Latest Articles - Volume 9 No. 1

  • Analysis of Pattern Identification Using Graph Database for Fraud Detection

    Navneet Kr. Kashyap*, B. K. Pandey and H. L. Mandoria

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    Internet is the main tool for e-business. E-transaction is made faster by Internet. With the increase of e-transaction internet fraud or e-business fraud is increasing. Credit fraud in the banking sector is a growing concern. Few sort of card (debit/credit) fraud is decreasing by providing detection and prevention system from banks and government. But card-not-present fraud losses are increasing at higher rate because of online transaction as there is no chance to use Chip and PIN as well as card is not used face-to-face. Card-not-present fraud losses are growing in an un-protective and un-detective way. This paper seeks to investigate the current debate regarding the fraud in the banking sector and vulnerabilities in online banking and to study some possible remedial actions to detect and prevent credit fraud. The research also reveals lots of channels of fraud in online banking which are increasing day by day. These kinds of fraud are the main barriers for the e-business in the banking sector. This paper devised a new approach for fraud detection in these sector with help of graph database & by matching pattern of previous frauds.

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  • Congestion Avoidance in Packet Networks using Network Simulator-3 (NS-3)

    Guneet Kaur Walia, O. P. Gupta and Sunil Kumar*

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    With the growth of internet, the user’s requirements in terms of scale, functionality of network and performance of internet also increases. It becomes very important to guarantee the efficiency, stability and performance of a given network as per the user’s requirements. There are many management techniques that need to be taken care of, so as to ensure network stability. These include queue management, queue scheduling, congestion control etc. But the most fundamental and foremost amongst these is congestion control, as it’s not possible to ensure Quality of Service (QoS) with a congested network. To avoid congestion, many algorithms are available; the most basic of them is Random Early Detection (RED). This paper implements RED and its enhancement Adaptive RED (ARED) in NS-3 simulator and comparative analysis of both the algorithms has been carried out.

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  • Development of Online Student Course Registration System

    Rattan Singh, Ravinder Singh, Harpreet Kaur and O. P. Gupta

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    With the advent of Information Technology in the last decade, the major focus has shifted from manual systems to computerised systems. Various systems viz. railway reservation, hospital management etc. involving manual work have been automated efficiently. Student course registration process in colleges involve filling registration forms manually, getting it signed by respective subject teachers, and then getting the documents acknowledged from the concerned Advisors, College Deans and Accounts Officers respectively. Finally the registration forms are submitted in the Administrative Branch. As is evident, this process is very laborious and time consuming. An Online Student Course Registration System has been developed to simplify the current manual procedure.This system has been developed using PHP, jQuery, Apache and MySQL. The front-end is designed using PHP with excerpts of code written using jQuery and back-end is designed and managed through MySQL. This system software is more secured, user-friendly and less time-consuming.

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  • Evaluation of Proposed Algorithm with Preceding GMT for Fraudulence Diagnosis

    Navneet Kr. Kashyap

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    Formerly existing graph mining algorithms regularly accept that database is generally static. To defeat that we proposed another algorithm which manages extensive database including the components which catches the properties of the graph in a couple of parameters and check the relationship among them in both left and additionally right course, in this way embracing DFS and in addition BFS approach. It furthermore discovers the subgraph by traversing the graph and removing the planned routine. The proposed calculation is utilized for identification of wrongdoing as a part of BANK & Financial organization by catching the properties and distinguishing the relationship and affiliations that may exist between the individual required in that wrongdoing which keep a few violations that may happen in future. We have utilized the Neo-ECLIPSE for the execution of proposed calculation and Neo4j is the graph database utilized for evaluation.

    On the off chance that a man  endeavoring to confer fraud or engage in some kind of illicit movement, they will endeavor to pass on their activities as near authentic activities as could reasonably be expected. Here in this paper, we are giving the data that a man who is in beginning the phase of the fraud, what co-related wrongdoings or illicit exercises he can do in future. The future exercises that can be performed by the individual can be ceased by demonstrating the associations with the entries saved in the database.

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  • Storage and Computation on Big Data: A Comparative study

    CH Sravan Kumar, P. Buddha Reddy, K. Srinivas

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    A huge data space includes set of interesting points; Skyline is an important operation in many applications to return a set of interesting points from a potentially huge data space [1].This survey paper highlights the characteristics of big data and their challenges. This paper also discusses the tools and techniques of big data.  The existing algorithms like SaLSa, SSPL are novel computation algorithms. SaLSa exploits the idea of presorting the input data so as to effectively limit the number of tuples to be read and compared [2].SSPL utilizes sorted positional index lists which require low space overhead to reduce I/O cost significantly [1].SSPL consists of two phases. In phase 1, SSPL computes scan depth of the involved sorted positional index lists. During retrieving the lists in a round-robin fashion, SSPL performs pruning on any candidate positional index to discard the candidate whose corresponding tuple is not skyline result. Phase 1 ends when there is a candidate positional index seen in all of the involved lists. In phase 2, SSPL exploits the obtained candidate positional indexes to get skyline results by a selective and sequential scan on the table [1].

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