degree (MD) routing algorithms, that possess attributes like network topologies and resource allocation schemes that show routing algorithms based on minimizing node usage (MNU) can balance the traffic load effectively also the resource allocation technique based on the node usage probability shows that the technique outperforms the uniform and degree-based allocation schemes. The analysis, as well as the results, gives an idea of what topology to be used, the routing method and the resource allocation scheme, for achieving optimal network performance.

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