DESIGN AND IMPLEMENTATION OF STATE CIVIL SERVICE PAYROLL ACCOUNTING SYSTEM

DESIGN AND IMPLEMENTATION OF STATE CIVIL SERVICE PAYROLL ACCOUNTING SYSTEM

ABSTRACT

Ever since man discovered the computer and hence computer science, attempt has been made to computerize every facet of human life.

This project work expresses the issue of designing and implementing a state civil payroll acknowledges (speed and accuracy) over the more tedious inefficient and true consuming manual system.

This system encounters the following problems.  late receipt of raw data each month, regular break down of the accounting machine presently used for data processing, irregular power supply, in any errors resulting from the manual methods of data processing and the office caused by bulky files and constant complaints for errors from the workers.  Consequently a computerized payroll accounting system when designed to take care of the above problems. The software is developed using Microsoft visual basic 6.0 programming language and access database.

 

Download Full Material-N5000

Leave a Reply

Your email address will not be published. Required fields are marked *

Related Post

Apr 22

DESIGN AND IMPLEMENTATION OF WEB BASED VETERINARY INFORMATION MANAGEMENT SYSTEM

DESIGN AND IMPLEMENTATION OF WEB BASED VETERINARY INFORMATION MANAGEMENT SYSTEM

Abstract

A computer system for collection of animal disease and related data was set up by the Directorate of Veterinary Services in 1985, and data are available from 1986 up to the present. The nationwide system consists of detailed disease and herd health data from field veterinarians, disease and related data (such as grazing and animal condition, vaccinations and farmer treatments) from animal health inspectors, laboratory results and investigations, and abattoir data. More recently, a socio-economic component has also been added. Considerable care has been taken to link related information. In addition, all data are geographically linked through a placename co-ordinate and attribute file, and a crude but effective automated map presentation system exists. The standard objectives of an information system (which were not historically met), namely, to facilitate the collection of complete, accurate, timely and relevant data which are easily accessible and processible, and in this way provide a better basis for management decision-making as well as for specific studies and queries, were strived for. The system has proved particularly valuable in terms of automation of routine reports and in answering ad hoc queries. It is underutilised with regard to epidemiological studies, but we feel that it holds great potential.

Download Full Material-N5000
Oct 04

MODELING AND SIMULATION OF A NETWORK ENVIRONMENT OVER SINGLE AND MULTIPLE LINKS

MODELING  AND SIMULATION OF A NETWORK ENVIRONMENT OVER SINGLE AND MULTIPLE LINKS

CHAPTER ONE/INTRODUCTION

Background to the Study

Considerable research has been conducted to model and quantify the performance of heterogeneous services and technologies (e.g (Kawasaki et al., 2006; Tsalgatidou et al., 2006; Liu et al., 2006). Accurate measurements and analyses of network characteristics (remote data transfers) are essential for robust network performance and management. Evaluating the performance of a computer networking usually involves constructing an appropriate model to predict the heterogeneous environment behaviour via simulation model. For example, several flow-level network traffic models have been proposed to describe/stimulate (Fred et al., 2001; Cong and Wolfinger, 2006). These models have been used to study fairness, response times, queue lengths and loss probabilities under different assumptions and using a variety of mathematical techniques. Queuing theory has been widely used to model and analyze the network performance of complex systems (Puigjaner, 2003) in contrast to other studies in the literature (Barakat et al., 2002; Bu and Towsley, 2001; Kherani and Kumar, 2000). This simulation model can be used to generate representative packet traffic (one-way delays and throughput) in a live network environment or in a simulated environment.

Moreover, in the future, the integration of data and communication services, almost every Internet Ready device will be a communicable device (Zhao et al., 2005). With the availability of this infrastructure, users are now demanding and expecting more services (Barakat et al., 2003; Bu and Towsley, 2001). Convergence is pushing towards an environment that requires new investment in infrastructure and able to support the delivery of rich services (various services), applications and content (Podhradsky, 2004; Nogueira, 2006). Network deployment is growing increasing complex as the industry lashes together a mix of wired and wireless technologies into large-scale heterogeneous network architecture and as user applications and traffic continue to evolve (Heidemann et al., 2001). The successful evolution of the Internet is tightly coupled to the ability to design simple and accurate models (Barakat et al., 2002). Many factors may contribute to the congestion of network interface, such as a heavy load in the network that usually generates higher traffic (Chang and Hon, 2002). Thus, this research is critical to be conducted in order to predict and measure of remote data transfers in heterogeneous environment.

Aims and Objectives

The main objectives of this study is on Modeling  and  Simulation of a network environment over single and multiple links.

The following are the specific objectives;

  1. predict the performance of various services in order to aid technology assessment and capacity planning
  2. predict the expected behavior of new services and designs through qualitative or quantitative estimates of network performance;
  3. assist network administrator to prepare, propose, plan and design network topology more effective and systematic and
  4. conduct What-If analysis for evaluating heterogeneous network environment performance.

Research Questions

This study posits several research questions:

  1. what is the performance level of the remote data transfers?
  2.  Is the simulation model for evaluating and measuring the heterogeneous (multiple links) environment performance effective?
Download Full Material-N5000
Sep 17

GLOBAL SYSTEM FOR MOBILE COMMUNICATION (GSM) SUBSCRIPTION FRAUD DETECTION SYSTEM USING ARTIFICIAL NEURAL NETWORK TECHNIQUE

GLOBAL SYSTEM FOR MOBILE COMMUNICATION (GSM) SUBSCRIPTION FRAUD DETECTION SYSTEM USING ARTIFICIAL NEURAL NETWORK TECHNIQUE
ABSTRACT

This project is concerned with GSM subscription fraud detection system using artificial neural network technique. Fraud is a multi-billion problem around the globe with huge loss of revenue. Fraud can affect the credibility and performance of telecommunication companies. The most difficult problem that faces the industry is the fact that fraud is dynamic, which means that whenever fraudsters feel that they will be detected, they device other ways to circumvent security measures. In such cases, the perpetrators intention is to completely avoid or at least reduce the charges for using the services. Subscription fraud is one of the major types of telecommunication fraud in which a customer obtain an account without intention to pay the bill. Thus at the level of a phone number, all transactions from the number will be fraudulent. In such cases abnormal usage occurs throughout the active period of the account; which is usually used for call selling or intensive self usage. This provides a means for illegal high profit business for fraudsters requiring minimal investment and relatively low risk of getting caught. A system to prevent subscription fraud in GSM telecommunications with high impact on long distance carriers is proposed to detect fraud. The system employs adaptive flexible techniques using advanced data analysis like Artificial Neural Networks (ANN). Fed with raw data, a neural network can quickly learn to pick up patterns of unusual variations that may suggest instances of fraud on a particular account. A total of 158 data samples were collected, trained and tested using a model that allows identifying potential fraudulent customers at the time of subscription. The result shows that 80% of the prediction accuracy has been obtained. From the result produced, artificial neural network has a potential to be used for detecting subscription fraud in telecommunication.
TABLE OF CONTETNS

Title Page – – – – – – – – – – i
Approval Page – – – – – – – – – ii
Dedication – – – – – – – – – – – iii
Acknowledgment – – – – – – – – – – – iv
Abstract – – – – – – – – – – – viii
Table of Contents — – – – – – – – – v
List of Figures – – – – – – – – – – x
CHAPTER ONE: INTRODUCTION
1.1 Background of the Study – – – – – – – 1
1.2 Statement of Problem- – – – – – – – 2
1.3 Objectives of the Study — – – – – – 2
1.4 Scope and Limitation of the Study – – – – – – – 3
1.5 Significance of the Study – – – – – – – 3
1.6 Definition of Terms – – – – – – – 4

CHAPTER TWO: LITERATURE REVIEW
2.1 Telecom Frauds – – – – – – – – 4
2. 1. 1 Subscription Fraud: – – – – – – – 7
2. 1. 2 Identity Theft: – – – – – – – – 7
2. 1.3 Roaming fraud: – – – – – – – – 8
2.1.4 DISA Fraud: – – – – – – – – 9
2. 1. 5 Phone Theft: – – – – – – – 10
2.1.6 Voicemail Fraud: – – – – – – – – 11
2. 1. 7 Some Factors Leading to Telecom Fraud – – – 11
2. 1. 8 Who Actually Commits Fraud- – – – – – – 12
2. 1. 9 Where do Fraudsters Work From – – – – – 13
2. 1. 10 Why do Fraudsters do it — – – – – – 12
2.2 Fraud Prevention and Detection – – – – – 12
2.2.1 Fraud Prevention – – – – – – – 17
2.2.2 Fraud Detection – – – – – – – – 20
2.2.2.1 Supervised Versus Unsupervised Learning – – – – 21
2.2.2.2 Supervised Methods of Fraud Detection – – – – 22
2.2.2.3 Unsupervised Methods of Fraud Detection- – – – 24
2.3 Fraud Detection Using Neural Network – – – – 25
2.4 GSM Subscription Fraud Detection Architectures – – – 27
2.4.1 System 1 Architecture – – – – – – – 27
2.4.1.1 Layer 1 – – – – – – – – – 28
2.4.1.2 Layer 2 – – – – – – – – – 28
2.4.1.3 Layer 3 – – – – – – – – – 28
2.4.2 System 2 – An ‘all in one’ Absolute Usage System – – – 28
2.4.3 System 3 – Introducing Diffential Usage – – – 29

CHAPTER THREE: SYSTEM ANALYSIS AND DESIGN
3.0 Analysis of the System – – – – – – – 31
3.1 Formulation of Model – – – – – – – 32
3.2 GSM Fraud Detection Using Neural Network- – – – 35
3.3 Object-Oriented Analysis of the System – – – – 38
3.4 Object-Oriented Design of the System – – – – – 39
3.4.1 Class Diagram of the System – – – – – – 40
3.4.2 Sequence Diagram of the System – – – – – 42
3.5 Database Design – – – – – – – – 46
3.5.1 Database diagram elements – – – – – – 46
3.6 Deployment diagram of the system – – – – – 47

CHAPTER FOUR: SYSTEM IMPLEMENTATION
4.0. Software Architecture – – – – – – – 49
4.1. System Requirements – – – – – – – 50
4.1.1 Hardware Requirements- – – – – – – – 50
4.1.2 Software Requirements – – – – – – – – 51
4.2. Development Environment, Coding and Testing Technique 51
4.3. Deployment Platform and Installation of the Software 53
4.4 Screen Shots of the Software Demos – – – – – – 53
4.5 User’s Guide – – – – – – 64
4.6 maintenance Guide – – – – – – 64
4.7 Program Documentation – – – – – – – – 65

CHAPTER FIVE: SUMMARY AND CONCLUSION
5.1 Summary – – – – – – – – – – 67
5.2 Conclusion – – – – – – – – – – 67
5.3 Recommendation – – – – – – – – – 68
5.4 Suggestion for further studies – – – – – – – 68
REFERENCE – – – – – – – – – 70
APPENDIX
Source Code

LIST OF FIGURES
Figure: 2.1 Flow of neutral network architecture 27
Figure: 2.2 An ‘all in one’ absolute usage system 29
Figure 2.3: Diffential usage system 30
Figure: 3.1 Architecture of the fraud detection tool 33
Figure 3.2 (a): Multilayer feed forward Neural network Architecture 36
Figure 3.2 (b): Back-propagation Neural network Architecture 38
Figure 3.3: Use Case boundary diagram of the system 39
Figure 3.4: Class diagram of the system 40
Figure 3.5 (a): Login sequence diagram 43
Figure 3.5(b): Make Call Sequence Diagram of the System 44
Figure 3.5 (c): End call Sequence Diagram of the System 45
Figure 3.5 (d): Check Detection Sequence Diagram of the System 45
Figure 3.6 Database Diagram of the System 47
Figure 3.7: Deployment Diagram of the System 48
Figure 4.1: The architecture of the software, GSM subscription fraud
detection using artificial neural network 49
Figure 4.2: Screen shot of the launch window 54
Figure 4.3 Screen shot of the welcome window 55
Figure 4.4: Screen shot of authenticate user window 56
Figure 4.4: Screen shot of make call window 57
Figure 4.6: Screen shot of timing window 58
Figure 4.7 Screen shot of call summary window 59
Figure 4.8 Screen shot of home page 60
Figure 4.9 Screen shot of add customer account detail page 61
Figure 4.10 Screen shot of check detection action 62
Figure 4.11 Screen shot of no detection page 63
Figure 4.12 Screen shot of fraud detections pageDownload Full Material-N5000